Telecommun Syst DOI 10.1007/s11235-016-0216-9

Towards the fulfillment of 5G network requirements: technologies and challenges Ali Alnoman1 · Alagan Anpalagan1

© Springer Science+Business Media New York 2016

Abstract Future 5G networks are expected to have the capabilities of providing extremely high data rates, seamless coverage, massive number of connected devices, low latency, etc., in order to support the internet of things era. The dynamic performance of 5G networks is a key feature for controlling the dense and rapidly changing communication environment. Technical issues such as limited frequency resources, interference, energy consumption, and network management are the main challenges facing 5G networks. This article presents a comprehensive study of 5G networks architecture, technologies, challenges, and possible solutions based on recent advances in technology and research. Keywords 5G systems · HetNets · Small cells · Interference cancellation · Network management · Energy efficiency

1 Introduction The growing demands for information and technology make the current wireless networks unable to deliver the required amount of data to the massive number of devices and machines expected to join the network in the next few years, where the peak data rate may increase up to 10 Gbps [1]. The envisioned data rate is 300 Mbps in downlink and 60 Mbps in uplink [2]. The density of connected devices is expected to range from 106 to 107 per km2 , whereas the volume density of network traffic is around 10 Tbps per km2 [3]. The main prospect of 5G networks is the internet of things (IoT) which provides tremendous benefits in many life aspects such as health care, smart cities, transportation,

B 1

Alagan Anpalagan [email protected] Ryerson University, Toronto, Canada

remote monitoring, etc. The IoT is classified as consumer and industrial. The consumer IoT involves the connection of electronic devices with anything that is related to the consumers environment such as homes and cities in the way that facilitates their lives. Whereas the industrial IoT improves business and market sectors using machine-to-machine communications such as remote sensors and self-organizing systems [4]. Compared to 4G LTE networks, 5G networks have to cope with the following requirements: • ×1000 higher capacity (data traffic) by 2020 and ×10, 000 higher by 2025 [5]. The ×1000 capacity can be obtained by having ×10 more cells (densification), ×10 spectrum increase (bandwidth), and ×10 spectral efficiency [6]. • ×10 to ×100 higher number of connected devices [7]. It is expected that by 2020 the connection density (number of connections per square kilometer) will increase from 140 thousand to 6 million [8]. • ×10 to ×100 higher user data rate [7]. • ×10 longer battery life for the low-power machine type communications [7]. • ×10 less latency (1ms latency) [9,10]. • ×100 to ×1000 increase in energy efficiency [9]. • ×10 to ×100 reduction in deployment costs [11]. One of the challenges facing 5G networks is the massive number of connected devices that require large bandwidths and more frequency resources. For this reason, spectrum has to be shared efficiently between the connected devices in centralized, distributed, or cooperative centralized/distributed manners [12]. Small cells are expected to be densely deployed in future networks because they can significantly increase the network capacity by allowing spatial frequency reuse in small areas. Besides network densification, the improvement of spectrum efficiency can be achieved

123

A. Alnoman, A. Anpalagan

by applying interference mitigation and massive multipleinput multiple-output (MIMO) techniques [13]. In addition, millimeter wave (mmWave) communications which provide high bandwidths and abundant frequency resources can be used to overcome the shortage in frequency resources. The co-existence of multiple tiers, radio access technologies (RATs), and large number of connected devices subject the radio environment to severe interference such as selfinterference, inter-cell interference, and intra-cell interference. Another challenge is the large amount of energy consumed by both the network infrastructure and mobile devices. Therefore, it is expected from both the transmitters and receivers to be aware of energy harvesting techniques including the exploitation of green energy resources. On the other hand, the management of such ultra dense networks regarding both cells and users is more complicated; therefore, intelligent management schemes which can be categorized into central, distributed, and cooperative central/distributed need to be adopted in order to facilitate the process. Once the wireless networks have the capabilities of providing adequate capacity, high energy efficiency, and low operational costs it will fulfill the requirements for the IoT which is going revolutionize the world. This article presents a comprehensive study on 5G network architecture, technologies and challenges. The main features of future 5G networks are the dynamic performance of the network, the capability to comprise the massive number of machines and users, and the high QoS requirements in order to enable the IoT era. Technical issues such as limited frequency resources, interference, energy consumption, and network management are the main challenges presented in this work. The rest of the paper is organized as follows: Sect. 2 illustrates the 5G network architecture. Section 3 explains the key enabling technologies. Section 4 presents interference cancellation techniques. Section 5 introduces the techniques required for energy-efficient networks. Section 6 demonstrates the techniques of network management. Section 7 suggests some resource allocation techniques. Finally, Sect. 8 concludes the work.

2 5G network architecture The high demands for data in 5G networks accompanied by the limited frequency resources make the spectrum allocation and frequency sharing process one of the main challenges. One way to optimally share frequency resources among all connected devices (things) is by dividing the network into multiple tiers, where each tier is capable of using frequencies that might be used concurrently by other tiers with the consideration of potential interference. This

123

Fig. 1 5G multi-tier architecture

paradigm of networks architecture is called the heterogeneous network (HetNet). Macrocell tier transmits signals with higher power than small cell tiers such as micro, pico and femto cells and hence must be considered by these small cells when planning to reuse frequencies that are already being used by macrocells to avoid interference. Figure 1 depicts the 5G multi-tier network architecture. The support of multi-RATs in 5G networks is required [3], and since multi-tier networks combine tiers with various transmission powers and multiple access techniques, the management of 5G HetNets becomes more complicated [2]. The components of 5G networks can be classified as follows: 2.1 Small cells and ultra dense networks (UDNs) The current available wireless channel capacity is close to the maximum theoretical limit, and in order to increase the capacity per unit area, small cells are expected to be widely deployed in 5G networks [14]. From the operator perspective, spectrum-sharing is more preferable than spectrum splitting due to the scarcity of frequency resources and to better utilize frequency resources; however, the cross-tier interference between macro base stations (BSs) and small BSs can severely degrade the system performance [15]. Small cells can improve the spectral efficiency by allowing more flexible frequency reuse in a particular area. They also provide indoor coverage in homes, buildings, enterprises, and vehicles [16,17], as more than 70 % of data traffic and 50 % of the phone calls traffic take place indoors [18]. In addition to improving the network coverage and capacity, small cell networks offers the capability of managing small

Towards the fulfillment of 5G network requirements: technologies and challenges

BSs from the users’ side, and can offload traffic from macro BSs to small BSs, thus reducing both operational expenditure (OPEX) and capital expenditure (CAPEX) [15]. Moreover, small cells can improve throughput and reduce energy consumption [19]. Further advances in the network architecture can be achieved by merging the available wireless local area networks (e.g., WiFi) with small cells to further improve the system efficiency [20]. The density of WiFi access points in some urban areas exceeds 1000 per km2 , and this large number of radio sources can improve the overall network capacity when cellular networks and WiFi access points are jointly deployed, provided that both cellular networks and WiFi access points cooperate such that interference is eliminated [21]. A resource allocation (subchannel and power allocation) scheme for OFDMA-based cognitive femtocells was proposed in [22] to maximize the total capacity of femtocell users under the QoS, fairness, and interference constraints. It is anticipated that the density of base stations (BSs) in 5G networks will be around 40-50 BSs per Km2 in order to provide seamless coverage; for this reason, 5G networks are described as ultra dense networks (UDNs) [19]. Due to their small coverage area, handovers are expected to occur more frequently [23], and in order to reduce the unnecessary handovers, macrocell BSs can provide handover control for small cells, while small cells convey data to users [19]. To avoid the co-channel interference and the unnecessary handovers in HetNets, low frequency signals (below 3GHz) can be used to provide mobility management and coverage, whereas the high frequency mmWaves can be used by the small cells to provide high data rates, this management scheme is called macro-assisted small cells [7]. Another paradigm of small cells utilization is the moving cells [24], where a group of users on board of a moving platform such as a train or bus are connected to a moving small cell on that platform. Here the data is conveyed to the core network via other small cells which are placed on the platforms route using wireless backhaul. Cloud radio-over-fiber technology is proposed in [20] to share the infrastructure of small cell radio access network (RAN) with multiple operators and services using optical fiber cables in order to deliver multiband data transmission in an integrated optical-wireless access technology. Along with small cells, spectrum efficiency can be further improved using two paradigms: (a) spectrum sensing where unlicensed users check the availability of channels before transmitting or receiving, and (b) spectrum database where unlicensed users can refer to spectrum database to check the availability of channels [22]. Moreover, the licensed spectrum that is not used by one network operator in certain locations can be granted to another network operator in order to enhance flexibility in frequency sharing [25].

Fig. 2 D2D communications

Small cells operate as plug-and-play devices that are not well planned as macro cells, and hence they are more vulnerable to outages caused by hardware or software faults. For these reasons, the operation of small cells is difficult to be managed manually, and hence the network needs powerful self-organizing features such as self-optimization and self-healing techniques. 2.2 D2D communications To add more flexibility in frequency reuse, devices that are relatively close to each other can exchange information without referring to BSs. In D2D communications, devices can communicate with each other either directly or by relaying through other devices or relays before reaching the desired device or BS, especially when the source and destination are far apart. The control links can be either formed by the devices themselves or by referring to the macro BSs. The system performance using BSs control is better in terms of information security and interference management; however, this will increase the signaling load on the network [12]. Figure 2 illustrates the different types of D2D communications. D2D communication gives the following advantages to the network: (1) Improves the spectrum efficiency in a given area by allowing flexible frequency reuse as the devices use short-range, low-power signals. Furthermore, by using spectrum that is out of the cellular band such as WiFi, D2D communications can improve spectrum efficiency [26]. Moreover, mmWave signals can be used in D2D networks to alleviate the co-channel interference by transmitting directed narrow beams, and hence the

123

A. Alnoman, A. Anpalagan

(2)

(3)

(4)

(5) (6)

(7)

frequency sharing scheme can be changed from the nonorthogonal to orthogonal since spectrum resources are highly available in mmWave technology [7]. The signal-to-self-interference-plus-noise ratio (SSINR) is larger in D2D networks due to the shorter distances between the connected pairs [27]. Therefore, devices can use full-duplex transmission without facing significant self-interference because the power gap between the paired terminals is small. In addition, to further optimize the performance in D2D networks some techniques such as beamforming, power control and scheduling may be applied [28]. D2D communication can provide service in situations when a lack of cellular coverage occurs. For example, in case of emergencies when cellular networks fail, D2D can still provide public safety services such as paramedics [29]. If the location of the destination is far from the source, devices can still communicate by relaying through other devices, relays or other operating BSs until achieving the desired link. Improves energy efficiency due to their low-power transmission. A scheme to find the optimal power allocation for wireless D2D networks using full duplex mode was proposed and tested by [27] to obtain the maximum ergodic capacity of the network. Reduces signaling loads on BSs. Reduces time delay between transmitters and receivers due to the short distance, this can be highly beneficial especially in delay sensitive applications. D2D applications can be extended to vehicle-to-vehicle (V2V) communications, where vehicles can communicate with each other directly in order to reduce latency and to avoid overloading BSs with the tremendous amount of data transmitted by vehicles. An LTE-D2D scheme was introduced in [30] to reduce latency and interference in V2V communications.

2.3 Massive machine communications (MMC) Massive machine communications (MMC) enable machines and devices to intelligently and autonomously exchange data with each other or with remote servers. MMC forms the basis of the IoT in next generation systems and is useful in a variety of life aspects such as health care, automated industry, road traffic control, distant monitoring, climate forecasting, agriculture, intelligent homes, etc. It is anticipated that in the time between 2020 and 2030, 100 billion machines (things) will be joining the 5G networks [32], while nowadays the number of connected things range from 1 to 10 billion [6]. Some conditions have to be fulfilled in order to enable an efficient machine-to-machine (M2M) or machine type communications (MTC) [33]. These conditions include seamless radio coverage, sufficient network capacity, low power consumption and finally MTC should not interfere with human-type communications (HTC). MMC is different from human-to-human (H2H) communications in that the total upload demands of MMC systems are higher due to the high density of machines required for applications such as automatic utility metering, control systems, and remote monitoring. However, the data generated by each machine is small and infrequent compared to the bursts of data that are required by human users on downlink. The performance of MMC systems can be enhanced by the support of D2D communications and network virtualization [34]. The number of concurrent online connections supported by the network is a challenge in MMC, and one solution can be provided by deploying aggregation gateways and forming capillary networks prior to BSs, while relays and multi-hopping techniques can be used to reduce the massive number of connections that might overload the BSs [32]. Table 1 summarizes the basic components, challenges and solutions of 5G network architecture.

3 5G network technologies Frequency sharing among D2D users can be either overlaid or underlaid [31]. In the overlaid scheme, the D2D users are assigned orthogonal resources together with the cellular users (static allocation), whereas in the underlaid scheme, the D2D users can reuse the same resources occupied by cellular users (dynamic allocation) to improve specrum efficiency [30,31]. Furthermore, software defined architecture is proposed in [26] for D2D networks, where a user equipment (UE) sends requests to nearby devices to form a cloud and once the cloud is formed; it will be managed and controlled by the central software defined network (SDN) controller in the core network. The proposed hierarchical architecture provides scalability and flexibility to network management as it distributes functionalities between the local and central SDN controllers.

123

In this section, recent advances in technologies and research that are expected to be applied in 5G networks are illustrated. 3.1 Full duplex transmission Using the same time-frequency channels to transmit and receive concurrently is a brilliant idea to double the channel capacity and to improve energy efficiency [35]. However, implementing full duplex (FD) transmission in wireless communications is a challenge due to the effects of interference. Interference sources in FD can be divided into three types: (a) BS to BS interference, where BSs interfere with each other because they transmit high power signals which are

Towards the fulfillment of 5G network requirements: technologies and challenges Table 1 Summary of 5G networks architecture Architecture

Description

Benefits

Challenges

Solutions

UDNs

The deployment of a large number of small cells in a particular area especially in the indoor environments

- Improves spectrum efficiency by allowing frequency reuse within small areas

- Control and management due to the large number of cells which are not well planned as macrocells

- Coordinating the operation of small cells with each other and with macrocells (CoMP)

- Improves energy efficiency due to the low-power, short-range communications

- Backhaul infrastructure, because providing optical fiber cables to a large number of small cells is complicated and expensive

- Using software defined network (SDN) and network function virtualization (NFV)

- Adopting self-organized operation

- Improves data throughput by exploiting the large bandwidths provided by mmWaves

- Using wireless backhaul (mmWaves) D2D

Devices communicate using direct links without referring to BSs except for control purposes

- Improves spectrum efficiency by allowing frequency reuse in small areas

- Self-interference due to the utilization of full duplex transmission

- Beamforming

- Improves energy efficiency because less power is required for short-distance communications

- Analog and digital interference cancellation

- Reduces latency due to the direct and short links

- Antenna separation

- Provides service when the cellular coverage is lost either directly or by relaying through other devices MMC

Massive numbers of machines communicate to each other or to remote servers autonomously

- MMC is the basis of IoT systems

- Requires sufficient capacity

- The support of small cells and D2D is required to provide sufficient capacity

- Can be used in a variety of life aspects such as health care, automated industry, road traffic control, distant monitoring, agriculture, intelligent homes, etc

- High upload demands

- To mitigate interference with HTC, MMC could be allocated frequencies lower than those used by HTC

- Should not interfere with human-type communications (HTC)

less affected by path loss and shadowing as they are usually mounted on the top of high buildings and towers. This type of interference can be mitigated by configuring the antennas elevation angle. (b) UE to UE interference due to the fact that users are spatially close to each other. This type of interference can be mitigated using intelligent scheduling and power allocation techniques. (c) Self-interference (SI) due to the high power gap between the transmitted and received signals which leads to low SIR at the receiver [36]. This power

gap (e.g., 100 dB) can be reduced by integrating FD in D2D communications and small cell networks where the communication distance is short, and therefore low-power signals can be used in order to achieve a better balancing in power and hence mitigate SI [28]. In addition, nodes in practical networks are governed by protocols such as the carrier sense multiple access (CSMA) which complicate the spatial frequency reuse due to the high potential self-interference and intercell-interference among

123

A. Alnoman, A. Anpalagan

various radio devices when using the same frequency concurrently. Therefore, even with perfect SI cancellation, it may not be guaranteed that FD doubles the capacity over half duplex (HD) transmission [28,37,38]. Due to the recent advances in radio frequency (RF) circuits and antennas, it is possible to use FD in wireless networks; however, careful planning and new scheduling schemes are required in order to avoid interference and the high energy losses due to SI cancellation circuitry. An efficient transmission mode that has advantages over either HD or FD is the hybrid HD/FD which facilitates switching between the two individual transmission modes [38]. By efficiently switching between HD and FD, better data throughput and energy efficiency can be obtained especially in multi-hop D2D communications, where HD is preferred for direct long-distance hops, whereas FD is preferred for short-distance multi-hops [28]. Moreover, the capacity gain can be obtained by allowing BSs to transmit in FD while users operate in HD mode [36]. A flexible duplex technique is another technology that can be implemented in 5G networks [3]. In this technique, frequency division duplexing (FDD) can be adapted to bandwidth requirements in uplink and downlink streams depending on the application. Whereas in time division duplexing (TDD), the frame ratio of uplink to downlink can be dynamically modified in order to provide the best data rate. In Catania et al. [39], an algorithm is proposed to adapt small cells to traffic imbalances between uplink and downlink using flexible TDD frame structure. Another paradigm is the decoupled duplex allocation, in which the uplink is active with a certain cell, whereas the downlink is active with another cell, if the first cell is overloaded in the downlink and the second cell is overloaded in the uplink [40]. 3.2 Millimeter wave (mmWave) communications Millimeter wave (mmWave) communications use bandwidths with multiple gigahertz, and therefore convey very high data rates and improve the channel capacity significantly [41]. Wavelengths of the mmWave signals range from 1 to 100 mm, and this range falls in the extremely high frequency (EHF) band from 30 to 300 GHz. This huge spectrum can solve the existing shortage in frequency resources in current wireless networks [7]. Due to their short wavelengths, mmWave signals suffer from high attenuation due to the absorption by atmospheric components especially oxygen, water vapor and rain; however, this attenuation improves the spatial frequency reuse in small areas. A 40 GHz signal may suffer an attenuation of 175 dB when penetrating through a 10 cm concrete material, whereas the attenuation of a signal with less than 3 GHz suffers an attenuation of only 17.7 dB [7]. Moreover, the

123

propagation loss in free space at 60 GHz is 28 dB higher than the 2.4 GHz since the propagation loss in free space is proportional to the square of the carrier frequency [41]. Therefore, the high path loss in mmWave signals is favorable in indoor environments as it facilitates the spatial frequency reuse [5]. The path loss (attenuation) in outdoors can be solved through the process of beamforming, where antennas transmit and receive directed narrow beams in order to increase the gain. Another benefit of using mmWaves is the applicability of tiny antennas that can be easily integrated into a chip. Besides, the utilization of mmWaves in the backhaul/fronthaul traffic can be considered as an efficient substitute of optical fiber cables [20]. Moreover, by combining both mmWave and MIMO technologies, an efficient wireless backhaul can be provided for small cells which can enhance the overall network performance [10]. For these reasons, mmWaves are considered as a powerful technique for 5G networks. Due to their electromagnetic characteristics, the lineof-sight component (LOS) of mmWaves is susceptible to blockage much more than low-frequency signals, and the non-line-of-sight (NLOS) components are significantly attenuated; therefore, link outages can occur when the LOS component is blocked [7,41]. In addition, the multipath effect in small cell mmWave propagation is significant due to the increase in delay spread which is inversely proportional with the distance between the BS and the mobile user [5]. The blockage problem can be solved by using relays, or by restricting the usage of mmWaves for data transfer while using low-frequency signals to provide tracking and control. 3.3 Massive MIMO The massive number (e.g., hundreds) of antennas in the technique of massive multiple-input multiple-output (MIMO), can jointly improve spectrum and energy efficiencies. Massive MIMO facilitates the spatial multiplexing of frequencies and therefore enables serving many users concurrently using the same frequency [12]. Directional beamforming in MIMO divides the coverage area of the cell into small sectors, thus minimizing interference and improves specrum efficiency which can reach high values such as 100 b/s/Hz [13]. MIMO can support other types of communications such as D2D and MTC, due to the fact that a large number of users and machines can be served in parallel. Figure 3 demonstrates the spatial frequency reuse using beamforming in massive MIMO, where directed narrow beams enable different UEs to use the same frequency concurrently. However, the large number of antennas in MIMO add more computational complexity and costs to the system due to the extra RF circuitry and signal processing requirements. Moreover, the number of orthogonal pilot signals which are

Towards the fulfillment of 5G network requirements: technologies and challenges

Fig. 3 Directed narrow beams in massive MIMO enable different UEs to use the same frequency concurrently

required for channel estimation will increase as number of antennas increase, and therefore, more radio resources are wasted [13].

Fig. 4 2-tier network with CoMP

3.4 Coordinated multi-point (CoMP) The CoMP technique facilitates the coordination of functions among different network nodes, and enable UEs to receive data from multiple heterogeneous base stations (HeNBs) simultaneously [18]. Moreover, by exchanging the channel state information (CSI) among the high-power and low-power transmitters, CoMP can coordinate the inter-cell interference (ICI), achieve minimum power transmission, and increase the bit rate especially for cell-edge users [42,43]. The combination of cloud computing and heterogeneous small cell networks can improve the network performance through the coordination of interference and handover management [44]. Figure 4 demonstrates the CoMP in a two-tier network. The femtocell UEs (FUEs) can help in the coordination process by measuring the path loss and power degradation experienced by the UE in different areas within the cell range and therefore provide information about the cell boundary [45]. 3.5 Cloud networks Many applications that are expected to be implemented in 5G systems such as remote monitoring, health care, environment and weather forecasts, measurements of social trends using social media, etc., are limited by the computational capabilities of smart devices (e.g., smart phones), which are incapable of handling such enormous amount of data. Therefore, cloud computing is considered as an excellent candidate for solving this problem by enabling users to access cloud

Fig. 5 C-RAN architecture

networks via any available wireless interface (e.g., WiFi) and use the powerful computational resources available by the cloud [46]. Figure 5 illustrates the C-RAN architecture. A promising network architecture is the cloud radio access network (C-RAN) in which the processing of baseband signals is performed in baseband units (BBUs), whereas the RF signals are processed in remote radio heads (RRHs). The BBUs and RRHs are linked by mmWaves fronthaul. The RRHs form coordinated clusters in which information and data are processed in static, semi-dynamic, and full-dynamic schemes depending on the desired coordination level and signaling overhead. The centralized processing in C-RAN architecture facilitates the CoMP performance in the network and reduces the number of required processing units

123

A. Alnoman, A. Anpalagan

especially in dense small cell environments. Therefore, this architecture can reduce energy, delay, OPEX and CAPEX [44]. In addition, the complicated management of ultra dense networks can be facilitated using the cloud which controls network elements, collects statistics, and configures the necessary parameters in a centralized manner. The cloud also facilitates the network scalability by dynamically adapting cells and users with low OPEX [47]. In cloud assisted HetNets, the cloud is only responsible for management, monitoring and maintenance [20]. Cloud computing can also facilitate the utilization of biometric authentication techniques such as face, iris, and fingerprint recognition for security demanding applications. These recognition algorithms are time consuming and their accuracy depends on the amount of training samples. In 5G systems, the transmission rate is very high and the number of training samples is collected online from millions of users which make the process more reliable and efficient [48].

3.6 Cognitive radio The majority of licensed frequencies are rarely exploited continuously in all spaces, and hence many spectrum resources are wasted. In order to maximize the spectrum efficiency, same frequencies can be used at different times within the same space, and in different spaces at the same time. Therefore, cognitive radio (CR) can be used to resolve the problem of spectrum underutilization in wireless networks [2]. In CR technology, secondary (unlicensed) users share frequency bands that are used by the primary (licensed) users provided that the priority is given to primary users. This technique can also improve energy efficiency [35]. In cognitive networks, secondary users test the availability of the licensed channels by comparing the received signals to either a predetermined energy threshold or a known statistical feature. Spectrum sharing between primary users (PUs) and secondary users (SUs) is categorized into three types: (a) overlay sharing which allows SUs to access the unoccupied spectrum of PUs, (b) underlay sharing which allows SUs to access the spectrum that is occupied by PUs provided that the interference level is kept below a threshold value, and (c) mixed sharing which combines the features of both overlay and underlay sharing [49]. The design of CR architecture should achieve maximum spectrum efficiency and guarantee the QoS for PUs. Two schemes are proposed to support efficient CR networks [49]: radio environment map (REM) whereby servers record information on geographical locations and interference of the radio environment, and primary exclusive zone (PEZ) in which the SUs are kept outside specific zones dedicated only for PUs.

123

CR technology assists D2D communications by enabling users to perform spectrum sensing locally and to identify the unused licensed spectrum and thus reducing the signaling overhead [29]. Similar cooperation can be achieved between cells and UEs in a peer-to-peer basis, where UEs can play a role in making decisions such as choosing the best channel from the ambient radio environment [23]. 3.7 Network virtualization Network virtualization is the process of sharing network resources such as the licensed spectrum, infrastructure, power, backhaul etc., among different cells and network operators in order to improve resource utilization, reduce CAPEX and OPEX, and to facilitate adding new services [16]. The mechanism of network virtualization enables the centralized operation of BBUs by virtualizing their functionalities in central servers. The centralized sharing and processing supports the dynamic coordination among different network nodes, and therefore provides the full benefits of CoMP [20]. Studies show that only 20–40 % of macrocell resources are utilized, whereas in network virtualization these resources can be intelligently and flexibly utilized based on the real needs and thus reducing wasted resources [50]. Virtual layer technology can provide solutions for the challenges experienced in the management of UDNs such as interference, backhaul resources, mobility, costs, etc., by dividing the network into two layers [3]: (1) The virtual layer which provides the wide coverage, monitoring and mobility management, and (2) The real network which contains the small cells that convey high data rates to users. 3.8 Software defined network (SDN) In SDN, the hardware and software are decoupled in order to improve resource sharing such that devices will no longer depend on a specific hardware. Network administrators can easily program the network functions rather than manipulating the sophisticated hardware. Moreover, the control and data planes are separated in SDN such that the control functions are executed by softwares without referring to the network infrastructure [32]. SDN facilitates the network management and simplifies the process of adding or upgrading new applications and services, and this feature supports the dynamic nature of 5G systems. In traditional networks, the control and data planes are combined in the node hardware. The control policies are set before the installation of the node devices, after that, data forwarding begins and when an adjustment is required, the device (hardware) has to be manually reconfigured. Whereas in SDN, the control is centralized in the network operating

Towards the fulfillment of 5G network requirements: technologies and challenges

system and is separated from the data forwarding (transfer) process. Some challenges face the migration from the traditional networks to SDNs such as the flexibility of current node devices to accept new sets of instructions (e.g., protocols, applications, etc.). A possible solution is the utilization of general-purpose processors that can provide high flexibility, as the replacement of currently deployed devices is a highly expensive and complicated process. Other technologies such as the programmable logic devices (PLDs) and field programmable gate arrays (FPGAs) can also be used to provide a hybrid control between the traditional networks and SDNs, leading to a smooth and practical transition and interoperation. Another challenge is the protection against malicious attacks which requires robust and efficient authentication and authorization mechanisms. To solve this problem, SDNs provide the insertion and alteration of security policies such as firewalls and intrusion detection systems (IDSs) [51]. The utilization of software defined networks (SDNs) with network function virtualization (NFV) provides excellent scalability and flexibility in terms of control and management [18,52]. It also reduces the signaling overhead in the core network and improves system capacity. Moreover, it improves networks performance by supporting decentralized mobility management, multi-RAT coordination, and distributed data forwarding [53].

ence between devices within the same cell. In this section, interference cancellation techniques are presented. 4.1 Self-interference (SI) cancellation Self-interference cancellation can be achieved using various techniques [28,38] such as: (1) Antenna separation: by using multiple antennas with phase shifted signals such that the transmitted and received signals are destructively combined at the receiver, or by using a single shared antenna while multiplexing signals using a circulator. (2) Minimizing the intersection between the transmit/receive radiation lobes using directed beams (beamforming). (3) Analog cancellation within the circuit level, which is achieved by training-based methods. (4) Digital cancellation to eliminate the residual SI after analog cancellation. It should be noted that the aforementioned SI cancellation techniques are classified as passive and active, where the first two techniques are passive, whereas the last two are active [38]. 4.2 Inter-cell interference (ICI) cancellation

3.9 Carrier aggregation Carrier aggregation is used to maximize data throughput by aggregating all component carriers, such that data throughput for a single UE can be increased by 100–300 %. In order to achieve a better utilization of this technique, the fairness problem has to be solved by equally distributing component carriers among concurrent users [18]. Spectrum aggregation in Long Term Evolution-Advance (LTE-A) divides the spectrum into fixed length fragments which are then merged to be assigned to a single user. Whereas in 5G communications, the aggregation involves flexible and small-scale fragments using multi-carrier transmission such as the non-contiguous orthogonal frequency division multiplexing (NC-OFDM) and non-contiguous filterbank multicarrier (NC-FBMC) techniques [54]. Table 2 summarizes the technologies, challenges and suggested solutions for 5G networks.

4 Interference cancellation techniques Interference can be classified as: (a) self-interference between the transmitted and received signals within the same device, (b) inter-cell interference between different cells especially at cell borders, and (c) intra-cell interfer-

In ultra dense networks, system performance is limited by the ICI especially at cell borders [11]. The inter-cell interference coordination (ICIC) scheme that is utilized in LTE, is capable of coordinating the interference without eliminating it, and hence the gain is restricted to low and medium load conditions [13]. Some techniques can be efficiently used to cancel ICI: (1) In CoMP, the coordination among different nodes in the network helps mitigating ICI and increasing data rates especially for cell-edge users who suffer strong ICI [20, 42]. (2) The large number of antennas in MIMO facilitate ICIC by dividing the coverage area into sectors and thus localizing the interference area. The remaining interference can then be eliminated using CoMP [13]. (3) Using mmWaves can significantly improve interference mitigation as the interference among concurrent mmWave links is negligible in outdoor environment [41]. (4) Fractional frequency reuse (FFR) can be used to cancel interference between adjacent cells by portioning the entire spectrum into blocks, and each block is subdivided into channels. Each cell can use channels within a specific block and hence avoiding cross interference. A drawback of this technique is the underutilization of frequency resources [18].

123

A. Alnoman, A. Anpalagan Table 2 Summary of the enabling technologies in 5G networks Technology

Description

Benefits

Challenges

Solutions

Full duplex

Transmitting signals using the same frequency in uplink and downlink

- Improves spectrum efficiency due to the fact that FD uses one frequency for both uplink and downlink instead of two different frequencies

- Self-interference mitigation

- Power allocation techniques can be used to reduce the power gap between the transmitted and received signals - Selfinterferencecancellation techniques

mmWaves

Massive MIMO

Using very high frequencies (very short wavelengths) for data transmission especially in small cell networks

Large number of antennas (e.g., hundreds) are used to provide cellular coverage such that each antenna transmits a directed beam concentrated in a specific region

- Provide very large bandwidths and data rates

- Suffer from high path loss especially in the LOS component

- Due to the abundant frequency resources, mmWaves can mitigate co-channel interference by allocating orthogonal channels to different users

- Significant multipath effects

- Improves spectrum efficiency by facilitating spatial frequency reuse

- High costs due to the extra RF circuitry and signal processing requirements

- Using less expensive hardware components (e.g., power amplifiers)

- Mitigates ICI, and hence increases data rates especially for cell-edge users

- Requires adequate backhaul infrastructure

- Integrating optical fiber cables and mmWave to provide an efficient backhaul system

- Improves energy efficiency by coordinating power transmission

- Performance is degraded by backhaul delay

- Users can benefit from the powerful computational resources available by the cloud

- Malicious attacks

- Supporting robust security policies (e.g., authentication mechanisms, firewalls, IDSs)

- Interference between primary and secondary users

- Applying interference cancellation techniques such as CoMP

- Use mmWaves for data transmission only (small cells), while control is provided using low frequency signals (macro cells)

- Improves energy efficiency by concentrating energy in small regions CoMP

Cloud networks

Coordinating functions among network elements (e.g., BSs and UEs)

Network elements are connected to remote servers in order to facilitate processing and management

- Facilitate network management and scalability - Reduce OPEX and CAPEX Cognitive radio

123

Enables secondary (unlicensed) users to utilize the licensed spectrum of primary users without harming the QoS of primary users

- Improves spectrum efficiency by allowing flexible frequency sharing

Towards the fulfillment of 5G network requirements: technologies and challenges Table 2 continued Technology

Description

Benefits

Challenges

Solutions - Efficient CR architecture such as radio environment map (REM) and primary exclusive zone (PEZ)

Network virtualization

Network resources such as - Simplifies network management spectrum, infrastructure, power, etc. are virtualized and shared among different cells and operators - Reduces OPEX and CAPEX

- High data processing requirements

- Implementation of SDNs

- Requires adequate backhaul infrastructure

- Exploiting mmWaves for backhaul

- Facilitates adding new technologies Software defined networks

The network functions are - Simplify network management controlled by programming softwares without configuring the infrastructure (hardware) - Facilitate adding new technologies

- Interoperability and - Using general-purpose flexibility with traditional processors to provide networks flexibile operation in the network nodes - Malicious attacks

- Support security and authentication policies

- Support network virtualization

4.3 Intra-cell interference cancellation In OFDM systems, the intra-cell interference that occurs among users within the same cell can be eliminated efficiently by allocating different frequency bands to different users. Thus, the major source of interference is the ICI [42]. Highly directional beamforming in mmWave systems can also mitigate this type of interference by transmitting directed narrow beams, dividing the cell area into sectors. As a result, the interference among users within the cell will be reduced enabling frequency bands to be efficiently reused in different sectors.

5 Energy efficiency Saving energy (i.e., bits per joule) is a priority in 5G systems design. Around 3 % of the worlds energy is consumed by the information and communication technology (ICT) infrastructure, and this is responsible for about 2 % of the global CO2 emissions [42]. Base stations are responsible for 80 % of these emissions [29], as they consume about 60–80 % of the overall network energy [18]. Energy efficiency can be improved in many aspects as follows: (1) Power control techniques, which are either networkcentric (centralized) such as CoMP and C-RAN or user-centric (decentralized or distributed) such as D2D

and small cells. In the centralized techniques, all network nodes cooperate to maximize energy efficiency, whereas in the decentralized techniques, nodes performance is described as self-organizing [55]. Intelligent power control techniques can be applied to both BSs and UEs. Transmitters and receivers enter a sleep state while they are idle, and will be activated when a call request is triggered. Therefore, power can be controlled dynamically, and BSs with light loads will be entering a sleep state in order to save energy [18]. An optimization algorithm was proposed in [56] to jointly optimize users association and dynamic ON/OFF activation to reduce energy consumption in HetNets. In addition, the utilization of shorter sub-frames facilitates maximizing the sleeping times between data bursts in high bit rate traffic and hence maximizes energy efficiency [11]. However, studies show that the ON/OFF scheme can severely degrade user experience especially when the activation and deactivation take long times [23]. Another scheme splits the network coverage into control plane (C-plane) and user (data) plane (U-plane). In the C-plane, coverage is provided by macrocell BSs which perform global and centralized management to all users, whereas the U-plane is covered by both macrocell and small cell BSs. Users can access the network through macrocell base stations that assign the small cells for data transmission [40]. A similar model was

123

A. Alnoman, A. Anpalagan

(2)

(3) (4) (5) (6)

(7)

(8)

(9)

proposed in [52] where three planes: data, cognitive, and control are organized in a hierarchical scheme based on cell-clustering in order to optimize resource utilization. Small cells: Due to their short-distance transmission, the required power in both uplink and downlink is small, and therefore energy efficiency can be improved [29]. D2D communications enable devices to use low-power signals for short distances. Using a large number of antennas with low power in MIMO [12]. The coordination of signal power among BSs and UEs in CoMP can improve the energy efficiency [42]. Green energy: the efficient utilization of green energy resources can protect the environment from the excessive CO2 emissions that made aggressive climate changes recently. In order to effectively achieve energy harvesting and to provide continuous energy supply for battery-equipped devices; renewable energy resources such as solar, wind, thermal, acoustic, vibration and ambient radio power should be efficiently exploited [2]. Green data centers for cloud-assisted ad-hoc networks in 5G was presented in [57] to minimize the excessive energy consumed by the cloud to search for a lost server that was connected to a mobile user before the occurrence of a link failure which disconnects the user from that server. Wireless charging: RF signals can be used for both information and energy transfer due to their physical characteristics; therefore, a receiver can exploit both data and energy using time or power multiplexing techniques [16]. One potential way to achieve this goal is the utilization of RF access points (APs) that transmit lowenergy signals. The energy of these signals is higher than that of the conventional information signals, enabling users to benefit from the RF energy. The amount of exploited RF energy depends to a large extent on the conversion efficiency of the electromagnetic energy into storable DC energy. Split indoor-outdoor communication: it has been shown that 80 % of wireless communications take place indoors, and this requires that signals have to penetrate through building walls which reduce energy efficiency, data rates, and spectrum efficiency. In order to overcome the aforementioned problems, antenna arrays can be mounted on top of buildings to communicate with macro BSs; meanwhile, these outdoor antennas are connected with the indoor small cells via optical fiber cables. This technology can be applied in high-speed moving vehicles such as high-speed trains, where users are connected with the indoor small BSs, whereas the connection with macro BSs is achieved via the outdoor antennas [12].

123

(10) Subcarrier allocation schemes can improve the energy efficiency of a full-duplex relay networks [58]. In Jing et al. [59], an energy-efficient algorithm was proposed for radio resource allocation in OFDMA femtocell networks under the constraints of delay-aware QoS and cross-tier interference. (11) Backhaul: in current cellular networks, the energy required for the backhaul infrastructure is low and not considered as a serious issue. However, in 5G networks, the backhaul infrastructure is more complicated due to the large number of small cells. Therefore, the energy required by the backhaul should be considered seriously in the design of future 5G networks [42].

6 Network management The large growth in 5G services and the huge number of connected devices lead to the fact that the management of such large networks is complicated and costly. In this section, centralized, distributed and cooperative management schemes are demonstrated. 6.1 Centralized management In the centralized management, all nodes including macrocells, small cells, and UEs are controlled by central servers that take responsibility for gathering the network’s information and statistics, analyze them, and perform the required actions to optimize performance. Small cells that are utilized in enterprises such as airports, universities and other sensitive facilities, are organized and managed by specialized operators; therefore, they can be easily managed by centralized servers that collect statistics from a large number of users and cells within that enterprise. Once a server obtains the required data, it starts optimizing the effective parameters until it achieves the optimum performance. 5G networks are expected to cover an ultra dense environment regarding small cells and users; therefore, more statistical information will be available for analysis and as a result, decisions will be more reliable. On the other hand, such large amount of data requires excessive amount of computations and processing. Moreover, centralized management performs global decisions where all cells might be reconfigured, and this could lead to unstable system performance especially with the expected frequent fault occurrence in small cells [17]. In addition, to obtain an efficient management system, network’s information that are collected from different base stations have to be conveyed through an efficient fronthaul and backhaul infrastructure. In Oliva et al. [60], an integrated fronthaul/backhaul architecture is proposed for 5G networks to centralize the processing of information collected from

Towards the fulfillment of 5G network requirements: technologies and challenges

multiple BSs, thus enhancing the performance of CoMP, carrier aggregation, and MIMO. Studies show that a backhaul delay exceeding 10 ms can degrade the performance of intersite CoMP. Moreover, short backhaul delay is also required for fast coordination schemes such as interference management [23]. 6.2 Distributed management Another paradigm where each cell is capable of organizing its own performance without referring to the core network is the distributed management. In distributed management, the cell monitors the surrounding environment and coverage status depending on statistics collected from the associated devices such as handover requests. Outage detection and compensation in femtocell networks is a process that can be processed using distributed management. Femto BSs sense the surrounding radio environment depending on the statistical measurements of associated users and signals of neighboring BSs. Once an outage is detected due to failures in hardware or software, the cell triggers the process of outage compensation in which the BSs increase the transmission power to compensate for the shortage [17]. Another process that can be controlled in a distributed manner is handover. Where cells can automatically tune their parameters such as the handover margin and time-to-trigger to minimize the number of dropped calls. This method of management is cost-efficient because no backhaul cooperation or additional expenditures are required from the operator’s side and cells can automatically manage their own performance. However, decisions taken by this management scheme depends on sparse statistical information obtained from few users (1–4 users), and hence does not provide certain and sufficient knowledge about the actual condition; which make the self-healing process more challenging [17]. 6.3 Cooperative centralized/distributed management A more sophisticated management system can be achieved by merging both centralized and distributed schemes in a way that reduces computational loads on the centralized servers and enables each cell to locally optimize its own performance. For example, in [17] outage detection and compensation scheme using cooperative management was proposed for femto cells using cooperative management architecture, where outages are detected using distributed trigger mechanism to minimize the computational and signaling overhead, whereas the outages compensation is performed centrally by reconfiguring a group of femtocells. In Zhang et al. [44], intercell interference is mitigated using cooperative interference management in heterogenous cloud small cell network (HCSNet), where RRHs perform a cluster that coordinate

their operation to serve the UEs. This cluster is connected to a central BBU pool which is responsible for the control and management of macro and small cells, thus reducing processing delay and enhancing CoMP. It was shown in [61] that energy efficiency under the hybrid (cooperative) management is 40 % higher than that of the centralized management, it also outperforms the fully distributed management technique. 6.4 Self-organized networks (SON) The OPEX expected during the utilization of 5G networks is high due to the large number of parameters that have to be dynamically tuned, frequent ON/OFF operation of small cells, and the sophisticated coordination required among different tiers and RATs in order to maintain high QoS. Therefore, self-organizing networks (SONs) are considered as a strong candidate to significantly reduce the OPEX by reducing the needs for human intervention. A SON features include self-configuration, self-optimization, and self-healing. In self-configuration, the parameters of newly deployed or booting BSs are autonomously initialized. In self-optimization, parameters such as the transmitted power, antenna tilt, handover, relation with neighboring BSs, scheduling, etc., are tuned to achieve the optimum performance. In self-healing, network failures are resolved automatically [45]. The process of self-healing involves fault detection, fault identification, action identification, and fault compensation [62]. In addition, mobility robustness optimization (MRO) techniques are required to support SONs in order to reduce the number of unnecessary handovers and radio link failures [63]. In Imran et al. [64] a big data SON (BSON) scheme is proposed to upgrade the current SON paradigm to a high level intelligent system capable of predicting users’ behaviors, and optimizing the related parameters accordingly. 5G networks are expected to have the characteristics of self-perception, self-optimization, self-organization and self-healing; therefore, can provide autonomous operation, configuration, fault detection, diagnosis, and finally fix problems [48]. In Duan et al. [65], various examples of SON inspired from biological systems and human social behavior are presented.

7 Resource allocation techniques By efficiently allocating network resources such as cells and subcarriers, the overall network performance can be improved. 7.1 Cell assignment Efficient cell assignment in multi-tier HetNets can improve link quality, mitigate interference, and improve the overall

123

A. Alnoman, A. Anpalagan

system capacity [10]. The signal-to-interference-plus-noise ratio (SINR) is the criterion used to assign cells in a singletier cellular networks; however, multi-tier networks are more challenging due to the variations in transmitted power in different tiers. Therefore, macrocells will have higher loads than small cells which transmit low-power signals. Load unbalancing can be alleviated by offloading users from macrocells to other RATs such as small cells or WiFi [10]. For example, an integrated LTE/WiFi system for ultra dense HetNets is proposed in [14]. Furthermore, a scheduling algorithm is proposed in [11] for outdoor 5G ultra dense networks to achieve uniform distribution in throughput. 7.2 Subcarrier allocation Non-orthogonal frequency division multiplexing can be used to improve spectrum efficiency and increase the number of connected devices [3]. Moreover, the spacing between subcarriers in 5G systems has to be increased compared to the current 15 kHz in LTE networks in order to reduce the peakto-average power ratio (PAPR). It also has to be robust against Doppler frequency shift [9]. The trade-off between bandwidth, MIMO and modulation order can achieve different data rates. For example, a 10 Gbps can be achieved using a system with 100 MHz, 64 QAM with 2/3 coding rate, and 32 MIMO. The same data rate can be obtained using a system with 500 MHz, 64 QAM with 2/3 coding rate, and 8 MIMO [9]. In Nam et al. [66], an algorithm for joint subcarrier assignment and power allocation is proposed for full-duplex OFDMA networks.

8 Conclusion This article presents a comprehensive study on 5G network architecture, technologies, challenges and possible solutions. The main features of future 5G networks are the dynamic performance of the network, the capability to comprise the massive number of machines and users, and the high QoS requirements in order to enable the IoT era. The scarcity of frequency resources is one of the main challenges that face the 5G networks. Several techniques such as the dense deployment of small cells, D2D communications, cognitive radio, MIMO, and mmWaves can provide high spectrum efficiency by providing flexible reuse and sharing of frequency resources. In addition, energy has to be efficiently exploited by adopting intelligent power control techniques and using sustainable energy resources. On the other hand, the complicated and costly network management can be facilitated using technologies such as network virtualization, software defined networks, cloud computing, and SON features. Opportunities for future research include interference

123

cancellation, CoMP, self-optimization and energy harvesting techniques.

References 1. Pierucci, L. (2015). The quality of experience perspective toward 5G technology. IEEE Wireless Communications, 22(4), 10–16. 2. Liu, Y., Zhang, Y., Yu, R., & Xie, S. (2016). Integrated energy and spectrum harvesting for 5G wireless communications. IEEE Network, 29(3), 75–81. 3. Liu, Y., She, X., Chen, P., Zhu, J., & Yang, F. (2015). The easy network: the way to go for 5G. China Communications, 12, 113– 120. 4. Palattella, M. R., et al. (2016). Internet of things in the 5G era: enablers, architecture, and business models. IEEE Journal on Selected Areas in Communications, 34(3), 510–527. 5. Muirhead, D., Imran, M. A., & Arshad, K. (2015). Insights and approaches for low-complexity 5G small-cell base-station design for indoor dense networks. IEEE Access, 3, 1562–1572. 6. Demestichas, P., Georgakopoulos, A., Tsagkaris, K., & Kotrotsos, S. (2015). Intelligent 5G networks: managing 5G wireless/mobile broadband. IEEE Vehicular Technology Magazine, 10(3), 41–50. 7. Wei, L., Hu, R. Q., Qian, Y., & Wu, G. (2014). Key elements to enable millimeter wave communications for 5G wireless systems. IEEE Wireless Communications, 21(6), 136–143. 8. Wang, J., Lv, Z., Ma, Z., Sun, L., & Sheng, Y. (2015). I-net: new network architecture for 5G networks. Communications Magazine, 53(6), 44–51. 9. Wang, H., Ni, J., Pan, Z., Sun, J., Pan, C., & Chih-Lin, I. (2014). Perspectives on new waveform design for 5G small cell. In General Assembly and Scientific Symposium (URSI GASS) (pp. 1–4). 10. Wang, N., Hossain, E., & Bhargava, V. K. (2015). Backhauling 5G small cells: a radio resource management perspective. IEEE Wireless Communications, 22(5), 41–49. 11. Kela, P., Turkka, J., & Costa, M. (2015). Borderless mobility in 5G outdoor ultra-dense networks. IEEE Access, 3, 1462–1476. 12. Gupta, A., & Jha, R. K. (2015). A survey of 5G network: architecture and emerging technologies. IEEE Access, 3, 1206–1232. 13. Jungnickel, V., et al. (2014). The role of small cells, coordinated multipoint, and massive MIMO in 5G. IEEE Communications Magazine, 52(5), 44–51. 14. Galinina, O., Pyattaev, A., Andreev, S., Dohler, M., & Koucheryavy, Y. (2015). 5G multi-RAT LTE-WiFi ultra-dense small cells: performance dynamics, architecture, and trends. IEEE Journal on Selected Areas in Communications, 33(6), 1224–1240. 15. Zhang, H., Jiang, C., Beaulieu, N. C., Chu, X., Wen, X., & Tao, M. (2014). Resource allocation in spectrum-sharing OFDMA femtocells with heterogeneous services. IEEE Transactions on Communications, 62(7), 2366–2377. 16. Hossain, E., & Hasan, M. (2015). 5G cellular: key enabling technologies and research challenges. IEEE Instrumentation and Measurement Magazine, 18(5), 11–21. 17. Wang, W., & Zhang, Q. (2014). Local cooperation architecture for self-healing femtocell networks. IEEE Wireless Communications, 21(2), 42–49. 18. Chuang, M.-C., Chen, M. C., & Sun, Y. S. (2015). Resource management issues in 5G ultra dense smallcell networks. In International Conference on Information networking (ICOIN) (pp. 159–1640). 19. Ge, X., Tu, S., Mao, G., Wang, C. X., & Han, T. (2016). The 5G ultra dense cellular networks. Wireless Communications, 23(1), 72–79. 20. Liu, C., Wang, J., Cheng, L., Zhu, M., & Chang, G. K. (2014). Key microwave-photonics technologies for next-generation cloud-

Towards the fulfillment of 5G network requirements: technologies and challenges

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37. 38.

39.

based radio access networks. Journal of Lightwave Technology, 32(20), 3452–3460. Zhang, H., Chu, X., Guo, W., & Wang, S. (2015). Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum. IEEE Communications Magazine, 53(3), 158–164. Zhang, H., Jiang, C., Mao, X., & Chen, H. (2016). Interferencelimited resource optimization in cognitive femtocells with fairness and imperfect spectrum sensing. IEEE Transactions on Vehicular Technology, 65(3), 1761–1771. Peng, H., Xiao, Y., Ruyue, Y. N., & Yifei, Y. (2016). Ultra dense network: challenges, enabling technologies and new trends. China Communications, 13(2), 30–40. Yasuda, H. et al. (2015). A study on moving cell in 5G cellular system. In IEEE Conference on Vehicular Technology (VTC) (pp. 1–5). Kliks, A., Holland, O., Basaure, A., & Matinmikko, M. (2015). Spectrum and license flexibility for 5G networks. IEEE Communications Magazine, 53(7), 42–49. Usman, M., Gebremariam, A. A., Raza, U., & Granelli, F. (2015). A software-defined device-to-device communication architecture for public safety applications in 5G networks. IEEE Access, 3, 1649–1654. Cheng, W., Zhang, X., & Zhang, H. (2014). Optimal power allocation for full-duplex D2D communications over wireless cellular networks. In IEEE Global Communications Conference (GLOBECOM) (pp. 4764–4769). Wang, L., Tian, F., Svensson, T., Feng, D., Song, M., & Li, S. (2015). Exploiting full duplex for device-to-device communications in heterogeneous networks. IEEE Communications Magazine, 53(5), 146–152. Wu, G., Yang, C., Li, S., & Li, G. Y. (2015). Recent advances in energy efficient networks and their application in 5G systems. IEEE Wireless Communications, 22(2), 145–151. Mumtaz, S., Huq, K. M. S., Ashraf, M. I., Rodriguez, J., Monteiro, V., & Politis, C. (2015). Cognitive vehicular communication for 5G. IEEE Communications Magazine, 53(7), 109–117. Pervaiz, H., Musavian, L., & Ni, Q. (2015). The area energy and area spectrum efficiency trade-off in 5G heterogeneous networks. In IEEE International Conference on Communication Workshop (ICCW) (pp. 1178–1183). Yang, F., Wang, H., Mei, C., Zhang, J., & Wang, M. (2015). A flexible three clouds 5G mobile network architecture based on NFV and SDN. China Communications, 12, 121–131. Condoluci, M., Dohler, M., Araniti, G., Molinaro, A., & Zheng, K. (2015). Toward 5G denseNets: architectural advances for effective machine-type communications over femtocells. IEEE Communications Magazine, 53(1), 134–141. Shariatmadari, H., et al. (2015). Machine-type communications: current status and future perspectives toward 5G systems. IEEE Communications Magazine, 53(9), 10–17. Zhang, X., Cheng, W., & Zhang, H. (2014). Heterogeneous statistical QoS provisioning over 5G mobile wireless networks. IEEE Network, 28(6), 46–53. Goyal, S., Liu, P., Panwar, S. S., Difazio, R. A., Yang, R., & Bala, E. (2015). Full duplex cellular systems: will doubling interference prevent doubling capacity? IEEE Communications Magazine, 53(5), 121–127. Xie, X., & Zhang, X. (2014). Does full-duplex double the capacity of wireless networks? In IEEE INFOCOM (pp. 253–261). Zhang, Z., Chai, X., Long, K., Vasilakos, A. V., & Hanzo, L. (2015). Full duplex techniques for 5G networks: self-interference cancellation, protocol design, and relay selection. IEEE Communications Magazine, 53(5), 128–137. Catania, D., Sarret, M. G., Cattoni, A. F., Frederiksen, F., Berardinelli, G., & Mogensen, P. (2014). The Potential of Flexible

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53.

54.

55.

56.

57.

58.

UL/DL Slot Assignment in 5G Systems. In IEEE Vehicular Technology Conference (pp. 1–6). Han, C. L. I. S., Xu, Z., Wang, S., Sun, Q., & Chen, Y. (2016). New paradigm of 5G wireless internet. IEEE Journal on Selected Areas in Communications, 34(3), 474–482. Qiao, J., Shen, X. S., Mark, J. W., Shen, Q., He, Y., & Lei, L. (2015). Enabling device-to-device communications in millimeter-wave 5G cellular networks. IEEE Communications Magazine, 53(1), 209– 215. Huq, K. M. S., Mumtaz, S., Bachmatiuk, J., Rodriguez, J., Wang, X., & Aguiar, R. L. (2014). Green HetNet CoMP: energy efficiency analysis and optimization. IEEE Transaction on Vehicular Technology, 64(10), 4670–4683. Mustafa, H. A. U., Imran, M. A., Shakir, M. Z., Imran, A., & Tafazolli, R. (2015). Separation framework: an enabler for cooperative and D2D communication for future 5G networks. IEEE Communications Surveys and Tutorials, 18(1), 419–445. Zhang, H., Jiang, C., Cheng, J., & Leung, V. C. M. (2015). Cooperative interference mitigation and handover management for heterogeneous cloud small cell networks. IEEE Wireless Communications, 22(3), 92–99. Zhang, H., Jiang, C., Hu, R. Q., & Qian, Y. (2016). Selforganization in disaster-resilient heterogeneous small cell networks. IEEE Network, 30(2), 116–121. Han, Q., Liang, S., & Zhang, H. (2015). Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world. IEEE Network, 29(2), 40–45. Zhang, N., Cheng, N., Gamage, A. T., Zhang, K., Mark, J. W., & Shen, X. (2015). Cloud assisted HetNets toward 5G wireless networks. IEEE Communications Magazine, 53(6), 59–65. Liu, X., Wang, P., Lan, Z., & Shao, B. (2015). Biological characteristic online identification technique over 5G network. IEEE Wireless Communications, 22(6), 84–90. Jiang, C., Zhang, H., Ren, Y., & Chen, H. (2014). Energy-efficient non-cooperative cognitive radio networks: micro, meso, and macro views. IEEE Communications Magazine, 52(7), 14–20. Rost, P., et al. (2015). Benefits and challenges of virtualization in 5G radio access networks. IEEE Communications Magazine, 53(12), 75–82. Sezer, S., et al. (2013). Are we ready for SDN? Implementation challenges for software-defined networks. IEEE Communications Magazine, 51(7), 36–43. Feng, Z., Qiu, C., Feng, Z., Wei, Z., Li, W., & Zhang, P. (2015). An effective approach to 5G: wireless network virtualization. IEEE Communications Magazine, 53(12), 53–59. Wang, H., Chen, S., Xu, H., Ai, M., & Shi, Y. (2015). SoftNet: a software defined decentralized mobile network architecture toward 5G. IEEE Network, 29(2), 16–22. Bogucka, H., Kryszkiewicz, P., & Kliks, A. (2015). Dynamic spectrum aggregation for future 5G communications. IEEE Communications Magazine, 53(5), 35–43. Zappone, A., Sanguinetti, L., Bacci, G., Jorswieck, E., & Debbah, M. (2015). Energy-efficient power control: a look at 5G wireless technologies. IEEE Transactions on Signal Processing, 64(7), 1668–1683. Tran, G.K., Shimodaira, H., Rezagah, R. E., Sakaguchi, K., & Araki, K. (2015). Dynamic cell activation and user association for green 5G heterogeneous cellular networks. In IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 2364–2368). Han, N. D., Chung, Y., & Jo, M. (2015). Green data centers for cloud-assisted mobile Ad Hoc networks in 5G. IEEE Network, 29(2), 70–76. Liu, G., Yu, F. R., Ji, H., & Leung, V. C. M. (2014). The energyefficient resource allocation in cellular networks with shared

123

A. Alnoman, A. Anpalagan

59.

60.

61.

62.

63.

64.

65.

66.

full-duplex relaying. IEEE Transactions on Vehicular Technology, 64(8), 3711–3724. Jing, W., Lu, Z., Zhang, H., Zhang, Z., Zhao, J., & Wen, X. (2014). Energy-Saving Resource Allocation Scheme With QoS Provisioning in OFDMA Femtocell Networks. In IEEE International Conference on Communications Workshops (ICC) (pp. 912–917). Oliva, A. D. L., et al. (2015). Xhaul: toward an integrated fronthaul/backhaul architecture in 5G networks. IEEE Wireless Communications, 22(5), 32–40. Geraci, G., Wildemeersch, M., & Quek, T. Q. S. (2015). Distributed network management for green wireless communications. In IEEE Global Communications Conference (GLOBECOM) (pp. 1–7). Barco, R., Lazaro, P., & Munoz, P. (2012). A unified framework for self-healing in wireless networks. IEEE Communications Magazine, 50(12), 134–142. Zheng, W., Zhang, H., Chu, X., & Wen, X. (2013). Mobility robustness optimization in self-organizing LTE femtocell networks. EURASIP Journal on Wireless Communications and Networking, 1, 1–10. Imran, A., Zoha, A., & Abu-Dayya, A. (2014). Challenges in 5G: how to empower SON with big data for enabling 5G. IEEE Network, 28(6), 27–33. Duan, D., Yang, L., Cao, Y., Wei, J., & Cheng, X. (2014). Selforganizing networks: from bio-inspired to social-driven. IEEE Intelligent Systems, 29(2), 86–90. Nam, C., Joo, C., & Bahk, S. (2015). Joint subcarrier assignment and power allocation in full-duplex OFDMA networks. IEEE Transactions on Wireless Communications, 14(6), 3108–3119.

Ali Alnoman is a Ph.D. student in the WINCORE Lab, Dept. of Electrical and Computer Engineering at Ryerson University. He graduated from the Department of Electrical Engineering – University of Baghdad, Iraq in 2009 and received his M.Sc. degree in Electronics and Communications in 2012 from the same university. During the period 2012–2015, he worked as a full-time lecturer at Ishik University, Erbil, Iraq. His research interests include wireless communications and signal processing.

123

Alagan Anpalagan received the B.A.Sc. M.A.Sc. and Ph.D. degrees in Electrical Engineering from the University of Toronto, Canada. He joined the Department of Electrical and Computer Engineering at Ryerson University in 2001 and was promoted to Full Professor in 2010. Dr. Anpalagan directs a research group working on radio resource management (RRM) and radio access & networking (RAN) areas within the WINCORE Lab. His current research interests include harvesting and green communications technologies, cognitive radio resource management, wireless cross layer design and optimization, cooperative communication, M2M communication, small cell and heterogeneous networks, and smart grid. He served as Associate Editor for the IEEE Communications Surveys & Tutorials (2012–14), IEEE Communications Letters (2010–13), Springer Wireless Personal Communications (2009–14), and EURASIP Journal of Wireless Communications and Networking (2004–2009). He also served as Guest Editor for two EURASIP SI in Radio Resource Management in 3G+ Systems (2006) and Fairness in Radio Resource Management for Wireless Networks (2008) and, MONET SI on Green Cognitive and Cooperative Communication and Networking (2012). He co-authored of three edited books, Design and Deployment of Small Cell Networks, Cambridge University Press (2014), Routing in Opportunistic Networks, Springer (2013), Handbook on Green Information and Communication Systems, Academic Press (2012).