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System Architecture and Key Technologies for 5G Heterogeneous Cloud Radio Access Networks

Compared with the fourth generation (4G) cellular systems, the fifth generation wireless communication systems (5G) are anticipated to provide spectral and energy efficiency growth by a factor of at least 10, and the area throughput growth by a factor of at least 25. To achieve these goals, a heterogeneous cloud radio access network (H-CRAN) is presented in this article as the advanced wireless access network paradigm, where cloud computing is used to fulfill the centralized large-scale cooperative processing for suppressing co-channel interferences. The state-of-the-art research achievements in aspects of system architecture and key technologies for H-CRANs are surveyed. Particularly, Node C as a new communication entity is defined to converge the existing ancestral base stations and act as the base band unit (BBU) pool to manage all accessed remote radio heads (RRHs), and the software-defined H-CRAN system architecture is presented to be compatible with software-defined networks (SDN). The principles, performance gains and open issues of key technologies including adaptive large-scale cooperative spatial signal processing, cooperative radio resource management, network function virtualization, and self-organization are summarized. The major challenges in terms of fronthaul constrained resource allocation optimization and energy harvesting that may affect the promotion of H-CRANs are discussed as well.


I. INTRODUCTION
With the rapid development of mobile internet and internet of things (IoTs), the demands for highspeed data applications, such as high-quality wireless video streaming, social networking and machineto-machine communication, have been growing exponentially recently. It is envisioned that the total daily mobile traffic in the representative Western European countries will grow 67 times from 186 terabyte (TB) to 12540 TB through 2010 to 2020, and the total worldwide mobile traffic of 351 exabyte (EB) in 2025 represents a 174% increase compared with 2020 [1]. Currently, the cellular networks including the first generation (1G), second generation (2G), third generation (3G) and fourth generation (4G) are far from satisfying the significant traffic increments and the high energy efficiency (EE) because a lot of power of a base station (BS) is used to overcome path loss, which in turn causes interferences to other users. The fifth generation (5G) system deployed initially in 2020 is expected to provide about 1000 times higher wireless area capacity and save up to 90% of energy consumption per service compared with the current 4G system. More than 1000 Gbit/s/km 2 area spectral capacity in dense urban environments, 10 times higher battery life time of connected devices, and 5 times reduced end-to-end (E2E) latency are anticipated in 5G systems. The new 5G air interface and spectrum should be combined together with the long term evolution (LTE) and WiFi to provide universal high-rate coverage and a seamless user experience [2].
To achieve these goals of 5G systems, advanced radio access technologies and all-internet protocol (IP) open internet network architectures should be evolved smoothly from 4G systems [3]. Accurately, the new breakthroughs in the baseband and radio frequency (RF) are required to enable computationally intensive and adapt to new air interfaces in 5G systems. A significant and advanced baseband computation is required to meet the complex requirements of new solutions like large-scale cooperative signal processing in the physical layer. Meanwhile, the new breakthroughs in the integrated access node and heterogeneous convergence are required to enable the ultra dense radio nodes to work efficiently. The plug-and-play function becomes essential to commercial deployments, in which the available spectral resources should be allocated and the corresponding parameters should be self-organized. Furthermore, the software-defined air interface technologies should be seamlessly integrated into the 5G radio access network (RAN) architectures. The cloud computing based radio access infrastructures would provide on-demand resource processing, delay-aware storage, and high network capacity wherever needed.

A. 5G C-RAN Solution
Some advanced technologies, such as the cloud radio access network (C-RAN) and ultra small cells based heterogeneous network (HetNet), have been presented as potential 5G solutions. C-RAN has attracted intense research interests from both academia and industry (such as China Mobile, Huawei, Alcatel Lucent, Qualcomm) [4]. In C-RANs, a large number of low-cost remote radio heads (RRHs) are randomly deployed and connect to the base band unit (BBU) pool through the fronthaul links. C-RANs have several advantages: First, by moving RRHs closer to the users, a higher system capacity and lower power consumption can be achieved because the signal doesn't need to propagate a long distance to reach the users; Second, since the baseband processing is centralized at the BBU pool, the cooperative processing techniques to mitigate interferences can be leveraged; Third, by exploiting the resource pooling and statistical multiplexing gain, C-RAN is much more efficient in both energy and cost aspects because it is needless to dimension the computing resource of each traditional BS according to the individual peak load. However, the fronthaul constraints have great impact on worsening performances of C-RAN, and the scale size of RRHs accessing the same BBU pool is limited and could not be too large due to the implementation complexity. Furthermore, many kinds of system architectures have been proposed by different mobile operators, manufactories and even researching institutes to explore potential advantages of C-RANs. Therefore, an unified C-RAN for 5G is still not straightforward.

B. 5G HetNet Solution
To increase the capacity of cellular networks in dense areas with high traffic demands, the low power node (LPN) serving for the pure "data-only" service with high capacity is identified as one of key components in HetNets [5]. One key advantage of HetNets is to decouple the control plane and user plane. LPNs only have the control plane, while the control channel overhead and cell-specific reference signals of LPNs can be fully shifted to macro base stations (MBSs). Unfortunately, an underlaid structure that MBSs and LPNs reuse the same spectral resources could lead to severe inter-tier interferences.
Hence, it is critical to suppress interferences through advanced signal processing techniques to fully unleash the potential gains of HetNets, such as adopting the advanced coordinated multi-point (CoMP) transmission and reception technique to suppress both intra-tier and inter-tier interferences. It was reported that the average spectral efficiency (SE) performance gain from the uplink CoMP in downtown Dresden field trials was only about 20 percents with non-ideal backhaul in [6].

C. 5G H-CRAN Solution
To fulfill new breakthroughs anticipated in 5G systems, and overcome the aforementioned challenges in both C-RANs and HetNets, we presented heterogeneous cloud radio access networks (H-CRANs) as 5G RANs in our prior work [7], which are fully backward compatible with different kinds of C-RANs and HetNets. The motivation of H-CRANs is to embed the cloud computing technology into HetNets to realize the large-scale cooperative signal processing and networking functionalities, and thus SE and EE performances are substantially improved beyond existing HetNets and C-RANs. In the H-CRAN based 5G system, the control plane and user plane are decoupled. MBSs are mainly used to deliver control signalling of the whole H-CRAN and provide the seamless coverage, while RRHs are used to provide the high speed data transmission in the hot spots with huge data services. The large-scale cooperative signal processing and networking techniques in H-CRANs are threefold. The first comprises advanced spatial signal processing techniques in the physical layer (PHY), including the centralized massive multipleinput-multiple-output (MIMO) and the distributed large-scale spatial cooperative processing; the second comprises large-scale cooperative radio resource management and cloudization in the medium access control (MAC) and upper layers; the third comprises intelligent and self-organizing network management in the network layer to support self-configuration, self-optimization, and self-healing in the ultra dense communication scenario with huge number of nodes deployed randomly.

D. Motivation and Organization
In this article, we are motivated to make an effort to offer a comprehensive discussion on recent advances of system architectures and technological principles in 5G H-CRANs. Specifically, the application scenarios and H-CRAN system architecture are presented, where the new communication entity Node C is defined and the software-defined H-CRAN architecture is designed. The large-scale cooperative signal processing and networking techniques strictly related to improve SE and EE in H-CRANs are surveyed, including advanced spatial signal processing, cooperative radio resource management, network function virtualization (NFV), and self-organizing network (SON). The challenging issues related to resource allocation optimization and energy harvesting are discussed as well.
The remainder of this paper is outlined as follows. The application architecture and system components of H-CRANs will be introduced in Section II. The promising large-scale cooperative signal processing and networking technologies will be shown in Section III. Future challenges will be highlighted in Section IV, followed by the conclusions in Section V.

II. APPLICATION ARCHITECTURE AND SYSTEM COMPONENTS
The H-CRAN based 5G systems have capabilities to provide anytime, anywhere gigabit data rate service to the desired user equipments (UEs), where an "edgeless" experience to UEs is provided under the help of heterogeneous MBSs and ultra dense RRHs. Based on cloud computing technologies, the on-demand resource processing, storage and network capacity wherever needed are fulfilled.
Software-defined air interfaces and networking technologies are seamlessly integrated into the H-CRAN architectures, which enables the flexibility to create new services and applications.

A. Application Architecture of H-CRANs
H-CRANs shown in Fig. 1 When Node C works to converge ACEs, it can be regarded as a convergence gateway to execute the cooperative multiple-radio resource managements (CM-RRM) and media independent handover (MIH) functionalities, and the functionalities of traditional radio network controller (RNC) and BS controller (BSC) can be embodied into Node C. When Node C is used to manage RRHs, it acts as the BBU pool, which is inherited from C-RANs. In addition, Node C has a powerful computing capabilities to execute the large-scale cooperative signal processing in the physical layer and large-scale cooperative networking in the upper layers.
In H-CRANs, RRHs are mainly used to provide the high speed data transmission without the control plane in hot spots. The control channel overhead and cell specific reference signals for the whole H-CRAN are delivered by ACEs. To guarantee the seamless coverage, UEs nearer to ACEs than RRHs are served by ACEs and called HUEs. To improve the intelligences and self-organizing capabilities, the universal plug and play functionality is critical, which allows both the new deployed RRHs and the existing ACEs to access the Node C instantaneously and automatically. With the help of Node C in H-CRANs, hundreds of RRHs and several tens of ACEs can be served simultaneously, and the control and user plane separation can be realized efficiently.
In view of the problems existing in internet protocol (IP) networks, software-defined network (SDN) is proposed as the IP evolution in 5G systems to simplify the network construction and operational costs by adopting the NFV technology [8]. The SDN based wireless network infrastructures are constantly emerging, in which SDN is co-located with the internet/IoT network entities and decentralizes RRHs/ACEs closer to the desired UEs. To be compatible with the SDN architecture, the adaptive signaling/control mechanism between connection-oriented and connectionless is supported in H-CRANs, which can achieve significant overhead savings in the radio connection/release by moving away from a pure connection-oriented mechanism. Node C is interfaced to SDN to complete NFV functionalities, which is urgent to be adaptive to the dramatically growing traffic features and demands. Different kinds of radio communication technologies in the physical layer can be adopted in RRHs to improve transmission bit rates, such as IEEE 802.11 ac/ad, millimeter wave communication, and even optical light communication.
To further decrease the power consumption in H-CRANs, the activating RRHs are adaptive to the traffic volume. When the traffic load is low, some potential RRHs fall into sleep under supervising of Node C. However, when the traffic load becomes tremendous in a small special zone, both the heterogeneous ACEs and dense RRHs work together to satisfy the huge capacity requirements. Further, based on the NFV technology, RRHs with high load can borrow the radio resources of neighboring homogeneous and heterogeneous RRHs with low load.

B. System Components of H-CRANs
As shown in Fig. 1 To improve SE and EE performances of the software-defined H-CRAN, there are 4 key functionalities to be implemented, such as the self-organizing, radio resource cloudization, cognitive processing, and big data mining. The big data mining functionality imports the machine learning technology into the cooperative signal processing and intelligent networking, which makes the 5G system adaptive to the Internet/IoT traffic's features and demands. The H-CRAN based 5G systems are traffic-driven and usercentrical. To avoid the inter-tier interference, the cognitive processing technique is utilized to make RRHs and ACEs work cooperatively in the overlaid scenario when the overall system load is not high. Furthermore, to improve the SE performance when the overall system load is high, the radio resource cloudization technique is used to decrease the inter-tier interference and improve the flexibility of radio resource's reuse. To decrease the operation cost and enhance the networking intelligences, the self-organizing H-CRAN is necessary. With measurement and analysis of SE and EE performances

A. Large-Scale Cooperative Spatial Signal Processing (LS-CSSP)
For the ultra dense nodes deployed in the traditional cellular networks or HetNets, although the impact of large-scale fading is alleviated, the existence of several co-channel interferences becomes the performance bottleneck. The LS-CSSP, including both distributed and centralized modes, is prominent to improve SE performance with the gigabit rate as shown in Fig. 2  dirty paper pre-coding can achieve a good capacity region, it is impractical because the complication increases with the number of connected RRHs and ACEs. The linear precoding is a good alternative with less complexity, which can be optimized jointly with combination of constraints on fronthaul [9].
Meanwhile, the cell association has great impact on precoding performances, the joint cell selection and precoding is key to improve EE and SE performances. As shown in Fig. 3 [10], in the uplink, the distributed LS-CSSP has good potential to strengthen uplink signal and suppress inter-RRH interference, whose gain can reach 40 ∼ 100% in the weak coverage area (that is the area where reference signal receive power (RSRP) is lower than -95dBm), and in some area the gain can exceed 100%. Due to the severe inter-RRH interference, the uplink throughput cannot reach peak at good coverage area (e.g. where RSRP higher than -90dBm) when the distributed LS-CSSP is not utilized. Distributed LS-CSSP can bring 1 ∼ 2 Mbps throughput boost and result in 20 ∼ 50% throughput gain at the test area.
The superior centralized processing capability of H-CRAN provides great convenience for making distributed LS-CSSP effective, even can support the combination with other advanced techniques, such By enlarging the special degrees of freedom, MIMO technique can improve SE and EE performances via achieving an extra diversity-multiplexing tradeoff. The centralized LS-CSSP is equipped with hundreds of low-power antennas at a co-located ACE site, which is mainly used to improve capacity, extend coverage, and decrease antenna deployment complexity. According to the law of large numbers, the channel propagation condition can be hardened, which ensures that the transmission capacity increases linearly as the number of antennas increases. The centralized LS-CSSP can increase the capacity 10 times or more, and improve the radiated EE performance on the order of 100 times as well [11].
Unlike RRHs, ACEs do not need to upload all their observations to Node C for the joint signal cooperative processing. By deploying ACEs with the centralized LS-CSSP, instead of using a huge number of RRHs in some coverage areas, the overload on fronthauls between RRHs and Node C is released, and the time latency of backhaul between MBSs and Node C is alleviated. Compared with ACEs without the centralized LS-CSSP, or the conventional C-RAN configuration, ACEs with the centralized LS-CSSP reduce inter-tier interferences to adjacent RRHs/ACEs because they can serve a large area with diluting the density of active RRHs. Moreover, the cooperative beamforming between ACEs and RRHs can suppress remaining inter-tier interferences.

B. Large-Scale Cooperative Radio Resource Managements (LS-CRRM)
To optimize performances of H-CRANs, more advanced radio resource management schemes are required than the traditional cellular network due to the additional computing resources. Since the physical radio resources can be completely reused among RRHs, the inter-tier interference between RRHs and ACEs should be suppressed with advanced LS-CRRM techniques. The traditional soft fractional frequency reuse (S-FFR) is considered to be an efficient inter-cell and inter-tier interference coordination technique, in which the service area is partitioned into spatial regions, and each subregion is assigned with different frequency sub-bands. Therefore, the cell-edge-zone UEs do not interfere with cell-center-zone UEs, and with an efficient channel allocation method, the cell-edge-zone UEs may not interfere with neighboring cell-edge-zone UEs [12]. Considering that the ACE is mainly used to deliver the control signalling and guarantee the seamless coverage, the QoS requirement of HUEs is not high, while the QoS requirement for UEs accessing RRHs (denoted as RUEs) is with a high priority.
Consequently, an enhanced S-FFR scheme should be proposed to suppress the inter-tier interference between the ACE and RRHs in Fig. 4(a), where only partial radio resources are allocated to both RUEs and HUEs with the low QoS requirement, and other radio resources are allocated to RUEs with high QoS requirements.
For the enhanced S-FFR, the RUEs with low QoS requirements will share the same radio resources with HUEs, which is absolutely different from that in the traditional S-FFR, where the radio resources allocated to cell-edge-zone UEs are orthogonal. If the traditional S-FFR is utilized in H-CRANs, only the cell-center-zone RUEs will share the same radio resources with HUEs, which decreases the SE performance significantly. Further, how to determine UEs located in the cell-edge or cell-center zone is challenging for the traditional S-FFR. Fortunately, these problems are solved via the enhanced S-FFR, where only the QoS requirement should be distinguished for RUEs. To avoid inter-tier interference between RRHs and ACEs, the outband frequency is preferred to be used according to the standards of HetNets in 3GPP, which means that RBs for ACEs should be different from those for RRHs. Meanwhile, to save the occupied frequency bands, the inband strategy is also defined in 3GPP, which means that both RUEs and HUEs will share the same RBs even when the inter-tier interference exists. To be completely compatible with inband and outband strategies in 3GPP, only two RB sets (namely Ω 1 and Ω 2 ) should be divided for the enhanced S-FFR scheme. Obviously, if the locations of RUEs could be known and the traffic volume in different zones are clearly anticipated, more RB sets can be divided in Ω 1 to achieve a higher performance gain. Ω 1 is only allocated to RUEs with high rate-constrained QoS requirements, and Ω 2 is allocated to RUEs and HUEs with low rate-constrained QoS requirements. Since all signal processing for different RRHs are executed on the BBU pool centrally, the inter-RRH interferences can be avoided and the same radio resource can be shared amongst RRHs, thus the radio resources among RRHs are reused and virtualized. Based on the enhanced S-FFR, the inter-tier interference among RRHs and ACEs can be suppressed efficiently.
To avoid the traffic congestion and obtain the near-optimal performances in H-CRANs, the LS-CRRM scheme should take the queue state information (QSI) into account to guarantee the fairness among UEs and improve the utilization efficiency of radio resource. Particularly, the joint optimization problem taking traffic admission control, RRH/MBS association, RB and power allocation into account subject to the average and instantaneous power consumption constraints should be researched, and performances of the corresponding joint-association-RB-power allocation (JARP) scheme are evaluated in Fig. 4(b), where the random-association-joint-RB-and-power allocation (RAJRP) scheme and joint-associationand-RB-equal-power allocation (JAREP) scheme are compared as baselines [13]. For RAJRP, the HUE randomly accesses the target RRH with the probability proportional to the throughput virtually, and the RB and power are jointly optimized using our algorithm. For JAREP, association and RBs are jointly optimized using our algorithm and the maximum average power is equally allocated on all RBs of each RRH.
The performances of average queue length of HUEs and RUEs versus mean traffic arrival rate of RUEs are compared among these three schemes. It is observed that the bigger the traffic arrival rate of RUEs, the larger the average queue length for HUEs. This can be understood by the fact that more RBs and power are allocated to RUEs to maintain the queue stability, leaving less resource for HUEs which are intended to access RRHs. As the mean arrival rate of RUEs increases, the system throughput utility increases, but with a diminishing slope. The rationale is that all traffic arrivals of RUEs can be admitted by these schemes according to the threshold-based admission control policy when the traffic arrival rate of RUEs is relatively small, and some traffic arrivals for RUEs have to be denied to stabilize the traffic queues with higher traffic arrival rate of RUEs. JARP achieves a higher utility and smaller average queue length than JAREP and RAJRP under all traffic arrival rates, which implies the importance of the joint optimization of association, RB, and power allocation.

C. Network function virtualization (NFV) in H-CRANs
Based on the software-defined H-CRAN architecture, NFV is an emerging technology for costeffective sharing of scarce radio and computing resources in 5G systems, which aims to evolve standard computing virtualisation technology to consolidate network equipments into high volume servers, switches and storage. Accurately, as highly complementary to the control and user plane separation in H-CRANs, NFV in SDNs is applicable to the packet processing in the U plane and the control function in the C plane, which offers the potential capabilities for both enhancing service delivery and reducing overall costs. By enabling NFV with OpenFlow-enabled SDN, network operators can realize significant benefits through utilizing the cloud computing technology.
The H-CRAN infrastructures provide potential capabilities to enhance resource availability and usage by means of orchestration and management mechanisms, which are applicable to the automatic instantiation of virtual appliances and the management of resources by assigning virtual appliances to the correct computing core, memory and interfaces. The NFV designed for H-CRANs is shown in Fig.   5, where the physical radio resources are cloudized, and computing resources in both intra-RANs and inter-RANs are virtualized. The functionalities of the virtual machine (VM) technology executed in Node C include: (1) Allow for quick creation and deletion of VM instances for management purposes; (2) Support a wide variety of distributions to allow customization of environment; (3) Allow the user to customize everything from the Kernel to the drivers; and (4) Provide means for easy administration. To fulfill the virtual infrastructure, the Node C should embody the generic high-volume computing servers, storage devices, high-volume network switches and be organized by the orchestration. H-CRANs have to consider resource sharing for uplink traffic, while UEs originate the uplink traffic randomly, which makes it hard to achieve the two conflicting goals of isolation and efficient resource utilization across slices. Meanwhile, H-CRANs often incur considerable overheads due to signaling and retransmissions that have to be properly accounted for, which hurts the resource utilization and flexibility.

D. Large-Scale Self-Organizing H-CRANs (LS-SON)
To fulfill the universal plug and play functionality, offload the traffic from the core network and manage computing and radio resources more efficiently using the service-aware controls, the large- Due to the existence of centralized Node C co-located with the SDN in the H-CRAN based 5G systems, the self-configuration, self-optimization, and self-healing functionalities are implemented in the centralized SON architecture. Since Node C needs to converge multiple RANs and process the RRHs with cloud computing cooperatively, which is shown in Fig. 6, the inter-RAN and intra-RAN SON functionalities should be implemented in Node C.
For the self-configuration, since RRHs are utilized to support transmissions with high data rates, the physical cell identifier (PCI) assignment is not necessary for RRHs, and only the radio resource should be self-assigned to each RRHs intelligently. However, the self-configuration of both PCI and radio resource should be fulfilled in ACEs. Thanks to Node C, these self-configuration cases for ACEs are handled in the centralized mode. For the self-optimization cases, energy saving and mobility load balance of the H-CRANs. Fronthaul with high bit rate and low time latency enables larger-scale cooperative processing and networking, which in turn makes the available scarce resources use more efficiently.

B. Energy Harvesting in H-CRANs
To save operational costs and decrease the energy consumptions, 5G systems are anticipated with renewable energy supply, such as solar and wind sources. Nevertheless, unlike the conventional energy from grid, the renewable energy (e.g., harvested through solar panel or wind turbine) is intermittent in nature and can have different availabilities over time and space. Traditional methods like energy storage with the use of capacity-limited and expensive battery are far from enough for 5G systems to manage the fluctuations. To mitigate such uncertain renewable energy fluctuations and shortages, the energy cooperation by sharing the excessive energy among RRHs/ACEs with the help of Node C is a promising solution. However, one key problem that remains unaddressed yet to implement this cooperation amongst large-scale ultra dense RRHs/ACEs is how to motivate the nodes with renewable energy to share its excessive energy to the others whose energy is insufficient with some benefits in return. Furthermore, the energy cooperation will occupy the radio resources, which will sacrifice the spectrum efficiency. To match peak-hour wireless traffics with limited spectrum, RRHs and ACEs can increase the transmission power to improve the spectrum efficiency, which indicates that the energy harvesting should be adaptive to the dynamical spectrum requirement and changeable traffic volume.
Most existing research works for optimizing the SE and EE performances are assumed that the transmission power is fixed and stable, however, based on the energy harvesting which has different availabilities over time and space, these works should be researched further. Summarily, the SE and EE performance optimizations in the ultra dense RRHs/ACEs with energy harvesting in H-CRANs is a promising and challenging work, where the transmission power of nodes should be adaptive to the packet traffic, radio channel fading, user's QoS, and offered energy.

V. CONCLUSION
In this article, we have provided a summary of recent advances in the application scenario, system architecture, and key techniques for achieving high throughput and low energy consumption in hetero-