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Rapid advancements in the development of smart terminals and infrastructure, coupled with a wide range of applications with complex requirements, are creating traffic demands that current networks may not be able to fully handle. Accordingly, the study of 6G networks deserves attention from both industry and academia. Artificial intelligence (AI) has emerged for application in the optimization and design process of new 6G networks. The developmental trend of 6G is towards effective resource management, along with the architectural improvement of the current network and hardware specifications. Cloud RAN (CRAN) is considered one of the major concepts in sixth- and fifth-generation wireless networks, being able to improve latency, capacity, and connectivity to huge numbers of devices. Besides bettering the current set-up in terms of setting the carriers’ network architecture and hardware specifications, among other potential enablers, the developmental trend of 6G also means that there must be effective resource management. As a result, this study covers a thorough analysis of resource management plans in CRAN, optimization, and AI taxonomy, and how AI integration might enhance existing resource management.

The main goals of fifth generation (5G) systems are to significantly increase the network capacity and to support new 5G service requirements. Ultra network densification with small cells is among the key pillars for 5G evolution. The inter-small-cell 5G backhaul network involves massive data traffic. Hence, it is important to have a centralized, efficient multi-hop routing protocol for backhaul networks to manage and speed up the routing decisions among small cells, while considering the 5G service requirements. This paper proposes a parallel multi-hop routing protocol to speed up routing decisions in 5G backhaul networks. To this end, we study the efficiency of utilizing the parallel platforms of cloud computing and high-performance computing (HPC) to manage and speed up the parallel routing protocol for different communication network sizes and set recommendations for utilizing cloud resources to adopt the parallel protocol. Our numerical results indicate that the HPC parallel implementation outperforms the cloud computing implementation, in terms of routing decision speed-up and scalability to large network sizes. In particular, for a large network size with 2048 nodes, our HPC implementation achieves a routing speed-up of 37x. However, the best routing speed-up achieved using our cloud computing implementation is 15.5x, and is recorded using one virtual machine (VM) for a network size of 1024 nodes. In summary, there is a trade-off between a better performance for HPC vs. flexible resources of cloud computing. Thus, choosing best fit platform for 5G routing protocols depends on the deployment scenarios at 5G core or edge network.

To alleviate the greenhouse effect, high network energy efficiency (EE) has become an important research target in wireless green communications. Therefore, an investigation for resource management to mitigate co-tier interference in a small-cell network (SCN) is provided. Moreover, with the merits of cloud radio access networks (C-RANs), small-cell base stations can be decomposed into central small cells (CSCs) and remote small cells (RSCs). To achieve coordination, split medium access control (MAC)-based functional splitting is adopted with a scheduler deployed at CSCs and retransmission functions implemented at RSCs. However, limited fronthaul has a compelling effect on RSCs owing to the requirements of user quality of service (QoS). In this paper, we propose a hybrid controlled user association and resource management (HARM) scheme to deal with the infeasibility of controlling all RSCs using a single CSC. A CSC performs the traffic-control-based user association and resource allocation (TURA) scheme for RSCs to mitigate intra-group interference within localized C-RANs, whereas the CSCs among separate C-RANs conduct cooperative resource competition (CRC) games to alleviate inter-group interference. Based on the regret-based learning algorithm, the proposed schemes are analytically proven to reach the correlated equilibrium (CE). The simulation results validate the capability of traffic control in the TURA scheme and the convergence of CRC. Moreover, a comparison of the proposed TURA, HARM, and CRC schemes with the benchmark is revealed. It is observed that the TURA scheme outperforms the other methods under ideal fronthaul control, whereas the HARM scheme can sustain EE performance considering practical implementation.

The emergence of future 5G technologies has given cloud radio access networks (C-RANs) considerable attention. In the C-RANs, distributed remote radio heads (RRHs) are connected to centralized baseband units (BBUs) which have high capacity processors through radio links to forward radio signals from users. For the BBU pool to control energy consumption and user satisfaction levels, reinforcement learning techniques become the best option. In this paper, we propose an autonomous cell activation framework and customized physical resource allocation schemes to balance energy consumption and QoS satisfaction in wireless networks. We formulate the cell activation problem as a Markov decision process and set up a relational reinforcement learning model based on online k-means clustering and anchor-graph hashing (AGH) to satisfy the user QoS demand and to achieve low energy consumption with the minimum number of the active RRHs under varying traffic demand and user mobility. The extensive simulations are conducted to show the effectiveness of our proposed solution under a mobility scenario compared with the state-of-the-art schemes.

For the new generation of cellular networks, new architectures are being examined to improve performance. Fog communication is a new paradigm that has been presented to exploit the power of edge computing in regards to throughput and latency. In this paper, we introduce a mathematical model to examine the performance of such proposals. Specifically, the delay, power consumption (PC) and energy efficiency (EE) of fog radio access networks (F-RANs) are tested in comparison with those of traditional cloud radio access networks (C-RANs). Although the literature has shown that F-RANs provide enhanced delay performance, this paper shows that they also consume a large amount of power, degrading their EE in comparison with their traditional cloud counterparts. However, the level of degradation depends on the number of deployed fog devices, which directly influence the PC. This paper also shows that improving the delay performance by using a fog architecture is not a straightforward process but rather requires particular care in terms of choosing the appropriate mode when placing/installing fog functions in fog devices.

Cloud-radio access networks (C-RAN) help in overcoming the scarcity of radio resources by enabling dense deployment of base-stations (BSs) and connecting them to a central-processor (CP). This paper considers the downlink of a C-RAN, where the cloud is connected to the BSs via limited-capacity backhaul links. We propose and optimize a C-RAN transmission scheme that combines rate splitting, common message decoding, and beamforming vectors design and clustering. To this end, this paper optimizes a transmission scheme that combines rate splitting (RS), common message decoding (CMD), and clustering and coordinated beamforming. In this paper, we focus on maximizing the weighted sum-rate subject to per-BS backhaul capacity and transmit power constraints, so as to jointly determine the RS-CMD mode of transmission, the cluster of BSs serving private and common messages of each user, and the associated beamforming vectors of each user private and common messages. This paper proposes solving such a complicated non-convex optimization problem using $l_{0}$ -norm relaxation techniques, followed by inner-convex approximations (ICA), so as to achieve stationary solutions to the relaxed non-convex problem. The numerical results show that the proposed method provides significant performance gain as compared to conventional interference mitigation techniques in C-RAN which simply treat interference as noise (TIN).

In this article, we develop a statistical framework to quantify the area spectral efficiency (ASE) and the energy efficiency (EE) performance of a user-centric cloud based radio access network (UC-RAN) downlink. We propose a user-centric remote radio head (RRH) clustering mechanism, which: 1) provides significant improvement in the received signal-to-interference-ratio through selection diversity; 2) enables efficient interference protection by inducing repulsion among scheduled user-centric RRH clusters; and 3) can self-organize the cluster radius to deal with spatio-temporal variations in user densities. It is shown that under the proposed user-centric clustering mechanism, the ASE (bits/s/Hz/m2) maximizes at an optimal cluster size. It is observed that this cluster size is sensitive to changes in both RRH and user densities and, hence, must be adapted with variations in these parameters. Next, we formulate the cost paid for the UC-RAN capacity gains in terms of power consumption, which is then translated into the EE (bits/s/Joule) of the UC-RAN. It is observed that the cluster radius which maximizes the EE of the UC-RAN is relatively larger as compared with that which yields maximum ASE. Consequently, we notice that the tradeoff between the ASE and the EE of UC-RAN manifests itself in terms of cluster radius selection. Such tradeoff can be exploited by leveraging a simple two player cooperative game. Numerical results show that the optimal cluster radius obtained from the Nash bargaining solution of the modeled bargaining problem may be adjusted through an exponential weightage parameter that offers a mechanism to utilize the inherent ASE-EE tradeoff in a UC-RAN. Furthermore, in comparison with existing state-of-the-art non user-centric network models, our proposed scheme, by virtue of selective RRH activation and non overlapping user-centric RRH clusters, offers higher and adjustable system ASE and EE, particularly in dense deployment scenarios.

Fronthaul networks are leveraging communication links between the remote radio heads and baseband units in cloud radio access networks, and common public radio interface (CPRI) is an interface to carry time-sensitive traffic over the fronthaul. For the fronthaul networks which impose stringent bandwidth, latency, and jitter requirements, Ethernet can be a cost-effective solution to carry the CPRI traffic. However, Ethernet networks have limitations to transport the multiple CPRI streams simultaneously and satisfy the strict service requirements due to the accumulation of unavoidable intrinsic delays at switches. To address these issues, this paper evaluates the impact of intrinsic delay on the performance of CPRI over Ethernet (CoE) networks with experimental results and proposes a virtual local area network-based CoE network architecture along with two transport algorithms to carry the multiple CPRI streams over Ethernet despite the intrinsic delays. We confirmed with computer simulations that with the proposed techniques, the end-to-end delays and jitter for a number of CPRI streams within the acceptable range of fronthaul requirements can be achieved.

Cloud radio access network (C-RAN) and massive multiple-input multiple-output (MIMO) are recognized as two key technologies for the fifth-generation mobile networks. In this paper, we consider the energy efficiency-based user association problem in massive MIMO empowered C-RAN, where multiple antennae are clustered at each remote radio head (RRH). We first obtain the deterministic equivalent expression of the energy efficiency, and then propose three user association algorithms, named nearest-based user association (NBUA), single-candidate RRH user association (SCRUA), and multi-candidate RRHs user association (MCRUA), respectively. In NBUA and SCRUA, each user is associated with only one RRH, and in MCRUA, multiple RRHs can serve the same user. In our algorithms, the impact of the power consumption of fronthaul links and antennas is considered by allowing inefficient RRHs to be turned into sleep mode. We provide the numerical comparisons of the proposed algorithms and a state-of-the-art baseline, which associates each user with the nearest RRH. The results show that our proposed algorithms achieve higher energy efficiency than the baseline algorithm. The proposed MCRUA algorithm achieves a good balance between spectral and energy efficiency, and the performance gain is more significant when the number of users is large.

For heterogeneous network, which has been viewed as one pioneering technology for making cellular networks be evolved into 5G systems, reducing energy consumption by dynamically switching off base stations (BSs) has attracted increasing attention recently. With aiming at optimization on energy saving only or another energy-related performance tradeoffs, several BS switch-off strategies have been proposed from different design perspectives, such as random, distance-aware, load-aware, and auction-based strategies. Furthermore, work has been done to consider joint design for BS switch-off strategy and another strategies, such as user association, resource allocation, and physical-layer interference cancellation strategies. Finally, there have been research results about this topic in emerging cloud radio access networks. In this paper, we take an overview on these technologies and present the state of the art on each aspect. Some challenges that need to be solved in this research filed for future work are also described.

Smart home technologies are growing actively all around the world. As a result, great pressures are imposed on internet of things networks by dynamic traffic and plenty of devices. The passive optical network is considered one of the most promising fronthaul technologies. In particular, the time and wavelength division multiplexing passive optical network has shown the advantage of high capacity and received attention recently. In support of internet of things networks, the energy and transmission efficiency has emerged as an important issue on the time and wavelength division multiplexing passive optical network enabled fronthaul networks. In this paper, we try to enhance the energy and transmission efficiency of the time and wavelength division multiplexing passive optical network enabled reconfigurable fronthaul. Fronthaul links’ load balancing is also taken into consideration. An integer non-linear programming model is employed to formulate the joint optimization problem. We also provide an adaptive genetic algorithm-based approach with fast convergence. The simulation results show that the active units of fronthaul can be dynamically switched on/off with the traffic variation and a significant energy saving is achieved. In addition, the maximum transmission efficiency increases by 87% with integer non-linear programming method in off-peak periods.

This paper investigates the achievable per-user degrees-of-freedom (DoF) in multi-cloud based sectored hexagonal cellular networks (M-CRAN) at uplink. The network consists of N base stations (BS) and K ≤ N base band unit pools (BBUP), which function as independent cloud centers. The communication between BSs and BBUPs occurs by means of finite-capacity fronthaul links of capacities C F = μ F · 1 2 log ( 1 + P ) with P denoting transmit power. In the system model, BBUPs have limited processing capacity C BBU = μ BBU · 1 2 log ( 1 + P ) . We propose two different achievability schemes based on dividing the network into non-interfering parallelogram and hexagonal clusters, respectively. The minimum number of users in a cluster is determined by the ratio of BBUPs to BSs, r = K / N . Both of the parallelogram and hexagonal schemes are based on practically implementable beamforming and adapt the way of forming clusters to the sectorization of the cells. Proposed coding schemes improve the sum-rate over naive approaches that ignore cell sectorization, both at finite signal-to-noise ratio (SNR) and in the high-SNR limit. We derive a lower bound on per-user DoF which is a function of μ BBU , μ F , and r. We show that cut-set bound are attained for several cases, the achievability gap between lower and cut-set bounds decreases with the inverse of BBUP-BS ratio 1 r for μ F ≤ 2 M irrespective of μ BBU , and that per-user DoF achieved through hexagonal clustering can not exceed the per-user DoF of parallelogram clustering for any value of μ BBU and r as long as μ F ≤ 2 M . Since the achievability gap decreases with inverse of the BBUP-BS ratio for small and moderate fronthaul capacities, the cut-set bound is almost achieved even for small cluster sizes for this range of fronthaul capacities. For higher fronthaul capacities, the achievability gap is not always tight but decreases with processing capacity. However, the cut-set bound, e.g., at 5 M 6 , can be achieved with a moderate clustering size.

Cloud radio access networks (C-RANs) are emerging architectural solutions to anticipate the increased capacity and quality demands of future 5G cellular networks at a reduced cost. In C-RANs, a transport segment referred to as fronthaul has been defined, which become a major constraint in practical implementations due to its high cost. A transport protocol referred to as eCPRI (enhanced common public radio interface), which was specifically designed for the fronthaul networks, imposes stringent end-to-end (E2E) latency and capacity requirements, which can be satisfied through the extortionate optical links. The high implementation cost of optical fronthaul networks significantly increased the system cost and made the fronthaul a hurdle to accomplish the cost−benefits of the C-RANs’ architecture. The globally deployed Ethernet networks could be leveraging solutions, but are inadequate to comply with the eCPRI requirements in fronthaul bridged networks and result in intolerable latencies due to ineffectual traditional quality of service aware forwarding schemes. Therefore, to realize the cost−benefits of ubiquitously deployed Ethernet infrastructure, this paper proposes the E2E latency aware path computation and packet forwarding schemes, which ameliorate the performance of Ethernet-based fronthaul bridged networks to transport the eCPRI traffic at tolerable latencies. The simulation results verify the feasibility of low-cost Ethernet to carry the eCPRI traffic streams up to 100 Gbps with the proposed schemes in fronthaul bridged networks.