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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.

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. [ABSTRACT FROM AUTHOR]

The Cloud Radio Access Network (C-RAN) is a state-of-the-art system paradigm that simultaneously improves spectral and energy efficiency. Capacity constraints of the fronthaul links connecting Remote Radio Heads (RRH) to the Cloud Unit are notable limitations of these networks. The multitude of RRHs and users make active user estimation and calculating Channel Side Information (CSI) between active users and RRHs necessary for implementing these networks. Moreover, in C-RAN, user activity detection is essential for energy-efficient resource allocation, calculating CSI, optimal precoder design, interference management, and multi-user detection. This study investigates active user detection in C-RAN as a joint block sparse signal recovery problem and evaluates the impact of fronthaul limitations, sparsity level, and other network parameters for different sparse signal reconstruction methods. We introduce an efficient method that is based on recovering multiple sparse signals sharing the same sparsity pattern or the same support set of non-zero entries. This method is developed using 5G training signals for user activity detection in C-RAN with fronthaul capacity limitations and without prior knowledge of the sparsity of users. In the end, we compare active user detection results for different sparse signal recovery methods, namely joint block sparse signal and block sparse signal algorithms, with different network specifications. [ABSTRACT FROM AUTHOR]

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.

Midhauls introduce new characteristics to wireless networks. We study implementation performance behaviour to make appropriate network and algorithm design decisions soft–real-time midhaul-based radio access network implementations. A model is developed based on data obtained using a testbed, which can be used to estimate midhaul latency as a function of the number of communicating nodes. The testbed comprises one central unit (CU) controlling a variable number of distributed units (DU) synchronized over Precision Time Protocol (PTP). Average reporting latencies of 266 μ s were observed with 16 DUs. Typical jitter performance was 99.99 % of values below 471.75 μ s but maximum values up to an order of magnitude larger. Variations in performance of up to 16.2 times more deadline misses were observed between best and worst performing DUs. A model fitted to the obtained data estimates the latency and jitter of CU-DU communication as a function of the number DUs. Results indicate suitability for application with moderate synchronization requirements, for example positioning, but insufficient for the most stringent uses case such ultra-reliable communication. [ABSTRACT FROM AUTHOR]

Cloud platforms play an important role in future computing networks and should deliver their expected functionality at all epochs. They are modular and use multivendor subsystems that should be fully interoperable. However, the realization of multivendor component interoperability and functionality may not exist when upgraded components do not function with existing server components. This results in a loss of the functionality of the cloud platform server. The identified challenge is addressed via a phased realization of multivendor component interoperability. In this approach (motivated by biological transplantation), interoperability is selectively realizable at different epochs within a server and using other server's components over a network. The performance is formulated using the cloud link availability factor (CLAF) and link capacity. Evaluation shows that the proposed mechanism enhances the CLAF and link capacity by 54% and 20.7% on average, respectively. [ABSTRACT FROM AUTHOR]

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.

This letter studies the resource allocation and passive beamforming for the cloud radio access network (C-RAN) with orthogonal frequency division multiple access (OFDMA) assisted by intelligent reflecting surface (IRS). Specifically, we consider the uplink transmission where remote radio heads (RRHs) quantize the received signals from the users by independent point-to-point compression and transmit the quantization bits to the baseband unit (BBU) pool through capacity-limited fronthaul links. We jointly optimize the resource allocation and passive beamforming to maximize the uplink sum-rate, using alternating approach and successive convex approximation (SCA) technique. Numerical results verify the performance gain brought by IRS and validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analytical derivation and experimental evaluation of the impact of harmonic and intermodulation distortion on data transmission over local and remote photonically generated millimeter wave (mmW) signals over an optical fronthaul based on a directly modulated laser (DML) and carrier-suppressed (CS) external modulation for frequency up-conversion. Frequency response of different harmonic distortion (HD) and intermodulation (IMD) terms are measured for a 40 GHz signal under back-to-back, local and remote scenarios for the sake of comparison. Furthermore, measurements of error vector magnitude (EVM) of single and multiband QPSK signals are presented in good agreement with the frequency measurements for $(2{{\boldsymbol{\omega }}_{\mathbf{k}}})$ , $({{{\boldsymbol{\omega }}_{\mathbf{k}}} - {{\boldsymbol{\omega }}_{\mathbf{l}}}})$ and $({{{\boldsymbol{\omega }}_{\mathbf{k}}} + {{\boldsymbol{\omega }}_{\mathbf{l}}}})$ -type distortion terms. Wideband signals with in-band distortion and multiband signals with out-of-band distortion are examples that need to be transmitted over local generation mmW approach in cloud-radio access networks (C-RAN), in spite of the high performance achieved by remote generation approach due to the combined effect of fiber dispersion and laser chirp. Results will serve as valuable guidelines for 6G networks deployment in concrete application scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose a rateless coded transmission scheme for the multi-user downlink of Cloud Radio Access Network (C-RAN). In the network, multiple users are served by a cluster of remote radio heads (RRH), which are connected to the building baseband units (BBU) pool through the fronthaul links with limited capacity. In the proposed transmission scheme, the precoder and compressor at the BBU pool, and the decoding algorithm at the users are designed. To further improve the performance of the proposed transmission scheme, we investigate the joint optimization of the precoder and the degree profiles of the rateless codes implemented at the BBU pool to maximize the sum throughput of the network. Explicitly, the optimization problem is formulated according to the extrinsic information transfer (EXIT) function analysis on the decoding process at the users. We give simulation results on the BER and throughput performance achieved by the proposed rateless coded transmission scheme, which verify the effectiveness of the joint optimization on the degree profiles and the precoder. [ABSTRACT FROM AUTHOR]

Cloud radio access networks (C-RANs) are promising paradigms for the fifth-generation (5G) networks due to their interference management capabilities. In a C-RAN, a central processor (CP) is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resource blocks (RRBs). In this paper, we develop a novel cross-layer network coding (CLNC) approach that proposes to optimize RRH’s transmit powers and user’s rates in making the coding decisions. As such, cross-layer throughput of the network is maximized. The joint user scheduling, file encoding, and power adaptation problem is solved by designing a subgraph for each RRB, in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and power levels (PLs) for one RRB. It is then shown that the C-RAN throughput maximization problem is equivalent to a maximum-weight clique problem over the union of all such subgraphs, called herein the CRAN-CLNC graph. Numerical results revealed that the proposed joint and iterative schemes offer improved throughput performances as compared to the existing algorithms in the literature. Compared to our proposed joint scheme, our proposed iterative scheme has a certain degradation, roughly in the range of 9%–14%. This small degradation in the throughput performance of the iterative scheme comes at the achieved low computational complexity as compared to the high complexity of the joint scheme. [ABSTRACT FROM AUTHOR]

Local and remote photonic millimeter wave (mmW) signal generation schemes are theoretically and experimentally evaluated in order to compare both approaches for practical deployment in a cloud radio access network (C-RAN) fronthaul network. The paper presents a full comprehensive formulation of the frequency response of a system based on a directly modulated laser transmitting data over 40 GHz signal which is generated by external carrier suppressed modulation and optical frequency multiplication. Theoretical and experimental characterization of the system response at baseband and mmW band for local and remote generation setups show very good agreement. The remote configuration leads to a higher electrical output power (i.e., 15 dB higher in 25 km fiber links) than the local generation setup in the mmW band due to the combined effect of chirp and fiber dispersion, although intermodulation distortion is higher in the former case. Transmission experiments using quadrature phase-shift keying (QPSK) signals with 250 MHz bandwidth centered at 0.5 GHz over 10 and 25 km fiber links also confirm the superior performance of the remote setup, whereas the local setup leads to similar results to optical back-to-back (OB2B) measurements, which is also validated with data signals centered at different frequencies within the laser bandwidth frequency range. Finally, experimental results show the quality of the recovered signals in terms of error vector magnitude (EVM) as a function of the received electrical power and demonstrate that no further penalties are introduced by photonic mmW signal generation with respect to electrical back-to-back (EB2B) levels. [ABSTRACT FROM AUTHOR]

This work studies multigroup multicasting transmission in cloud radio access networks (C-RANs) with simultaneous wireless information and power transfer where densely packed remote radio heads (RRHs) cooperatively provide information and energy services for information users (IUs) and energy users (EUs) respectively. To maximize the weighted sum rate (WSR) of information services while satisfying the energy harvesting levels at EUs an optimization of joint beamforming design for the fronthaul and access links is formulated which is however neither smooth nor convex and is indeed NP-hard. To tackle this difficulty the smooth and successive convex approximations are used to transform the original problem into a sequence of convex problems and two first-order algorithms are developed to find the initial feasible point and the nearly optimal solution respectively. Moreover an accelerated algorithm is designed to improve the convergence speed by exploiting both Nesterov and heavy-ball momentums. Numerical results demonstrate that the proposed first-order algorithms achieve almost the same WSR as that of traditional second-order approaches yet with much lower computational complexity and the proposed scheme outperforms state-of-the-art competing schemes in terms of WSR. [ABSTRACT FROM AUTHOR]

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).

The cloud radio access network (C-RAN) can potentially reduce a network's deployment cost and energy consumption. However, a connection between a centralized baseband unit and distributed remote radio heads, known as a fronthaul, introduces an additional delay to both control and user planes. This delay is a serious limiting factor for radio resource management functionalities, such as scheduling, because the radio resources are assigned to users according to outdated channel quality information if the fronthaul delay is non-zero. This article provides an overview of existing scheduling approaches suitable for C-RAN and identifies their potential limitations. Based on these limitations, we outline a framework for hierarchical scheduling. The hierarchical scheduling mitigates a negative impact of the fronthaul delay on the throughput of non-cell-edge users and enables efficient retransmission of erroneous data. Besides, cell-edge users can still benefit from interference mitigation techniques requiring centralized control. We compare individual scheduling approaches and show that hierarchical scheduling increases the network throughput (by up to 26 percent) and reduces the number of retransmissions with respect to the existing solutions. [ABSTRACT FROM AUTHOR]

With the assistance of non-orthogonal multiple access (NOMA), the spectrum efficiency and the number of users in cloud radio access network (CRAN) can be greatly improved. In this paper, the system performance of NOMA-assisted CRAN is investigated. Specially, the outage probability (OP) and ergodic sum rate (ESR), are derived for performance evaluation of the system, respectively. Based on this, by minimizing the OP of the system, a suboptimal power allocation (PA) scheme with closed-form PA coefficients is proposed. Numerical simulations validate the accuracy of the theoretical results, where the derived OP has more accuracy than the existing one. Moreover, the developed PA scheme has superior performance over the conventional fixed PA scheme but has smaller performance loss than the optimal PA scheme using the exhaustive search method. [ABSTRACT FROM AUTHOR]

Large‐scale multi‐user multiple‐input multiple‐output (MU‐MIMO) systems and cloud radio access networks (C‐RANs) are promising technologies for the fifth generation (5G) of wireless networks. In this context, the use of low‐resolution analogue‐to‐digital converters (ADCs) is key for energy efficiency and for complying with constrained fronthaul links. Processing signals with a few bits implies performance loss and, therefore, techniques that can compensate for quantisation distortion are fundamental. In wireless systems, an automatic gain control (AGC) precedes the ADCs to adjust the input signal level in order to reduce the impact of quantisation. In this work, the authors propose the joint optimisation of the AGC, which works in the remote radio heads (RRHs), and a low‐resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works in the cloud unit (CU), for large‐scale MU‐MIMO systems with coarsely quantised signals. They develop linear and successive interference cancellation receivers based on the proposed joint AGC and LRA MMSE (AGC‐LRA‐MMSE) approach. An analysis of the achievable sum rates along with a computational complexity study is also carried out. Simulations show that the proposed AGC‐LRA‐MMSE design obtains substantial gains in bit error rates and achievable information rates over existing techniques. [ABSTRACT FROM AUTHOR]

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) is a promising network architecture to reduce energy consumption and the increasing number of base station deployment costs in mobile networks. However, the necessity of enormous fronthaul bandwidth between a remote radio head and a baseband unit (BBU) calls for novel solutions. One of the solutions introduces the edge-cloud layer in addition to the centralized cloud (CC) to keep resources closer to the radio units (RUs). Then, split the BBU functions between the center cloud (CC) and edge clouds (ECs) to reduce the fronthaul bandwidth requirement and to relax the stringent end-to-end delay requirements. This paper expands this architecture by combining it with renewable energy sources in CC and ECs. We explain this novel system and formulate a mixed-integer linear programming (MILP) problem, which aims to reduce the operational expenditure of this system. Due to the NP-Hard property of this problem, we solve the smaller instances by using a MILP Solver and provide the results in this paper. Moreover, we propose a faster online heuristic to find solutions for high user densities. The results show that make splitting decisions by considering renewable energy provides more cost-effective solutions to mobile network operators (MNOs). Lastly, we provide an economic feasibility study for renewable energy sources in a CRAN architecture, which will encourage the MNOs to use these sources in this architecture. [ABSTRACT FROM AUTHOR]

In this paper, we propose an energy efficient Sparse Beamforming Strategy (SBS) in Cloud Radio Access Networks (C-RAN) to achieve an optimimum energy efficiency. The stochastic geometry method is used to derive some expressions for ergodic rate and coverage probability in downlink transmission. In this system model, Remote Radio Heads (RRHs) are coordinated by a Baseband Unit to transmit data toward users. We assume using RRH clusters where each cluster includes one or two RRHs. To investigate this system, we have used Gauss–Poisson process (GPP). The GPP well describes this clustering scenarios. Considering the intra-cell interference power control, we propose SBS to gain the best performance subject to the energy efficiency (EE) metric. SBS which is based on sparse selections of RRHs, is characterized by introducing analytical expressions and simulation of a C-RAN scenario which the allocation of the RRHs follows the GPP distribution. The numerical results demonstrate that the proposed SBS method improves the overall EE of C-RAN scenario up to 15% with respect to full RRHs coordination, at the high intra-cell interference conditions, and about 30% with respect to no coordination state between RRHs in low intra-cell interference regime. [ABSTRACT FROM AUTHOR]

The incorporation of cloud computing in cloud radio access networks (C-RAN) implies that drawbacks of existing cloud platforms influence wireless network performance. Two of such drawbacks are power usage effectiveness and cloud content access latency. New locations such as the ocean, stratosphere and outer space are suitable alternative locations for cloud computing platforms. These new locations have cooling benefits and enhance the power usage effectiveness of future cloud computing platforms. The latency is also reduced when cloud computing platforms in these locations are closer to subscribers. This paper proposes the cloud diversity concept for C-RANs. Cloud diversity proposes combining cloud computing platforms at locations close to subscribers and with cooling benefits. Cloud computing platforms in the aforementioned locations are closer to different subscribers for which they reduce the latency of data access. This paper presents an architecture showing a future C-RAN that incorporates cloud diversity. It formulates a performance model and presents simulation results. The results obtained show that the incorporation of cloud diversity enhances power usage effectiveness by up to 74.9% on average. Cloud diversity also enhances data transmission by reducing the number of round trips associated with data access by up to 63.6% on average. [ABSTRACT FROM AUTHOR]

Featured Application: The results of the research can be used in the practical deployments of the leveraging Ethernet fronthaul bridged networks in the 5G cloud radio access networks. 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. [ABSTRACT FROM AUTHOR]

We study the downlink of a limited fronthaul capacity cloud-radio access networks (C-RANs). Three energy efficiency metrics, namely, global energy efficiency (GEE), weighted sum energy efficiency (WSEE), and energy efficiency fairness (EEF) are maximized by jointly designing transmit beamforming, remote radio head (RRH) selection, and RRH-user association. Furthermore, we incorporate a rate-dependent fronthaul power model, in which the fronthaul power consumption is proportional to the user sum rate. The formulated problems are difficult to solve. Our first contribution is to customize a branch and reduce and bound (BRB) method based on monotonic optimization to find globally optimal solutions for the three energy efficiency maximization problems. Subsequently, for a more practical approach, we propose a unified framework based on successive convex approximation (SCA) method that can be applied to all the considered problems. Our novelty lies in the equivalent transformations leading to more tractable problems that are amenable to the SCA. Specifically, appropriate continuous relaxation and convex approximation techniques are employed to arrive at a sequence of second-order cone programs (SOCPs) for which dedicated solvers are available. Then, a post-processing algorithm is devised to obtain a high-performance feasible solution from the continuous relaxation. The numerical results demonstrate that the proposed SCA-based algorithms converge rapidly and achieve near-optimal performance as well as outperform the known methods. They also highlight the importance of the rate dependent fronthaul power model in designing the energy efficient C-RANs. [ABSTRACT FROM AUTHOR]

To satisfy the rapidly growing demands of wireless communications, new structures have been proposed for the fifth-generation (5G) mobile communication networks, such as cloud radio access networks (C-RANs), which have advantages including high energy efficiency, large network capacity, and high flexibility. This paper concentrates on the problem of remote radio head (RRH) activation, user association, and power allocation in C-RANs. To tackle the problem with $l_{0}$ norm, we transform it into a mixed-integer nonlinear programming (MINLP) problem. Instead of solving it by centralized solvers, we propose a novel algorithm based on Benders decomposition, which can obtain the optimal solution of the MINLP problem. To solve the primal problem in Benders decomposition efficiently, we adopt the alternating direction method of multipliers (ADMM) to achieve a parallel implementation. To further reduce the complexity of solving the MINLP problem, a distributed two-stage iterative algorithm combining the ADMM and the max-sum algorithm is also proposed. The simulation results demonstrate that the first proposed algorithm can obtain the optimal solution, and the second proposed algorithm outperforms conventional algorithms significantly. [ABSTRACT FROM AUTHOR]

Wireless networks have experienced fast development in the past decades. Various advancing wireless technologies have been proposed. To catch up with the ever-increasing diverse communication needs, cloud-radio access networks (C-RAN), which decouples the baseband processing unit (BBU) from the remote radio head (RRH), has been proposed. On the other hand, it has been widely recognized that huge energy consumption has been raised due to the massive deployment of cellular networks. Lowering the network energy consumption therefore becomes a widely concerned topic. To combat the limitations in traditional power grid, smart grid, with the emphasis on distributed energy resource (DER) and bidirectional energy sharing, is advocated to power the wireless networks. In this paper, we are motivated to investigate a joint RRH-BBU association and energy sharing problem towards brown energy usage minimization in green energy powered C-RAN. The problem is formulated into a mixed integer linear programming (MILP) form. To deal with the computation complexity of solving MILP, a two-phase heuristic polynomial-time algorithm is proposed and evaluated via extensive simulation based studies. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of power-efficient resource management in Cloud Radio Access Networks (C-RANs). Specifically, we consider the case where Remote Radio Heads perform data transmission, and signal processing is executed in a virtually centralized Base-Band Units pool. Users request to transmit at different time instants; they demand minimum signal-to-noise-plus-interference ratio (SINR) guarantees, and their requests must be accommodated within a given deadline. These constraints pose significant challenges to the management of C-RANs and, as we will show, considerably impact the allocation of processing and radio resources in the network. Accordingly, we analyze the power consumption of the C-RAN system, and we formulate the power consumption minimization problem as a weighted joint scheduling of processing and power allocation problem for C-RANs with minimum SINR and finite horizon constraints. The problem is a Mixed Integer Non-Linear Program, and we propose an optimal offline solution based on Dynamic Programming. We show that the optimal solution is of exponential complexity; thus, we propose a sub-optimal greedy online algorithm of polynomial complexity. We assess the performance of the two proposed solutions through extensive numerical results. Our solution aims to reach an appropriate tradeoff between minimizing the power consumption and maximizing the percentage of satisfied users. We show that it results in power consumption that is only marginally higher than the optimum, at significantly lower complexity. [ABSTRACT FROM AUTHOR]

In this paper, we study joint interference cancellation and resource allocation for full-duplex (FD) cloud radio access networks (C-RANs) with antenna correlation. The target is to maximize the capacity of downlink (DL) user equipments (UEs) with guaranteed quality of service (QoS) of uplink (UL) UEs under the constraints of DL and UL transmit power. Intractability of the considered problem involves non-convexity and coupling between postcoding and remote radio head (RRH) selection. To deal with the high coupling between system variables, an estimation-free self-interference (SI) cancellation (EFSC) scheme with merit of significantly reducing signaling overhead for channel estimation is proposed. The reduced signaling overhead is also derived in closed-form expression. Furthermore, we propose a generalized Bender’s decomposition-based resource allocation (GRA) algorithm, which separates the continuous and discrete variables to solve the optimization problem. With the design of a flexible utility function, the tradeoff between DL capacity and co-channel interference (CCI) can be achieved. Moreover, we identify the scenario in which antenna correlation will be beneficial for both the UL and DL communications in FD C-RAN with the implementation of EFSC scheme. The effectiveness of the proposed methods and theorems are verified via simulation results. [ABSTRACT FROM AUTHOR]

Due to the centralized signal processing and powerful computational ability, cloud radio access networks (C-RAN) is considered as a promising technique to meet the increasing demand of high-data-rate services of the fifth generation wireless communications. This paper investigates the stochastic optimization problem of C-RAN by joint remote radio heads and baseband units pool resource scheduling to achieve the throughput utility maximization. We formulate a problem to maximize the average throughput utility while stabilizing all processing and transmission queues under the power, subcarrier and computational resources constraints. By utilizing Lyapunov optimization technique, the primal problem can be decomposed into four subproblems. Throughput utility maximization, admission control and computational resource allocation subproblems can be solved easily. For the joint power and subcarrier allocation subproblem, we utilize time-sharing and alternating approaches to obtain a feasible solution. By utilizing the results of four subproblems, a joint admission control and resource allocation (JACRA) algorithm is proposed. The simulation results are provided to show that the total average throughput of JACRA algorithm is increased by 4.02% compared with joint Hungarian and iterative waterfilling (JHIW) algorithm, but the total average backlogs of JACRA algorithm are decreased by 7.04% when the control parameter grows from 1 to 10. [ABSTRACT FROM AUTHOR]

Traffic delay is a key metric to measure the quality-of-service of next-generation wireless communication networks. In this paper, we consider a cloud radio access network architecture with a hierarchical structure of virtual controllers and multiple clusters of remote radio heads (RRHs). A high-level controller coordinates control plane decisions among local controllers and each local controller is in charge of a cluster of RRHs. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute the users’ baseband processing tasks. Then, under the considered architecture, we aim to minimize the average delay consisting of task execution delay and signal transmission delay under total power constraint, by joint optimization of task scheduling and resource allocation, including VM allocation and RRH assignment. Due to the non-deterministic polynomial-time hardness (NP-hardness) of the joint optimization problem, we translate it into a matroid constrained submodular maximization problem and propose heuristic algorithms to find solutions with 0.5-approximation. Besides, both centralized and distributed control schemes are considered. In the centralized control scheme, all decisions about task scheduling, VM allocation, and RRH assignment are made in the high-level controller. But in the distributed control scheme, the high-level controller is only in charge of task scheduling based on graph theory and the local controllers are responsible for their respective VM allocation and RRH assignment. The simulation results show that the proposed algorithms can achieve better performance than the separate optimization of VM allocation and RRH assignment. [ABSTRACT FROM AUTHOR]

In this paper, a simultaneous wireless information and power transfer-based traffic offloading scheme is proposed for cloud radio access networks with energy harvesting-aided device-to-device (D2D) communications. Wherein, the traffic offloading via D2D communications is utilized to alleviate the heavy burden on the capacity-limited fronthauls, while the energy harvesting design is adopted to stimulate offloading by compensating the energy consumption at the D2D transmitters. Different from the conventional works that harvest energy from ambient radio-frequency signals, dedicated energy signals are designed to provide a more flexible wireless power supply for the D2D transmitters with the data-energy beamforming technique. However, transmitting the dedicated energy signals impairs the performances of the wireless information transfer. To achieve a better balance between the traffic offloading gains and the wireless power transfer costs, a weighted sum-rate maximization problem is formulated. Nevertheless, the binary variables introduced by the offloading decision and the constraints on the data-energy beamforming make the optimization problem non-convex. To solve this intractable problem, a layered optimization method with the iterative optimizing and the ellipsoid method is proposed. Furthermore, an algorithm with lower complexity is proposed with a separated data-energy beamforming design. Simulation results reveal that a significant sum-rate gain can be achieved via the proposed scheme. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel cooperative hierarchical caching framework in a Cloud Radio Access Network (C-RAN), in which a new cloud-cache at Cloud Processing Unit (CPU) is envisioned to bridge the storage-capacity/delay-performance gap between the traditional edge-based and core-based caching paradigms. A delay-cost model is introduced and the cache placement problem is formulated that aims at minimizing the average delay-cost of content delivery in the network. Given the NP-completeness of the cache placement problem, we propose a low-complexity heuristic cache-management strategy comprising of a proactive cache-distribution algorithm and a reactive cache-replacement algorithm. Furthermore, a Cache-Aware Request Scheduling (CARS) algorithm is devised in order to optimize online the tradeoff between content download rate and content access delay. Via extensive numerical simulations—carried out using both real-world YouTube video requests and synthetic content requests—it is demonstrated that the proposed cache-management strategy outperforms traditional caching strategies in terms of cache hit ratio, average content access delay, and backhaul traffic load. Additionally, it is shown that the proposed CARS algorithm achieves superior tradeoff performance over traditional approaches that optimize either users’ rate or access delay alone. [ABSTRACT FROM AUTHOR]

Full duplex (FD) in cloud radio access networks (C-RAN) has improved the overall system throughput at most to double the half duplex (HD) counterpart. Although there is an overall improvement in the throughput, it is mainly due to the downlink (DL) rate, while the uplink (UL) rate is highly degraded due to the substantial DL interference. In this paper, we showed how by employing a downlink power control (DLPC), the overall capacity could be improved along with UL rate improvement. In DL, we could achieve 2.9 times the HD rate and 1.5 times that of FD without DLPC. In UL, 1.6 times the HD rate and the improvement up to 3.49 bps/Hz from rates of the order 1 0 − 4 bps/Hz of FD without DLPC. We have also shown how by appropriately selecting DL and UL power control parameters, we can reduce the cluster size requirement in the FD C-RAN system and the fronthaul capacity requirements. We use Matern Cluster Process (MCP) to model the network. We provide coverage and rate as integral expressions in terms of special functions and algebraic terms. We also derive closed-form approximations for asymptotic rates and reflect few insights into the system design. [ABSTRACT FROM AUTHOR]

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.