Downlink Cloud Radio Access Network Research Articles

Robust Design for IRS-Aided C-RAN With Constrained Wireless Fronthaul Links

In this paper, an intelligent reflecting surface (IRS)-aided downlink cloud radio access network (C-RAN) is studied, wherein the baseband unit (BBU) pool communicates with the users through multiple remote radio heads (RRH). Specifically, the IRSs are deployed to assist both the wireless fronthaul link and the user access link. Due to the limitation of the fronthaul capacity, the BBU pool deals with the baseband signals by point-to-point compression or multivariate compression and sends the quantization bits to the RRHs via the fronthaul link. Considering the imperfect channel state information (CSI) for both the conventional direct channel and the IRS cascaded channel, we investigate the robust design by jointly optimizing the transmit beamformers for BBU pool and RRHs, the passive beamformer for IRSs and the fronthaul compression under point-to-point compression and multivariate compression, respectively. Specifically, we derive the closed-form user downlink rate and the achievable rate for the wireless fronthaul under imperfect CSI. To tackle the non-convex problem, we adopt the successive convex approximation (SCA) approach and transform it into a tractable form. Then utilizing the alternating optimization (AO) approach, we divide the transformed problem into three sub-problems which can be efficiently solved. Furthermore, since the fronthaul link and access link work on time division duplex (TDD) mode, we also discuss the joint optimization taking the time allocation into consideration. Finally, via numerical results, the effectiveness of the proposed robust design is verified.

Joint Optimization of Transmit Beamforming and Base Station Cache Allocation in Multi-Cell C-RAN

This paper studies a downlink cloud radio access network (C-RAN) consisting of a centralized processor (CP) and multiple cells each one of which has one cache-enabled base station (BS) connected to the CP through wireless backhaul links. In such a network, we aim to determine sizes of the cache allocated to the BSs such that the long-term delivery time of the available files is minimized. Considering the two-stage nature of this problem, we formulate a beamforming optimization problem and a cache allocation problem. The purpose of the first one is to deliver un-cached portions of files to the BSs in the shortest possible time during the beamforming stage. The second problem aims to minimize long-term expectation of delivery time through optimal cache allocation during the cache allocation stage. We rigorously prove that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-varying beamformers</i> can be assumed to be piece-wise constant functions of time. Based on this finding, the beamforming optimization problem does not appear to be amenable to a computationally efficient solution. Hence, we resort to the zero-forcing (ZF) beamforming approach to tackle the beamforming optimization problem. Using the results of the beamforming optimization problem, we prove that the cache allocation problem is a convex optimization problem; hence it can be solved by any convex optimization solvers. Simulation results show that the loss of optimality due to adopting ZF beamforming is negligible. Moreover, the superiority of our proposed cache allocation scheme over several other heuristic schemes including proportional cache size allocation is shown.

Energy-Efficient Hybrid Powered Cloud Radio Access Network (C-RAN) for 5G

Owing to the ever-increasing energy consumption, energy efficiency (EE) is an important parameter for next generation 5G network. The Cloud Radio Access Network (C-RAN) is a viable solution for tackling 5G network problems in an energy-efficient manner. Integrating renewable energy with C-RAN can be a powerful tool for reducing operating costs and carbon dioxide emission. But, the regional unpredictability and intermittency nature of renewable energy can result in energy outages and worsening the service quality. As a consequence, the most reliable way is to combine commercial grid supplies with renewable energy. In this paper, we proposed a network model for the downlink C-RAN with hybrid power supplies. The goal of the proposed hybrid supply with solar and grid energy collaboration is to optimize the use of renewable solar energy by reducing grid Power Consumption (PC) and, most significantly, enhancing energy efficiency and preserving service quality. A comprehensive analysis is performed to evaluate EE performance of the proposed C-RAN under a variety of network settings. The numerical results affirm the proposed C-RAN models establishing a significant improvement in network EE compared to the conventional one.

Convolutional neural network-based deep Q-network (CNN-DQN) resource management in cloud radio access network

The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.

A Theoretical Performance Bound for Joint Beamformer Design of Wireless Fronthaul and Access Links in Downlink C-RAN

It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution to this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a candidate solution in 5G and future communication networks, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink C-RAN with a wireless fronthaul and aim to minimize total power spent by jointly designing beamformers for fronthaul and access links. We consider the case where perfect channel state information is not available in the CP. We first derive a novel theoretical performance bound for the problem defined. Then we propose four algorithms with different complexities to show the tightness of the bound. The first two algorithms apply successive convex optimizations with semi-definite relaxation ideas where the other two are adapted from well-known beamforming design methods. The detailed simulations under realistic channel conditions show that as the complexity of the algorithm increases, the corresponding performance becomes closer to the bound.

A Bound Based Method for Beamformer Design in Downlink Cloud Radio Access Networks

Cloud Radio Access Network is a candidate solution in 5G and beyond where we intend to serve as many users as possible with minimum transmit power and minimum fronthaul data transmission. In this study, we aim to maximize a mixed expression of the number of users served, the number of fronthaul links used, and the total transmitted power by beamforming optimization under imperfect channel state information. We find a theoretical upper bound and propose a method based on the bound derivation. We compare its performance with heuristic search and norm approximation methods solving a series of convex sub-problems. The detailed simulations show that the proposed method outperforms other baseline techniques in terms of the number of users served, fronthaul data rate, power consumption, and computational complexity.

On the Capacity Regions of Cloud Radio Access Networks With Limited Orthogonal Fronthaul

Uplink and downlink cloud radio access networks are modeled as two-hop ${K}$ -user ${L}$ -relay networks, whereby small base-stations act as relays for end-to-end communications and are connected to a central processor via orthogonal fronthaul links of finite capacities. Simplified versions of network compress–forward (or noisy network coding) and distributed decode–forward are presented to establish inner bounds on the capacity region for uplink and downlink communications, that match the respective cutset bounds to within a finite gap independent of the channel gains and signal to noise ratios. These approximate capacity regions are then compared with the capacity regions for networks with no capacity limit on the fronthaul. Although it takes infinite fronthaul link capacities to achieve these “fronthaul-unlimited” capacity regions exactly, these capacity regions can be approached approximately with finite-capacity fronthaul. The total fronthaul link capacities required to approach the fronthaul-unlimited sum-rates (for uplink and downlink) are characterized. Based on these results, the capacity scaling law in the large network size limit is established under certain uplink and downlink network models, both theoretically and via simulations.

Energy-Efficient Joint Association and Precoding in Ultra-Dense C-RAN

In this work, we consider the joint optimization of virtual cell association and the precoder design for a multi-user downlink cloud radio access network (C-RAN). The optimal design of cell associations and precoders are particularly relevant due to the anticipated ultra-dense networks suffering from heavy interference. As the resulting optimization is a non-convex and combinatorial problem, we propose an iterative low complexity algorithm, employing the recently developed penalty dual decomposition (PDD) framework. In contrast to predominantly used sparsity inducing methods, the proposed method incorporates the penalty/re-weighting strategy into the algorithm, which is then solved as a sequence of convex second-order cone programs. The aforementioned feature leads to a high convergence rate and subsequently a low computational cost for the proposed algorithm. Furthermore, the proposed algorithm meets the requirements to converge to a KKT solution. Numerical simulations verify the superior performance of our proposed method in ultra-dense networks, in comparison to commonly used methods, such as the reweighted $\ell _1$ -norm, in terms of system energy efficiency.

Beamformer Design With Smooth Constraint-Free Approximation in Downlink Cloud Radio Access Networks

It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network, which is a candidate solution in 5G and beyond, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink scenario and aim to minimize total power spent by designing beamformers. We consider the case where perfect channel state information is not available in the CP. The original problem includes discontinuous terms with many constraints. We propose a novel method which transforms the problem into a smooth constraint-free form and a solution is found by the gradient descent approach. As a comparison, we consider the optimal method solving an extensive number of convex sub-problems, a known heuristic search algorithm and some sparse solution techniques. Heuristic search methods find a solution by solving a subset of all possible convex sub-problems. Sparse techniques apply some norm approximation (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) or convex approximation to make the objective function more tractable. We also derive a theoretical performance bound in order to observe how far the proposed method performs off the optimal method when running the optimal method is prohibitive due to computational complexity. Detailed simulations show that the performance of the proposed method is close to the optimal one, and it outperforms other methods analyzed.

Robust Power Optimization for Downlink Cloud Radio Access Networks with Physical Layer Security.

Since the cloud radio access network (C-RAN) transmits information signals by jointly transmission, the multiple copies of information signals might be eavesdropped on. Therefore, this paper studies the resource allocation algorithm for secure energy optimization in a downlink C-RAN, via jointly designing base station (BS) mode, beamforming and artificial noise (AN) given imperfect channel state information (CSI) of information receivers (IRs) and eavesdrop receivers (ERs). The considered resource allocation design problem is formulated as a nonlinear programming problem of power minimization under the quality of service (QoS) for each IR, the power constraint for each BS, and the physical layer security (PLS) constraints for each ER. To solve this non-trivial problem, we first adopt smooth -norm approximation and propose a general iterative difference of convex (IDC) algorithm with provable convergence for a difference of convex programming problem. Then, a three-stage algorithm is proposed to solve the original problem, which firstly apply the iterative difference of convex programming with semi-definite relaxation (SDR) technique to provide a roughly (approximately) sparse solution, and then improve the sparsity of the solutions using a deflation based post processing method. The effectiveness of the proposed algorithm is validated with extensive simulations for power minimization in secure downlink C-RANs.

Energy Efficient SCMA Supported Downlink Cloud-RANs for 5G Networks

Cloud-radio access networks (C-RANs) are regarded as a promising solution to provide low cost services among users through the centralized coordination of baseband units for 5G wireless networks. The coordinated multi-point access, visualization and cloud computing technologies enable C-RANs to provide higher capacity and wider coverage, as well as manage the interference and mobility in a centralized coordinated way. However, C-RANs face many challenges due to massive connectivity and spectrum scarcity. If not properly handled, these challenges may degrade the overall performance. Recently, the non-orthogonal multiple access (NOMA) scheme has been suggested as an attractive solution to support multi-user resource sharing in order to improve the spectrum and energy efficiency in 5G wireless networks. In this paper, among various NOMA schemes, we consider and implement the sparse code multiple access (SCMA) scheme to jointly optimize the codebook (CB) and power allocation in the downlink of C-RANs, where the utilization of SCMA in C-RANs to improve the energy efficiency has not been investigated in detail in the literature. To solve this NP-hard joint optimization problem, we decompose the original problem into two sub-problems: codebook allocation and power allocation. Using the conflict graph, we propose the throughput aware SCMA CB selection (TASCBS) method, which generates a stable codebook allocation solution within a finite number of steps. For the power allocation solution, we propose the iterative level-based power allocation (ILPA) method, which incorporates different power allocation approaches (e.g., weighted and NOMA successive interference cancellation (SIC)) into different levels to satisfy the maximum power requirement. Simulation results show that the sum data rate and energy efficiency performances of SCMA supported C-RANs depend on the selected power allocation approach. In terms of energy efficiency, the performance significantly improves with the number of users when the NOMA-SIC aware geometric water-filling based power allocation method is used.

Generalized Compression Strategy for the Downlink Cloud Radio Access Network

This paper studies the downlink of a cloud radio access network (C-RAN) in which a centralized processor (CP) communicates with mobile users through base stations (BSs) that are connected to the CP via finite-capacity fronthaul links. Information theoretically, the downlink of a C-RAN is modeled as a two-hop broadcast-relay network. Among the various transmission and relaying strategies for such model, this paper focuses on the compression strategy, in which the CP centrally encodes the signals to be broadcast jointly by the BSs, then compresses and sends these signals to the BSs through the fronthaul links. We characterize an achievable rate region for a generalized compression strategy with Marton’s multicoding for broadcasting and multivariate compression for fronthaul transmission. We then compare this rate region with the distributed decode-forward (DDF) scheme, which achieves the capacity of the general relay networks to within a constant gap, and show that the difference lies in that DDF performs Marton’s multicoding and multivariate compression jointly as opposed to successively as in the compression strategy. A main result of this paper is that under the assumption that the fronthaul links are subject to a sum capacity constraint, this difference is immaterial; so, for the Gaussian network, the compression strategy based on successive encoding can already achieve the capacity region of the C-RAN to within a constant gap, where the gap is independent of the channel parameters and the power constraints at the BSs. As a further result, for C-RAN under individual fronthaul constraints, this paper also establishes that the compression strategy can achieve to within a constant gap to the sum capacity.

Large system analysis of downlink C-RAN with phase noise and fronthaul compression

This paper studies the effect of phase noise and fronthaul compression on a downlink cloud radio access network (C-RAN), where several remote radio heads (RRHs) are coordinated to communicate with users by a baseband unit (BBU) on the cloud server. In the system, the baseband signals are precoded at BBU, and then compressed before being transmitted to RRHs through capacity-limited fronthaul links which results in the compressive quantization noise. We assume the regularized zero-forcing precoding is performed with an imperfect channel state information and a compression strategy is applied at BBU. The effect of phase noise arising from nonideal local oscillators both at RRHs and users is considered. We propose an approximate expression for the downlink ergodic sum-rate of considered C-RAN utilizing large dimensional random matrix theory in the large-system regime. From simulation results, the accuracy of the approximate expression is validated, and the effect of phase noise and fronthaul compression can be analyzed theoretically based on the approximate expression.

Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.

Edge Caching and Resource Allocation Scheme of Downlink Cloud Radio Access Networks With Fronthaul Compression

With the rapid development of real-time applications in the Internet of Things, people have paid more and more attention on the traffic delay. Cloud radio access networks (C-RANs) are seen as a novel network architecture with significant advantages in reducing latency on control and data planes. In this paper, we aim to minimize the average user delay in a downlink C-RAN with a hierarchical structure of virtual controllers and high-speed but limited-capacity fronthaul links, by simultaneously considering edge caching, user association, and computing resource allocation. We first propose a caching scheme based on user preference and user mobility. Then the user association scheme is carried out according to the distances between users and remote radio heads. Finally, we reformulate the computing resource allocation problem as a matroid-constrained submodular function maximization problem and propose a heuristic scheme to find a sub-optimal solution. Besides, fronthaul compression technique is adopted to alleviate the capacity constraint of fronthaul links. Simulation results reveal that the proposed scheme achieves a better performance in terms of average system delay than three baseline schemes.

Energy Optimization Algorithms for MIMO-OFDM Based Downlink C-RAN System

In this paper, we consider a green communication design in a multiple-input-multiple-output orthogonal frequency-division multiplexing-based downlink cloud radio access network system with per-user quality of service constraints and remote antenna units (RAUs) power constraints. Two problems are solved in this paper: a total transmits power minimization problem and a total network consumption minimization problem. The first problem is to minimize the total transmit power of RAUs by designing beamforming. While the other is to minimize the total network consumption by beamforming and switching off unnecessary RAUs, which is a joint design of sparse beamforming and RAU selection. It is a mixed integer nonlinear programming problem and hard to deal with. Approximations are made to convert the problems into convex ones by using the difference of convex programming and a mixed $\ell _{1}/\ell _{2}$ -norm. Then, the iterative algorithms are proposed to solve the problems. The simulation results show that by using proposed algorithms, the transmit power consumption and the network power consumption can be significantly reduced. Meanwhile, the number of active RAUs can be effectively decreased.

Energy Efficiency of Downlink C-RAN With Edge Caching and Fronthaul Compression

In this letter, we study the impact of edge caching on the energy efficiency (EE) of a downlink cloud radio access network. The fronthaul compression strategy is introduced to tackle the problem of the limited fronthaul capacity. To reduce computational complexity, we resort to the large dimensional random matrix theory to derive the deterministic equivalent of the EE based on which the optimal cache capacity that maximizes the EE is derived. We compare the performance of the fronthual compression scheme with that of the data-sharing scheme and reveal the tradeoff between the EE and the transmit power. Simulation results show that the approximation is accurate and validate the dependency of the EE on the cache capacity.

Power Minimization-Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue, whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this paper provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.

Hybrid Data-Sharing and Compression Strategy for Downlink Cloud Radio Access Network

This paper studies transmission strategies for the downlink of a cloud radio access network, in which the base stations are connected to a centralized cloud computing-based processor with digital fronthaul or backhaul links. We provide a system-level performance comparison of two fundamentally different strategies, namely, the data-sharing strategy and the compression strategy, which differ in the way the fronthaul/backhaul is utilized. It is observed that the performance of both strategies depends crucially on the available fronthaul or backhaul capacity. When the fronthaul/backhaul capacity is low, the data-sharing strategy performs better, while under moderate-to-high fronthaul/backhaul capacity, the compression strategy is superior. Using insights from such a comparison, we propose a novel hybrid strategy, combining the data-sharing and compression strategies, which allows for better control over the fronthaul/backhaul capacity utilization. An optimization framework for the hybrid strategy is proposed. Numerical evidence demonstrates the performance gain of the hybrid strategy.

Energy Efficiency Maximization for Downlink Cloud Radio Access Networks With Data Sharing and Data Compression

This paper aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN). Here, data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. Both data sharing and compression-based strategies are considered for fronthaul data transfer. New mixed-integer nonlinear problems are formulated, in which the ratio of network throughput and total power consumption is maximized. These challenging problem formulations include practical constraints on routing, predefined minimum data rates, fronthaul capacity, and maximum remote radio head transmit power. By employing the successive convex quadratic programming, iterative algorithms are proposed with guaranteed convergence to the Fritz John solutions of the formulated problems. Significantly, each iteration of the proposed algorithms solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization designs markedly improve the C-RAN’s energy efficiency compared to benchmark schemes. They also show that the fronthaul data-sharing strategy outperforms its compression-based counterpart in terms of energy efficiency, in both single-hop and multi-hop network scenarios.