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

This paper considers a heterogeneous downlink cloud radio access network (C-RAN) where all the base stations (BSs) in the network are connected to a central processor (CP) via capacity-limited backhaul links. Under this model, we investigate the message-sharing transmission strategy where the CP shares each user's message with a fixed subset of BSs, which then serve the user through joint beamforming. In this setting, although the overall long-term average backhaul consumption is limited by the fixed cluster size, the instantaneous backhaul consumption at each BS may vary significantly depending on the data rates of the scheduled users at each time slot. To avoid such large fluctuations in backhaul consumption, this paper proposes a backhaul-aware multicell scheduling and beamforming strategy that explicitly accounts for backhaul consumption. Specifically, a beamforming design algorithm is proposed to maximize the network utility for a downlink C-RAN under both per-BS power constraints and per- BS backhaul constraints in each time slot. Although this problem has already been considered in our previous work, this present paper proposes a new beamforming design algorithm that not only has guaranteed convergence but also achieves better system performance. This paper also shows that the performance of the proposed algorithm can be further improved by iterating with an additional power minimization step.

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.

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.

Physical layer (PHY) security is recently regarded as a promising technique to improve the security performance of wireless communication networks. Current developments in PHY security are often based on the assumption of perfect channel state information (CSI). In this paper, both security and reliability performance for the downlink cloud radio access network with optimal remote radio heads (RRHs) node selection are investigated in a practical scenario by considering channel estimation (CE) errors. In particular, a three-phase transmission scheme is proposed and the linear minimum mean-square error (MMSE) estimation method is utilized to obtain the CSI. Based on the CSI estimates and the statistics of CE errors, the outage probability and intercept probability are derived in closed-form expression to evaluate the security and reliability performance, respectively. In addition, two possible cases (with or without intercepting signals from baseband unit) are considered for the eavesdropper. It is found that the suggested optimal RRHs selection scheme outperforms the nonselection scheme, and that the increasing number of RRHs can lower the outage probability as well as the intercept probability. It is also shown that there exists an optimal training number to minimize the sum of the outage probability and intercept probability. Finally, simulation results are provided to corroborate our proposed studies.

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.

Cloud radio access network (CRAN) is a promising candidate for the next-generation wireless communication systems. In CRAN, remote radio heads (RRHs) are deployed to serve users in a target area, which are connected to a central processor (CP) via limited-capacity links termed the fronthaul. In practice, the large amount of information transferred between each RRH and the CP for centralized processing can easily exceed its fronthaul capacity. This motivates our study in this paper on a new hybrid decoding scheme here the RRHs can either locally decode-and-forward the user messages to save the fronthaul capacity, or quantize and forward their signals to the CP for joint decoding as in the conventional CRAN (i.e., forward-and-decode). We consider the uplink transmission in an orthogonal frequency division multiplexing (OFDM)-based CRAN with multiple RRHs to serve a single user, where the proposed hybrid decoding is performed on each OFDM sub-channel (SC). We consider a joint optimization of the processing mode selections (decode-and-forward or forward-and-decode) along with the user's power allocation over all SCs to maximize the sum-rate for the user subject to the RRHs' individual fronthaul capacity constraints and the user's total power constraint. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We also compare the performance of the proposed hybrid decoding with existing schemes that perform either decode-and-forward or forward-and-decode processing at all SCs, which shows promising throughput gains.

In this paper, the power allocation (PA) for maximizing sum rate in downlink cloud radio access networks based on non-orthogonal multiple access (C-RAN-NOMA) is explored. Specifically, we consider a downlink C-RAN which includes two paired NOMA-users served by several uniformly distributed remote radio heads (RRHs) simultaneously. Based on the C-RAN-NOMA model, a linear programming (LP) algorithm is proposed to maximize the sum rate of downlink, and the corresponding optimal PA scheme is developed for C-RAN-NOMA. Simulation results illustrate that the developed power allocation scheme can achieve higher sum rate than the conventional equal power allocation scheme, and has the same sum rate as the exhaustive search algorithm but with lower complexity. Thus, the effectiveness of the proposed scheme and the corresponding algorithm are well verified.

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.

The compression step of the Cloud Radio Access Networks (C-RAN) is studied in this paper. In order to maximize the achievable sum rate in the uplink of C-RAN, constrained by the finite-capacity fronthaul links to the Central Processor (CP) in the cloud, an optimization algorithm of quantizers used for compression is proposed. In C-RAN, there are multiple Radio Units (RUs) compressing and forwarding the correlated received signals simultaneously. Due to the correlations, Wyner-Ziv coding is used. Hence a joint optimization of the quantizers is required at the CP. In this paper we extend the Information Bottleneck (IB) method for this joint optimization. It is a combination of the Alternating Information Bottleneck method (AIB) and the Alternating Bi-Section method, which are both proposed in this paper. Then we use the proposed algorithms to study some key features of C-RAN.

We consider downlink transmission in cloud radio access networks with multiple cochannel multicasting groups served by a group of remote radio heads (RRHs), which receive information from a base band unit via finite-capacity fronthaul links. Our aim is to jointly design RRH selection and beamforming vectors such that the minimum weighted data rate among users is maximized under the constraints of maximum transmit power and fronthaul capacity at each specific RRH. The problem is intractable due to the numerical difficulties of combination and nonconvex functions. Based on a semidefinite relaxation technique, bisection search, and a branch-and-bound method, we develop an upper bound, which is also the optimal solution to the original problem if the relaxation is tight. More importantly, we propose a heuristic low-complexity iterative procedure for practical applications based on the state-of-the-art sequential convex approximation. Subsequently, we modify the proposed methods for the uncertain channel state information case. To be specific, the upper bound and its suboptimal solution are altered based on the $\mathcal {S}$ -lemma while the low-complexity algorithm is tailored by using two different approximations of intractable robust counterpart. The validity of the proposed methods in the region of limited fronthaul capacity is confirmed by numerical results.

Over-The-Air computation (AirComp) is an efficient solution to enable federated learning on wireless channels. Air-Comp assumes that the wireless channels from different devices can be controlled, e.g., via transmitter-side phase compensation, in order to ensure coherent on-Air combining. Intelligent reflecting surfaces (IRSs) can provide an alternative, or additional, means of controlling channel propagation conditions. This work studies the advantages of deploying IRSs for AirComp systems in a large-scale cloud radio access network (C-RAN). In this system, worker devices upload locally updated models to a parameter server (PS) through distributed access points (APs) that communicate with the PS on finite-capacity fronthaul links. The problem of jointly optimizing the IRSs' reflecting phases and a linear detector at the PS is tackled with the goal of minimizing the mean squared error (MSE) of a parameter estimated at the PS. Numerical results validate the advantages of deploying IRSs with optimized phases for AirComp in C-RAN systems.

This study addresses the achievable sum-rate for the uplink of a cloud radio access network (C-RAN) operating in a linear Wyner-type topology, i.e., with partial channel connectivity. In the system, the radio units (RUs) communicate with a central, or cloud, unit (CU) by means of digital finite-capacity fronthaul links. The messages sent by the user equipments (UEs) are jointly decoded by the CU based on the compressed baseband signals received on the fronthaul links. Unlike prior works, each RU is assumed to be also connected to its neighboring RUs via finite-capacity fronthaul links. Under the standard assumption that the RUs do not perform channel decoding (i.e., oblivious RUs), each RU performs in-network processing of the uplink received signal and of the compressed baseband signal received from the adjacent RU, with the CU carrying out channel decoding. A closed-form expression of the achievable sum-rate is derived assuming point-to-point compression, and then analytical expressions are provided for more advanced fronthaul compression schemes that leverage side information. Numerical examples provide insights into the advantages of inter-RU communications and into the performance gap to existing sum-rate upper bounds.

This work considers the downlink of a cloud radio access network (C-RAN) in which a baseband processing unit (BBU) sends independent messages to multiple user equipments (UEs) by controlling a set of remote radio heads (RRHs) which are connected to the BBU via finite-capacity fronthaul links. In standard C-RAN systems, it is prescribed that the fronthaul links are used in a soft-transfer mode whereby quantized signals of the baseband signals, that encode the UEs' messages, are transferred on the fronthaul links. Although this approach is advantageous in a sense that the RRHs do not need to have baseband signal processing functionalities, it has been reported that one can achieve better performance with hard-transfer fronthauling scheme, whereby the fronthaul links are used to transfer hard information of the UEs' messages, particularly when the fronthaul links have relatively large capacity. This work proposes a unified scheme that leverages hybrid soft-transfer and hard-transfer fronthauling strategies. This is enabled by a superposition coding where each UE message is split into two submessages that are delivered to the UEs by means of soft-transfer or hard-transfer schemes. Some numerical results are provided to confirm the advantages of the proposed hybrid scheme.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by merging RAN and cloud computing together. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The introduced Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.