Robust Optimization for Energy Efficiency in Multicast Downlink C-RAN

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.

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.

In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed l1/l2-norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.

In this paper, we consider a downlink cloud radio access network where a hierarchical structure of virtual controllers is in the center and massive remote radio heads (RRHs) in the coverage area are divided into multiple clusters. Each cluster has one local controller and a high-level controller coordinates control plane decisions among local controllers. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute users' tasks. Then the output data is transmitted to their respective users through RRHs. This work aims to minimize the average delay including task execution delay and signal transmission delay by joint optimization of task scheduling and resource allocation under power constraint. Specifically, resource allocation consists of VM allocation and RRH assignment. Due to the NP-hardness of the joint optimization problem, we translate it into a matroid constrained submodular function maximization problem and propose a heuristic algorithm to find solutions with 0.5-approximation. Numerical results show that the proposed algorithm can achieve a better performance than two baseline algorithms, i.e., VM prioritized allocation algorithm and RRH prioritized assignment algorithm.

We investigate the problem of robust joint transmitter and receiver power allocation for uplink multi-input multi-output (MIMO) transmissions. The channel model is assumed to have Rayleigh flat fading. The objective of power allocation is to minimize the total MSE each of which has limited transmit power. The problem is formulated as a non-linear optimization problem. In addition, we investigate robust joint transmitter and receiver power allocation with imperfect channel state information (CSI). The CSI error is assumed to be unknown but bounded by a constant. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results indicate that, with imperfect CSI, the BER performance improvement obtained by taking robustness into account in the joint power allocation process.

This paper studies the energy efficiency (EE) of the cloud radio access network (C-RAN), consisting of multiple remote radio heads (RRHs) equipped with electronically steerable parasitic array radiator (ESPAR) antennas, which provide multiple antenna functionality with a single radio frequency (RF) chain. An EE optimization problem is formulated to obtain the configuration of ESPAR and the closed-form expressions of the voltage feeding and the loadings are derived for signal transmission at each RRH. Specifically, we obtain the closed-form expressions for precoder and power allocation that are applicable not only for the ESPAR based system but also for standard MIMO antenna (SMA) system with multiple RF chains. It is shown that EA system can be configured with less complexity compared with SMA system in block fading channel. Furthermore, symbol error rate (SER) and EE performances are compared for EA and SMA based systems. It is shown that the system with EA provides better EE performance while providing similar SER performance. From our results, it is proved that EA system can provide better performance to satisfy the requirement of 5G wireless communication networks.

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

In this paper, we develop a statistical group sparse beamforming framework to minimize the network power consumption for green cloud radio access networks (Cloud-RANs). It will promote group sparsity structures in the beamforming vectors, which will provide a good indicator for remote radio head (RRH) ordering to enable adaptive RRH selection for power saving. In contrast to the previous works that depend heavily on instantaneous channel state information (CSI), the proposed algorithm only depends on the long-term channel state attenuation for RRH ordering, which does not require frequent update, thereby significantly reducing the computation overhead. This is achieved by developing a smoothed l p -minimization approach to induce group sparsity in beamforming vectors, followed by an iterative reweighted-l 2 algorithm via the principles of the majorization-minimization (MM) algorithm and the Lagrangian duality theory. With the well-structured closed-form solutions at each iteration, we further leverage the large-dimensional random matrix theory to derive deterministic approximations for the squared l 2 -norm of the induced group sparse beamforming vectors in the large system regimes. The deterministic approximation results only depend on statistical CSI and will guide the RRH ordering. Simulation results demonstrate the near-optimal performance of the proposed algorithm, even in finite systems.

Heterogeneous cloud radio access networks (HCRANs) are potential solutions to improve both spectral and energy efficiencies by embedding cloud computing into heterogeneous networks (HetNets). The interference among remote radio heads (RRHs) can be suppressed with centralized cooperative processing in the base band unit (BBU) pool, while the intertier interference between RRHs and macro base stations (MBSs) is still challenging in H-CRANs. In this paper, to mitigate this inter-tier interference, a contract-based interference coordination framework is proposed, where three scheduling schemes are involved, and the downlink transmission interval is divided into three phases accordingly. The core idea of the proposed framework is that the BBU pool covering all RRHs is selected as the principal that would offer a contract to the MBS, and the MBS as the agent decides whether to accept the contract or not according to an individual rational constraint. An optimal contract design that maximizes the rate-based utility is derived when perfect channel state information (CSI) is acquired at both principal and agent. Furthermore, contract optimization under the situation where only the partial CSI can be obtained from practical channel estimation is addressed as well. Monte Carlo simulations are provided to confirm the analysis, and simulation results show that the proposed framework can significantly increase the transmission data rates over baselines, thus demonstrating the effectiveness of the proposed contract-based solution.

Many papers have studied the joint optimal transceiver design based on the assumption that channel state information (CSI) is perfect at both sides of transceiver, the same imperfect CSI is available at both ends or that transmitter has knowledge of channel distribution. To our knowledge, little effective work has been done on the practical situation that transmitter only knows the imperfect CSI different from the imperfect CSI at the receiver. So, we proposed a simple practical joint transceiver scheme for such CSI mismatch in this paper and applied it to three popular joint transceiver designs to analyze its performance. Simulation results show that the proposed scheme outperforms the conventional scheme at different channel estimation errors and vector quantization errors.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the energy efficiency of the UAVs cells using non-orthogonal multiple access (NOMA) with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we maximize energy efficiency in the downlink NOMA UAV network considering imperfect CSI between the UAV and users. Resource allocation schemes including user scheduling as well as power allocation are designed for system energy efficiency optimization. Because of the non-convexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a nonprobability problem and then decoupled into two convex subproblems by successive convex approximation method. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm. The simulation results prove that our proposed algorithm is more effective compared with existing resource allocation schemes.

Since Maddah-Ali and Tse showed that the completely stale transmitter-side channel state information (CSIT) still benefits the Degrees of Freedom (DoF) of the Multiple-Input-Multiple-Output (MISO) Broadcast Channel (BC), there has been much interest in the academic literature to investigate the impact of imperfect CSIT on DoF region of time correlated broadcast channel. Even though the research focus has been on time correlated channels so far, a similar but different problem concerns the frequency correlated channels. Indeed, the imperfect CSIT also impacts the DoF region of frequency correlated channels, as exemplified by current multi-carrier wireless systems. This contribution, for the first time in the literature, investigates a general frequency correlated setting where a two-antenna transmitter has imperfect knowledge of CSI of two single-antenna users on two adjacent subbands. A new scheme is derived as an integration of Zero-Forcing Beamforming (ZFBF) and the scheme proposed by Maddah-Ali and Tse. The achievable DoF region resulted by this scheme is expressed as a function of the qualities of CSIT.

In this paper, with the aid of new analytical method, we present a new closed-form expression for the ergodic capacity of full-duplex relaying (FDR) systems, with multiple antenna amplify-and-forward (AF) cooperative networks and imperfect channel state information (CSI). At the relay, the loopback self-interfernce (LI) is mitigated by using receive zero-forcing (ZF) precoding scheme, then steering the signal to the destination by using the maximum-ratio transmission (MRT) technique. To facilitate comparison, a closed-form expression for the ergodic capacity of half-duplex relaying (HDR) systems with multiple antenna AF cooperative networks is also addressed, when maximum-ratio combining (MRC)/MRT precoding schemes are performed at the relay input/output, respectively. Therefore, since perfect CSI is not available in practice, this paper investigates the impact of channel estimation errors on the performance of receive ZF/MRT and MRC/MRT schemes. These expressions are exact in the case of perfect CSI and form a very tight indistinguishable lower-bound in the case of imperfect CSI. Numerical results sustained by Monte-Carlo simulations show the exactness of the proposed analytical expressions in perfect CSI and the tightness of the lower-bounds in imperfect CSI. In addition, it is seen here that the FDR AF cooperative systems outperforms the HDR AF cooperative systems in terms of capacity. Furthermore, in contrast to perfect CSI, in the presence of imperfect CSI, an ergodic capacity ceiling is seen as SNR increases. Besides, it is seen that the ergodic capacity degradation due to imperfect CSI is higher in the FDR case because of the extra LI estimation errors.

A robust scheme is proposed to optimize the linear precoders/decoders for MIMO downlinks where the available channel state information (CSI) at base station (BS) is imperfect. The objective is to minimize the sum mean square error (SMSE) over all data sub-streams under a per-user transmit power constraint. By using an iterative procedure, the proposed scheme extends the existing optimization algorithm for the single-user MIMO system with perfect CSI to solve approximately the minimizing SMSE problem in the multiuser MIMO downlink with imperfect CSI at BS. Simulation results demonstrate that the proposed scheme can effectively mitigate the performance loss induced by the imperfect CSI and can provide largest possible diversity as well as increased throughput.

This paper studies a cognitive small cell downlink network, where one cognitive base station (CBS) transmits information to a cognitive user and transfers energy to energy harvesting receivers (EHRs). The spectrum sensing interval, the spectrum sensing time, and the beamforming matrices of the CBS are jointly optimized to maximize the energy efficiency (EE) of the CBS and to minimize the energy cost of the CBS under both the bounded channel state information (CSI) model and the probabilistic CSI model. The interference constraints of the macrocell users, the secrecy rate constraint, the transmit power constraint of the CBS and the energy harvesting constraints of the EHRs are all considered in this paper. All the three formulated optimization problems are nonconvex, for which semidefinite relaxation, a 2-D line search method, S-Procedure, and Bernstein-type inequalities are exploited. This paper also derives the conditions under which the EE maximization problem and the energy cost of the CBS minimization problem using the bounded CSI model have rank-one solutions. Simulation results demonstrate that the proposed algorithms have significant gain on the CBS EE under the perfect CSI and also gain on the CBS energy cost under imperfect CSI, all compared to the benchmark scheme.