Yuanming Shi

Group Sparse Beamforming for Green Cloud-RAN

A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near-optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted ℓ1/ℓ2-norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption.

Large-scale convex optimization for ultra-dense cloud-RAN

The heterogeneous cloud radio access network (Cloud-RAN) provides a revolutionary way to densify radio access networks. It enables centralized coordination and signal processing for efficient interference management and flexible network adaptation. Thus it can resolve the main challenges for next-generation wireless networks, including higher energy efficiency and spectral efficiency, higher cost efficiency, scalable connectivity, and low latency. In this article we will provide an algorithmic approach to the new design challenges for the dense heterogeneous Cloud-RAN based on convex optimization. As problem sizes scale up with the network size, we will demonstrate that it is critical to take unique structures of design problems and inherent characteristics of wireless channels into consideration, while convex optimization will serve as a powerful tool for such purposes. Network power minimization and channel state information acquisition will be used as two typical examples to demonstrate the effectiveness of convex optimization methods. Then we will present a twostage framework to solve general large-scale convex optimization problems, which is amenable to parallel implementation in the cloud data center.

Large-Scale Convex Optimization for Dense Wireless Cooperative Networks

Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

CSI overhead reduction with stochastic beamforming for cloud radio access networks

Cloud radio access network (Cloud-RAN) is a promising network architecture to meet the explosive growth of the mobile data traffic. In this architecture, as all the baseband signal processing is shifted to a single baseband unit (BBU) pool, interference management can be efficiently achieved through coordinated beamforming, which, however, often requires full channel state information (CSI). In practice, the overhead incurred to obtain full CSI will dominate the available radio resource. In this paper, we propose a unified framework for the CSI overhead reduction and downlink coordinated beamforming. Motivated by the channel heterogeneity phenomena in large-scale wireless networks, we first propose a novel CSI acquisition scheme, called compressive CSI acquisition, which will obtain instantaneous CSI of only a subset of all the channel links and statistical CSI for the others, thus forming the mixed CSI at the BBU pool. This subset is determined by the statistical CSI. Then we propose a new stochastic beamforming framework to minimize the total transmit power while guaranteeing quality-of-service (QoS) requirements with the mixed CSI. Simulation results show that the proposed CSI acquisition scheme with stochastic beamforming can significantly reduce the CSI overhead while providing performance close to that with full CSI.

Smoothed

The cloud radio access network (Cloud-RAN) has recently been proposed as one of the cost-effective and energy-efficient techniques for 5G wireless networks. By moving the signal processing functionality to a single baseband unit (BBU) pool, centralized signal processing and resource allocation are enabled in cloud-RAN, thereby providing the promise of improving the energy efficiency via effective network adaptation and interference management. In this paper, we propose a holistic sparse optimization framework to design green cloud-RAN by taking into consideration the power consumption of the fronthaul links, multicast services, as well as user admission control. Specifically, we first identify the sparsity structures in the solutions of both the network power minimization and user admission control problems, which call for adaptive remote radio head (RRH) selection and user admission. However, finding the optimal sparsity structures turns out to be NP-hard, with the coupled challenges of the ℓ0-norm-based objective functions and the nonconvex quadratic QoS constraints due to multicast beamforming. In contrast to the previous works on convex but nonsmooth sparsity inducing approaches, e.g., the group sparse beamforming algorithm based on the mixed ℓ1/ℓ2-norm relaxation, we adopt the nonconvex but smoothed ℓp-minimization (0 <; p ≤ 1) approach to promote sparsity in the multicast setting, thereby enabling efficient algorithm design based on the principle of the majorization-minimization (MM) algorithm and the semidefinite relaxation (SDR) technique. In particular, an iterative reweighted-ℓ2 algorithm is developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of the relaxed smoothed ℓp-minimization problem from the SDR technique. We illustrate the effectiveness of the proposed algorithms with extensive simulations for network power minimization and user admission control in multicast cloud-RAN.

L_p

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.

-Minimization for Green Cloud-RAN With User Admission Control

Transmit optimization and resource allocation for wireless cooperative networks with channel state information (CSI) uncertainty are important but challenging problems in terms of both the uncertainty modeling and performance optimization. In this paper, we establish a generic stochastic coordinated beamforming (SCB) framework that provides flexibility in the channel uncertainty modeling, while guaranteeing optimality in the transmission strategies. We adopt a general stochastic model for the CSI uncertainty, which is applicable for various practical scenarios. The SCB problem turns out to be a joint chance constrained program (JCCP) and is known to be highly intractable. In contrast to all of the previous algorithms for JCCP that can only find feasible but sub-optimal solutions, we propose a novel stochastic DC (difference-of-convex) programming algorithm with optimality guarantee, which can serve as the benchmark for evaluating heuristic and sub-optimal algorithms. The key observation is that the highly intractable probability constraint can be equivalently reformulated as a dc constraint. This further enables efficient algorithms to achieve optimality. Simulation results will illustrate the convergence, conservativeness, stability and performance gains of the proposed algorithm.

Robust Group Sparse Beamforming for Multicast Green Cloud-RAN With Imperfect CSI

A cloud radio access network (C-RAN) is a promising network architecture to meet the explosive growth of the mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost network. However, as all the RRHs need to be connected to the BBU through backhaul links, the backhaul power consumption becomes significant and cannot be ignored. In this paper, we propose a new framework to design green C-RAN. Instead of only focusing on the RRH power consumption, we will minimize the network power consumption which includes the power consumed by both the RRHs and the backhaul links. The design problem is formulated as a joint RRH selection and power minimization beamforming problem, which turns out to be a convex-cardinality optimization problem and is NP-hard. We will first propose a global optimization algorithm based on the branch-and-bound method. By inducing the group-sparsity of the beamformers, we then propose two low-complexity algorithms, which essentially decouple the RRH selection and the power minimization beamforming. Simulation results demonstrate that the proposed algorithms can significantly reduce the network power consumption.

Optimal Stochastic Coordinated Beamforming for Wireless Cooperative Networks With CSI Uncertainty

To meet the ever growing demand for both high throughput and uniform coverage in future wireless networks, dense network deployment will be ubiquitous, for which cooperation among the access points is critical. Considering the computational complexity of designing coordinated beamformers for dense networks, low-complexity and suboptimal precoding strategies are often adopted. However, it is not clear how much performance loss will be caused. To enable optimal coordinated beamforming, in this paper, we propose a framework to design a scalable beamforming algorithm based on the alternative direction method of multipliers (ADMM). Specifically, we first propose to apply the matrix stuffing technique to transform the original optimization problem to an equivalent ADMM-compliant problem, which is much more efficient than the widely-used modeling framework CVX. We will then propose to use the ADMM algorithm, a.k.a. the operator splitting method, to solve the transformed ADMM-compliant problem efficiently. In particular, the subproblems of the ADMM algorithm at each iteration can be solved with closed-forms and in parallel. Simulation results show that the proposed techniques can result in significant computational efficiency compared to the state-of-the-art interior-point solvers. Furthermore, the simulation results demonstrate that the optimal coordinated beamforming can significantly improve the system performance compared to sub-optimal zero forcing beamforming.

Group Sparse Beamforming for Green Cloud Radio Access Networks

Relaying is a promising technique to extend coverage and improve throughput in wireless networks, but its performance is degraded in the presence of co-channel interference. In this paper, we consider coordinated relay beamforming to suppress interference and improve the date rates of two-hop interference networks. We first propose optimal coordinated relay beamforming algorithms to characterize the achievable rate region and maximize the sum-rate. By imposing a constraint on the desired signals, a low-complexity iterative algorithm is then proposed to maximize the sum-rate. Through performance comparison, we show that the proposed relaying strategy provides a promising tradeoff between complexity and performance. To further reduce design complexity, we propose a new interference management scheme, interference neutralization, to cancel the interferences over the air at the second hop. We show that this scheme yields a closed-form solution for the beamforming design and provides good performance especially at high signal-to-noise ratio (SNR).