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Gagnon, François
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Gagnon, François
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Gagnon, François
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Now showing 1 - 3 of 3
- PublicationOptimal Joint Remote Radio Head Selection and Beamforming Design for Limited Fronthaul C-RANThis paper considers the downlink transmission of cloud-radio access networks (C-RANs) with limited fronthaul capacity. We formulate a joint design of remote radio head (RRH) selection, RRH-user association, and transmit beamforming for simultaneously optimizing the achievable sum rate and total power consumption, using the multiobjective optimization concept. Due to the nonconvexity of perfronthaul capacity constraints and introduced binary selection variables, the formulated problem lends itself to a mixed-integer nonconvex program, which is generally non-deterministic polynomial-time hard. Motivated by powerful computing capability of C-RAN and for benchmarking purposes, we propose a branch and reduce and bound-based algorithm to attain a globally optimal solution. For more practically appealing approaches, we then propose three iterative low-complexity algorithms. In the first method, we iteratively approximate the continuous nonconvex constraints by convex conic ones using successive convex approximation framework. More explicitly, the problem obtained at each iteration is a mixed-integer second-order cone program (MI-SOCP) for which dedicated solvers are available. In the second method, we first relax the binary variables to be continuous to arrive at a sequence of SOCPs and then perform a postprocessing procedure on the relaxed variables to search for a high-performance solution. In the third method, we solve the considered problem in view of sparsity-inducing regularization. Numerical results show that our proposed algorithms converge rapidly and achieve near-optimal performance as well as outperform the known algorithms.
494Scopus© Citations 50 - PublicationOptimal Energy-Efficient Beamforming Designs for Cloud-RANs With Rate-Dependent Fronthaul PowerWe 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.
338Scopus© Citations 8 - PublicationJoint Virtual Computing and Radio Resource Allocation in Limited Fronthaul Green C-RANsWe consider the virtualization technique in the downlink transmission of limited fronthaul capacity cloud-radio access networks. A novel virtual computing resource allocation (VCRA) method which can dynamically split the users workload into smaller fragments to be served by virtual machines is presented. Under the proposed scheme, we aim at maximizing the network energy efficiency by a joint design of virtual computing resources, transmit beamforming, remote radio head (RRH) selection, and RRH-user association. Moreover, we construct a more realistic fronthaul power consumption model, which is directly proportional to users' rate transmitted by the corresponding RRHs. The formulated problem is combinatorial and difficult to solve in general. Our first contribution is to customize a branch-and-reduce-and-bound method to attain a globally optimal solution. To compute a high-quality approximate solution, a standard routine is used to deal with the continuous relaxation of the original problem. However, the proposed continuous relaxation is non-convex which implies another challenge. For a practically appealing solution approach, we resort to a local optimization method, namely the difference of convex algorithm. Our second contribution is on the use of Lipschitz continuity to arrive at a sequence of convex quadratic programs, which can be solved efficiently by modern convex solvers. Finally, a post-processing procedure is proposed to obtain a high-performance feasible solution from the continuous relaxation. Extensive numerical results demonstrate that the proposed algorithms converge rapidly and achieve near-optimal performance as well as outperform other known methods. Moreover, we numerically show that the VCRA scheme significantly improves the system energy efficiency compared to the existing schemes.
475Scopus© Citations 32