Optimal Joint Remote Radio Head Selection and Beamforming Design for Limited Fronthaul C-RAN

Files in This Item:
File Description SizeFormat 
Phuong_TSP_CRAN_FINAL_VERSION.pdf3.47 MBAdobe PDFDownload
Title: Optimal Joint Remote Radio Head Selection and Beamforming Design for Limited Fronthaul C-RAN
Authors: Luong, PhuongGagnon, FrançoisDespins, CharlesTran, Le-Nam
Permanent link: http://hdl.handle.net/10197/10325
Date: 11-Aug-2017
Online since: 2019-05-07T14:20:56Z
Abstract: This 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.
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Transactions on Signal Processing
Volume: 65
Issue: 21
Start page: 5605
End page: 5620
Copyright (published version): 2017 IEEE
Keywords: Base station selectionBeamformingCloud radio access networksLimited fronthaulMixed integer second order cone programmingOptimization
DOI: 10.1109/tsp.2017.2739102
Language: en
Status of Item: Peer reviewed
Appears in Collections:Electrical and Electronic Engineering Research Collection

Show full item record

Citations 20

Last Week
Last month
checked on Oct 11, 2019

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.