Distributed Downlink Beamforming With Cooperative Base Stations

• Published in: IEEE transactions on information theory 2008-12, Vol.54 (12), p.5491-5499
• Main Authors: Boon Loong Ng, Evans, J.S., Hanly, S.V., Aktas, D.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Array signal processing; Base stations; Beamforming; Cellular; Cellular communication; Convergence; Cooperative base stations; Detection, estimation, filtering, equalization, prediction; distributed algorithm; Downlink; downlink beamforming; Equipments and installations; Estimating techniques; Estimation error; Exact sciences and technology; Graphs; Information theory; Information, signal and communications theory; Kalman filters; Kalman smoothing; Land mobile radio cellular systems; linear mean square error (LMMSE); localized interference; Mean square error methods; Mean square errors; Message passing; Mobile radiocommunication systems; multicell processing; multiple-input-multiple-output (MIMO); Networks; Radiocommunications; Signal and communications theory; Signal, noise; Smoothing methods; Stations; Sum product algorithm; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments)
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2008.2006426

In this paper, we consider multicell processing on the downlink of a cellular network to accomplish ldquomacrodiversityrdquo transmit beamforming. The particular downlink beamformer structure we consider allows a recasting of the downlink beamforming problem as a virtual linear mean square error (LMMSE) estimation problem. We exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighboring base stations. For 1-D networks, we use the Kalman smoothing framework to obtain a forward-backward beamforming algorithm. We also propose a limited extent version of this algorithm that shows that the delay need not grow with the size of the network in practice. For 2-D cellular networks, we remodel the network as a factor graph and present a distributed beamforming algorithm based on the sum-product algorithm. Despite the presence of loops in the factor graph, the algorithm produces optimal results if convergence occurs.