Browsing by Author "Ng, B. L."

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Open Access
Distributed downlink beamforming with cooperative base stations
(Institute of Electrical and Electronics Engineers, 2008) Ng, B. L.; Evans, J. S.; Hanly, S. V.; Aktas, D.
In this paper, we consider multicell processing on the downlink of a cellular network to accomplish "macrodiversity" 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.
Open Access
Transmit beamforming with cooperative base stations
(IEEE, 2005-09) Ng, B. L.; Evans, J. S.; Hanly, S. V.; Aktaş, Defne
We consider a cellular network where base stations can cooperate to determine the signals to be transmitted on the downlink. In such a scenario, it would be possible to use "macroscopic" transmit beamforming to improve system performance. The downlink beamformer of interest is generalised from some transmit beamformers that have been shown to meet various optimality criteria in the literature. The particular down-link beamformer structure enables us to recast our downlink beamforming problem as a virtual LMMSE estimation problem. Based on this virtual set up, we exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighbouring base stations. Two algorithms are outlined, both of which are based on the Kalman smoothing framework. The first algorithm is a forward-backward algorithm that produces optimal performance, but it has the disadvantage of a delay that grows linearly with array size. The second algorithm, which is a limited extent algorithm, solves the delay problem by using only local information.