Mean-variance optimal linear precoders for random MISO broadcast channels
Information Theory Proceedings（2011）
摘要
We consider the problem of designing linear precoders for Gaussian multiple input, single output (MISO) broadcast (BC) channels with a random channel matrix, where the randomness models imperfect channel knowledge due to feedback delay, channel time variations, limited training, or quantized feedback. First, we introduce a design framework where the goal is to minimize outage probability (or risk) or maximize rate. It is shown that full-power precoders, i.e. those that meet the transmit power constraint with equality, satisfy the design criterion. Next, we motivate and solve a related mean-variance optimization problem by supposing the channel density is a Gaussian mixture model (GMM) and deriving efficient formulae to compute the objective and constraint functions.
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关键词
Gaussian channels,minimisation,precoding,probability,Gaussian mixture model,Gaussian multiple input single output broadcast channel,channel density,channel time variation,constraint function,design framework,feedback delay,full-power precoder,mean-variance optimal linear precoder,mean-variance optimization problem,objective function,outage probability minimisation,quantized feedback,random MISO broadcast channel,random channel matrix,rate maximization
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