Performance of space-time modulation for a generalized time-varying Rician channel model

We analyze the performance of trained and differential space-time modulation for channels with a constant specular component and time-varying diffuse fading. We examine the case where the channel varies from sample to sample within a space-time symbol matrix according to a first-order time-varying model. We show that the effect of the time-varying diffuse channel can be described by an effective signal-to-noise ratio (SNR) that decreases with time. We derive pairwise probability of error expressions based on these effective SNR values that accurately describe performance for unitary modulation. We quantify the significant advantage that differential modulation provides at high SNR where the effect of the time-varying channel dominates. At low SNR where additive noise dominates, we note that trained modulation with perfect channel state information provides a 3-dB advantage over differential modulation, but decoding based on a maximum likelihood channel estimate yields worse performance than differential modulation at all SNR values. Simulation results are provided to support our analysis.