Optimal Recursive Spatial Multiplexing Strategies for Slowly Time-Varying MIMO Channels

We consider a simple closed-loop spatial multiplexing architecture which can be incorporated into an outer system with off-the-shelf single-input single-output encoders/decoders. Instead of relying on transmitter-side channel state information for matching the layers of the multidimensional signal to the eigenmodes of the multiple-input multiple-output channel through a precoding, the eigenmodes with high attenuation are identified in the receiver and a retransmission of the affected signal parts requested through a feedback channel. The requested backup facilitates a linear array signal reconstruction subject to limited noise amplification. Since the backup is always embedded in a subsequent signal frame, the multiplexing becomes a recursive procedure. We propose appropriate transmit antenna selection policies for limiting the number of recursions and thereby the memory requirement and incurred delay. Optimal policies are obtained using the theory of Markov decision processes. Moreover, the use of a simple unsupervised learning method lets the system adapt to unknown channel conditions while always guaranteeing a target signal-to-noise ratio at the decoder input.