On The Capacity Of Time-Varying Channels With Periodic Feedback

TL;DR

This paper evaluates the capacity of time-varying channels with periodic feedback, analyzing finite state Markov and correlated Gaussian fading channels, and derives optimal coding and power allocation strategies.

Contribution

It provides a comprehensive analysis of channel capacity with periodic feedback, including achievable schemes and optimal adaptive coding for different channel models.

Findings

Capacity is achievable via multiplexing multiple codebooks.

Optimal adaptive coding uses a single Gaussian codebook.

Power allocation can be optimized based on feedback information.

Abstract

The capacity of time-varying channels with periodic feedback at the transmitter is evaluated. It is assumed that the channel state information is perfectly known at the receiver and is fed back to the transmitter at the regular time-intervals. The system capacity is investigated in two cases: i) finite state Markov channel, and ii) additive white Gaussian noise channel with time-correlated fading. In the first case, it is shown that the capacity is achievable by multiplexing multiple codebooks across the channel. In the second case, the channel capacity and the optimal adaptive coding is obtained. It is shown that the optimal adaptation can be achieved by a single Gaussian codebook, while adaptively allocating the total power based on the side information at the transmitter.