Feedback Compression for Correlated Broadcast Channels

Abstract

Abstract-In this paper we apply Predictive Vector Quantization (PVQ) to quantize time-correlated broadcast channels. PVQ exploits the time-correlation of the channel to reduce the quantization error of the channels, and thus to improve the sum rate of the system. PVQ predicts the actual channel based on a number of previous channels, and then quantizes the difference between the prediction and the true channel. In this paper we show how the corresponding codebooks can be designed, and we present a prediction strategy. The performance of PVQ for a broadcast system is depicted through numerical simulations.

Grassmannian predictive coding for limited feedback multiuser MIMO systems

“…Then, the predicted channel vectors are used to form the composite channel matrix to compute the zero forcing precoder. The channel to each user is assumed to be temporally correlated with correlation according to J 0 (2πf D T s ) [46]. Each user's channel is independently generated assuming same temporal correlation.…”

On the Minimum Differential Feedback for Time-Correlated MIMO Rayleigh Block-Fading Channels

“…In [10], it modeled the time-correlated fading channel as a finitestate Markov chain to reduce the feedback rate by ignoring some states occurred with small probabilities. In [11] and [12], a predictive vector quantization scheme was proposed, provided that the previous quantization CSI is known. In [13], variable-length code was applied for feedback rate reduction.…”