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Abstract: Most communication systems use some form of feedback, often related tochannel state information. In this paper, we study diversity multiplexingtradeoff for both FDD and TDD systems, when both receiver and transmitterknowledge about the channel is noisy and potentially mismatched. For FDDsystems, we first extend the achievable tradeoff region for 1.5 rounds ofmessage passing to get higher diversity compared to the best known scheme, inthe regime of higher multiplexing gains. We then break the mold of all currentchannel state based protocols by using multiple rounds of conferencing toextract more bits about the actual channel. This iterative refinement of thechannel increases the diversity order with every round of communication. Theprotocols are on-demand in nature, using high powers for training and feedbackonly when the channel is in poor states. The key result is that the diversitymultiplexing tradeoff with perfect training and K levels of perfect feedbackcan be achieved, even when there are errors in training the receiver and errorsin the feedback link, with a multi-round protocol which has K rounds oftraining and K-1 rounds of binary feedback. The above result can be viewed as ageneralization of Zheng and Tse, and Aggarwal and Sabharwal, where the resultwas shown to hold for K=1 and K=2 respectively. For TDD systems, we alsodevelop new achievable strategies with multiple rounds of communication betweenthe transmitter and the receiver, which use the reciprocity of the forward andthe feedback channel. The multi-round TDD protocol achieves adiversity-multiplexing tradeoff which uniformly dominates its FDD counterparts,where no channel reciprocity is available.

Autor: Vaneet Aggarwal, Ashutosh Sabharwal


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