This work develops a novel receiver methodology to integrate signal detection and forward error correction in multiple-input-multiple-output (MIMO) communications and various diversity transmissions. Moving beyond traditional approaches relying on belief-propagation, this investigation into the rather classic open problem of integrated signal detection and decoding involves novel joint optimization formulations that incorporate binary field parity constraints imposed by the low-density parity check within maximum likelihood detection frameworks for unified optimization. This novel framework is general and encompasses a number of practical transmission models, including distributed antennas, opportunistic cooperative networking, and signal retransmission as well as their integrations. This project has broad impacts on engineering, education, and society. Its success can lead to new research directions, new tools, and results to help advance other science and engineering fields.
Focusing on multicarrier MIMO signal reception, the investigators will develop and optimize integrated receivers for important wireless network diversities including distributed transmissions, cooperative MIMO, and hybrid-ARQ retransmission systems. The new design methodology emphasizes integration of multiple constraints from incompatible fields. By reformulating and relaxing joint detection and decoding problems into convex optimization, the investigators will design high performance receivers that are efficient and are robust to various forms of uncertainties in channel state information. Such a novel approach to receiver design integration represents a fundamental and practical design paradigm that can fully utilize various a priori signaling and code constraints for joint detection and decoding against channel distortions and other non-idealities to achieve high performance, efficiency, and reliability. The research findings can contribute practically to improvement of future wireless services and broaden their applications in many practical fields where quality, efficiency, and distributivity concerns are paramount.
