Semidefinite Relaxation-Based Soft MIMO Demodulation via Efficient Dual Scaling

Abstract

<p>Soft multiple-input multiple-output (MIMO) demodulators are a core component of iterative receivers for MIMO communication systems that employ bit-interleaved coded modulation (BICM). The role of these demodulators is to extract a good approximation of the posterior likelihood of each bit transmitted at each channel use. The main challenge in designing a soft MIMO demodulator is to achieve the desired level of performance at a reasonable computational cost. This is important because in the case of a memoryless MIMO channel, the computational cost of the exact soft demodulator increases exponentially with the number of bits transmitted per channel use, and the cost grows faster in the case of the channels with memory.</p> <p>Several approximate low-complexity soft demodulators for memoryless channels have been proposed in the literature. In this thesis, we develop a low-complexity soft MIMO demodulator that is based on semidefinite relaxation (SDR) and uses the max-log approximation to reduce the cost of the demodulation. In particular, we develop a customized dual-scaling algorithm to solve the semidefinite program that constitutes the core computational task of the SDR-based soft demodulator. The computational cost per iteration of the customized dual algorithm is about half that of the existing customized primal-dual algorithm, and this leads to a reduction in the overall computational cost. We apply the customized dual-scaling algorithm to two different list-based soft demodulators, the list-SDR and single-SDR demodulators, and compare the performance, computational cost, and EXIT chart characteristics of these demodulators with other existing methods. This comparison shows that the developed demodulator provides a desirable trade-off between performance and complexity.</p>