We develop a computationally efficient and memory efficient approach to (near) maximum a posteriori probability demodulation for MIMO systems with QPSK signalling, based on semidefinite relaxation. Existing approaches to this problem require either storage of a large list of candidate bit-vectors, or the solution of multiple binary quadratic problems. In contrast, the proposed demodulator does not require the storage of a candidate list, and involves the solution of a single (efficiently solvable) semidefinite program per channel use. Our simulation results show that the resulting computational and memory efficiencies are obtained without incurring a significant degradation in performance.

This letter presents a novel method for network echo cancelation, based on a combination of normalized least mean square (NLMS) and proportionate NLMS (PNLMS) adaptive filtering algorithms. First, based on a rough analysis of PNLMS adaptation, it is indicated why after PNLMS initial fast convergence, it slows down. Then, the method used to overcome this deficiency is presented. Last, by showing some of the simulations, its improvement over PNLMS algorithm is shown.Index Terms-Adaptive filters, composite proportionate normalized least mean squares (PNLMS) and normalized least mean squares (NLMS) (CPNLMS), echo cancelation, proportionate normalized least mean squares (PNLMS).

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