Index assignment optimization for joint source-channel MAP decoding
Channel-optimized quantizer index assignment and maximum a posteriori (MAP) decoding have been extensively studied for error-resilient communications. An interesting and largely untreated problem is how to optimize the index assignment with respect to joint source-channel MAP decoding. In this paper we formulate the above problem as one of quadratic assignment, and discuss its solutions from very general to some special cases. For highly correlated Gaussian Markov sources and Hamming distortion, we can construct the optimal index assignment analytically. For general cases, simulated annealing algorithm is adopted to search for the optimal index assignment. Experimental results are presented to demonstrate the performance improvement of the index assignments optimized for MAP decoding over those designed for hard-decision decoding (e.g. Gray code). The reduction of symbol error rate and mean squared error can be as large as 40% and 50% respectively for highly correlated Gaussian Markov sources.