Channel Optimized Vector Quantization: Iterative Design Algorithms

Joint source-channel coding (JSCC) has emerged to be a major field of research recently. Channel optimized vector quantization (COVQ) is a simple feasible JSCC scheme introduced for communication over practical channels. In this work, we propose an iterative design algorithm, referred to as the iterative maximum a posteriori (MAP) decoded (IMD) algorithm, to improve COVQ systems. Based on this algorithm, we design a COVQ based on symbol MAP harddecision demodulation that exploits the non-uniformity of the quantization indices probability distribution. The IMD design algorithm consists of a loop which starts by designing a COVQ, obtaining the index source distribution, updating the discrete memoryless channel (DMC) according to the achieved index distribution, and redesigning the COVQ. This loop stops when the point-to-point distortion is minimized. We consider memoryless Gaussian and Gauss-Markov sources transmitted over binary phase-shift keying modulated additive white Gaussian noise (AWGN) and Rayleigh fading channels. Our scheme, which is shown to have less encoding