Energy-Efficient Full Diversity Collaborative Unitary Space-Time Block Code Design via Unique Factorization of Signals

In this paper, a novel concept called a \textit{uniquely factorable constellation pair} (UFCP) is proposed for the systematic design of a noncoherent full diversity collaborative unitary space-time block code by normalizing two Alamouti codes for a wireless communication system having two transmitter antennas and a single receiver antenna. It is proved that such a unitary UFCP code assures the unique identification of both channel coefficients and transmitted signals in a noise-free case as well as full diversity for the noncoherent maximum likelihood (ML) receiver in a noise case. To further improve error performance, an optimal unitary UFCP code is designed by appropriately and uniquely factorizing a pair of energy-efficient cross quadrature amplitude modulation (QAM) constellations to maximize the coding gain subject to a transmission bit rate constraint. After a deep investigation of the fractional coding gain function, a technical approach developed in this paper to maximizing the coding gain is to carefully design an energy scale to compress the first three largest energy points in the corner of the QAM constellations in the denominator of the objective as well as carefully design a constellation triple forming two UFCPs, with one collaborating with the other two so as to make the accumulated minimum Euclidean distance along the two transmitter antennas in the numerator of the objective as large as possible and at the same time, to avoid as many corner points of the QAM constellations with the largest energy as possible to achieve the minimum of the numerator. In other words, the optimal coding gain is attained by intelligent constellations collaboration and efficient energy compression

Error Resilient Multiple Description Compression of Vector Graphics

This research is motivated by the needs of robust streaming of vector graphics contents over the Internet, wireless and other lossy networks. We present a multiple description coding (MDC) technique for error resilient compression and transmission of 2D vector graphics contents. An object is coded into two or more so-called co-descriptors, which are transmitted in separate data packets and generally via different network routes from a server to a client. Each co-descriptor can autonomously provide an approximation of the input object, and it can collaborate with other co-descriptors, if also available at the decoder, to refine the approximation

Optimal two-description scalar quantizer design

Abstract Multiple description quantization is a signal compression technique for robust networked multimedia communication. In this paper we consider the problem of optimally quantizing a random variable into two descriptions, while each description being produced by a side quantizer of convex codecells. The optimization objective is to minimize the expected distortion given the probabilities of receiving either and both descriptions. The problem is formulated as one of shortest path in a weighted directed acyclic graph with constraints on the number and types of edges. An O(K 1 K 2 N 3 ) time algorithm for designing the optimal two-description quantizer is presented, where N is the cardinality of the source alphabet, and K 1 , K 2 are the number of codewords of the two quantizers respectively. This complexity is reduced to O(K1K2N 2 ) by exploiting the Monge property of the objective function. Furthermore, if K 1 = K 2 = K and the two descriptions are transmitted through two channels of the same statistics, then the optimal two-description quantizer design problem can be solved in O(KN 2 ) time