Advanced signal processing techniques for wireless communications

Inexpensive and rapid computational power provided powerful tools to overcome the limitations of current technologies and enabled us to apply several advanced statistical signal processing techniques for the design of receivers in wireless communications systems. In this presentation, we first mention the knowledge gaps in general. Then, we briefly explain two techniques, namely, the expectation maximization (EM) and its modified version called the space-alternating generalized expectation maximization (SAGE) algorithm and the Monte Carlo Markov Chain (MMCC) technique based on the Gibbs Sampling algorithm. To demonstrate the applications of these techniques to wireless communications, we will then give two examples from our recent research, carried out with Princeton University jointly. The first one is on computationally efficient, joint transmission delay and channel parameter estimation algorithm for uplink asynchronous direct-sequence CDMA systems, and the second one is on OFDM receiver design in the presence of high mobility fading channels. The presentation will end with a conclusion.