The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Adaptive space-time processing for wireless communications
Author:	Bernstein, Xiao Cheng
View Online:	njit-etd1996-087 (xi, 70 pages ~ 2.8 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Doctor of Philosophy
Program:	Electrical Engineering
Document Type:	Dissertation
Advisory Committee:	Haimovich, Alexander (Committee chair) Bar-Ness, Yeheskel (Committee member) Porter, Michael Blair (Committee member) Siveski, Zoran (Committee member) Winters, Jack H. (Committee member)
Date:	1996-01
Keywords:	Digital communications. Signals--Array processing.
Availability:	Unrestricted
Abstract:	Adaptive space-time processing techniques have been found to increase the capacity of two major, multiple-access wireless communication systems: Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA). In an IS-54 TDMA system, the frequency re-use factor has to be set to 7 so that cells with the same spectrum are separated far enough to meet a required carrier-to-interference ratio (CIR). Space processing uses multiple antennas which, in turn, provide alternative signal paths in order to cancel interferences and combat multipath fading. We have proposed the eigencanceler method and have reviewed the theoretical optimum combining and the feasible direct matrix inverse (DMI) technique. An analysis of the system performance reveals that when data sets are small, the eigencanceler is superior to DMI. Furthermore, we have proposed a. simple projection-based algorithm and have analyzed its performance. The capacity of CDMA communication systems is restricted by multiple-access interferences (MAI). We have shown that spatial and temporal processing can be combined to increase the capacity of CDMA-based wireless communications systems. The degrees of freedom provided by space-time processing can be exploited to combat both fading and MAI. Specifically, we have discussed the following methods: (1) space-time diversity, (2) cascade optimum spatial-diversity temporal, (3) cascade optimum spatial-optimum temporal, and (4) joint-domain optimum processing. We have proved that, due to its interference cancellation capability, optimum combining provides significantly better performance than diversity techniques.