Various iterative algorithms for interference avoidance (IA) in networks with co-located receivers, suitable for distributed implementation, have been proposed in the literature. In this paper, the IA problem is cast in a game-theoretic framework and is formulated as a potential game. This formulation accommodates previously proposed algorithms and, in addition, gives us a framework that enables the design of new distributed and convergent algorithms for IA including algorithms with nonidentical utility functions for the users. Two new convergence results for potential games are then derived. The first result establishes the convergence of a class of potential games to the global solution while following best response iterations and when noise is added. The second result establishes the convergence of potential games to the Nash equilibria of the game while following random better response iterations. The first result combined with the potential game formulation allows us to show that for a large class of network scenarios, arbitrarily small noise assures the convergence of best response IA algorithms, including the eigeniterations, to an arbitrarily small neighborhood of the globally optimal signature sequence set. The second result enables the design of reduced feedback mechanisms for IA that converge to desirable solutions.

We show that the fixed power, synchronous interference avoidance (IA) scheme of (C. Rose et al, IEEE Trans. on Wireless Comm., vol.1, no.3, p. 415-427, 2002) employing the (greedy) eigen-iteration can be modeled as the recently developed potential game of (D. Monderer et al, Journal of Games and Economic Behavior, vol.14, no.0044, p.124-143, 1996). Motivated by the fact that receivers can make small mistakes, we consider the convergence of the eigen-iteration when noise is added in a manner similar to (P. Anigstein, IEEE Trans. On Inf. Theory vol.49, no.4, 2003). Further, we restrict ourselves to a class of signal environments that we call levelable environments. Applying game-theory, we obtain a convergence result similar to that of the Anigstein method, for levelable environments: arbitrarily small noise assures that the eigen-iteration almost surely converges to a neighborhood of the optimum signature set.

This paper considers the application of minimum mean squared error linear space-time processing in tandem with successive interference cancellation (MMSE-SIC) to the extraction of symbol-asynchronous /spl pi//4-DQPSK signals with RRCOS pulse shaping (35%-75% rolloff) in overloaded environments. The performance is analyzed through simulation in terms of the symbol error rate ranked by cancellation order. The effect of signal excess-bandwidth, adjacent channel interference, the number of users, SNR, and array type are considered. The channel statistics of all users are assumed known.

An antenna array is overloaded when the number of cochannel signals in its operating environment exceeds the number of elements. This paper proposes an iterative joint detection technique, spatially reduced search joint detection (SRSJD), that well approximates the joint maximum likelihood (JML) receiver, while reducing its computational complexity by several orders of magnitude. This complexity reduction is achieved by first exploiting the spatial separation between interfering signals with a linear preprocessing stage, and second, performing iterative joint detection with a reduced-state trellis formed over space instead of time. These novel joint detection trellises are possibly tail-biting and vary in structure from stage to stage. Through simulation, SRSJD is shown to demodulate over 2M synchronous quaternary phase-shift keying signals of zero excess bandwidth with an M element circular array. The channels of all users are assumed known.