Distributed adaptive algorithms for large dimensional MIMO systems

An algorithm for multi-input multi-output (MIMO) adaptive filtering is intr oduced that distributes the adaptive computation over a set of linearly con nected computational modules, Each module has an input and an output and tr ansmits data to and receives data from its nearest neighbor, A gradient-based algorithm for adapting the parameters in each module to mi nimize the global mean-squared error is derived using principles of hack pr opagation, The performance surface is explored to understand the characteri stics of the adaptive algorithm, The minimum mean-squared error is a many t o one function of the parameters; therefore, upper bounds on each parameter are used to prevent excessive parameter drift and insure stability with fi xed step sizes, Guidelines for choosing the LMS algorithm step sizes and in itial conditions are developed. Several examples illustrate the performance of the algorithm.