On optimal tracking of rapidly varying telecommunication channels

When parameters of mobile telecommunication channels change rapidly, classical adaptive filters, such as exponentially weighted least squares algorithms or gradient algorithms, fail to estimate them with sufficient accuracy. In cases like this, one can use identification methods based on explicit models of parameter changes such as the method of basis functions (BF). When prior knowledge about parameter changes is available the BF approach can be optimized. The paper presents and compares three locally optimized BF algorithms differing in computational requirements. It also demonstrates how the performance of the proposed algorithms can be enhanced in cases where prior knowledge depends on unknown and/or time-varying environmental factors.