An LMS-based decision feedback equalizer for IS-136 receivers

Abstract

In digital mobile communication systems, inter-symbol interference is one of the main causes of degrading system performance. Decision feedback equalization (DFE) is the commonly used remedy for this problem. Since the channel is fast-varying, an adaptive algorithm possessing a fast convergence property is then required. The least mean square (LMS) algorithm is well known for its simplicity and robustness; however, its convergence is slow. As a consequence, the LMS algorithm is rarely considered in this application. In this paper, we consider an LMS-based DFE for the North American IS-136 system. We propose an extended multiple-training LMS algorithm accelerating the convergence process. The convergence properties of the multiple-training LMS algorithm are also analyzed. We prove that the multiple-training LMS algorithm can converge regardless of its initial value and derive closed-form expressions for the weight error vector power. We further take advantage of the IS-136 downlink slot format and divide a slot into two subslots. Bidirectional processing is then applied to each individual subslot. The proposed LMS-based DFE has a low computational complexity and is suitable for real-world implementation. Simulations with a 900- MHz carrier show that our algorithm can meet the 3% bit error rate requirement for mobile speeds up to 100 km/hr.