A suboptimal approach to channel equalization based on the nearest neighbor rule

Recent applications of clustering and neural network techniques to channel equalization have revealed the classification nature of this problem. This paper illustrates an implementation of a global system for mobile communica tions (GSM) receiver in which channel equalization and demodulation are rea lized by means of the nearest neighbor (NN) classifier algorithm. The most important advantage in using such techniques is the significant reduction i n terms of computational complexity compared with the maximum likelihood se quence estimation (MLSE) equalizer. The proposed approach involves symbol-b y-symbol interpretation and the knowledge of the channel is embedded in the mapping process of the received symbols over the symbols of the training s equence. This means that no explicit channel estimation need be carried out , either with correlative blocks or using neural networks thus speeding up the entire process. The performance of the proposed receiver, evaluated thr ough a channel simulator for mobile radio communications, is compared with the results obtained by means of a 16-state Viterbi algorithm and other sub optimal receivers. It is shown that the presented algorithm increases the b it error rate (BER) compared with the MLSE demodulator, but the performance degradation, despite the simplicity of the receiver, is kept within the li mits imposed by the GSM specifications.