Adaptive Near-Optimal Multiuser Detection Using a Stochastic and Hysteretic Hopfield Net Receiver

Gábor Jeney,János Levendovszky,Ec Van Der Meulen,László Pap

doi:10.1155/s1110865702209130

Abstract

This paper proposes a novel adaptive MUD algorithm for a wide variety (practically any kind) of interference limited systems, for example, code division multiple access (CDMA). The algorithm is based on recently developed neural network techniques and can perform near optimal detection in the case of unknown channel characteristics. The proposed algorithm consists of two main blocks: one estimates the symbols sent by the transmitters and the other identifies each channel of the corresponding communication links. The estimation of symbols is carried out either by a stochastic Hopfield net (SHN), by a hysteretic neural network (HyNN) or by both. The channel identification is based on either the self-organizing feature map (SOM) or the learning vector quantization (LVQ). The combination of these two blocks yields a powerful real-time detector with near optimal performance. The performance is analyzed by extensive simulations.

Highlights

Multiuser detection (MUD) has gained much attention in the world of telecommunication research
The claim for multiuser detection (MUD) primarily arises in systems suffering from the limitation of interference, such as code division multiple access (CDMA) which has been adopted as the main multiple access method of the third generation universal mobile telecommunication system (UMTS)
Many different approaches to MUD have been proposed [1]

Summary

Introduction

Multiuser detection (MUD) has gained much attention in the world of telecommunication research. The claim for MUD primarily arises in systems suffering from the limitation of interference, such as code division multiple access (CDMA) which has been adopted as the main multiple access method of the third generation universal mobile telecommunication system (UMTS). MUD carries out joint detection for a group of users or single-user detection for a specific user in the presence of other users in the channel. Many different approaches to MUD have been proposed [1] (e.g., some authors regard this field as a task of joint detection, others implement signal processing methods to get rid of unwanted interference, while a third group of authors still regard it as a classification or hypothesis testing problem). We should keep in mind that the purpose of MUD is to provide a robust, low cost, reliable, and fast method to separate signals arriving from different sources over the same

Methods

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Conclusion