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

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, the other identifies each channel of the corresponding communication links. The estimation of symbols is carried out either by a stochastic Hopfield net (SHN) or by a hysteretic neural network (HyNN) or 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

Risk-sensitive optimal control for stochastic recurrent neural networks

“…On the other hand, stochastic recurrent neural networks have emerged to be a very useful tool in various application areas, for example, sub-optimal detection in telecommunication of code division multiple access (CDMA) systems, traveling salesman problem in combinatorial optimization, etc [8]. However, with regard to the analysis of both stability and controllability of stochastic recurrent neural networks, there has been little work in the literature until the very recent years [9].…”

Inverse optimal noise‐to‐state stabilization of stochastic recurrent neural networks driven by noise of unknown covariance

“…On the other hand, stochastic recurrent neural networks have emerged to be a very useful tool in various application areas, for example, sub-optimal detection in telecommunication of code division multiple access systems, traveling salesman problem in combinatorial optimization, etc. [11][12][13][14]. Hence, it is worthwhile to analytically explore the stabilization of stochastic recurrent neural networks.…”