TY - JOUR

T1 - A neural network for detection of signals in communication

AU - Bang, Sa H.

AU - Sheu, Bing J.

PY - 1996

Y1 - 1996

N2 - An architecture of densely connected compact neural networks is presented for the maximum-likelihood sequence estimation (MLSE) of signals in digital communications. The combinatorial minimization of the detection cost is performed through the optimization of a concave Laypunov function associated with the network, and truly paralleled operations can be achieved via the collective computational behaviors. In addition, the MLSE performance can be improved by a paralleled annealing technique which has been developed for obtaining optimal or near-optimal solutions in high-speed, real-time applications. Given a sequence of length n, the network of complexity and throughput rate are O(L] and n/'lc, respectively, where L is the number of symbols the inference spans and 71 is the convergence time. The hardware architecture as well as network models, neuron models, and methods of feeding the input to the network are addressed in terms of the probability of error. Through the simulations, it is demonstrated that the proposed compact neural network approach is an efficient method of realizing the MLSE receiver.

AB - An architecture of densely connected compact neural networks is presented for the maximum-likelihood sequence estimation (MLSE) of signals in digital communications. The combinatorial minimization of the detection cost is performed through the optimization of a concave Laypunov function associated with the network, and truly paralleled operations can be achieved via the collective computational behaviors. In addition, the MLSE performance can be improved by a paralleled annealing technique which has been developed for obtaining optimal or near-optimal solutions in high-speed, real-time applications. Given a sequence of length n, the network of complexity and throughput rate are O(L] and n/'lc, respectively, where L is the number of symbols the inference spans and 71 is the convergence time. The hardware architecture as well as network models, neuron models, and methods of feeding the input to the network are addressed in terms of the probability of error. Through the simulations, it is demonstrated that the proposed compact neural network approach is an efficient method of realizing the MLSE receiver.

UR - http://www.scopus.com/inward/record.url?scp=0030216743&partnerID=8YFLogxK

U2 - 10.1109/81.526680

DO - 10.1109/81.526680

M3 - 文章

AN - SCOPUS:0030216743

SN - 1057-7122

VL - 43

SP - 644

EP - 655

JO - IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications

JF - IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications

IS - 8

ER -