One-dimensional signal processor with optimized solution capabil

Details One-dimensional signal processor with optimized solution capabil One-dimensional signal processor with optimized solution capabil

1995-09-14

1998-06-09

Hafiz, Tariq R.

395 21, 395 24, G06E 100, G06E 300

Patent

active

057648581

ABSTRACT:
An architecture and design of compact neural networks is presented for the maximum-likelihood sequence estimation (MLSE) of one-dimensional signals, such as sound, in digital communications. Optimization of a concave Lyapunov function associated with a compact neural network performs a combinatorial minimization of the detection cost, and truly paralleled operations in the analog domain are achievable via the collective computational behaviors. In addition, the MLSE performance can be improved by paralleled hardware annealing, a technique for obtaining optimal or near-optimal solutions in high-speed, real-time applications. For a sequence of length n, the network of complexity and throughput rate are O(L) and n/T.sub.c, respectively, where L is the number of symbols the inference spans and T.sub.c 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.

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