Dynamic adaptation of quantization thresholds for soft-decision viterbi decoding with a reinforcement learning neural network

Two reinforcement learning neural network architectures which enhance the performance of a soft-decision Viterbi decoder used for forward error-correction in a digital communication system have been investigated and compared. Each reinforcement learning neural network is designed to work as a co-processor to a demodulator dynamically adapting the soft quantization thresholds toward optimal settings in varying noise environments. The soft quantization thresholds of the demodulator are dynamically adjusted according to the previous performance of the Viterbi decoder, with updates occurring in fixed intervals (every 200 decoded bits out of the Viterbi decoder.) To facilitate implementaiton in digital hardware, each weight of the neural network and related parameters are specified as binary numbers. Computer simulation results demonstrate that, on average, the performance of a Viterbi decoder on an AWGN channel with nonuniformly-spaced soft decision thresholds dynamically adjusted by these neural networks is better than the performance of a Viterbi decoder with uniformly-spaced thresholds. This approach may be used for a variety of other digital communication applications such as channel estimation, adaptive equalization, and signal acquisition.