Demodulator Statistics for Enhanced Soft-Decision Decoding in CDMA Packet Radio Systems

Next generation packet radio networks will have far greater processing capabilities than current radio systems. We propose and evaluate decoding techniques that make use of such capabilities to increase the probability of successful decoding. We propose a metric derived from statistics collected during demodulation in a binary CDMA receiver. We investigate several methods to apply the proposed metric to the demodulator´s soft-decision outputs prior to decoding. Our soft-decision decoding techniques are designed to mitigate the effects of interference from other signals in the frequency band. We compare the performance of our proposed metric to the log-likelihood ratio (LLR) metric, which requires that the mean signal level and noise variance are known for each bit position. Rather than attempt to estimate these parameters directly, our metric uses demodulator statistics and thus does not require pilot symbols or training sequences typically required by an LLR-based metric.