New Cramer-Rao lower bound and SNR estimation in hybrid ARQ system

By using multiple repeated signal replicas to formulate the accumulative observed noisy signal sequence (AONSS) in the hybrid ARQ system, a novel data-aided maximum likelihood (DA ML) SNR estimation is proposed for the AWGN channel. Based on the AONSS, new lower hounds, i.e., the generalized deterministic and random Cramer-Rao lower bounds (GCRLB´s), which include traditional Cramer-Rao lower bounds (CRLB´´) as special cases, are derived. It is indicated that the conventional DA ML estimate is a special case of the novel DA ML estimate. An alternative differential observed noisy signal sequence (DONSS) is also proposed, yielding a blind ML SNR estimation technique. It is shown by numerical analysis and simulation results that both the proposed DA ML and the proposed blind ML SNR estimation techniques can offer satisfaction SNR estimation.