PROJECT TITLE :
Noise-Enhanced Blind Multiple Error Rate Estimators in Wireless Relay Networks
Data detection or fusion based on output from multiple wireless links often requires channel state information (CSI) about the links' error rate (ER) performance. We consider the scenario that these links include direct source-destination (SD) links and two-hop links that require an intermediate decode-and-forward (DF) node to relay the source signal. Conventional destination-based estimators suffer from slow convergence and are incapable of simultaneously blind estimating all ERs, including, in particular, those of the source-relay (SR) links. They may also require various degrees of CSI about the ERs of the SD and relay-destination (RD) links to remove the ambiguity arising from the insufficient number of links in the network and from that due to the symmetric nature of a cascaded source-relay-destination link's ER as a function of its component SR and RD links' ERs. We propose novel Monte-Carlo-based estimators that overcome all these shortcomings. The estimation process involves injecting noise into the samples received by the destination node to create virtual links and alter link output statistics. We show that the latter scheme exhibits a stochastic resonance effect, i.e., its mean squared estimation error (MSEE) performance is enhanced by injecting proper noise, and there exists an optimal injected noise power level that achieves the maximum improvement. The stochastic resonance effects are analyzed, and numerical examples are provided to display our estimators' MSEE behaviors, as well as to show that the ER performance of the optimal detector using the proposed estimators is almost as good as that with perfect ER information.
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