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This paper reexamines and fundamentally improves the Schmidl-and-Cox (S&C)algorithm, which is extensively used for packet detection in wireless networks,and enhances its adaptability for multi-antenna receivers. First, we introducea new "compensated autocorrelation" metric, providing a more analyticallytractable solution with precise expressions for false-alarm andmissed-detection probabilities. Second, this paper proposes the Paretocomparison principle for fair benchmarking packet-detection algorithms,considering both false alarms and missed detections simultaneously. Third, withthe Pareto benchmarking scheme, we experimentally confirm that the performanceof S&C can be greatly improved by taking only the real part and discarding theimaginary part of the autocorrelation, leading to the novel real-part S&C(RP-S&C) scheme. Fourth, and perhaps most importantly, we utilize thecompensated autocorrelation metric we newly put forth to extend thesingle-antenna algorithm to multi-antenna scenarios through a weighted-sumapproach. Two optimization problems, minimizing false-alarm andmissed-detection probabilities respectively, are formulated and solutions areprovided. Our experimental results reveal that the optimal weights for falsealarms (WFA) scheme is more desirable than the optimal weights for misseddetections (WMD) due to its simplicity, reliability, and superior performance.This study holds considerable implications for the design and deployment ofpacket-detection schemes in random-access networks.

Reliable Packet Detection for Random Access Networks: Analysis, Benchmark, and Optimization