  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 introduce
a new "compensated autocorrelation" metric, providing a more analytically
tractable solution with precise expressions for false-alarm and
missed-detection probabilities. Second, this paper proposes the Pareto
comparison principle for fair benchmarking packet-detection algorithms,
considering both false alarms and missed detections simultaneously. Third, with
the Pareto benchmarking scheme, we experimentally confirm that the performance
of S&C can be greatly improved by taking only the real part and discarding the
imaginary part of the autocorrelation, leading to the novel real-part S&C
(RP-S&C) scheme. Fourth, and perhaps most importantly, we utilize the
compensated autocorrelation metric we newly put forth to extend the
single-antenna algorithm to multi-antenna scenarios through a weighted-sum
approach. Two optimization problems, minimizing false-alarm and
missed-detection probabilities respectively, are formulated and solutions are
provided. Our experimental results reveal that the optimal weights for false
alarms (WFA) scheme is more desirable than the optimal weights for missed
detections (WMD) due to its simplicity, reliability, and superior performance.
This study holds considerable implications for the design and deployment of
packet-detection schemes in random-access networks.

Networking and Internet Architecture