Detecting changes in wireless signal types quickly with low false alarms
Sequential Change Point Detection for PSK Modulation Switching
Information Theory
Summary
In wireless communication, devices often change how they send data, but the receiver doesn’t know when this happens. The authors study ways to quickly detect when the type of signal (specifically phase shift keying or PSK) switches, without mistakenly raising too many false alarms. They develop mathematical methods to monitor the incoming signal and decide the exact moment the change occurs, balancing quick detection with accuracy. Their experiments show that these methods work well and match the theoretical predictions closely.
What this means in practice
- •For communication system engineers: Improve receivers to quickly recognize modulation changes in noisy wireless channels, enabling adaptive signal decoding with fewer errors.
- •For wireless device manufacturers: Enhance device firmware to detect signal modulation switches promptly, leading to more robust and efficient wireless communication hardware.
Authors
Gopal Kataria, Davin Sequeira, Arti Yardi
Abstract
Transmitters in adaptive communication systems may switch their modulation scheme at an unknown time, and a receiver must detect this switch quickly without raising frequent false alarms. Existing modulation classification methods label a finite block of received data and do not provide stopping rules with controlled false-alarm and delay performance. We study the sequential detection of a switch from one phase shift keying (PSK) scheme to another over an additive white Gaussian noise channel. We build cumulative-sum (CUSUM) and moving-sum (MOSUM) detectors from the log-likelihood ratio and its low-SNR approximation. For a switch from quadrature PSK to binary PSK, we derive the moment-generating function and probability density function of the approximate statistic. We use them to relate the CUSUM threshold to the average run length to false alarm and the worst-case detection delay. For a switch between any two PSK orders, we approximate the vector of overlapping MOSUM statistics as multivariate Gaussian and obtain bounds on both measures. Monte Carlo simulations show that our derived expressions for ARL2FA and WADD closely match the empirical results across thresholds, validating the accuracy of the proposed analytical approximations and theoretical bounds.