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Cooperative Distributed Detection for Oversampled Random Multiplexing Systems

2026 · IEEE Transactions on Wireless Communications · Vol 25, pp. 23048-23062 · 0 citations · 39 references

Abstract

Random multiplexing (RM) with approximate message passing (AMP)-type detectors effectively address doubly-selective fading in high-mobility scenarios while achieving maximum a posteriori (MAP) bit-error rate performance. To further improve RM performance, this paper considers oversampling at an integer multiple of the symbol rate. Traditional oversampling detectors are challenged by the high complexity of processing large-dimension stacked sampled data and substantial overhead from correlated noise whitening. To overcome these issues, we present the oversampled RM system model, treating each oversampling instance as a distributed node and modeling the system as a distributed detection problem. In this context, we propose a cooperative distributed AMP-type detection framework with inner-iterative local estimation and outer-iterative global fusion. Then, we analyze the correlation among the local messages caused by the shared RM matrix and the oversampled noise. The orthogonality of the estimation errors ensures that the matrix formed by local information from distributed nodes has columns that are independent and identically distributed, while each row follows a joint Gaussian distribution. By leveraging this correlation, we derive an optimal fusion strategy to design a low-complexity cooperative distributed memory AMP detector and a high-stability cooperative distributed orthogonal AMP detector. Our simulation results show that the proposed detectors outperform both the AMP-type detectors with Nyquist sampling and the oversampled centralized detectors without noise whitening. The performance of RM is superior to that of other multiplexing schemes when the same detector is employed. In the outer iteration, our optimal fusion outperforms the Gaussian fusion that does not consider correlation. Moreover, with sufficient inner iterations, a single outer iteration matches the performance of full outer iterations, minimizing communication overhead.

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