Phase-modulated continuous wave (PMCW) has emerged as a promising waveform candidate for next-generation integrated sensing and communication systems due to its favorable sensing performance and multiplexing capability. In multiple-input and multiple-output (MIMO) PMCW systems, fast-time code-division multiplexing enables simultaneous transmission from multiple transmitters but causes inter-transmitter code interference due to non-ideal cross-correlation properties. The interference is observed to manifest as a low-rank component in the range–Doppler domain while target echoes appear as sparse components. This structural distinction motivates the use of robust principal component analysis (RPCA) for interference mitigation. In practice, conventional RPCA incurs high computational complexity due to the singular value decomposition (SVD) required at every iteration. To address this limitation, we propose an unfolded RPCA network in which each iterative step is mapped to a network stage and SVD is replaced by a factorized low-rank approximation. The proposed network also incorporates stage-wise learnable parameters for adaptive interference mitigation in MIMO PMCW systems. Simulation results demonstrate that the proposed method achieves interference mitigation performance comparable to conventional RPCA with 21.2 times lower inference latency. These results confirm the effectiveness and computational efficiency of the proposed method for real-time mitigation of inter-transmitter code interference in MIMO PMCW systems.
Lee et al. (2026) studied this question.