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<p>Orthogonal time–frequency space modulation combined with multiple-input multiple-output transmission (MIMO-OTFS) has emerged as a strong waveform candidate for sixth-generation (6G) wireless networks because of its robustness against high mobility and doubly selective channels. However, reliable signal detection in large-scale MIMO-OTFS systems remains challenging owing to severe delay–Doppler coupling and channel state information (CSI), particularly under Rayleigh and Rician fading conditions. This paper proposes a residual-learning minimum mean square error neural detector (RL-MMSE-ND) to address these challenges under both perfect and imperfect CSI, including scenarios with up to a 20% channel estimation error. The proposed detector integrates a conventional minimum mean square error (MMSE) front-end with a lightweight residual-learning neural network that learns only the residual interference caused by CSI mismatch and delay–Doppler effects, thereby preserving MMSE stability while introducing minimal learning overhead. Extensive simulations were conducted for representative large-scale MIMO-OTFS configurations to evaluate the bit error rate (BER) versus signal-to-noise ratio (SNR), BER versus delay spread, power spectral density (PSD) characteristics, inference latency, and training convergence. Numerical results demonstrate that the proposed RL-MMSE-ND achieves 10–13&#xa0;dB SNR gain at a BER of 10<sup>−3</sup> compared to zero-forcing equalization and MMSE detectors, while requiring 3–6&#xa0;dB lower SNR than the maximum likelihood, QR decomposition–based M-algorithm detection, and deep learning–based detectors under both Rayleigh and Rician fading. Moreover, the proposed method reduces inference latency by over 60% compared to long short-term memory and bidirectional long short-term memory detectors and achieves 5–8&#xa0;dB lower out-of-band emissions while adding only linear computational overhead beyond MMSE detection. These results confirmed the novelty and practical significance of the proposed approach for robust, low-latency, and low-complexity MIMO-OTFS detection in future 6G wireless systems.</p>

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A residual-learning MMSE neural detector for 6G MIMO-OTFS systems under diverse channel conditions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Arun Kumar,
  • Mansor Alohali,
  • Venkatachalam Revathi,
  • Mehedi Masud,
  • Nishant Gaur,
  • Aziz Nanthaamornphong

摘要

Orthogonal time–frequency space modulation combined with multiple-input multiple-output transmission (MIMO-OTFS) has emerged as a strong waveform candidate for sixth-generation (6G) wireless networks because of its robustness against high mobility and doubly selective channels. However, reliable signal detection in large-scale MIMO-OTFS systems remains challenging owing to severe delay–Doppler coupling and channel state information (CSI), particularly under Rayleigh and Rician fading conditions. This paper proposes a residual-learning minimum mean square error neural detector (RL-MMSE-ND) to address these challenges under both perfect and imperfect CSI, including scenarios with up to a 20% channel estimation error. The proposed detector integrates a conventional minimum mean square error (MMSE) front-end with a lightweight residual-learning neural network that learns only the residual interference caused by CSI mismatch and delay–Doppler effects, thereby preserving MMSE stability while introducing minimal learning overhead. Extensive simulations were conducted for representative large-scale MIMO-OTFS configurations to evaluate the bit error rate (BER) versus signal-to-noise ratio (SNR), BER versus delay spread, power spectral density (PSD) characteristics, inference latency, and training convergence. Numerical results demonstrate that the proposed RL-MMSE-ND achieves 10–13 dB SNR gain at a BER of 10−3 compared to zero-forcing equalization and MMSE detectors, while requiring 3–6 dB lower SNR than the maximum likelihood, QR decomposition–based M-algorithm detection, and deep learning–based detectors under both Rayleigh and Rician fading. Moreover, the proposed method reduces inference latency by over 60% compared to long short-term memory and bidirectional long short-term memory detectors and achieves 5–8 dB lower out-of-band emissions while adding only linear computational overhead beyond MMSE detection. These results confirmed the novelty and practical significance of the proposed approach for robust, low-latency, and low-complexity MIMO-OTFS detection in future 6G wireless systems.