Deep Learning-Enabled Signal Detection for MIMO-OTFS-Based 6G and Future Wireless Networks

Orthogonal time frequency space (OTFS) modulation stands out as a promising waveform for sixth generation (6G) and beyond wireless communication systems, offering superior performance over conventional methods, particularly in high-mobility scenarios and dispersive channel conditions. Recent research has demonstrated that the reduced computational complexity of deep learning (DL)-based signal detection (SD) methods constitutes a compelling alternative to conventional techniques. In this study, low-complexity DL-based SD methods are proposed for a multiple-input multiple-output (MIMO)-OTFS system and examined under Nakagami-$m$ channel conditions. The symbols obtained from the receiver antennas are combined using maximum ratio combining (MRC) and detected with the help of a DL-based detector implemented with multi-layer perceptron (MLP), convolutional neural network (CNN), and residual network (ResNet). Complexity analysis reveals that the MLP architecture offers significantly lower computational complexity compared to CNN, ResNet, and classical methods such as maximum likelihood detection (MLD). Furthermore, numerical analyses have shown that the proposed DL-based detectors, despite their low complexity, achieve comparable bit error rate (BER) performance to that of a high-performance MLD under various system conditions.

A Comprehensive Survey of Channel Estimation Techniques for OTFS in 6G and Beyond Wireless Networks

Orthogonal time-frequency space (OTFS) modulation has emerged as a powerful wireless communication technology that is specifically designed to address the challenges of high-mobility scenarios and significant Doppler effects. Unlike conventional modulation schemes that operate in the time-frequency (TF) domain, OTFS projects signals to the delay-Doppler (DD) domain, where wireless channels exhibit sparse and quasi-static characteristics. This fundamental transformation enables superior channel estimation (CE) performance in challenging propagation environments characterized by high-mobility, severe multipath effects, and rapidly time-varying channel conditions. This article provides a systematic examination of CE techniques for OTFS systems, covering the extensive research landscape from foundational methods to cutting-edge approaches. We present a detailed analysis of DD and TF domain CE techniques presented in the literature, including separate pilot, embedded pilot, and superimposed pilot approaches. The article encompasses various algorithmic frameworks including Bayesian learning, matching pursuit-based techniques, message passing algorithms, deep learning (DL)-based methods, and recent CE approaches. Additionally, we explore joint CE and signal detection (SD) strategies, the integration of OTFS with next-generation wireless systems including massive multiple-input multiple-output (MIMO), millimeter wave (mmWave) communications, reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC) systems. Critical implementation challenges are presented, including leakage suppression, inter-Doppler interference mitigation, impulsive noise handling, signaling overhead reduction, guard space requirements, peak-to-average power ratio (PAPR) management, beam squint effects, and hardware impairments.

Bit Error Rate and Performance Analysis of Multi-User OTFS under Nakagami-m Fading for 6G and Beyond Networks

Orthogonal Time-Frequency Space modulation stands out as a promising waveform for 6G and beyond wireless communication systems, offering superior performance over conventional methods, particularly in high-mobility scenarios and dispersive channel conditions. Error performance analysis remains crucial for accurately characterizing the reliability of wireless communication systems under practical constraints. In this paper, we systematically investigate the bit error rate performance of OTFS modulation over Nakagami-m fading channels in both single-user and multi-user scenarios. In analytical approaches, mathematical frameworks are employed for distinct receiver configurations: the Single-input Single-output scenario leverages Erlang probability density function of squared-Nakagami variables to derive closed-form BER expressions, while the Single-input Multiple-output case applies moment matching techniques with Gamma approximation to model multiple user interference, subsequently yielding Signal-to-interference-plus-noise Ratio characterizations through Meijer-G functions. This study examines single-path and multi-path channel conditions, evaluating the relationship between path multiplicity and error performance metrics while considering various fading intensities through Nakagami-m fading parameters. The derived closed-form BER expressions are validated through maximum likelihood detection based Monte Carlo simulations, demonstrating strong correlation between analytical and numerical results across various SNR regions. Furthermore, comparative benchmark evaluations against conventional orthogonal frequency division multiplexing with MLD reveal that OTFS consistently achieves superior error performance in high-mobility scenarios. In multipath fading environments, OTFS achieves superior diversity gain compared to conventional OFDM, which refers to enhanced error performance.

Orthogonal Time-Frequency Space (OTFS) Aided Media-Based Modulation System For 6G and Beyond Wireless Communications Networks

This paper proposes a new orthogonal time frequency space (OTFS)-based index modulation system called OTFS-aided media-based modulation (MBM) scheme (OTFS-MBM), which is a promising technique for high-mobility wireless communication systems. The OTFS technique transforms information into the delay-Doppler domain, providing robustness against channel variations, while the MBM system utilizes controllable radio frequency (RF) mirrors to enhance spectral efficiency. The combination of these two techniques offers improved bit error rate (BER) performance compared to conventional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. The proposed system is evaluated through Monte Carlo simulations over high-mobility Rayleigh channels for various system parameters. Comparative throughput, spectral efficiency, and energy efficiency analyses are presented, and it is shown that OTFS-MBM outperforms traditional OTFS and OTFS-SM techniques. The proposed OTFS-MBM scheme stands out as a viable solution for sixth generation (6G) and next-generation wireless networks, enabling reliable communication in dynamic wireless environments.

A New OTFS-Based Index Modulation System for 6G and Beyond: OTFS-Based Code Index Modulation

This paper proposes the orthogonal time frequency space-based code index modulation (OTFS-CIM) scheme, a novel wireless communication system that combines OTFS modulation, which enhances error performance in high-mobility Rayleigh channels, with CIM technique, which improves spectral and energy efficiency, within a single-input multiple-output (SIMO) architecture. The proposed system is evaluated through Monte Carlo simulations for various system parameters. Results show that increasing the modulation order degrades performance, while more receive antennas enhance it. Comparative analyses of error performance, throughput, spectral efficiency, and energy saving demonstrate that OTFS-CIM outperforms traditional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. Also, the proposed OTFS-CIM system outperforms benchmark systems in many performance metrics under high-mobility scenarios, making it a strong candidate for sixth generation (6G) and beyond.