Precoded Faster-than-Nyquist OTFS Modulation for Enhanced Spectral Efficiency and Doppler Resilience in Doubly Selective Fading Channels

Core Concepts

This research paper introduces a novel precoded faster-than-Nyquist (FTN) orthogonal time frequency space (OTFS) modulation scheme that enhances spectral efficiency and improves Doppler resilience in challenging wireless communication environments.

Abstract

• Bibliographic Information: Hong, Z., Sugiura, S., Xu, C., & Hanzo, L. (2024). Precoded Faster-than-Nyquist Signaling Using Optimal Power Allocation for OTFS. IEEE Wireless Communications Letters.

• Research Objective: This paper proposes a novel precoded OTFS-FTN architecture to enhance the spectral efficiency and Doppler resilience of wireless communication systems operating in doubly selective fading channels.

• Methodology: The authors derive the input-output relationship of FTN signaling in the delay-Doppler domain and employ eigenvalue decomposition (EVD) to mitigate inter-symbol interference and correlated noise. They further optimize power allocation coefficients for individual frames to maximize mutual information under transmit power constraints.

• Key Findings: The proposed OTFS-FTN scheme demonstrates superior performance compared to conventional Nyquist-based OTFS systems using root-raised-cosine (RRC) filters. Simulation results show enhanced information rate and improved BER performance, particularly in high Doppler shift scenarios.

• Main Conclusions: The research concludes that incorporating FTN signaling into OTFS modulation, combined with EVD precoding and optimal power allocation, significantly enhances spectral efficiency and robustness against Doppler effects in challenging wireless channels.

• Significance: This research contributes to the advancement of OTFS technology, paving the way for high-data-rate, reliable wireless communication in high-mobility scenarios, such as vehicular communication and next-generation wireless networks.

• Limitations and Future Research: The study primarily focuses on a theoretical analysis and simulation-based evaluation of the proposed scheme. Future research could explore practical implementation aspects, including channel estimation, synchronization, and hardware complexity, to further validate its real-world feasibility and performance gains.

Stats

The symbol packing ratio was set to α = 0.9 and 0.8.
The basic system parameters are set to (P, M, N) = (20, 128, 12), and the maximum integer Doppler-shift tap is given by kmax = 5 (≥kp).
We considered QPSK, ∆f = 30 kHz, and (M, N, α, β) = (64, 30, 0.82, 0.25).
We considered QPSK, ∆f = 60 kHz, and (M, N, α, β) = (256, 6, 0.85, 0.25).
The parameters are set as ∆f = 30 kHz, νmax = 7.5 kHz and (M, N) = (128, 12).

Quotes

"Our simulation results will demonstrate that the proposed OTFS-FTN scheme achieves a higher information rate and better BER performance than its conventional time-orthogonal signaling counterpart using the same RRC shaping filter."

Key Insights Distilled From

Precoded faster-than-Nyquist signaling using optimal power allocation for OTFS

by Zekun Hong, ... at arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.01244.pdf

Deeper Inquiries

How does the computational complexity of the proposed OTFS-FTN scheme compare to existing OTFS techniques, and what are the potential implications for its implementation in practical devices?

The proposed OTFS-FTN scheme, while offering spectral efficiency and Doppler resilience benefits, introduces additional computational complexity compared to traditional OTFS techniques. This complexity primarily arises from the precoding and decoding operations required to mitigate the inherent inter-symbol interference (ISI) introduced by faster-than-Nyquist (FTN) signaling.
Here's a breakdown of the computational complexity:

EVD operations: The core of the OTFS-FTN scheme lies in employing Eigenvalue Decomposition (EVD) for both precoding at the transmitter and decoding at the receiver. These EVD operations, while enabling ISI mitigation and noise whitening, are computationally intensive, particularly for large frame sizes (M and N). The complexity of EVD scales cubically with the matrix dimensions, making it a bottleneck for real-time processing.

Matrix multiplications: The precoding and decoding processes involve multiple matrix multiplications, further adding to the computational burden. These multiplications involve matrices with dimensions related to the frame size, impacting the overall processing time.

Power allocation: The optimal power allocation strategy, while maximizing mutual information, necessitates iterative water-filling algorithms. These algorithms, though not as computationally demanding as EVD, contribute to the overall complexity, especially in dynamic channel conditions requiring frequent power allocation updates.
Implications for practical implementation:
The increased computational complexity of OTFS-FTN poses challenges for its implementation in practical, resource-constrained devices.

Hardware requirements: Devices would require more powerful processors and dedicated hardware accelerators to handle the complex precoding and decoding operations in real-time. This translates to increased power consumption and device cost.

Processing latency: The time required for EVD and other matrix operations could introduce processing latency, potentially impacting latency-sensitive applications like real-time video streaming or remote control scenarios.
Potential solutions and future directions:

Reduced-complexity EVD algorithms: Exploring and developing computationally efficient EVD algorithms tailored for the specific structure of matrices encountered in OTFS-FTN could significantly reduce the processing burden.

Approximate solutions: Investigating near-optimal precoding and decoding techniques with lower complexity, such as iterative methods or those exploiting channel sparsity, could provide a practical trade-off between performance and complexity.

Hardware acceleration: Designing dedicated hardware accelerators optimized for the specific operations involved in OTFS-FTN, like EVD and matrix multiplications, can significantly speed up processing and reduce power consumption.

Could the performance gains of OTFS-FTN be compromised in highly congested network environments with significant inter-cell interference, and how can this challenge be addressed?

Yes, the performance gains of OTFS-FTN, particularly its spectral efficiency advantage, could be compromised in highly congested network environments with significant inter-cell interference (ICI). This degradation stems from the fact that OTFS-FTN, similar to other multicarrier techniques, is sensitive to the orthogonality of its subcarriers. ICI, by introducing non-orthogonal signals from neighboring cells, disrupts this orthogonality, leading to performance degradation.
Here's how ICI impacts OTFS-FTN:

Increased interference levels: ICI manifests as additional noise at the receiver, raising the noise floor and reducing the signal-to-interference-plus-noise ratio (SINR). This directly impacts the achievable data rates and error performance.

Distorted channel estimation: Accurate channel estimation is crucial for OTFS-FTN's precoding and decoding processes. ICI can severely impair channel estimation by introducing errors, leading to suboptimal precoding and decoding, further degrading performance.
Addressing ICI in OTFS-FTN:
Mitigating ICI in OTFS-FTN requires a multi-pronged approach:

Interference coordination and cancellation: Employing advanced interference coordination techniques between cells, such as coordinated scheduling and power control, can help manage ICI. Additionally, exploring multi-cell processing techniques, where multiple base stations cooperate to jointly decode signals and mitigate interference, holds promise.

Robust channel estimation: Developing robust channel estimation algorithms that can effectively estimate the channel in the presence of ICI is crucial. Techniques like pilot contamination mitigation and blind/semi-blind channel estimation can be explored.

Advanced receiver designs: Employing advanced receiver architectures with interference cancellation capabilities, such as those based on minimum mean squared error (MMSE) or interference alignment principles, can help suppress ICI and improve performance.

Fractional frequency reuse (FFR): Implementing FFR schemes, where adjacent cells utilize different subcarrier sets, can reduce ICI, particularly at cell-edge regions.

What are the broader implications of this research for the future of wireless communication, particularly in the context of emerging technologies like 6G and beyond?

This research on OTFS-FTN carries significant implications for the future of wireless communication, particularly as we transition towards demanding scenarios envisioned for 6G and beyond:

Increased spectral efficiency for future demands: 6G aims to support significantly higher data rates and massive device connectivity. OTFS-FTN's ability to enhance spectral efficiency aligns perfectly with this goal, enabling more efficient utilization of the scarce radio spectrum.

Robustness for high-mobility and dense deployments: Future wireless networks will need to seamlessly support high-mobility applications and ultra-dense deployments. OTFS-FTN's inherent resilience to Doppler shifts, a characteristic inherited from OTFS, makes it well-suited for high-mobility scenarios.

Enabling technology for demanding applications: The increased data rates and robustness offered by OTFS-FTN open doors for supporting demanding applications like augmented/virtual reality (AR/VR), holographic communications, and the tactile internet, which require high bandwidth and low latency.

Catalyst for further research and innovation: This research serves as a stepping stone for further exploration and development of advanced signal processing techniques. It encourages the investigation of reduced-complexity algorithms, novel precoding and decoding schemes, and optimized hardware implementations to unlock the full potential of OTFS-FTN.

Integration with other emerging technologies: OTFS-FTN can be integrated with other promising technologies like massive MIMO, millimeter-wave (mmWave) communication, and reconfigurable intelligent surfaces (RIS) to further enhance spectral efficiency, coverage, and reliability in future wireless networks.
However, realizing the full potential of OTFS-FTN for 6G and beyond requires addressing the challenges of computational complexity, ICI mitigation, and practical implementation complexities. Overcoming these hurdles will pave the way for OTFS-FTN to play a pivotal role in shaping the future of wireless communication.