Efficient Message Passing-Based Joint Channel Estimation and Signal Detection for OTFS with Superimposed Pilots

Core Concepts

A message passing-based iterative receiver called the SP-DD receiver is proposed, which drastically reduces the computational complexity of joint channel estimation and signal detection in OTFS systems with superimposed pilots, while achieving marginal performance loss compared to existing receivers.

Abstract

The content discusses the design of an efficient message passing-based iterative receiver for OTFS (Orthogonal Time Frequency Space) systems with superimposed pilots (SP). Key highlights: Existing OTFS receivers with SP scheme require high-dimensional matrix operations and inversions, leading to high computational complexity. The proposed SP-DD receiver leverages message passing techniques to achieve joint channel estimation (CE) and signal detection with significantly reduced complexity compared to existing receivers. The SP-DD receiver adopts the generalized complex exponential basis expansion modeling (GCE-BEM) for efficient CE. To further improve the efficiency of the SP scheme, the authors design the pilot signals to achieve power concentration in the frequency domain, leading to the SP-DD-D receiver that can reduce the pilot power without performance degradation. Extensive simulations demonstrate the superior performance and reduced complexity of the proposed SP-DD and SP-DD-D receivers compared to existing OTFS receivers.

Stats

The received signal can be expressed as: yT = HT (FH N ⊗ IM) (√ρxp + √1 −ρxd) + w The time domain channel matrix HT can be modeled as: HT = PQ−1 q=0 diag{bq}Cq + Em The received signal in the frequency domain can be written as: xF = √1 −ρF ⊙xF;d + √ρF ⊙xF;p

Quotes

"Receivers with joint channel estimation (CE) and signal detection using superimposed pilots (SP) can achieve high transmission efficiency in orthogonal time frequency space (OTFS) systems." "To reduce the number of unknown channel parameters and improve the accuracy of CE, we adopt the GCE-BEM CE." "To make the SP scheme more efficient, we design the pilot signals carefully to achieve pilot power concentration, facilitating the reduction of the pilot power without decreasing the performance of the receiver, leading to a receiver named SP-DD-D, which can also reduce the peak-to-average power ratio (PAPR) of OTFS signals."

Key Insights Distilled From

Message Passing-Based Joint Channel Estimation and Signal Detection for OTFS with Superimposed Pilots

by Fupeng Huang... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2309.08177.pdf

Deeper Inquiries

How can the proposed message passing-based receivers be extended to handle more complex OTFS system models, such as MIMO-OTFS or multi-user OTFS

To extend the proposed message passing-based receivers to handle more complex OTFS system models like MIMO-OTFS or multi-user OTFS, several modifications and enhancements can be implemented: MIMO-OTFS: For MIMO-OTFS systems, the receivers can be extended to handle multiple input multiple output scenarios by incorporating multiple antennas at both the transmitter and receiver ends. This would involve adapting the message passing algorithms to account for the additional spatial dimensions introduced by the multiple antennas. The channel estimation and signal detection processes would need to be modified to accommodate the increased complexity of MIMO systems. Multi-User OTFS: In the case of multi-user OTFS systems, where multiple users share the same time-frequency resources, the receivers would need to be designed to handle interference from other users. This could involve implementing interference cancellation techniques within the message passing framework to separate the signals from different users. Additionally, the receivers would need to support multiple access schemes such as TDMA, FDMA, or CDMA to allocate resources efficiently among multiple users. By incorporating these enhancements, the message passing-based receivers can be extended to handle more complex OTFS system models like MIMO-OTFS or multi-user OTFS, enabling them to support a wider range of communication scenarios.

What are the potential limitations of the GCE-BEM approach used in the proposed receivers, and how could alternative channel modeling techniques be explored to further improve performance

The GCE-BEM approach used in the proposed receivers may have some limitations that could impact performance in certain scenarios: Modeling Accuracy: One potential limitation of the GCE-BEM approach is its reliance on the assumption of a specific channel model, which may not always accurately capture the true channel characteristics. In scenarios where the channel exhibits non-linear or time-varying behavior, the GCE-BEM model may not provide an optimal representation, leading to performance degradation. Complexity: While the GCE-BEM approach helps reduce the number of unknown channel parameters and computational complexity, it may still struggle to capture the full complexity of real-world channels. Alternative channel modeling techniques, such as deep learning-based models or adaptive algorithms, could be explored to enhance the accuracy of channel estimation in OTFS systems. Generalization: The GCE-BEM approach may be limited in its ability to generalize across different channel conditions and environments. Exploring more adaptive and versatile channel modeling techniques could help improve the robustness and performance of the receivers in diverse operating scenarios. To address these limitations, alternative channel modeling techniques such as deep learning-based models, adaptive algorithms, or hybrid approaches could be investigated to further enhance the performance of the receivers in OTFS systems.

Given the focus on computational complexity reduction, how could the proposed receivers be adapted to leverage emerging hardware acceleration technologies, such as GPU or FPGA, to enable real-time OTFS implementations

Adapting the proposed receivers to leverage emerging hardware acceleration technologies like GPU or FPGA can significantly enhance their computational efficiency and enable real-time OTFS implementations. Here are some ways to adapt the receivers for hardware acceleration: Parallel Processing with GPU: The message passing algorithms can be parallelized to take advantage of the massively parallel architecture of GPUs. By offloading the computationally intensive tasks to the GPU, the receivers can achieve significant speedup in processing time. Implementing GPU-accelerated libraries like CUDA or OpenCL can help optimize the algorithms for GPU execution. Custom FPGA Implementations: For real-time processing and low-latency requirements, the receivers can be implemented on FPGAs to achieve hardware-level acceleration. By designing custom FPGA architectures tailored to the specific algorithms and computations involved in the receivers, high throughput and low latency can be achieved. Utilizing high-level synthesis tools like Vivado HLS can facilitate the design and optimization of FPGA implementations. Hybrid CPU-GPU-FPGA Architecture: A hybrid architecture combining the strengths of CPU, GPU, and FPGA can be employed to maximize performance and efficiency. Task partitioning strategies can be utilized to distribute computations across the different hardware components based on their strengths, ensuring optimal utilization of resources and achieving real-time processing capabilities. By adapting the proposed receivers to leverage GPU or FPGA acceleration technologies, the computational complexity can be significantly reduced, enabling efficient and real-time implementation of OTFS systems in practical applications.