Pilot Design Method for OTFS System in High-Speed Mobile Scenarios

LI Yibing; TANG Yunhe; JIAN Xin; SUN Qian; CHEN Hao

doi:10.11999/JEIT240349

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LI Yibing, TANG Yunhe, JIAN Xin, SUN Qian, CHEN Hao. Pilot Design Method for OTFS System in High-Speed Mobile Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240349

Citation:

LI Yibing, TANG Yunhe, JIAN Xin, SUN Qian, CHEN Hao. Pilot Design Method for OTFS System in High-Speed Mobile Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240349

LI Yibing, TANG Yunhe, JIAN Xin, SUN Qian, CHEN Hao. Pilot Design Method for OTFS System in High-Speed Mobile Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240349

Citation:

LI Yibing, TANG Yunhe, JIAN Xin, SUN Qian, CHEN Hao. Pilot Design Method for OTFS System in High-Speed Mobile Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240349

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Pilot Design Method for OTFS System in High-Speed Mobile Scenarios

doi: 10.11999/JEIT240349

1.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
2.
Wuhan Maritime Communication Research Institute, Wuhan 430010, China

Funds: The National Natural Science Foundation of China General Project (52271311), 2022 Innovation Foundation of China State Shipbuilding Corporation 722 Institute

Received Date: 2024-05-07
Rev Recd Date: 2025-01-24

Available Online: 2025-02-09

Abstract

Abstract

Objective Orthogonal Time Frequency Space (OTFS) have attracted significant attention in recent years due to excellent performance in high-speed mobile communication scenarios characterized by time-frequency double-selective channels. Accurate and efficient channel state information acquisition is critical for these systems. To address this, a channel estimation method based on compressed sensing is employed, using specialized pilot sequences. The performance of such channel estimation algorithms based on compressed sensing and the cross-correlation properties of the dictionary sets generated by these pilot sequences. which vary depending on the sequence design. This study addresses the pilot design problem in OTFS communication systems, proposing an optimization method to identify pilot sequences that enhance channel estimation accuracy effectively. Methods A pilot-assisted channel estimation algorithm based on compressed sensing is employed to estimate the delay and Doppler channel state information in OTFS systems for high-speed mobile scenarios. To improve channel estimation accuracy in the Delay-Doppler domain and achieve better performance than traditional pseudo-random sequences, this study proposes a pilot sequence optimization method using an Improved Genetic Algorithm (IGA). The algorithm takes the cross-correlation among dictionary set columns as the optimization goal, leveraging the GA's strong integer optimization capabilities to search for optimal pilot sequences.An adaptive adjustment strategy for crossover and mutation probabilities is also introduced to enhance the algorithm's convergence and efficiency. Additionally, to address the high computational complexity of the fitness function, the study analyzes the expressions for calculating cross-correlation among dictionary set columns and simplifies redundant calculations, thereby improving the overall optimization efficiency. Results and Discussions This study investigates the channel estimation performance of OTFS systems using different pilot sequences. The simulation parameters are presented in (Table 1), and the simulation results are shown in (Figure 2), (Figure 3), and (Figure 4). (Figure 2) illustrates the convergence performance of several commonly used group heuristic intelligent optimization algorithms applied to the pilot optimization problem, including the Particle Swarm Optimization (PSO) algorithm, Discrete Particle Swarm Optimization (DPSO) algorithm, Snake Optimization (SO) algorithm, and Genetic Algorithm (GA). The results indicate that the performance of common continuous optimization algorithms, such as PSO and SO, is comparable, while DPSO slightly outperforms traditional PSO, GA, due to its unique genetic and mutation mechanisms, demonstrates significantly faster convergence and better solutions. Furthermore, this study proposes a targeted IGA capable of adaptively adjusting crossover and mutation probabilities, leading to better solutions with fewer iterations. The objective function calculation process is also analyzed and simplified, reducing its computational complexity from $ {O}({\lambda ^2}k_p^2{l_p}) $ to $ {O}(\lambda {k_p}{l_p}) $ without altering the cross-correlation coefficient, which significantly reduces the computational load while maintaining optimization efficiency. (Figure 3) and (Figure 4) depict the Normalized Mean Square Error (NMSE) and Bit Error Rate (BER) performance of OTFS systems using different pilot sequences for channel estimation. The commonly used pseudo-random sequences, including m-sequences, Gold sequences, Zadoff-Chu sequences, and the optimized sequences generated by the proposed algorithm, are compared. The results demonstrate that the optimized pilot sequences generated by the proposed algorithm achieve superior channel estimation performance compared with other pilot sequences. Conclusions This study analyzes a pilot-assisted channel estimation method for OTFS systems based on compressed sensing and proposes a pilot sequence optimization approach using an IGA to address the pilot optimization challenge. The optimization objective function is constructed based on the correlation among dictionary set columns, and an adaptive adjustment strategy for crossover and mutation probabilities is proposed to enhance the algorithm's convergence speed and optimization capability, outperforming other commonly used group heuristic optimization algorithms. To address the high computational complexity associated with directly calculating cross-correlation coefficients, the calculation steps are simplified, reducing the complexity from $ {O}({\lambda ^2}k_p^2{l_p}) $ to $ {O}(\lambda {k_p}{l_p}) $, while preserving the cross-correlation properties, thereby improving optimization efficiency. Simulation results demonstrate that the proposed optimized pilot sequences offer better channel estimation performance than traditional pseudo-random pilot sequences, with relatively low optimization complexity.
- Orthogonal Time Frequency Space (OTFS),
- Compressed sensing,
- Pilot optimization,
- Genetic Algorithm (GA)

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References(20)

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