面對高速移動場景的OTFS系統(tǒng)導頻設計方法
doi: 10.11999/JEIT240349 cstr: 32379.14.JEIT240349
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哈爾濱工程大學信息與通信工程學院 哈爾濱 150001
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武漢船舶通信研究所 武漢 430010
基金項目: 國家自然科學基金(52271311),中國船舶集團有限公司第七二二所 2022 年創(chuàng)新基金(2022J-4)
Pilot Design Method for OTFS System in High-Speed Mobile Scenarios
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College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
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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 (2022J-4)
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摘要: 正交時頻空(Orthogonal Time Frequency Space, OTFS)系統(tǒng)由于在面對高速移動通信場景下的時頻雙色散信道時的優(yōu)異性能受到了廣泛關注。為了準確獲取信道狀態(tài)信息,采用基于壓縮感知的信道估計方法,并輔以特殊的導頻序列完成信道估計。該文針對導頻優(yōu)化問題,提出了一種基于改進遺傳算法的OTFS導頻序列優(yōu)化方法,該方法以互相關最小化為優(yōu)化目標,采用遺傳算法進行尋優(yōu),并能夠自適應調整交叉和變異概率,在較少的迭代次數(shù)下即可實現(xiàn)比傳統(tǒng)偽隨機序列更優(yōu)的互相關性,能夠有效提高信道估計的準確性。此外,考慮到目標函數(shù)的計算量較大,該文分析了互相關的計算過程,并對其中的冗余計算進行了化簡,與直接計算字典集的互相關值相比大大提高了算法的優(yōu)化效率。
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關鍵詞:
- 正交時頻空(OTFS) /
- 壓縮感知 /
- 導頻優(yōu)化 /
- 遺傳算法
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}) $, while preserving the cross-correlation properties, thereby improving optimization efficiency. Simulation results demonstrate that the proposed optimized pilot sequences offer better channel estimation performance than traditional pseudo-random pilot sequences, with relatively low optimization complexity. -
表 1 OTFS系統(tǒng)仿真參數(shù)
參數(shù) 值 時延點數(shù)$M$ 16 多普勒點數(shù)$N$ 16 中心頻率${f_{\rm{c}}}$ 4×109 Hz 子載波間隔$\Delta f$ 105 Hz 最大時延${\tau _{\max }}$ 2 510 ns 最大移動速度${v_{\max }}$ 1 000 km/h 字典集過采樣因子$\lambda $ 10 下載: 導出CSV
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