A Dual-Functional Sensing-Communication Waveform Design Based on OFDM

被引:1
作者
He, Yinghui [1 ]
Yu, Guanding [1 ]
Tang, Zhenzhou [2 ]
Wang, Jianfeng [3 ]
Luo, Haiyan [4 ]
机构
[1] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China
[2] Wenzhou Univ, Wenzhou Key Lab Intelligent Networking, Wenzhou 325035, Peoples R China
[3] Lenovo Grp Ltd, Lenovo Res, Beijing 100094, Peoples R China
[4] Lenovo Shanghai Informat Technol Co Ltd, Shanghai 201203, Peoples R China
关键词
Integrated sensing and communication; waveform design; OFDM waveform; majorization-minimization; precoding; WIRELESS COMMUNICATIONS; INTEGRATED RADAR; SYSTEMS;
D O I
10.1109/TWC.2024.3448456
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation mobile networks to embed sensing function on communication waveforms. A major challenge in ISAC is the effective integration of sensing and communication functions. Addressing this, this paper introduces a dual-functional waveform design that builds on the existing orthogonal frequency division multiplexing (OFDM) waveform. Unlike prior approaches that generally sacrifice communication performance to enhance sensing performance, our design contains a null-space sensing precoder that utilizes the null space of the communication channel to project additional sensing signals, thus improving the sensing functionality of the OFDM waveform without degrading any communication performance. We formulate a waveform optimization problem aimed at maximizing the sensing performance under the null-space sensing precoder and then propose a majorization-minimization (MM)-based waveform design algorithm. Additionally, to meet the real-time communication requirement in practice, we analyze the intrinsic characteristics of the high-performance sensing waveform and then develop a low-complexity waveform design algorithm. Simulation results show that the proposed MM-based algorithm can dramatically improve sensing performance without incurring any additional sensing power and degrading the communication performance. Furthermore, the low-complexity algorithm achieves substantial improvements in the sensing performance with much reduced computational complexity.
引用
收藏
页码:16962 / 16975
页数:14
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