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

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 introdu...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 23; H. 11; S. 16962 - 16975
Hauptverfasser: He, Yinghui, Yu, Guanding, Tang, Zhenzhou, Wang, Jianfeng, Luo, Haiyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Communication
Complexity
Design optimization
Discrete Fourier transforms
Functionals
Integrated sensing and communication
majorization-minimization
Null space
OFDM
OFDM waveform
Orthogonal Frequency Division Multiplexing
precoding
Real time
Sensors
Vectors
waveform design
Waveforms
Wireless communication
Wireless sensor networks
ISSN:1536-1276, 1558-2248
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Zusammenfassung: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.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3448456