2026· IEEE Transactions on Wireless Communications· Vol 25, pp. 23519-23535· 0 citations· 31 references
Abstract
This paper proposes a novel multi-user multiple-input multiple-output orthogonal frequency-division multiplexing (MU-MIMO-OFDM) based integrated sensing and communication (ISAC) framework. By partitioning the time-frequency resource grid into sub-blocks, the architecture enables 4D sensing (range, velocity, azimuth, elevation) and facilitates radar data cube formation for high-speed standard radar processing. A key innovation is a radar supplement signal featuring double-orthogonality to both the communication channel and signal. Unlike conventional null-space projection (NSP) methods, which transmit radar signals solely through the channel’s null-space, our approach explicitly eliminates the cross-correlation between sensing and communication signals by exploiting the communication signal’s null-space. This minimizes radar estimation error while strictly preserving communication performance. Furthermore, we derive an optimal power allocation strategy and incorporate stabilization techniques, such as block selection, to ensure robust sensing in practical environments. Simulation results demonstrate that the proposed framework achieves superior radar detection performance with an average precision (AP) of 0.90, significantly outperforming both the separate resource allocation (SRA) method, which assigns distinct resources to communication and radar, and schemes that neglect orthogonality with respect to the communication signal. These gains are achieved while maintaining spectral efficiency comparable to a communication-only baseline, effectively validating the efficacy of the stabilized ISAC architecture.
This paper analyzes a multi-input multi-output (MIMO)-OFDM-ISAC system in which multiple data streams jointly illuminate a sensing target, and derives second-order moment expressions for the RDM under matched filtering (MF) and reciprocal filtering (RF), and uses them to characterize the dynamic range (DR).
Random communication symbols allow integrated sensing and communications (ISAC) systems to exploit payload signals for sensing, but the resulting matched-filter response varies with the transmitted data. Existing statistical analyses of sensing with random orthogonal frequency division multiplexing (OFDM) waveforms hav...
Pin-Jun Zeng, Yu-Yang Lu, Jia-Lan Tang et al.· 0 citations
In this paper, we investigate the design of multiple orthogonal frequency-division multiplexing (OFDM) waveforms for joint radar and communications applications. The design objectives are to simultaneously minimize the integrated sidelobe level (ISL) and the peak-to-mean envelope power ratio (PMEPR) of the OFDM wavefor...
Xiao-Nan Xu, Yong-Zhe Li, Ran Tao et al.· IEEE Transactions on Signal...· 0 citations
Orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) reuses the entire time-frequency frame, including pilots and data payloads, for radar sensing. The random data payload, however, reduces the sensing dynamic range and degrades the target detection and parameter estimation perf...
Don-Lin Yang, Kai-Tao Meng, Fan Liu et al.· 0 citations
Orthogonal frequency division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC) systems due to its high spectral efficiency and inherent compatibility with modern wireless standards. However, its fundamental estimation-theoretic sensing performance under random data modulation remain...
Kawon Han, Kai-Tao Meng, Alexandra Chatzicharistou et al.· 2 citations· ⚡1
Integrated sensing and communication (ISAC) is regarded as a key enabling technique in future 6th-generation (6G) mobile communication systems. However, existing multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC designs generally rely on the fixed-position antennas and fixed allocat...
Cheng-Zhi Ye, Ruo-Yu Zhang, Lei Yao et al.· 4 citations
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