Off-Grid Blind Near-Field Integrated Sensing and Communication: Algorithm Design and Lower Bound

YUAN Zhengdao; GUO Qinghua; HUANG Chongwen; GAO Dawei; MEI Fengtong; LIAO Guisheng

doi:10.11999/JEIT260404

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YUAN Zhengdao, GUO Qinghua, HUANG Chongwen, GAO Dawei, MEI Fengtong, LIAO Guisheng. Off-Grid Blind Near-Field Integrated Sensing and Communication: Algorithm Design and Lower Bound[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260404

Citation:

YUAN Zhengdao, GUO Qinghua, HUANG Chongwen, GAO Dawei, MEI Fengtong, LIAO Guisheng. Off-Grid Blind Near-Field Integrated Sensing and Communication: Algorithm Design and Lower Bound[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260404

Citation:

YUAN Zhengdao, GUO Qinghua, HUANG Chongwen, GAO Dawei, MEI Fengtong, LIAO Guisheng. Off-Grid Blind Near-Field Integrated Sensing and Communication: Algorithm Design and Lower Bound[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260404

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Off-Grid Blind Near-Field Integrated Sensing and Communication: Algorithm Design and Lower Bound

doi: 10.11999/JEIT260404 cstr: 32379.14.JEIT260404

1.
School of Information Engineering, Henan Open University, Zhengzhou, Henan 450008, China
2.
University of Wollongong, Wollongong, NSW 2522, Australia
3.
College of Information Science , Zhejiang University, Hangzhou 310027, China
4.
Hangzhou Institute of Technology, Xidian University, Hangzhou, 311200, China

Funds: The National Natural Science Foundation of China(62301394, 62331023, 62394292), Science and Technology Research Project of Henan (262102211109), National Key Research and Development Program (2021YFA1000500, 2023YFB2904804)

Received Date: 2026-04-07
Accepted Date: 2026-06-17
Rev Recd Date: 2026-06-16

Available Online: 2026-06-23

Abstract

Abstract

Objective With the widespread deployment of extra-large-scale antenna arrays in 6G networks, user terminals are increasingly located in the near-field region. Existing Near-Field Integrated Sensing And Communication (NF-ISAC) algorithms face key challenges, including off-grid power leakage, severe model mismatch, and strong pilot dependence. These limitations make them unsuitable for low-overhead, high-performance 6G transmission. This paper aims to design an off-grid blind NF-ISAC algorithm and derive the theoretical performance bound for near-field sensing. Methods To overcome the limitations of analytical geometric steering vectors and adapt to more accurate electromagnetic propagation characteristics without closed-form expressions, an amplitude-phase separation method is first proposed. This method decomposes the nonlinear near-field steering vector into amplitude and phase terms, enabling high-precision characterization of the steering vector using a single-hidden-layer neural network. Second, the NF-ISAC problem is formulated as a constrained matrix factorization problem, and a corresponding factor graph model is constructed. The trained neural network is embedded into the factor graph as a function node. Message passing through the embedded neural network is then achieved, enabling joint blind coordinate sensing, channel estimation, and signal detection in a pilot-free manner. Finally, the Cramér-Rao Lower Bound (CRLB) for multi-user near-field joint distance and angle sensing in polar coordinates is derived based on the neural-network-fitted steering vector. Results and Discussions Extensive Monte Carlo simulations are conducted to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm achieves millimeter-level position sensing. Compared with existing mainstream algorithms, it improves both communication Bit Error Rate (BER) and sensing accuracy. The proposed algorithm achieves a 2～3 dB gain in sensing accuracy over the state-of-the-art near-field off-grid algorithm, and its performance is closest to the derived theoretical CRLB. These results indicate that the proposed algorithm effectively mitigates off-grid power leakage and model mismatch. Conclusions The proposed off-grid blind NF-ISAC algorithm overcomes the pilot dependence and model mismatch of existing NF-ISAC schemes. It achieves integrated high-precision sensing and reliable communication for near-field users in a pilot-free manner. The derived CRLB provides a theoretical benchmark for evaluating the sensing performance of NF-ISAC systems. This work provides technical support for the design of 6G NF-ISAC systems.
- Near-field communication,
- Integrated sensing and communication,
- Off-grid estimation algorithm,
- Blind signal detection,
- Factor graph

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References(24)

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