Detection in Bistatic ISAC with Deterministic Sensing and Gaussian Information Signals
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909901115097088 |
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| author | Song, Xianxin Yu, Xianghao Xu, Jie Ng, Derrick Wing Kwan |
| author_facet | Song, Xianxin Yu, Xianghao Xu, Jie Ng, Derrick Wing Kwan |
| contents | Integrated sensing and communications (ISAC) is a disruptive technology enabling future sixth-generation (6G) networks. This paper investigates target detection in a bistatic ISAC system, in which the base station (BS) transmits superimposed ISAC signals comprising both Gaussian information-bearing and deterministic sensing components to simultaneously provide communication and sensing functionalities. First, we develop a Neyman-Pearson (NP)-based detector that effectively utilizes both the deterministic sensing and random communication signals. Closed-form analysis reveals that both signal components contribute to improving the overall detection performance. Subsequently, we optimize the BS transmit beamforming to maximize the detection probability, subject to a minimum signal-to-interference-plus-noise ratio (SINR) constraint for the communication user (CU) and a total transmit power budget at the BS. The resulting non-convex beamforming optimization problem is addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which leverages both types of signals, over benchmark schemes that treat information signals as interference. They also reveal that a higher communication-rate threshold directs more transmit power to Gaussian information-bearing signals, thereby diminishing deterministic-signal power and weakening detection performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detection in Bistatic ISAC with Deterministic Sensing and Gaussian Information Signals Song, Xianxin Yu, Xianghao Xu, Jie Ng, Derrick Wing Kwan Signal Processing Integrated sensing and communications (ISAC) is a disruptive technology enabling future sixth-generation (6G) networks. This paper investigates target detection in a bistatic ISAC system, in which the base station (BS) transmits superimposed ISAC signals comprising both Gaussian information-bearing and deterministic sensing components to simultaneously provide communication and sensing functionalities. First, we develop a Neyman-Pearson (NP)-based detector that effectively utilizes both the deterministic sensing and random communication signals. Closed-form analysis reveals that both signal components contribute to improving the overall detection performance. Subsequently, we optimize the BS transmit beamforming to maximize the detection probability, subject to a minimum signal-to-interference-plus-noise ratio (SINR) constraint for the communication user (CU) and a total transmit power budget at the BS. The resulting non-convex beamforming optimization problem is addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which leverages both types of signals, over benchmark schemes that treat information signals as interference. They also reveal that a higher communication-rate threshold directs more transmit power to Gaussian information-bearing signals, thereby diminishing deterministic-signal power and weakening detection performance. |
| title | Detection in Bistatic ISAC with Deterministic Sensing and Gaussian Information Signals |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.10897 |