Communication-Assisted Sensing in 6G Networks
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866912004197842944 |
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| author | Dong, Fuwang Liu, Fan Lu, Shihang Xiong, Yifeng Zhang, Qixun Feng, Zhiyong Gao, Feifei |
| author_facet | Dong, Fuwang Liu, Fan Lu, Shihang Xiong, Yifeng Zhang, Qixun Feng, Zhiyong Gao, Feifei |
| contents | Exploring the mutual benefit and reciprocity of sensing and communication (S\&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems. This paper investigates a novel communication-assisted sensing (CAS) system within 6G perceptive networks, where the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the estimated information to end-users. In such a CAS system, we first establish an optimal waveform design framework based on the rate-distortion (RD) and source-channel separation (SCT) theorems. After analyzing the relationships between the sensing distortion, coding rate, and communication channel capacity, we propose two distinct waveform design strategies in the scenario of target impulse response estimation. In the separated S\&C waveforms scheme, we equivalently transform the original problem into a power allocation problem and develop a low-complexity one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S\&C waveform. In the dual-functional waveform scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff in this scheme. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07157 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Communication-Assisted Sensing in 6G Networks Dong, Fuwang Liu, Fan Lu, Shihang Xiong, Yifeng Zhang, Qixun Feng, Zhiyong Gao, Feifei Signal Processing Exploring the mutual benefit and reciprocity of sensing and communication (S\&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems. This paper investigates a novel communication-assisted sensing (CAS) system within 6G perceptive networks, where the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the estimated information to end-users. In such a CAS system, we first establish an optimal waveform design framework based on the rate-distortion (RD) and source-channel separation (SCT) theorems. After analyzing the relationships between the sensing distortion, coding rate, and communication channel capacity, we propose two distinct waveform design strategies in the scenario of target impulse response estimation. In the separated S\&C waveforms scheme, we equivalently transform the original problem into a power allocation problem and develop a low-complexity one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S\&C waveform. In the dual-functional waveform scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff in this scheme. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations. |
| title | Communication-Assisted Sensing in 6G Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2311.07157 |