Inference-Optimal ISAC via Task-Oriented Feature Transmission and Power Allocation
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| Format: | Preprint |
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2025
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| _version_ | 1866913148900999168 |
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| author | Dong, Biao Cao, Bin Zhang, Qinyu |
| author_facet | Dong, Biao Cao, Bin Zhang, Qinyu |
| contents | This work is concerned with the coordination gain in integrated sensing and communication (ISAC) systems under a compress-and-estimate (CE) framework, wherein inference performance is leveraged as the key metric. To enable tractable transceiver design and resource optimization, we characterize inference performance via an error probability bound as a monotonic function of the discriminant gain (DG). This raises the natural question of whether maximizing DG, rather than minimizing mean squared error (MSE), can yield better inference performance. Closed-form solutions for DG-optimal and MSE-optimal transceiver designs are derived, revealing water-filling-type structures and explicit sensing and communication (S\&C) tradeoff. Numerical experiments confirm that DG-optimal design achieves more power-efficient transmission, especially in the low signal-to-noise ratio (SNR) regime, by selectively allocating power to informative features and thus saving transmit power for sensing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20429 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inference-Optimal ISAC via Task-Oriented Feature Transmission and Power Allocation Dong, Biao Cao, Bin Zhang, Qinyu Signal Processing This work is concerned with the coordination gain in integrated sensing and communication (ISAC) systems under a compress-and-estimate (CE) framework, wherein inference performance is leveraged as the key metric. To enable tractable transceiver design and resource optimization, we characterize inference performance via an error probability bound as a monotonic function of the discriminant gain (DG). This raises the natural question of whether maximizing DG, rather than minimizing mean squared error (MSE), can yield better inference performance. Closed-form solutions for DG-optimal and MSE-optimal transceiver designs are derived, revealing water-filling-type structures and explicit sensing and communication (S\&C) tradeoff. Numerical experiments confirm that DG-optimal design achieves more power-efficient transmission, especially in the low signal-to-noise ratio (SNR) regime, by selectively allocating power to informative features and thus saving transmit power for sensing. |
| title | Inference-Optimal ISAC via Task-Oriented Feature Transmission and Power Allocation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.20429 |