{"ID":2859580,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06429","arxiv_id":"2510.06429","title":"Distributed Detection and Bandwidth Allocation with Hybrid Quantized and Full-Precision Observations over Multiplicative Fading Channels","abstract":"A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive a locally most powerful test (LMPT)--based hybrid detector from the composite probability distribution of the compound observations received by the fusion center, and then analyze its asymptotic detection performance. Subsequently, we optimize the sensor-wise quantization thresholds to achieve near-optimal asymptotic performance at the local sensor level. Moreover, we propose a mixed-integer linear programming approach to solve the optimization problem of transmission bandwidth allocation accounting for bandwidth constraints and error-prone channels. Finally, simulation results demonstrate the superiority of the proposed hybrid detector and the bandwidth allocation strategy, especially in challenging error-prone channel conditions.","short_abstract":"A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive a locally most powerful test (LMPT)--based hybrid detector from the composite probability distribution of the compound observations received...","url_abs":"https://arxiv.org/abs/2510.06429","url_pdf":"https://arxiv.org/pdf/2510.06429v1","authors":"[\"Linlin Mao\",\"Zeping Sui\",\"Michail Matthaiou\",\"Hongbin Li\"]","published":"2025-10-07T20:09:04Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}