{"ID":2876761,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21614","arxiv_id":"2508.21614","title":"Energy Detection over Composite $κ-μ$ Shadowed Fading Channels with Inverse Gaussian Distribution in Ultra mMTC Networks","abstract":"This paper investigates the characteristics of energy detection (ED) over composite $κ$-$μ$ shadowed fading channels in ultra machine-type communication (mMTC) networks. We have derived the closed-form expressions of the probability density function (PDF) of signal-to-noise ratio (SNR) based on the Inverse Gaussian (\\emph{IG}) distribution. By adopting novel integration and mathematical transformation techniques, we derive a truncation-based closed-form expression for the average detection probability for the first time. It can be observed from our simulations that the number of propagation paths has a more pronounced effect on average detection probability compared to average SNR, which is in contrast to earlier studies that focus on device-to-device networks. It suggests that for 6G mMTC network design, we should consider enhancing transmitter-receiver placement and antenna alignment strategies, rather than relying solely on increasing the device-to-device average SNR.","short_abstract":"This paper investigates the characteristics of energy detection (ED) over composite $κ$-$μ$ shadowed fading channels in ultra machine-type communication (mMTC) networks. We have derived the closed-form expressions of the probability density function (PDF) of signal-to-noise ratio (SNR) based on the Inverse Gaussian (\\e...","url_abs":"https://arxiv.org/abs/2508.21614","url_pdf":"https://arxiv.org/pdf/2508.21614v1","authors":"[\"He Huang\",\"Zeping Sui\",\"Zilong Liu\",\"Wei Huang\",\"Md. Noor-A-Rahim\",\"Haishi Wang\",\"Zhiheng Hu\"]","published":"2025-08-29T13:29:31Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}