{"ID":2923648,"CreatedAt":"2026-06-02T04:05:25.881865328Z","UpdatedAt":"2026-06-04T13:12:39.622923895Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.02281","arxiv_id":"2606.02281","title":"Distributed MoE-based Uplink Detection for Cell-Free Communication Systems","abstract":"Cell-free Massive multiple input and multiple output (MIMO) is recognized as a key technology for beyond-5G networks, where distributed access points (APs) jointly serve user equipments (UEs) to address the inherent inter-cell interference issue inherent in cellular systems. While conventional distributed signal detection methods offer a practical balance between performance and fronthaul load, they are fundamentally limited by linear processing constraints. In this paper, we propose a novel deep learning based uplink detection framework by introducing the distributed mixture of experts detection network (DMoE-DetNet). In this architecture, each AP acts as a local expert employing convolutional neural networks (CNNs) for non-linear feature extraction, and transmits the local minimum mean square error (MMSE) detection results and statistical channel information to the central processing unit (CPU). In the CPU, an attention-based encoder module captures complex spatio-temporal dependencies among users for global feature fusion, with a gating network at the central processor dynamically weighting the contributions from different APs. At last, a linear detector outputs the symbol probability. Simulation results demonstrate that the proposed DMoE-DetNet significantly outperforms conventional linear processing based cell-free signal detection methods in terms of symbol error rate, showcasing the potential of artificial intelligence-enabled communication systems.","short_abstract":"Cell-free Massive multiple input and multiple output (MIMO) is recognized as a key technology for beyond-5G networks, where distributed access points (APs) jointly serve user equipments (UEs) to address the inherent inter-cell interference issue inherent in cellular systems. While conventional distributed signal detect...","url_abs":"https://arxiv.org/abs/2606.02281","url_pdf":"https://arxiv.org/pdf/2606.02281v1","authors":"[\"Le Zhao\",\"Xuesong Pan\",\"Xinyi Wang\",\"Zhong Zheng\",\"Zesong Fei\"]","published":"2026-06-01T14:05:27Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Mixture of Experts\",\"Convolutional Neural Network\"]","has_code":false}