面向密集物联网网络的基于深度学习的多用户通信设计：干扰感知有限块长通信及初步MIMO扩展
Deep Learning-Based Multi-User Communication Design for Dense IoT Networks: Interference-Aware Finite-Blocklength Communication and Preliminary MIMO Extensions
- Indian Institute of Technology Madras（印度马德拉斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对密集物联网网络的通信需求，该研究扩展SiameseNet框架实现多用户干扰抑制，其单用户解码器复杂度随用户数线性扩展，在BLER性能上优于传统非正交接入，还验证了其MIMO扩展潜力。
AI中文摘要：
密集物联网网络需要在频谱有限、多用户干扰严重的情况下实现可靠通信，同时保持可管理的接收机复杂度。本研究针对受干扰限制的有限块长物联网场景，聚焦短和中等块长，提出一种基于深度学习的端到端多用户通信设计。我们将先前的2用户SiameseNet收发器框架扩展至支持2、4和8个用户，利用学习到的冗余进行干扰抑制和噪声鲁棒性。与传统非正交接入基线相比，我们的方法在各种场景下展现出优异的块错误率（BLER）性能，且无需采用联合检测；单用户解码器的复杂度随用户数量大致呈线性扩展。此外，我们还测试了其在干扰失配和不等干扰强度下的鲁棒性，这对异构设备的实际部署至关重要。潜在空间分析显示，随着有效单用户速率降低，学习到的码字距离增大，与观测到的BLER提升相印证。另外，我们还给出了固定信道状态信息（CSIT）和信道状态信息（CSIR）下2X2 MIMO设置的初步结果，表明该框架具备扩展至多天线物联网网关的潜力。
英文摘要：
Dense IoT networks require reliable communication despite limited spectrum and substantial multi-user interference while maintaining manageable receiver complexity. This work introduces a deep-learning-based end-to-end multi-user communication design for interference-limited finite-blocklength IoT scenarios, focusing on short and medium blocklengths. We extend a prior 2-user SiameseNet transceiver framework to accommodate 2, 4, and 8 users, leveraging learned redundancy for interference suppression and noise robustness. Compared to conventional non-orthogonal access baselines, our method demonstrates strong Block Error Rate (BLER) performance across various scenarios without resorting to joint detection; the per-user decoder scales roughly linearly with the number of users. Further, we examine the robustness under interference mismatch and unequal interference strengths, critical for practical deployments with heterogeneous devices. The Latent-space analysis reveals that the learned codeword distance increases as the effective per-user rate decreases, corroborating with the observed BLER improvements. In addition, we also present preliminary results for a 2X2 MIMO setup under fixed-channel CSIT and CSIR, indicating potential for extending the framework to IoT gateways with multiple antennas.
