面向基于OTFS的免授权随机接入的结构化稀疏感知联合用户活动检测与信道估计
Structured-Sparsity-Aware Joint User Activity Detection and Channel Estimation for OTFS-Based Grant-Free Random Access
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中文总结 AI 辅助
该研究针对高移动性场景下OTFS与GFRA集成的用户活动检测和信道估计难题，提出基于结构化稀疏的SS-EP算法，构建对应框架并通过仿真验证其性能优于现有基准方案。
中文摘要 AI 辅助
免授权随机接入（GFRA）是未来无线网络中面向海量机器类通信（mMTC）的有前景解决方案。然而，可靠的用户活动检测与信道估计是关键挑战，尤其是当正交时频空间（OTFS）调制与GFRA集成以应对高移动性引发的双选择性信道时。本文提出一种基于OTFS的GFRA框架，该框架利用延迟-多普勒信道的固有结构化稀疏性。通过采用基扩展模型（BEM），我们将联合用户活动检测与信道估计建模为结构化压缩感知问题。识别出一种双层稀疏结构，包含多接收天线间的公共稀疏性以及mMTC用户间的激活稀疏性。为有效利用该结构，我们构建了两层因子图，并开发了结构化稀疏期望传播（SS-EP）算法以实现高效贝叶斯推理。仿真结果表明，所提方案显著优于现有基准方案。
英文摘要
Grant-free random access (GFRA) is a promising solution for massive machine-type communications (mMTC) in future wireless networks. However, reliable user activity detection and channel estimation are critical challenges, particularly when orthogonal time-frequency space (OTFS) modulation is integrated with GFRA to address doubly selective channels induced by high mobility. In this paper, we propose an OTFS-based GFRA framework that exploits the inherent structured sparsity of delay-Doppler channels. By adopting a basis expansion model (BEM), we formulate joint user activity detection and channel estimation as a structured compressive sensing problem. A bi-level sparsity structure is identified, consisting of common sparsity across multiple receive antennas and activation sparsity across mMTC users. To effectively leverage this structure, we construct a two-layer factor graph and develop a structured sparsity expectation propagation (SS-EP) algorithm for efficient Bayesian inference. Simulation results demonstrate that the proposed scheme significantly outperforms existing benchmarks.
