AI 中文总结
针对大规模 MIMO 系统中多设备向边缘服务器传输特征进行分布式推理的需求，基于统计信道状态信息开发随机矩阵理论框架设计慢时标预编码器，用最大编码率降低衡量类可分性并推导确定性近似优化，提升了任务感知模式分配和推理性能。
AI 中文摘要
未来无线网络需支持网络人工智能服务，多设备向边缘服务器传输特征进行分布式推理，这要求面向任务的物理层优化。关键控制变量是多输入多输出预编码器。现有方法依赖瞬时信道状态信息，开销大。本文基于统计信道状态信息开发随机矩阵理论框架，设计慢时标预编码器，采用最大编码率降低衡量接收特征的类可分性，推导确定性近似并优化，实验验证了近似效果及预编码器优势。
英文摘要
Future wireless networks are expected to support networked artificial intelligence (AI) services, where multiple devices transmit learned features to an edge server for distributed inference. This setting calls for task-oriented physical-layer optimization, where wireless transmission should preserve useful information for inference rather than only maximize the rate or reconstruct the transmitted signals. A key physical-layer control variable is the multiple-input multiple-output precoder, which determines how device features are shaped and combined over wireless channels. Existing task-oriented precoding methods typically adapt the precoder to instantaneous channel state information at the transmitter (CSIT). However, in multi-device MIMO systems, acquiring the aggregate channel, feeding back CSI or optimized precoders, and reoptimizing across coherence blocks introduce substantial overhead. This paper develops a random-matrix-theoretic framework based on statistical CSIT that designs a slow-timescale precoder from channel covariance statistics and training-set feature statistics, without requiring instantaneous CSIT. We adopt maximal coding rate reduction (MCR${^2}$) to measure the class separability of the received features, yielding a task-aware utility for MIMO precoder design. Since this utility still depends on random small-scale fading, we derive a deterministic approximation that converts it into a fixed-point objective depending only on long-term statistics and large-system dimension ratios via random matrix theory. A projected block-coordinate ascent and successive convex approximation algorithm is developed to optimize this deterministic objective under per-device power constraints. Experiments on ModelNet10 verify the approximation and show that the proposed statistical precoder improves task-aware mode allocation and inference performance over competitive benchmarks.
Comments13 pages, 4 figures, submitted to IEEE for possible publication
