A Universal Random Precoding Framework for MIMO Systems

Current wireless systems combat inter-symbol interference (ISI) by diagonalizing or sparsifying the channel matrix, yet they remain vulnerable to selective fading. To address this, we propose a universal random precoding (RP) transmission framework based on the universality class. RP leverages random transforms to statistically exploit all subchannels and construct an equivalent channel belonging to the universality class, thereby enhancing diversity gain while maintaining backward compatibility with existing waveforms. Low-complexity implementations include the randomly permuted fast transform (FT-RP) and the interleaved block-sparse fast transform (IBSFT-RP). A cross-domain OAMP/MAMP (CD-OAMP/MAMP) detector is designed for RP systems, which is replica maximum \textit{a posteriori} (MAP)-optimal according to state evolution (SE). Simulation results on MIMO systems demonstrate that RP with CD-OAMP/MAMP achieves near-RM performance with much lower complexity, with additional benefits of flexible compression ratios for spectral efficiency.

RaStream: Edge-Deployable Streaming Human Mesh Recovery from mmWave Radar

Millimeter-wave (mmWave) radar enables privacy-preserving human sensing for edge applications, but streaming SMPL-X recovery on edge devices requires accurate spatial evidence extraction and temporally stable predictions under lightweight causal inference. Sparse radar reflections make dense mesh recovery difficult, and heavy multi-scale spatial backbones can be costly for volumetric radar tensors while still diluting weak body evidence with background clutter. Frame-wise mesh estimates further exhibit jitter, while generic temporal models often mix slowly varying body morphology with fast pose and translation dynamics. We present RaStream, an edge-deployable radar-tensor streaming mesh recovery framework that combines a radar-aware spatial encoder with dual-state causal temporal refinement. The Radar-aware Spatial Structure (RaSS) encoder preserves 3D radar structure, localizes the subject, extracts body-centered evidence, and produces compact radar-aware tokens from short radar windows. The dual-state temporal module separates slow morphology state from fast motion state: it accumulates morphology evidence for shape and gender estimation through a token-conditioned update gate and tracks dynamic motion with a causal recurrent state. The resulting model keeps streaming memory fixed and avoids full-volume buffering. We formulate temporal sampling parameters $(T_w, T, s)$ that expose radar observation density, finite unroll horizon, warm-up/replay behavior, and output-rate tradeoffs, and evaluate reconstruction accuracy, temporal smoothness, and edge efficiency on M4Human. RaSS-Base reduces single-window MVE from 90.90 mm to 84.27 mm over RT-Mesh with fewer parameters, while RaStream further reduces MVE to 72.05 mm under the random-split protocol. Jetson Orin Nano profiling shows 26.93 ms FP32 latency for the Base configuration.