SwinLSTM Autoencoder for Temporal-Spatial-Frequency Domain CSI Compression in Massive MIMO Systems

This study presents a parameter-light, low-complexity artificial intelligence/machine learning (AI/ML) model that enhances channel state information (CSI) feedback in wireless systems by jointly exploiting temporal, spatial, and frequency (TSF) domain correlations. While traditional frameworks use autoencoders for CSI compression at the user equipment (UE) and reconstruction at the network (NW) side in spatial-frequency (SF), massive multiple-input multiple-output (mMIMO) systems in low mobility scenarios exhibit strong temporal correlation alongside frequency and spatial correlations. An autoencoder architecture alone is insufficient to exploit the TSF domain correlation in CSI; a recurrent element is also required. To address the vanishing gradients problem, researchers in recent works have proposed state-of-the-art TSF domain CSI compression architectures that combine recurrent networks for temporal correlation exploitation with deep pre-trained autoencoder that handle SF domain CSI compression. However, this approach increases the number of parameters and computational complexity. To jointly utilize correlations across the TSF domain, we propose a novel, parameter-light, low-complexity AI/ML-based recurrent autoencoder architecture to compress CSI at the UE side and reconstruct it on the NW side while minimizing CSI feedback overhead.

Urban Outdoor Propagation Measurements and Channel Models at 6.75 GHz FR1(C) and 16.95 GHz FR3 Upper Mid-Band Spectrum for 5G and 6G

Global allocations in the upper mid-band spectrum (4-24 GHz) necessitate a comprehensive exploration of the propagation behavior to meet the promise of coverage and capacity. This paper presents an extensive Urban Microcell (UMi) outdoor propagation measurement campaign at 6.75 GHz and 16.95 GHz conducted in Downtown Brooklyn, USA, using a 1 GHz bandwidth sliding correlation channel sounder over 40-880 m propagation distance, encompassing 6 Line of Sight (LOS) and 14 Non-Line of Sight (NLOS) locations. Analysis of the path loss (PL) reveals lower directional and omnidirectional PL exponents compared to mmWave and sub-THz frequencies in the UMi environment, using the close-in PL model with a 1 m reference distance. Additionally, a decreasing trend in root mean square (RMS) delay spread (DS) and angular spread (AS) with increasing frequency was observed. The NLOS RMS DS and RMS AS mean values are obtained consistently lower compared to 3GPP model predictions. Point data for all measured statistics at each TX-RX location are provided to supplement the models and results. The spatio-temporal statistics evaluated here offer valuable insights for the design of next-generation wireless systems and networks.

Sub-Terahertz Channel Measurements and Characterization in a Factory Building

Sub-Terahertz (THz) frequencies between 100 GHz and 300 GHz are being considered as a key enabler for the sixth-generation (6G) wireless communications due to the vast amounts of unused spectrum. The 3rd Generation Partnership Project (3GPP) included the indoor industrial environments as a scenario of interest since Release 15. This paper presents recent sub-THz channel measurements using directional horn antennas of 27 dBi gain at 142 GHz in a factory building, which hosts equipment manufacturing startups. Directional measurements with co-polarized and cross-polarized antenna configurations were conducted over distances from 6 to 40 meters. Omnidirectional and directional path loss with two antenna polarization configurations produce the gross cross-polarization discrimination (XPD) with a mean of 27.7 dB, which suggests that dual-polarized antenna arrays can provide good multiplexing gain for sub-THz wireless systems. The measured power delay profile and power angular spectrum show the maximum root mean square (RMS) delay spread of 66.0 nanoseconds and the maximum RMS angular spread of 103.7 degrees using a 30 dB threshold, indicating the factory scenario is a rich-scattering environment due to a massive number of metal structures and objects. This work will facilitate emerging sub-THz applications such as super-resolution sensing and positioning for future smart factories.