With the advent of extreme massive multiple-input multiple-output (X-MIMO) systems and the emerging Frequency Range 3 (FR3, 7.125–24.25 GHz) for 6G networks, the dimensionality of channel state information (CSI) has increased significantly. However, to ensure scalable operation and avoid an excessive increase in overhead, the feedback size is expected to increase only marginally. This constraint necessitates an extremely high compression ratio during CSI feedback, which in turn severely degrades the reconstruction performance of standardized codebooks. Moreover, even when employing AI methods, training models from scratch under such severe compression conditions leads to vanishing or exploding gradients, resulting in unstable training and diminished performance. In this paper, we propose a novel hierarchical transfer learning with progressive compression (HTL-PC) method to address these challenges. Our approach leverages a pretrained autoencoder extraction block from a large-feedback model and progressively transfers it to models with smaller latent spaces, thereby enabling stable training under severe compression conditions. Extensive system-level simulations demonstrate that the proposed HTL-PC method achieves remarkable improvements in squared generalized cosine similarity and downlink cell throughput over benchmark techniques.

To mitigate the growing overhead resulting from the increasing number of antennas in massive multiple-input multiple-output (MIMO) systems, various autoencoder-based schemes employing two-sided artificial intelligence (AI) models for channel state information (CSI) feedback-specifically, feedback of the precoding matrix-have been proposed. In these twosided AI models, the encoder and decoder are separately deployed at the user equipment (UE) and the base station (BS), posing significant challenges in maintaining synchronization between them in real-world scenarios with multiple UEs and BSs. In this paper, we propose a novel transformer-based temporaldomain multi-length optimized (TMO) decoder that effectively leverages temporal correlations in MIMO channels to enhance CSI reconstruction. Unlike existing schemes that require different models or additional signaling overhead for re-synchronization between the encoder and decoder under varying conditions, the transformer-based TMO decoder can robustly recover CSI by utilizing feedback of the precoding matrix with temporal information using a single fixed model for inputs of varying sequential time steps. Performance evaluations demonstrate that the proposed transformer-based TMO decoder achieves significantly higher squared generalized cosine similarity and average downlink throughput compared to state-of-the-art techniques.

As wireless local area network (WLAN) devices become more prevalent in various environments, the IEEE 802.11ax standard (Wi-Fi 6), released in 2021, aims to improve the spectral efficiency. The overlapping basic service set (OBSS) packet detection (PD)-based spatial reuse (SR) scheme is a main technology for this purpose which can increase channel access opportunities by adjusting parameters. However, the standard only provides boundary values for parameter adjustment and does not provide details on how to optimize the performance, which can lead to a performance degradation. The IEEE 802.11be standard (Wi-Fi 7), established to satisfy extremely high-throughput (EHT) demands of next-generation applications, includes the OBSS PD-based SR scheme and the coordinated SR (CSR) scheme to maximize area throughput in dense networks. The CSR scheme optimizes the transmission performance through coordination between access points (APs), which results in additional signaling overhead and protocol complexity. In this article, an enhanced SR scheme named optimized transmission power-based OBSS PD (OTOP) SR is proposed to achieve high throughput in dense networks. The OTOP scheme enhances the parameter control operation of OBSS PD-based SR to take advantage of its distributed nature which keeps the signaling overhead and protocol complexity low. The OTOP scheme derives the optimal transmission power that can maximize the transmission success probability of WLAN devices based on stochastic geometry analysis. The simulation results show that the proposed OTOP scheme performs better than the existing SR gain maximizing and OBSS PD-based SR schemes in terms of throughput, frame error rate, and fairness.

5G networks were designed to provide sufficient throughput and reliable data communication services. To achieve the targeted QoS requirements of the 5G specifications, the use of mmWaves are essential. However, mmWavs are very vulnerable to signal blockages due their high frequency. Multi-path transmission control protocol (MPTCP) can deal with this problem by transmitting data through multiple subflows using TCP. The current congestion control scheme of MPTCP was designed for wired networks and is not suitable for use in wireless networks. In this paper, a new MPTCP congestion control algorithm named round trip time (RTT) estimation based Veno (REVeno) is proposed to better support wireless networks by distinguishing between loss due to congestion and wireless channel errors. The proposed REVeno scheme uses a novel backlog estimation formula that considers the total buffer size and parameters that distinguish loss due to congestion and loss due to the wireless channel. The REVeno scheme will use these estimations to minimizes the reordering delay by equalizing the equilibrium RTT of all subflows. The simulation results show that the proposed REVeno scheme performs better than the existing schemes in terms of goodput and latency without adversely affecting other concurrent TCP connections in the same network.