TL;DR
The findings demonstrate that DL methods provide significant performance gains at the physical layer, but significant challenges remain in critical areas such as data efficiency, generalizability, computational cost, and scalability to real-time applications.
To address the limitations of conventional deep learning models in antenna selection for Multiple Input Multiple Output (MIMO) systems—particularly in terms of insufficient feature extraction and degraded channel state information (CSI) modeling—this paper proposes a structurally enhanced ResNet-based joint transmit–re...
Massive multiple-input multiple-output (MIMO) systems is one of the key scenarios for 5G and future 6G networks. Power allocation is a critical yet computationally expensive component in multi-cell MIMO downlink systems. While the weighted minimum mean-square error (WMMSE) algorithm achieves near-optimal sum-rate perfo...
DEBKA is developed, a training-free optimizer that assigns different search roles to two complementary mechanisms: a BKA-inspired Cauchy-Gaussian attack for local refinement and adaptive DE/current-to-pbest migration for population-difference exploration, and results support the contribution of the coupled search and c...
An autoencoder-based solution to compress CSI at the UE side and reconstruct it at BS and outperforms the conventional CSI feedback scheme by approximately 21% in terms of throughput is proposed.
An advanced Deep Reinforcement Learning (DRL)-based approach is proposed for efficient dynamic spectrum allocation in 6G MIMO systems and it is guaranteed that the recommended FMTQA-ADMRL-CA can allocate the spectrum efficiently and robustly in 6G MIMO systems than the existing methods.
Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain degree of freedom. Integrating radiation-pattern reconfigurability, realized as EM-domain precoding, with conventional hybrid analog-digital precoding yields tri-hybrid mul...
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