Multiple-Input Multiple-Output (MIMO) systems play a crucial role in advancing 5 G and 6 G networks by improving spectral efficiency and data rates. In Frequency Division Duplex (FDD) systems, since the channel reciprocity does not hold, timely feedback of Channel State Information (CSI) from receiver to transmitter is essential to enable beamforming and precoding - which are fundamental techniques in MIMO systems. However, in MIMO configurations with large antenna arrays, this feedback mechanism introduces significant overhead to the system. Among different compression methods, deep learning based approaches outperform traditional compressive sensing techniques, but their large parameter count poses challenges for deployment on resource-limited devices, such as user equipment. Moreover, factors such as short channel coherence time and imperfect wireless channels demand more robust models. This paper introduces CLLWCsiNet (Convolutional Latent-space Low Weight CsiNet), a novel architecture that employs convolutional latent-space learning under imperfect channel conditions. CLLWCsiNet demonstrates superior predictive accuracy in noisy environments. We provide a comprehensive overview of previous models and introduce convolutional quantization techniques, which significantly reduce parameters and enhance computational efficiency. Our approach effectively compresses CSI while maintaining predictive accuracy, making it well-suited for deployment on resource-constrained user equipment. Simulations show that CLLWCsiNet achieves -11 dB Normalized Mean Square Error (NMSE) in low-SNR regimes (0-20 dB), achieving 31% lower NMSE than DNNet with only 70 K parameters.

Abstract Invisible to naked human eyes, micro‐movements are barely noticeable. There are wide ranges of micro‐movement applications from spotting subtle changes in a volcano, vascular pulse, and blood vessel to micro‐expression detection. In the latter, some evil thoughts can be unveiled, resulting in identifying crooks and lawbreakers and it arises from involuntary subtle and short‐duration of facial muscles movements. Precise spotting of these tiny movements is possible only when multiple aspects of temporal images are scrutinized. Meanwhile, since motions often happen in one or two directions, it is rudimentary to extract complete feature sets in textural‐based approaches such as cubic Local Binary Pattern (cubic‐LBP). Approaches like cubic‐LBP also have imposed an unnecessary computation burden. Hence, in this research, a novel method named intelligent cubic‐LBP is proposed, which incorporates Convolutional Neural Network (CNN) model. This model learns to select the useful plane(s) automatically. Apex is then detected by applying Partial Autocorrelation Coefficient (PACF) on the selected plane(s). The experimental results show significant improvement in micro‐movements identification. The accuracy of the apex frame detection has elevated to 10% and 17% in the Chinese Academy of Sciences Micro‐Expressions (CASME) and the CASME II databases, respectively.

Energy harvesting (EH) has emerged as a promising technique for Green Communications and it is a novel technique to prolong the lifetime of the wireless networks with replenishable nodes. In this study, the authors consider the energy shortage analysis of fixed rate transmission in communication systems with EH nodes. First, the authors study the finite‐horizon transmission and provide the general formula for the energy shortage probability (ESP). The authors also give some examples as benchmarks. Then, the authors continue to derive a closed‐form expression for infinite‐horizon transmission, which is a lower bound for the ESP of any finite‐horizon transmission. These results are proposed for both Additive White Gaussian Noise (AWGN) and fading channels. Moreover, the authors show that even under random energy arrival , one can transmit at a fixed rate equal to capacity in the AWGN channels with negligible aggregate shortage time. The authors achieve this result using our practical transmission schemes, proposed for finite‐horizon. Also, comprehensive numerical simulations are performed in AWGN and fading channels with no Channel State Information (CSI) available at the transmitter, which corroborate our theoretical findings. Furthermore, the authors improve the performance of our transmission schemes in the fading channel with no CSI at the transmitter by optimising the transmission initiation threshold.