Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM) wave control capabilities, H-MIMO opens up the opportunity for signal processing in a more fundamental EM-domain, paving the way for realizing holographic imaging level communications in supporting the extremely high spectral efficiency and energy efficiency in future networks. In this article, we propose a generalized EM-domain near-field channel modeling and study its capacity limit of point-to-point H-MIMO systems that equips arbitrarily placed surfaces in a line-of-sight (LoS) environment. Two effective and computational-efficient channel models are established from their integral counterpart, where one is with a sophisticated formula but showcases more accurate, and another is concise with a slight precision sacrifice. Furthermore, we unveil the capacity limit using our channel model, and derive a tight upper bound based upon an elaborately built analytical framework. Our result reveals that the capacity limit grows logarithmically with the product of transmit element area, receive element area, and the combined effects of 1/ d ² _mn , 1/ d ⁴ _mn , and 1/ d ⁶ _mn over all transmit and receive antenna elements, where d_mn indicates the distance between each transmit element n and receive element m . Particularly, 1/ d ⁶ _mn dominates in the near-field region whereas 1/ d ² _mn dominates in the far-field region. Numerical evaluations validate the effectiveness of our channel models, and showcase the slight disparity between the upper bound and the exact capacity, which is beneficial for predicting practical system performance.

Radio frequency (RF) chain circuits play a major role in digital receiver architectures, allowing passband communication signals to be processed in baseband. When operating at high frequencies, these circuits tend to be costly. This increased cost imposes a major limitation on future multiple-input-multiple-output (MIMO) communication technologies. A common approach to mitigate the increased cost is to utilize hybrid architectures, in which the received signal is combined in analog into a lower dimension, thus reducing the number of RF chains. In this article we study the design and hardware implementation of hybrid architectures via minimizing channel estimation error. We first derive the optimal solution for complex-gain combiners and propose an alternating optimization algorithm for phase-shifter combiners. We then present a hardware prototype implementing analog combining for RF chain reduction. The prototype consists of a specially designed configurable combining board as well as a dedicated experimental setup. Our hardware prototype allows us evaluating the effect of analog combining in MIMO systems using actual communication signals. The experimental study, which focuses on channel estimation accuracy in MIMO channels, demonstrates that using the proposed prototype, the achievable channel estimation performance is within a small gap in a statistical sense from that obtained using a costly receiver in which each antenna is connected to a dedicated RF chain.

Recently, sub-Nyquist sampling (SNS) based wideband spectrum sensing has emerged as a promising approach for cognitive radios. However, most of existing SNS based approaches cannot effectively deal with the wireless channel fading due to the lack of space diversity exploitation, which would lead to poor sensing performance. To address the problem, we propose a multiantenna system, referred to as the multiantenna generalized modulated converter (MAGMC), to realize the SNS, where spatially correlated Multiple-Input Multiple-Output (MIMO) channel is considered. Based on the multiantenna system, two compressive subspace learning (CSL) approaches (mCSL and vCSL) are proposed for signal subspace learning, where wideband sectrum sensing is realized based on the signal subspace. Both proposed CSL approaches exploit space diversity, where the mCSL utilizes an antenna averaging temporal decomposition, and the vCSL (which is formulated based on a vectorization of sample matrix in the mCSL) uses a spatial-temporal joint decomposition. We further establish analytical relationships between eigenvalues of statistical covariance matrices in statistical sense in both multiantenna and single antenna scenarios. Space diversity and superiority over the single antenna scenario for both proposed CSL approaches are analyzed based on the derived analytical relationships. Moreover, the mCSL and vCSL based wideband spectrum sensing algorithms are proposed based on the system model of MAGMC and their computational complexities are given. The proposed CSL based wideband spectrum sensing algorithms can effectively deal with the wireless channel fading and simulations show the improvement on performance of wideband spectrum sensing over related works.

The sub-Nyquist sampling (SNS) has emerged as an appealing technique for wideband signal sampling and has found its applications in many areas, such as, cognitive radios, radar and medical imaging, etc.. However, existing SNS based approaches generally assume that the output noise of SNS (termed as SNS noise) is generated as the additive white Gaussian noise without considering the SNS effect. To give more insights on the noise after SNS, we propose a generalized modulated converter to represent existing prevalent SNS systems and give an analysis on statistics of the SNS noise in terms of the proposed SNS system. The noise folding factor and the uncorrelated and white keeping properties are derived and concluded from the derived statistics, in which the former is used to show the noise enhancement by SNS and the latter describes the SNS noise uncorrelation and equal power intensity in different frequencies, respectively. In the final, simulation results validate the derived results and conclusions.

Data-driven deep learning techniques for wafer defect image classification provide wafer manufacturers with a tool to rapidly identify surface defects. However, the defect data and computational capabilities of a single wafer manufacturer are often insufficient to support the training of deep learning models. In response, we introduce federated learning, a paradigm that leverages the data and computational capabilities of various wafer manufacturers, all while ensuring that the original data from different manufacturers remain unexposed to each other. Due to variations in manufacturing processes and image acquisition equipment, identical wafer defects can exhibit different features in different manufacturing settings, leading to statistically heterogeneous datasets. This heterogeneity can reduce model convergence speed and accuracy. To counteract this issue, we propose a personalized federated learning approach with clustering. In the personalization phase, we train distinct network layers for each client’s local model, capitalizing on the feature extraction capability of the global model’s shallow network, while also achieving commendable performance on each client’s unique dataset. During the clustering phase, we provide a theoretical analysis, demonstrating that the divergence of weights between two models is bounded above, laying a theoretical foundation for the clustering operation. We then enhance a density-based clustering method, enabling the clustering of clients with similar data features without the need to specify the number of cluster centers, thus mitigating the problem of global model oscillation. We have conducted experiments under various data heterogeneity scenarios. The experiments show that our method can achieve a 2.8% accuracy improvement average versus the compared state-of-the-art federated methods with a faster convergence rate.