Mohamed Akrout

We present an evolution of multiple-input multiple-output (MIMO) wireless communications known as the tri-hybrid MIMO architecture. In this framework, the traditional operations of linear precoding at the transmitter are distributed across digital beamforming, analog beamforming, and reconfigurable antennas. Compared with the hybrid MIMO architecture, which combines digital and analog beamforming, the tri-hybrid approach introduces a third layer of electromagnetic beamforming through antenna reconfigurability. This added layer offers a pathway to scale MIMO spatial dimensions, important for 6G systems operating in centimeter-wave bands, where the tension between larger bandwidths and infrastructure reuse necessitates ultra-large antenna arrays. We introduce the key features of the tri-hybrid architecture by (i) reviewing the benefits and challenges of communicating with reconfigurable antennas, (ii) examining tradeoffs between spectral and energy efficiency enabled by reconfigurability, and (iii) exploring configuration challenges across the three layers. Overall, the tri-hybrid MIMO architecture offers a new approach for integrating emerging antenna technologies in the MIMO precoding framework.

This paper introduces an algorithmic solution to a broader class of unlabeled sensing problems with multiple measurement vectors (MMV). The goal is to recover an unknown structured signal matrix, X, from its noisy linear observation matrix, Y, whose rows are further randomly shuffled by an unknown permutation matrix U. A new Bayes-optimal unlabeled compressed sensing (UCS) recovery algorithm is developed from the bilinear vector approximate message passing (Bi-VAMP) framework using non-separable and coupled priors on the rows and columns of the permutation matrix U. In particular, standard unlabeled sensing is a special case of the proposed framework, and UCS further generalizes it by neither assuming a partially shuffled signal matrix X nor a small-sized permutation matrix U. For the sake of theoretical performance prediction, we also conduct a state evolution (SE) analysis of the proposed algorithm and show its consistency with the asymptotic empirical mean-squared error (MSE). Numerical results demonstrate the effectiveness of the proposed UCS algorithm and its advantage over state-of-the-art baseline approaches in various applications. We also numerically examine the phase transition diagrams of UCS, thereby characterizing the detectability region as a function of the signal-to-noise ratio (SNR).

Background: Artificial intelligence (AI) has been used to create diagnostic models, such as VisualDx, to assist in rapidly diagnosing skin conditions. AI diagnostic models are typically trained on image databases of dermatologic conditions, which are known to underrepresent patients with richly pigmented skin. Objectives: We investigated whether VisualDx performed differently when classifying conditions across different skin phenotypes and whether images of conditions processed to resemble richly pigmented skin impacts diagnostic accuracy. Methods: Our image dataset consisted of sixteen common conditions. For each condition, three subgroups were curated: “Fitzpatrick I-III”, “Fitzpatrick IV-VI”, and “Processed”. The “Processed” subgroup contained images from the “Fitzpatrick I-III” subgroup altered to resemble richly pigmented skin. Images were processed by VisualDx to obtain a differential diagnosis list and diagnostic performance was analysed. Results: Across all subgroups, the highest sensitivity ( 97%) was seen in hidradenitis suppurativa, prurigo nodularis, and tinea versicolor. Atopic dermatitis, post-inflammatory hyperpigmentation, and basal cell carcinoma demonstrated the lowest sensitivity (23%, 23%, and 27%, respectively). Significantly greater diagnostic sensitivity was noted for all conditions in the “Fitzpatrick I-III” subgroup (p < 0.001) except acanthosis nigricans, melasma, and melanoma compared to the “Fitzpatrick IV-VI” and “Processed” subgroups. For all conditions, a reduction in sensitivity and specificity was observed in processed images (p < 0.001). Conclusion: Overall, VisualDx demonstrated diagnostic bias for images in the “Fitzpatrick I-III” subgroup, and colour-editing reduced diagnostic accuracy. These results suggest comprehensive databases should be used for future training of AI diagnostic tools to improve performance in all skin phototypes.

The large language models GPT-4 Vision and Large Language and Vision Assistant are capable of understanding and accurately differentiating between benign lesions and melanoma, indicating potential incorporation into dermatologic care, medical research, and education.

Multiple-Input Multiple-Output (MIMO) technology has emerged as a critical component in modern wireless communication systems. As antenna systems continue to evolve, the development and refinement of the corresponding information-theoretic bounds and achievability results remain essential in unlocking the full potential of future MIMO technologies in information transmission. To harness the benefits of such information-theoretic analysis, accurate modeling and analysis of the antenna's behavior are essential. Equivalent circuit models provide a valuable tool for understanding the performance limits of MIMO antennas. By providing a simplified representation of complex antenna structures, these models enable researchers to gain insights into the antenna's behavior, predict performance metrics, and optimize the system for optimal operation. This magazine paper reviews the advancements in the physical consistent modeling techniques of MIMO communications and sheds light on the potential avenues for further research and development in the field of information theory for advanced antenna structures and the role of circuit-based modeling.

UNSTRUCTURED The large language models GPT-4 Vision and Large Language and Vision Assistant are capable of understanding and accurately differentiating between benign lesions and melanoma, indicating potential incorporation into dermatologic care, medical research, and education.

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generation wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin “bandwidth gain”. Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted “disconnected” designs that disregard MC by putting faith in the half-wavelength spacing rule.