DCU 250 Arabic dependency bank: an LFG gold standard resource for the Arabic Penn treebank

This paper describes the construction of a dependency bank gold standard for Arabic, DCU 250 Arabic Dependency Bank (DCU 250), based on the Arabic Penn Treebank Corpus (ATB) (Bies and Maamouri, 2003; Maamouri and Bies, 2004) within the theoretical framework of Lexical Functional Grammar (LFG). For parsing and automatically extracting grammatical and lexical resources from treebanks, it is necessary to evaluate against established gold standard resources. Gold standards for various languages have been developed, but to our knowledge, such a resource has not yet been constructed for Arabic. The construction of the DCU 250 marks the first step towards the creation of an automatic LFG f-structure annotation algorithm for the ATB, and for the extraction of Arabic grammatical and lexical resources

Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training. This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches. In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model. We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion. We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings. Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time

Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations 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

Super-Wideband Massive MIMO

We present a unified model for connected antenna arrays with a massive (but finite) 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 and hence not clearly understood. 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 light-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

From Multilayer Perceptron to GPT: A Reflection on Deep Learning Research for Wireless Physical Layer

Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disadvantage of this common practice, we revisit a data decoding example from one of the first papers introducing DL-based end-to-end wireless communication systems to the research community and promoting the use of artificial intelligence (AI)/DL for the wireless physical layer. We then put forward two key trade-offs in designing DL models for communication, namely, accuracy versus generalization and compression versus latency. We discuss their relevance in the context of wireless communications use cases using emerging DL models including large language models (LLMs). Finally, we summarize our proposed evaluation guidelines to enhance the research impact of DL on wireless communications. These guidelines are an attempt to reconcile the empirical nature of DL research with the rigorous requirement metrics of wireless communications systems

Optimization of a cationic dye desorption from a loaded-lignocellulosic biomass: factorial design experiments and investigation of mechanisms

The sustainable management of loaded adsorbents with organic pollutants represents an important environmental challenge. The current research work investigates the regeneration process optimization of raw orange tree sawdust (ROS) loaded with methylene blue (MB) by using NaCl solutions as eluent. The MB desorption was assessed in static mode under different process variables, notably the desorbing NaCl solution’s pH and concentration and the MB-loaded biomass dose. A full factorial design composed of 24 experiments was employed to apprehend the statistical significance of each followed parameter. Experimental results showed that the maximum desorption yield was estimated to be about 82.4% for the following parameter’s values: aqueous pH $=$ 3, [NaCl] $=$ 0.2 M and MB-loaded-ROS dosage in the desorbing solution $=$ 1 g$\cdot$L$^{-1}$. The statistical study confirmed the good fit of the experimental data with the statistical model used as well as regression and adjusted regression coefficients of about 99.0% and 96.6%, respectively. Moreover, the ranking of the effect of each studied parameter in terms of standardized effect on the desorption efficiency of MB from ROS was assessed through ANOVA test. The desorption mechanisms involved were explored by using multiple analysis techniques. It appears that the MB molecules’ desorption from ROS’s particles is mainly driven by a counter chemisorption process based on cationic exchange with the sodium and hydronium ions present in the desorbing solutions

Optimization of a cationic dye desorption from a loaded-lignocellulosic biomass: factorial design experiments and investigation of mechanisms

The sustainable management of loaded adsorbents with organic pollutants represents an important environmental challenge. The current research work investigates the regeneration process optimization of raw orange tree sawdust (ROS) loaded with methylene blue (MB) by using NaCl solutions as eluent. The MB desorption was assessed in static mode under different process variables, notably the desorbing NaCl solution’s pH and concentration and the MB-loaded biomass dose. A full factorial design composed of 24 experiments was employed to apprehend the statistical significance of each followed parameter. Experimental results showed that the maximum desorption yield was estimated to be about 82.4% for the following parameter’s values: aqueous pH $=$ 3, [NaCl] $=$ 0.2 M and MB-loaded-ROS dosage in the desorbing solution $=$ 1 g$\cdot$L$^{-1}$. The statistical study confirmed the good fit of the experimental data with the statistical model used as well as regression and adjusted regression coefficients of about 99.0% and 96.6%, respectively. Moreover, the ranking of the effect of each studied parameter in terms of standardized effect on the desorption efficiency of MB from ROS was assessed through ANOVA test. The desorption mechanisms involved were explored by using multiple analysis techniques. It appears that the MB molecules’ desorption from ROS’s particles is mainly driven by a counter chemisorption process based on cationic exchange with the sodium and hydronium ions present in the desorbing solutions