The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC’17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point.

Employing Reconfigurable Intelligent Surface (RIS) is an advanced strategy to enhance the efficiency of wireless communication systems. However, the number and positions of the RISs elements are still challenging and require a smart optimization framework. This paper aims to optimize the number of RISs subject to the technical limitations of the average achievable data rate with consideration of the practical overlapping between the associated multi-RISs in wireless communication systems. In this regard, the Differential evolution optimizer (DEO) algorithm is created to minimize the number of RIS devices to be installed. Accordingly, the number, positions, and phase shift matrix coefficients of RISs are then jointly optimized using the intended DEO. Also, it is contrasted to several recent algorithms, including Particle swarm optimization (PSO), Gradient-based optimizer (GBO), Growth optimizer (GO), and Seahorse optimization (SHO). The outcomes from the simulation demonstrate the high efficiency of the proposed DEO and GO in obtaining a 100% feasibility rate for finding the minimum number of RISs under different threshold values of the achievable rates. PSO scores a comparable result of 99.09%, while SHO and GBO attain poor rates of 66.36% and 53.94%, respectively. Nevertheless, the excellence of the created DEO becomes evident through having the lowest average number of RISs when compared to the other algorithms. Numerically, the DEO drives improvements by 5.13%, 15.68%, 30.58%, and 51.01% compared to GO, PSO, SHO and GBO, respectively.

An innovative method to raise wireless communication systems’ efficiency is to use Reconfigurable Intelligent Surface (RIS). Unfortunately, determining the quantity and locations of the RIS elements continues to be difficult, requiring a clever optimization framework. Concerning the practical overlap between the related multi-RISs in wireless communication systems, this article attempts to minimize the number of RISs while considering the average possible data rate and technological constraints. In this regard, a novel enhanced artificial rabbits algorithm (EARA) is developed to minimize the number of RISs to be installed. The novel EARA is inspired by the natural survival strategies of rabbits, including detour eating and random concealment. A more effective method of exploring the search space around the best solution so far is produced by the suggested EARA by combining an upgraded collaborative searching operator (CSO) arrangement. Also, an adaptive time function is included to increase the effect of this exploitation tactic by the increasing number of iterations. The simulation results show that the suggested EARA is highly efficient in reaching the maximum success rate of producing the smallest number of RISs under various feasible rate threshold settings. When EARA is compared to standard artificial rabbits optimizer (ARO), growth optimizer (GO), artificial ecosystem optimizer (AEO), and particle swarm optimization (PSO), the average number of RISs is improved by 5.32%, 6.7%, 16.73%, and 20.06%, respectively. Furthermore, according to simulation data, the EARA outperforms AEO, GO, ARO, and PSO in terms of success rate at $\delta = 1.4$ by 6.66%, 6.66%, 45.43%, and 99%, respectively.

The rapid and low-power configuration capabilities of Reconfigurable Intelligent Surfaces (RISs) have made them an attractive option for future wireless networks in terms of energy efficiency. They have the ability to greatly increase connection and facilitate low-latency communications. However, because RIS-based systems often have a large number of RIS unit elements and unique hardware constraints, accurate and low-overhead channel estimate remains a crucial challenge. In this study, we offer a channel estimation framework and concentrate on the uplink of a multi-user multiple-input multiple-output (MU-MIMO) communication system driven by RIS. Our primary goal is to enhance the achievable rate and system capacity. We derive a closed-form deterministic expression for the uplink achievable rate under practical scenarios where channel state information (CSI) is not directly known and must be estimated. In contrast to previous studies assuming perfect CSI, our approach incorporates the channel estimation process, leading to a more realistic performance assessment. Extensive simulations validate the tightness of our derived expression compared to the actual achievable rate across various system parameters (with discrepancies typically within 2-5%). The results highlight the significant impact of RIS on system performance enhancement, even with imperfect CSI. Our findings provide crucial insights into the deployment and optimization of RIS-assisted multi-user wireless networks, underscoring their potential for substantial improvements in rate and capacity.

Gesture recognition (GR) has many applications for human-computer interaction (HCI) in the healthcare, home, and business arenas. However, the common techniques to realize gesture recognition using video processing are computationally intensive and expensive. In this work, we propose to task existing visible light communications (VLC) systems with gesture recognition. Different finger movements are identified by training on the light transitions between fingers using the long short-term memory (LSTM) neural network. This paper describes the design and implementation of the gesture recognition technique for a practical VLC system operating over a distance of 48 cm. The platform uses a single low-cost light-emitting diode (LED) and photo-diode sensor at the receiver side. The system recognizes gestures from interruptions in the direct light transmission, and is therefore suitable for high-speed communication. Gesture recognition accuracies were conducted for five gestures, and results demonstrate that the proposed system is able to accurately identify the gestures in up to 88% of cases.