The widespread distribution of intelligent applications and devices, coupled with the vast array of mobile data, technologies, and architectures within Sixth Generation (6G) networks, highlights the importance of optimization strategies. Concurrently, state-of-the-art Metaheuristic Algorithms (MHAs) have emerged as a promising optimization approach for wireless networks, bridging game theory and convex optimization domains. Falling within the domain of Artificial Intelligence (AI), MHAs draw their inspiration from Evolutionary Algorithms (EAs), which are rooted in the principle of natural selection, the principles of Swarm Intelligence (SI), and Trajectory-based Algorithms (TAs). In recent times, MHAs have been implemented in the 6G network. These simple solutions with limited capabilities have proven effective in solving complex, challenging and high-dimensional problems. Nevertheless, the literature has yet to study the MHAs comprehensively, especially within 6G networks. The main objective of this study is to investigate how to merge the 6G and MHAs domains. We begin by giving a comprehensive overview of MHAs, from fundamental to popular optimization methods. Then, we examine the utilization of MHAs to address the challenges in 6G, such as network security, resource allocation, spectrum management, edge computing, wireless caching, and others. Finally, we identify the current literature's limitations and suggest avenues for further research directions. 

Metaheuristic algorithms for 6G wireless communications: Recent advances and applications

This article presents a model through which the reflection coefficient amplitude as well as phase of reflective intelligent surfaces can be estimated accurately. The reconfigurability of the surface was achieved by incorporating the varactor diodes into the surface of the cell unit. The manipulation of the phase of the reflection coefficient can be achieved by making adjustments to the biasing state of the varactors. The model, which makes use of a physics-based methodology and is based on a transmission-line circuit description of the Reconfigurable Intelligent surfaces (RIS) unit cells, considers every pertinent electrical and geometrical characteristics of the proposed surface. With the method proposed in this paper, fast and accurate RIS-based communication lines can be created. The recommended accuracy of the proposed method was confirmed through the use of a CST microwave studio full-wave simulations. 

https://online-journals.org/index.php/i-joe/article/download/37585/13097

Design and Implementation Unit Cell for 6G Reconfigurable Intelligent Surface Application

Online delay optimization for MEC and RIS-assisted wireless VR networks

In order to achieve thousand-fold throughput improvements for future applications, heterogeneous cellular networks (HCNs) with ultra-densely deployed small cells have gained a great deal of attention in recent years. However, energy consumption is an increasing concern as base station deployment (BSs) becomes denser. In order to address this issue, a novel framework is proposed for integrating multiple reconfigurable intelligent surfaces (RISs) in heterogeneous cellular networks (HCNs), where the communication in each BS is enhanced by its associated RIS. The multi-objective optimization is formulated to obtain the optimal trade-off between throughput and energy consumption. Moreover, an improved non-dominated sorting genetic algorithm II (NSGA-II) based algorithm is proposed to optimize the sub-carrier assignment, user equipment (UE) association, power allocation, and RISs’ phase shift. The proposed algorithm’s potential solutions are first encoded as individuals and optimized with the conventional NSGA-II algorithm. In order to further tackle the coupling characters of the optimization variables, the Zoutendijk method is further employed for searching for better solutions. Numerical results illustrate that: (i) the proposed multiple-RIS-assisted HCNs outperform conventional HCNs without RIS. (ii) the performance can be improved by deploying the RIS closer to UEs or increasing the number of reflecting elements. 

Multi-objective oriented resource allocation in reconfigurable intelligent surface assisted HCNs

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Multi-objective oriented resource allocation in reconfigurable intelligent surface assisted HCNs