This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two nearly-passive reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmitter, and one in the immediate vicinity of the receiver. In this scenario, the system energy efficiency is optimized with respect to the transmit covariance matrix, and the reflection matrices of the two metasurfaces. A low-complexity algorithm is developed, which is converges to a first-order optimal point of the maximization problem.Moreover, closed-form expressions are derived for the metasurface matrices in the special case of single-antenna or single-stream transmission. A numerical performance analysis shows, in particular, that the considered architecture can provide significant energy efficiency gains compared to fully digital beamforming architectures.

This work addresses the problem of secrecy energy efficiency (SEE) maximization in RIS-aided wireless networks. The use of active and nearly-passive RISs are compared and their trade-off in terms of SEE is analyzed. Considering both perfect and statistical channel state information, two SEE maximization algorithms are developed to optimize the transmit powers of the mobile users, the RIS reflection coefficients, and the base station receive filters. Numerical results quantify the trade-off between active and nearly-passive RISs in terms of SEE, with active RISs yielding worse SEE values as the static power consumed by each reflecting element increases.

The rapid progression of 5G networks has ushered in a new era of communication, marked by challenges in optimizing enhanced Mobile Broadband (eMBB) throughput and Ultra-Reliable Low Latency Communications (URLLC) latency. This research delves into Deep Reinforcement Learning (DRL) to address these challenges, with a particular emphasis on the Proximal Policy Optimization (PPO) algorithm. By leveraging a judiciously crafted environment and reward structure, our DRL agents were trained to optimize eMBB throughput and URLLC latency concurrently. Using the Colosseum O-RAN COMMAG Dataset, our agents achieved an average eMBB throughput of 0.4451 Gbps and an average URLLC latency of 0.4719 ms. These outcomes highlight the potency of DRL as a tool for 5G optimization, presenting a promising avenue for future advancements in intelligent 5G network management.

The field of wireless communication networks has witnessed a dramatic change over the last decade due to sophisticated technologies deployed to satisfy various demands peculiar to different data‐intensive wireless applications. Consequently, this has led to the aggressive use of the available propagation channels to fulfill the minimum quality of service (QoS) requirement. A major barometer used to gauge the performance of a wireless communication system is the spectral efficiency (SE) of its communication channels. A key technology used to improve SE substantially is the multiple input multiple output (MIMO) technique. This article presents a detailed survey of MIMO channel models in wireless communication systems. First, we present the general MIMO channel model and identified three major MIMO channel models, viz., the physical, analytical, and standardized models. The physical models describe the MIMO channel using physical parameters. The analytical models show the statistical features of the MIMO channel with respect to the measured data. The standardized models provide a unified framework for modern radio propagation architecture, advanced signal processing, and cutting‐edge multiple access techniques. Additionally, we examined the strengths and limitations of the existing channel models and discussed model design, development, parameterization, implementation, and validation. Finally, we present the recent 3GPP‐based 3D channel model, the transitioning from 2D to 3D channel modeling, discuss open issues, and highlight vital lessons learned for future research exploration in MIMO communication systems.

The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.