The migration of wireless communications from 5G to the sixth generation (6G) of cellular communications and next-generation wireless local area networks (Wi-Fi 7/8) has emerged to support ultra-high data rate, massive connectivity, and ultra-low latency. Nonetheless, these needs exacerbate both energy and spectral resource challenges, especially in ultra-dense and heterogeneous networks. The available techniques have focused dominantly on energy efficiency or spectral efficiency, making them less effective in multi-radio access technology (multi-RAT) networks. In this paper, a hybrid framework that simultaneously leverages energy and spectral efficiency for converged 5G/6G communications and Wi-Fi 7/8 networks based on the integration of resource control based on artificial intelligence (AI), cognitive spectrum management, and reconfigurable intelligent surfaces (RIS) is proposed. A model of a multi-RAT system assisted by reconfigurable intelligent surfaces has been established, and an optimisation problem that maximises energy efficiency while satisfying quality-of-service constraints has been formulated. The challenge of the resulting mathematical model being a non-convex optimisation problem has been resolved using a controller designed based on deep reinforcement learning to dynamically adjust both the power allocation to all the radios involved and the reconfigurable intelligent surfaces' phases. Simulation results have confirmed that the proposed scheme can obtain energy efficiency improvement of up to 35% compared to the typical scheme, and simultaneously, the spectral efficiency gain has also significantly improved when operating at a high frequency. The results have confirmed that integrated energy and spectral resource optimisation could provide a new solution for a sustainable 6G and Wi-Fi 8 network.

The transition towards the sixth generation (6G) of wireless networks requires ultra-high data rates and seamless connectivity, even in frequency bands like mmWave and THz, which are highly susceptible to physical blockages. RIS have emerged as a key technology in mitigating NLOS limitations through programmable steering of electromagnetic waves by passive reflection. However, real-time optimization of high-dimensional RIS phase shifts in dynamic multi-user environments remains an NP-hard challenge that is hardly solvable efficiently by traditional methods based on mathematical optimization. This paper presents a new DRL framework, utilizing a TD3 architecture, for the optimization of sum-rate performance of multi-user links. The model incorporates real-time environmental feedback and imperfect CSI for ensuring robust connectivity. Simulation results show that the DRL-RIS framework proposed here achieves remarkable gains in the sum-rate performance and outperforms conventional convex optimization baselines in computational latency and energy efficiency. Keywords: 6G Networks, Reconfigurable Intelligent Surfaces (RIS), Deep Reinforcement Learning (DRL), Non-Line-of-Sight (NLOS), Beamforming

Simultaneous transmission and reflection reconfigurable intelligent surfaces (STAR-RIS) have been proposed as a promising technique to improve the spectral and energy efficiency of future wireless communication networks. In this paper, we examine the joint rate and energy optimization problem in STAR-RIS assisted simultaneous wireless information and power transfer (SWIPT) systems using a collaborative Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MA-TD3) framework. Differing from existing single-agent solutions, the proposed solution allows multiple agents to work together to learn beamforming, power control, and mode splitting decisions in a manner that takes into account realistic constraints such as imperfect channel state information, nonlinear energy harvesting, and discrete phase shifts. The problem is formulated as a Pareto frontier analysis, which captures the rate-energy tradeoff for multiple users. Simulation results show that the proposed MA-TD3 solution can significantly outperform existing benchmark methods, achieving a near-optimal rate–energy trade-off while ensuring user fairness. Specifically, numerical results show that the MA-TD3 framework outperforms the conventional MADDPG method by 18.5% in sum rate convergence, and the STAR-RIS configuration offers a 30% larger coverage area for energy harvesting than the conventional reflecting surface. In addition, the work sheds light on the effect of STAR-RIS configurations, collaboration strategies among agents, and environmental uncertainties on the overall system efficiency. These results indicate the potential of collaborative learning to facilitate intelligent and energy-efficient wireless communication networks and facilitate the large-scale deployment of STAR-RIS assisted SWIPT in future 6G IoT and UAV-enabled communications.

The migration of wireless networks towards sixth-generation (6G) cellular communications and Wi-Fi 8 is targeting ultra-high data rates, massive connectivity, and in-built sensing. However, these sophisticated applications raise new complexities for improving high levels of energy efficiency, spectrum efficiency, and accuracy in ultra-dense and heterogeneous wireless networks. The existing literature has mainly addressed energy efficiency or spectral efficiency in hybrid multi-radio access technology (multi-RAT)-based wireless communications. Therefore, in this manuscript, an Intelligent Integrated Sensing and Communication (ISAC) solution for hybrid 6G and Wi-Fi 8 communications is proposed in order to concurrently maximize energy efficiency (EE), spectral efficiency (SE), and sensing performance. The system model used in this manuscript utilizes Reconfigurable Intelligent Surfaces (RIS) and formulates a multi-objective problem considering communications quality of service and sensing constraints. For this non-convex problem, an optimized deep reinforcement learning (DRL)-based controller to dynamically control RIS phases, beamforming, as well as power allocation in multiple radio communications links has been proposed. The simulation results indicate that the proposed method effectively enhances EE and SE with a high degree of accuracy in environment sensing with a spectral efficiency of 7.1 bits/s/Hz with 32 RIS elements and a spectral efficiency of 12.3 bits/s/Hz with 128 RIS elements. This work proposes the first framework that performs joint energy efficiency, spectral efficiency, and sensing performance optimization for hybrid 6G and Wi-Fi 8 networks. The proposed approach utilizes RIS-aided ISAC to enable intelligent multi-objective optimization using deep reinforcement learning

The realization of sixth generation (6G) wireless networks heavily relies on incorporating space-air-ground integrated network (SAGIN) capabilities to realize global ubiquity. However, the inherent stochasticity of non-terrestrial network (NTN) node mobility, in conjunction with terrestrial resource variability, creates a multidimensional orchestration problem. Traditional optimization and supervised learning schemes are often prohibitive due to high computational complexity and the extreme cost of data labeling. This manuscript therefore presents a novel autonomous resource orchestration scheme with the application of self-supervised learning. By learning efficient spatial-temporal patterns, the scheme supports optimized power allocation together with beamforming for RIS-assisted wireless links. Extensive simulations for the application of the scheme confirm near-optimal sum-rate performance with 40% lower data requirements when compared with standard deep reinforcement learning schemes.

This study proposes an architecture that employs an Agentic Edge Computing layer for offloading computationally intensive task processing from the cloud, thereby achieving sub-millisecond synchronization for multi-modal sensor data, including torque, vibration, and thermal patterns. To evaluate real-world performance, edge-native execution is conducted on an NVIDIA Jetson AGX Orin module for hardware-in-the-loop (HiL) validation. Hardware-in-the-loop (HiL) testing performed on the physical UR10e robot confirms that the Agentic P-DT maintains a 98.2% similarity with simulation responses, even under mechanical friction and stochastic workloads. A new Deep Reinforcement Learning (DRL) method is also developed at the edge to optimize resource allocation and predict Remaining Useful Life (RUL) under stochastic operational conditions. By embedding physical kinematic constraints within the neural network’s loss function, this approach shows a 28.4% relative improvement in fault detection sensitivity over traditional data-driven models. HiL validation results in dense industrial settings show that the P-DT approach preserves a 99.7% level of synchronization accuracy while lowering unplanned downtime by 34%. Additionally, the approach facilitates proactive self-correction capabilities to compensate for mechanical degradation by adjusting motion plans. These results offer a robust solution for the deployment of autonomous and self-healing robotic systems in latency-sensitive industrial settings.