Autonomous vehicles (AVs) and 5G wireless networks need low latency, reliability, and energy efficiency. AVs generate massive amounts of heterogeneous, real-time data, requiring efficient energy and resource allocation for large-scale vehicular communications. Vehicles with fast mobility patterns, channel conditions, a...
As the demand for high-speed, low-latency connectivity escalates, fifth generation (5G) and emerging sixth generation (6G) networks face significant challenges in managing energy consumption while maintaining performance standards. This paper investigates the application of Reinforcement Learning (RL) for dynamic energ...
The fast development of the Industrial Internet of Things (IIoT) system during the era of 6G communications requires intelligent and adaptable network management systems to address the highly dynamic and resource-constrained environment. Conventional network optimization methods are not able to pursue several conflicti...
The high rate of Internet of Things (IoT) networks development has posed a serious problem of effective resource allocation because devices are heterogeneous, the traffic conditions are dynamic, and the energy and latency requirements are severe. Traditional resource allocation methods and classical reinforcement learn...
Sophisticated and adaptive resource-management functions are critical to ensure high performance in dynamic traffic and network conditions of modern communication networks. In this paper, we present a hybrid Deep Learning (DL) and Reinforcement Learning (RL) framework on, specifically constructed through the use of Dee...
An Adaptive Deep Reinforcement Learning (ADRL) based dynamic spectrum allocation framework for AVNs can ensure efficient spectrum allocation and reliable communication in a fast-growing network density and degraded channel environment and has stable convergence characteristics in its training behavior.
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