Uplink OFDMA-based random access (UORA) is a new channel access mechanism that supports uplink multiuser access in the new generation WiFi systems. Any associated stations (STAs) can use UORA to send their requests or data to the access point (AP) in a contention manner. In this paper, we provide a comprehensive evaluation for simulation study that investigates two power control strategies combined with capture effect in UORA and observes the fairness issue for spatial distribution of STAs. The results demonstrate that power control strategies can improve the performance of access success probability, delay, resource utilization, and power efficiency of UORA.
As the demand for wireless connectivity expands from high-speed data transmission to high-reliability applications, such as the Industrial Internet of Things and immersive communications, traditional Wi-Fi technologies optimized primarily for peak throughput face new challenges i…
This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link…
Deep learning is a promising approach to optimize wireless communication by simplifying the search for near-optimal solutions. Prior studies on deep learning-based wireless communication optimization have explored supervised learning approaches that map raw user information, such…
One of the key promises of Mobile Edge Computing (MEC) is its low latency. Current large-scale IoT deployments rely on cloud for their reliability, low cost, and ease of use. For outdoor IoT deployments, 5G cellular networks offer significantly enhanced bandwidth and dramatically…
The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based approach for network control that enables adaptive configuration of crucial parameters. The world model…
Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments. However, poisoning attacks pose a…