Related papers: Power Control for Wireless VBR Video Streaming: Fr…
Future wireless networks require high throughput and energy efficiency. This paper studies using Reinforcement Learning (RL) to do transmission rate and power control for maximizing a joint reward function consisting of both throughput and…
We consider wireless transmission over fading channel powered by energy harvesting and storage devices. Assuming a finite battery storage capacity, we design an online power control strategy aiming at maximizing the long-term time-averaged…
Wireless support of virtual reality (VR) has challenges when a network has multiple users, particularly for 3D VR gaming, digital AI avatars, and remote team collaboration. This work addresses these challenges through investigation of the…
Nowadays, the use of mobile applications and terminals faces fundamental challenges related to energy constraint. This is due to the limited battery lifetime as compared to the increasing hardware evolution. Video streaming is one of the…
Offline reinforcement learning (RL) enables policy optimization using static datasets, avoiding the risks and costs of extensive real-world exploration. However, it struggles with suboptimal offline behaviors and inaccurate value estimation…
We consider a single source transmitting data to one or more receivers/users over a shared wireless channel. Due to random fading, the wireless channel conditions vary with time and from user to user. Each user has a buffer to store…
Deep Reinforcement Learning (DRL) has become a popular method for solving control problems in power systems. Conventional DRL encourages the agent to explore various policies encoded in a neural network (NN) with the goal of maximizing the…
In streaming Reinforcement Learning (RL), transitions are observed and discarded immediately after a single update. While this minimizes resource usage for on-device applications, it makes agents notoriously sample-inefficient, since…
This paper delves into the synergistic potential of adaptive video streaming over emerging 6G wireless networks, emphasizing innovative buffer control techniques and detailed analysis of user viewing behaviors. As 6G technology heralds a…
This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance…
Providing a high Quality of Experience (QoE) for video streaming in 5G and beyond 5G (B5G) networks is challenging due to the dynamic nature of the underlying network conditions. Several Adaptive Bit Rate (ABR) algorithms have been…
In this paper, we propose a network scenario where the baseband processes of the virtual small cells powered solely by energy harvesters and batteries can be opportunistically executed in a grid-connected edge computing server, co-located…
Adaptive bitrate streaming (ABR) has become thede factotechnique for videostreaming over the Internet. Despite a flurry of techniques, achieving high quality ABRstreaming over cellular networks remains a tremendous challenge. First, the…
We study behavior-regularized reinforcement learning (RL), where regularization toward a reference distribution (the dataset in offline RL or the base model in LLM RL finetuning) is essential to prevent value over-optimization caused by…
This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a WiFi network system, and evaluates the performance with…
This paper considers controlled scalar systems relying on a lossy wireless feedback channel. In contrast with the existing literature, the focus is not on the system controller but on the wireless transmit power controller that is…
Data-driven learning-based control methods such as reinforcement learning (RL) have become increasingly popular with recent proliferation of the machine learning paradigm. These methods address the parameter sensitiveness and unmodeled…
Bandwidth hungry video content has become the dominant contributor to the data traffic world over. Cellular networks are constantly evolving to meet the growing traffic demands. Over the past few years, wireless multicast has been garnering…
Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its…
The rise in residential photovoltaics and other distributed energy sources poses unprecedented challenges for the operation of power distribution grids. When high amounts of active power are injected into the grid by such power sources, the…