相关论文: Refined-Deep Reinforcement Learning for MIMO Bista…
Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a…
Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error…
In this paper, we address the challenge of dynamic spectrum access in a cognitive Internet of Things (CIoT) network where a secondary user (SU) operates under both energy constraints and adversarial interference from a smart jammer. The SU…
In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several…
With the high development of wireless communication techniques, it is widely used in various fields for convenient and efficient data transmission. Different from commonly used assumption of the time-invariant wireless channel, we focus on…
To support the newly introduced multimedia services with ultra-low latency and extensive computation requirements, resource-constrained end user devices should utilize the ubiquitous computing resources available at network edge for…
Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban and non-line-of-sight scenarios, thus limiting their effectiveness in practical deployments. To deal…
Backscatter wireless communication is an emerging technique widely used in low-cost and low-power wireless systems, especially in passive radio frequency identification (RFID) systems. Recently, the requirement of high data rates, data…
This paper studies backscatter communication (BackCom) systems with a passive backscatter tag. The effectiveness of these tags is limited by the amount of energy they can harness from incident radio signals, which are used to backscatter…
Symbiotic radio (SR) backscatter systems are possible techniques for the future low-power wireless communications for Internet of Things devices. In this paper, we propose a multiple-input-multiple-output (MIMO) SR backscatter system, where…
Solar sensor-based monitoring systems have become a crucial agricultural innovation, advancing farm management and animal welfare through integrating sensor technology, Internet-of-Things, and edge and cloud computing. However, the…
Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-world applications, the agents can only acquire a partial view…
Reinforcement Learning (RL) approaches are lately deployed for orchestrating wireless communications empowered by Reconfigurable Intelligent Surfaces (RISs), leveraging their online optimization capabilities. Most commonly, in RL-based…
Continued progress towards energy-neutral Internet of Things (IoT) nodes expose the wireless communication link as the dominant energy bottleneck. While low-power wide-area network (LPWAN) technologies achieve long-range communication with…
Cell-free massive multiple-input-multiple-output is promising to meet the stringent quality-of-experience (QoE) requirements of railway wireless communications by coordinating many successional access points (APs) to serve the onboard users…
Ambient backscatter communication (AmBC) has been introduced to address communication and power efficiency issues for short-range and low-power Internet-of-Things (IoT) applications. On the other hand, reconfigurable intelligent surface…
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexity and the safety…
Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in…
Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven approach for adaptive traffic signal control in complex urban…
Device-to-device (D2D) technology enables direct communication between adjacent devices within cellular networks. Due to its high data rate, low latency, and performance improvement in spectrum and energy efficiency, it has been widely…