Related papers: Multicast Scheduling over Multiple Channels: A Dis…
Multi-channel multi-interface Wireless Mesh Networks permit to spread the load across orthogonal channels to improve network capacity. Although broadcast is vital for many layer-3 protocols, proposals for taking advantage of multiple…
We consider a multi-agent reinforcement learning problem where each agent seeks to maximize a shared reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to…
Multicast device-to-device (D2D) transmission is important for applications like local file transfer in commercial networks and is also a required feature in public safety networks. In this paper we propose a tractable baseline multicast…
This paper re-visits a multi-agent deployment problem where agents are restricted from requesting information from other agents as well as sending acknowledgments when information is received. These communication constraints relax the…
This paper proposes an adaptive channel contention mechanism to optimize the queuing performance of a distributed millimeter wave (mmWave) uplink system with the capability of environment and mobility sensing. The mobile agents determine…
Scheduling problems pose significant challenges in resource, industry, and operational management. This paper addresses the Unrelated Parallel Machine Scheduling Problem (UPMS) with setup times and resources using a Multi-Agent…
Massive multiple-input-multiple-output (MIMO) is unquestionably a key enabler of the fifth-generation (5G) technology for mobile systems, enabling to meet the high requirements of upcoming mobile broadband services. Physical-layer…
The surge in wireless devices and data traffic volume necessitates more efficient transmission methods. Multicasting has garnered consistent attention as a means to fulfill the increasing demand for more efficient data transmission methods.…
Modern radars often adopt multi-carrier waveform which has been widely discussed in the literature. However, with the development of civil communication, more and more spectrum resource has been occupied by communication networks. Thus,…
Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of distributed multi-agent learning without resorting to…
Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of…
Massive internet of things microservices require integrating renewable energy harvesting into mobile edge computing (MEC) for sustainable eScience infrastructures. Spatiotemporal mismatches between stochastic task arrivals and intermittent…
We consider problems in sequential decision making with natural multi-level structure, where sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure has remained a…
Cell-free massive multiple-input multiple-output (CF-mMIMO) is a breakthrough technology for beyond-5G systems, designed to significantly boost the energy and spectral efficiencies of future mobile networks while ensuring a consistent…
Integrated sensing, communication, and computation (ISCC) is emerging as a unified design paradigm for future vehicular networks that require joint environment perception, safety-critical information exchange, and latency-sensitive task…
In important applications involving multi-task networks with multiple objectives, agents in the network need to decide between these multiple objectives and reach an agreement about which single objective to follow for the network. In this…
The thesis proposes a generalized charging framework for multiple mobile chargers to maximize the network lifetime and ensure target coverage and connectivity in large scale WRSNs. Moreover, a multi-point charging model is leveraged to…
Multiuser resource allocation has recently been recognized as an effective methodology for enhancing the power and spectrum efficiency in OFDM (orthogonal frequency division multiplexing) systems. It is, however, not directly applicable to…
This paper proposes a multi-agent reinforcement learning based medium access framework for wireless networks. The access problem is formulated as a Markov Decision Process (MDP), and solved using reinforcement learning with every network…
In this paper, we consider the problem of real-time transmission scheduling over time-varying channels. We first formulate the transmission scheduling problem as a Markov decision process (MDP) and systematically unravel the structural…