相关论文: Traffic Prediction and Random Access Control Optim…

We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional communication systems minimize interference by orthogonally allocating resources (time and/or…

This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. Individual nodes decide their optimal states with distributed…

The capacity of wireless networks is fundamentally limited by interference. However, little research has focused on the interference correlation, which may greatly increase the local delay (namely the number of time slots required for a…

This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is partially inspired by…

We propose a novel random access (RA) protocol that accounts for the network traffic in mixed URLLC-mMTC scenarios. By considering an IoT environment under high mMTC traffic demand, we model the traffic of each service using realistic…

Wireless random access protocols are attracting a revived research interest as a simple yet effective solution for machine-type communications. In the quest to improve reliability and spectral efficiency of such schemes, the use of multiple…

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this…

The thesis is dedicated to studying methods to improve the efficiency of random access schemes and to facilitate their deployment in machine-type communications (MTC). First, a joint user activity identification and channel estimation…

There is a paucity of random access protocols designed for alleviating collisions in visible light communication (VLC) systems, where carrier sensing is hard to be achieved due to the directionality of light. To resolve the problem of…

As a main use case of 5G and Beyond wireless network, the ever-increasing machine type communications (MTC) devices pose critical challenges over MTC network in recent years. It is imperative to support massive MTC devices with limited…

Characterizing and comparing the optimal energy efficiency in energy-aware machine-to-machine (M2M) random access networks remains a challenge due to the distributed nature of the access behavior of nodes. To address this issue, this letter…

Learning-based traffic signal control is typically optimized for average performance under a few nominal demand patterns, which can result in poor behavior under atypical traffic conditions. To address this, we develop a distributionally…

In this work, we consider a remote monitoring scenario in which multiple sensors share a wireless channel to deliver their status updates to a process monitor via an access point (AP). Moreover, we consider that the sensors randomly arrive…

Making judicious channel access and transmission scheduling decisions is essential for improving performance as well as energy and spectral efficiency in multichannel wireless systems. This problem has been a subject of extensive study in…

In this paper, we study a layered random access scheme based on non-orthogonal multiple access (NOMA) to improve the throughput of multichannel ALOHA. At a receiver, successive interference cancellation (SIC) is carried out across layers to…

Two-way relaying can significantly improve performance of next generation wireless networks. However, due to its dependence on multi-node cooperation and transmission coordination, applying this technique to a wireless network in an…

We propose a coordinated random access scheme for industrial internet-of-things (IIoT) scenarios, with machine-type devices (MTDs) generating sporadic correlated traffic. This occurs, e.g., when external events trigger data generation at…

Non-orthogonal multiple access (NOMA) has been considered as a promising solution for improving the spectrum efficiency of next-generation wireless networks. In this paper, the performance of a p-persistent slotted ALOHA system in support…

Reinforcement learning (RL) has attracted increasing interest for adaptive traffic signal control due to its model-free ability to learn control policies directly from interaction with the traffic environment. However, several challenges…

Massive machine-type communications protocols have typically been designed under the assumption that coordination between users requires significant communication overhead and is thus impractical. Recent progress in efficient activity…