相关论文: Decentralised Learning MACs for Collision-free Acc…

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable to the others. In particular, finding time efficient paths…

Full Duplex (FD) technology is considered as one of the next big leap in the evolution of modern WLANs. Allowing a node to simultaneously transmit a data frame while in receive mode, can theoretically double the system throughput. However,…

In this paper, we propose a novel adaptive carrier sense multiple access scheme with collision avoidance (CSMA/CA) to perform efficient and reliable data transfer with increased throughput across multiple coexisting wireless body area…

Recently several CSMA algorithms based on the Glauber dynamics model have been proposed for multihop wireless scheduling, as viable solutions to achieve the throughput optimality, yet are simple to implement. However, their delay…

Recently there has been considerable interest in the design of efficient carrier sense multiple access(CSMA) protocol for wireless network. The basic assumption underlying recent results is availability of perfect carrier sense information.…

Networking protocols are designed through long-time and hard-work human efforts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual efforts to tune individual protocol parameters.…

Many TDMA- and CSMA-based protocols try to obtain fair channel access and to increase channel utilization. It is still challenging and crucial in Wireless Sensor Networks (WSNs), especially when the time synchronization cannot be well…

High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision…

Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication. It is known that the convergence speed of decentralized optimization…

Deep learning (DL)-based solutions have recently been developed for communication protocol design. Such learning-based solutions can avoid manual efforts to tune individual protocol parameters. While these solutions look promising, they are…

Existing work that schedules concurrent transmissions collision-free suffers from low channel utilization. We propose the Optimal Node Activation Multiple Access (ONAMA) protocol to achieve maximal channel spatial reuse through a…

The emergence of large-scale wireless networks with partially-observable and time-varying dynamics has imposed new challenges on the design of optimal control policies. This paper studies efficient scheduling algorithms for wireless…

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling…

We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints. By performing the same task…

Enterprise Wireless Local Area Networks (WLANs) consist of multiple Access Points (APs) covering a given area. Finding a suitable network configuration able to maximize the performance of enterprise WLANs is a challenging task given the…

This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the…

In distributed control systems where devices are connected through Wi-Fi, direct access to low-level MAC operations may help applications to meet their timing constraints. In particular, the ability to timely control single transmission…

The objective of meta-learning is to exploit the knowledge obtained from observed tasks to improve adaptation to unseen tasks. As such, meta-learners are able to generalize better when they are trained with a larger number of observed tasks…

In this paper, we consider CSMA policies for scheduling of multihop wireless networks with one-hop traffic. The main contribution of this paper is to propose Unlocking CSMA (U-CSMA) policy that enables to obtain high throughput with low…

In this paper a candidate protocol to replace the prevalent CSMA/CA medium access control in Wireless Local Area Networks is presented. The proposed protocol can achieve higher throughput than CSMA/CA, while maintaining fairness, and…