The Story of 1/e: ALOHA-based and Reinforcement-Learning-based Random Access for Delay-Constrained Communications

by   Lei Deng, et al.
Shenzhen University
The University of North Carolina at Greensboro

Motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, and cyber-physical systems, supporting delay-constrained traffic becomes critical for such systems. In delay-constrained traffic, each packet has a hard deadline; when it is not delivered before its deadline is up, it becomes useless and will be removed from the system. In this work, we focus on designing random access schemes for delay-constrained wireless communications. We first investigate three ALOHA-based schemes and prove that the system timely throughput of all three schemes under corresponding optimal transmission probabilities asymptotically converges to 1/e, same as the well-known throughput limit for delay-unconstrained ALOHA systems. The fundamental reason why ALOHA-based schemes cannot achieve asymptotical system timely throughput beyond 1/e is that all active ALOHA stations access the channel with the same probability in any slot. To go beyond 1/e, we propose a reinforcement-learning-based scheme for delay-constrained wireless communications, called RLRA-DC, under which different stations collaboratively attain different transmission probabilities by only interacting with the access point. Our numerical result shows that the system timely throughput of RLRA-DC can be as high as 0.8 for tens of stations and can still reach 0.6 even for thousands of stations, much larger than 1/e.


page 1

page 2

page 3

page 4


Reinforcement Learning for Improved Random Access in Delay-Constrained Heterogeneous Wireless Networks

In this paper, we for the first time investigate the random access probl...

Delay-Constrained Input-Queued Switch

In this paper, we study the delay-constrained input-queued switch where ...

Admission Control based Traffic-Agnostic Delay-Constrained Random Access (AC/DC-RA) for M2M Communication

The problem of wireless M2M communication is twofold: the reliability as...

Reinforcement Learning Random Access for Delay-Constrained Heterogeneous Wireless Networks: A Two-User Case

In this paper, we investigate the random access problem for a delay-cons...

Delay-Constrained Topology-Transparent Distributed Scheduling for MANETs

Transparent topology is common in many mobile ad hoc networks (MANETs) s...

DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications

Closing the gap between high data rates and low delay in real-time strea...

Frame Size Optimization Using a Machine Learning Approach in WLAN Downlink MU-MIMO Channel

The IEEE 802.11ac/n introduced frame aggregation technology to accommoda...

Please sign up or login with your details

Forgot password? Click here to reset