Low Latency Wireless Networks for Mission Critical Communications

Abstract

Increasingly wireless data services extend beyond traditional best-effort communication to enhanced data applications such as interactive video, real-time multimedia streaming, high-throughput data access and Voice-over-IP. Additionally, the United States military greatly relies on wireless communications for its operations, including command, control, surveillance, reconnaissance, and targeting system. Invariably, meeting the quality-of-service (QoS) requirements of these applications translates into strict delay and throughput constraints. However, meeting such requirements over an unreliable wireless channel is a very challenging task, due to mobility, time-varying channel quality, energy and power limitations, and packet losses. Our goal is to develop transmission scheduling schemes for traffic with both latency and throughput requirements. We propose to develop new approaches for supporting delay sensitive traffic over multi-hop wireless networks subject to interference constraints. In particular, we plan to develop routing and scheduling schemes for delay constrained traffic over multi-hop wireless networks using a combination of machine-learning, optimization, and stochastic control techniques. Our proposed research include the following interdependent goals: 1) Develop transmission scheduling schemes for real-time traffic over wireless networks subject to interference constraints. We will consider both combinatorial interference models (as described by link activation sets) as well as SINR-based interference models. 2) Develop joint routing and scheduling schemes for multi-hop networks with delay constraints. 3) Develop delay sensitive routing schemes for hybrid networks, consisting of wired, wireless, and ``black-box" components. 4) Develop a utility optimization framework for networks with delay constraints.

Document Details

Document Type
DoD Grant Award
Publication Date
Sep 11, 2018
Source ID
W911NF1710508

Entities

People

  • Eytan Modiano

Organizations

  • Army Contracting Command
  • Massachusetts Institute of Technology
  • United States Army

Tags

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms