Optimal scheduling of real-time traffic in wireless networks with delayed feedback

In this paper we consider a wireless network composed of a base station and a number of clients, with the goal of scheduling real-time traffic. Even though this problem has been extensively studied in the literature, the impact of delayed acknowledgment has not been assessed. Delayed feedback is of increasing importance in systems where the round trip delay is much greater than the packet transmission time, and it has a significant effect on the scheduling decisions and network performance. Previous work considered the problem of scheduling real-time traffic with instantaneous feedback and without feedback. In this work, we address the general case of delayed feedback and use Dynamic Programming to characterize the optimal scheduling policy. An optimal algorithm that fulfills any feasible minimum delivery ratio requirements is proposed. Moreover, we develop a low-complexity suboptimal heuristic algorithm which is suitable for platforms with low computational power. Both algorithms are evaluated through simulations.National Science Foundation (U.S.) (CNS-1217048)United States. Office of Naval Research (Grant N00014-12-1-00640Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks

We consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients through unreliable channels. The Age of Information (AoI), namely the amount of time that elapsed since the most recently delivered packet was generated, captures the freshness of the information. We formulate a discrete-time decision problem to find a transmission scheduling policy that minimizes the expected weighted sum AoI of the clients in the network. We first show that in symmetric networks a Greedy policy, which transmits the packet with highest current age, is optimal. For general networks, we develop three low-complexity scheduling policies: a randomized policy, a Max-Weight policy and a Whittle's Index policy, and derive performance guarantees as a function of the network configuration. To the best of our knowledge, this is the first work to derive performance guarantees for scheduling policies that attempt to minimize AoI in wireless networks with unreliable channels. Numerical results show that both Max-Weight and Whittle's Index policies outperform the other scheduling policies in every configuration simulated, and achieve near optimal performance

Transmission scheduling of periodic real-time traffic in wireless networks

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages [77]-79).An increasing number of applications rely on wireless networks for distributing information. Communicating time-sensitive data such as position, video, voice and telemetry, can be particularly challenging in wireless networks due to packet losses. In this thesis, we consider a single-hop wireless network, in which a base station is sending time-sensitive data packets to a set of clients. Our goal is to study transmission scheduling strategies for real-time traffic. Even though this problem has been explored in the literature, we present novel results that provide useful insight into the optimal scheduling problem. Previous work considered the problem of maximizing the throughput of networks with instantaneous feedback and without feedback. We address the general case of delayed feedback. Delayed feedback is particularly important for communication systems in which the round trip delay is much greater than the packet transmission time, and it has a significant impact on the scheduling decisions and network performance. In addition, we consider the case of clients receiving multiple parallel packet flows with heterogeneous deadlines. It is a well-known result that the Shortest Time to Extinction (STE) policy optimizes the throughput in wired networks. In this thesis, we establish a class of wireless networks for which the STE policy is throughput-optimal, i.e. minimizes the expected number of packets that expire due to the deadlines. Finally, we study the wireless network from the perspective of the Age of Information (AoI). This recently proposed performance metric represents the freshness of the information at the clients. We use Dynamic Programming to formulate and solve the problem of characterizing the scheduling policy that minimizes the AoI. The AoI metric is compared with throughput, and insights are drawn from numerical results. Simulations suggest that AoI-optimal policies are always throughput-optimal, while the converse is not true.by Igor Kadota.S.M

Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals

© 2002-2012 IEEE. We consider a wireless network with a base station serving multiple traffic streams to different destinations. Packets from each stream arrive to the base station according to a stochastic process and are enqueued in a separate (per stream) queue. The queueing discipline controls which packet within each queue is available for transmission. The base station decides, at every time t, which stream to serve to the corresponding destination. The goal of scheduling decisions is to keep the information at the destinations fresh. Information freshness is captured by the Age of Information (AoI) metric. In this paper, we derive a lower bound on the AoI performance achievable by any given network operating under any queueing discipline. Then, we consider three common queueing disciplines and develop both an Optimal Stationary Randomized policy and a Max-Weight policy under each discipline. Our approach allows us to evaluate the combined impact of the stochastic arrivals, queueing discipline and scheduling policy on AoI. We evaluate the AoI performance both analytically and using simulations. Numerical results show that the performance of the Max-Weight policy is close to the analytical lower bound