2,308 research outputs found
Optimizing Age of Information in Wireless Networks with Perfect Channel State Information
Age of information (AoI), defined as the time elapsed since the last received
update was generated, is a newly proposed metric to measure the timeliness of
information updates in a network. We consider AoI minimization problem for a
network with general interference constraints, and time varying channels. We
propose two policies, namely, virtual-queue based policy and age-based policy
when the channel state is available to the network scheduler at each time step.
We prove that the virtual-queue based policy is nearly optimal, up to a
constant additive factor, and the age-based policy is at-most factor 4 away
from optimality. Comparing with our previous work, which derived age optimal
policies when channel state information is not available to the scheduler, we
demonstrate a 4 fold improvement in age due to the availability of channel
state information
Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals
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
Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-aided and Wireless-powered Wireless Networks
In this paper, we focus on a wireless-powered sensor network coordinated by a
multi-antenna access point (AP). Each node can generate sensing information and
report the latest information to the AP using the energy harvested from the
AP's signal beamforming. We aim to minimize the average age-of-information
(AoI) by adapting the nodes' transmission scheduling and the transmission
control strategies jointly. To reduce the transmission delay, an intelligent
reflecting surface (IRS) is used to enhance the channel conditions by
controlling the AP's beamforming vector and the IRS's phase shifting matrix.
Considering dynamic data arrivals at different sensing nodes, we propose a
hierarchical deep reinforcement learning (DRL) framework to for AoI
minimization in two steps. The users' transmission scheduling is firstly
determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and
then the inner-loop optimization is used to adapt either the uplink information
transmission or downlink energy transfer to all nodes. A simple and efficient
approximation is also proposed to reduce the inner-loop rum time overhead.
Numerical results verify that the hierarchical learning framework outperforms
typical baselines in terms of the average AoI and proportional fairness among
different nodes.Comment: 31 pages, 6 figures, 2 tables, 3 algorithm
- …