13 research outputs found
Fairness for Freshness: Optimal Age of Information Based OFDMA Scheduling with Minimal Knowledge
It is becoming increasingly clear that an important task for wireless
networks is to minimize the age of information (AoI), i.e., the timeliness of
information delivery. While mainstream approaches generally rely on the
real-time observation of user AoI and channel state, there has been little
attention to solve the problem in a complete (or partial) absence of such
knowledge. In this article, we present a novel study to address the optimal
blind radio resource scheduling problem in orthogonal frequency division
multiplexing access (OFDMA) systems towards minimizing long-term average AoI,
which is proven to be the composition of time-domain-fair clustered round-robin
and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic
solutions that are near-optimal as shown by simulation results are also
proposed to effectively improve the performance upon presence of various
degrees of extra knowledge, e.g., channel state and AoI.Comment: Accepted on 05.06.2021 by the IEEE Transactions on Wireless
Communications for publicatio
Status Updating under Partial Battery Knowledge in Energy Harvesting IoT Networks
We study status updating under inexact knowledge about the battery levels of
the energy harvesting sensors in an IoT network, where users make on-demand
requests to a cache-enabled edge node to send updates about various random
processes monitored by the sensors. To serve the request(s), the edge node
either commands the corresponding sensor to send an update or uses the aged
data from the cache. We find a control policy that minimizes the average
on-demand AoI subject to per-slot energy harvesting constraints under partial
battery knowledge at the edge node. Namely, the edge node is informed about
sensors' battery levels only via received status updates, leading to
uncertainty about the battery levels for the decision-making. We model the
problem as a POMDP which is then reformulated as an equivalent belief-MDP. The
belief-MDP in its original form is difficult to solve due to the infinite
belief space. However, by exploiting a specific pattern in the evolution of
beliefs, we truncate the belief space and develop a dynamic programming
algorithm to obtain an optimal policy. Moreover, we address a multi-sensor
setup under a transmission limitation for which we develop an asymptotically
optimal algorithm. Simulation results assess the performance of the proposed
methods.Comment: 32 Pages. arXiv admin note: text overlap with arXiv:2203.10400,
arXiv:2212.0597