age of information

This paper investigates resource management in device-to-device (D2D) networks coexisting with mobile cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, measured by minimizing the average age of information (AoI), and to effectively utilize energy harvesting (EH) technology to satisfy the network’s energy needs, we formulate an online optimization problem.

Aiming the analytical modeling of Age of Information (AoI) and Peak-AoI, uploaded codes construct and solve analytical models for Non-preemptive Bufferless, Probabilistic Generate-at-will (GAW) and Random Arrival with Single Buffer (RA-SB) servers using the theory of absorbing Markov Chains. In particular, they output per-source PAoI/AoI distributions in a setting with general number of sources where the sources may have different (i) general phase-type service time distributions, (ii) packet error probabilities and (iii) arrival rates.

The script "numericalExample.m" obtains per-source average AoI and per-source age violation probabilities for First Source First Serve (FSFS), Earliest Served First Serve (ESFS) and Single Buffer with Replacement (SBR) policies for N sources using analytical models based on Markov Fluid Queues (MFQ). It is also possible to obtain the exact per-source CDF using this script. To do so, only the input parameters of used functions, namely "FSFS.m", "ESFS.m" and "SBR.m", in this script should be modified accordingly.

In this paper, we consider that the unmanned aerial vehicles (UAVs) with attached intelligent reflecting surfaces (IRSs) play the role of flying reflectors that reflect the signal of users to the destination, and utilize the power-domain non-orthogonal multiple access (PD-NOMA) scheme in the uplink. We investigate the benefits of the UAV-IRS on the internet of things (IoT) networks that improve the freshness of collected data of the IoT devices via optimizing power, sub-carrier, and trajectory variables, as well as, the phase shift matrix elements.