Optimizing Information Freshness in Wireless Networks: A Stochastic\n Geometry Approach

Optimization of information freshness in wireless networks has usually been\nperformed based on queueing analysis that captures only the temporal traffic\ndynamics associated with the transmitters and receivers. However, the effect of\ninterference, which is mainly dominated by the interferers' geographic\nlocations, is not well understood. In this paper, we leverage a spatiotemporal\nmodel, which allows one to characterize the age of information (AoI) from a\njoint queueing-geometry perspective, for the design of a decentralized\nscheduling policy that exploits local observation to make transmission\ndecisions that minimize the AoI. To quantify the performance, we also derive\naccurate and tractable expressions for the peak AoI. Numerical results reveal\nthat: i) the packet arrival rate directly affects the service process due to\nqueueing interactions, ii) the proposed scheme can adapt to traffic variations\nand largely reduce the peak AoI, and iii) the proposed scheme scales well as\nthe network grows in size. This is done by adaptively adjusting the radio\naccess probability at each transmitter to the change of the ambient\nenvironment.\n