Heuristic approach for forecast scheduling

Abstract

Forecast Scheduling (FS) is a scheduling concept that utilizes rate prediction along the users' trajectories in order to optimize the scheduler allocation. The rate prediction is based on Signal to Interference plus Noise Ratio (SINR) or rate maps provided by a Radio Environment Map (REM). The FS has been formulated as a convex optimization problem namely the maximization of an α−fair utility function of the cumulated rates of the users along their trajectories [1]. This paper proposes a fast heuristic for the FS problem based on two FS users' scheduling. Furthermore, it is shown that in the case of two users, the FS problem can be solved analytically, making the heuristic computationally very efficient. Numerical results illustrate the throughput gain brought about by the scheduling solution. a

Location-Aware Wireless Resource Allocation in Industrial-Like Environment

“…In the area of network optimization, past work has mainly focused on building maps of metrics based on geolocated measurements, a concept denoted as REM, from geo-localized measurement [8], and being applied to problems such as frequency reuse planning [9], coverage analysis [10], or forecast scheduling [2], [11]. The latest refers to a scheduling concept that utilizes rate prediction along the users' trajectories in order to optimize the scheduler allocation.…”

“…The idea is to take advantage of positioning data as a type of context information to enable reliable mobile communications, where context refers to the information considered to forecast the system evolution. Past work has mainly focused on the problem of deriving Radio Environmental Map (REM) from geo-localized measurement and applied that to theoretical understanding of how to perform rate prediction along the users' trajectories in order to optimize the scheduler allocation [2]. On the other hand, there is lack of experimental work in this area.…”