Open AccessThroughput Prediction Across Heterogeneous Boundaries in Wireless Communications
Author(s) -
Rayyan Sayeed,
Raymond E. Miller,
Zulfiquar Sayeed
Publication year - 2016
Publication title -
journal of cyber security and mobility
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.198
H-Index - 9
eISSN - 2245-4578
pISSN - 2245-1439
DOI - 10.13052/2245-1439.441
Subject(s) - handover , computer science , throughput , quality of service , unobservable , performance metric , metric (unit) , wireless , computer network , focus (optics) , mean squared prediction error , algorithm , telecommunications , mathematics , engineering , operations management , physics , management , optics , economics , econometrics
In this paper we demonstrate how an estimated functional kernel-regression polynomial from a particular RF technology can be created by the mobiles being served by that technology. A 3rd order polynomial description of the regression can be used to predict future throughput by observing the “pilot” quality prior to handover. The UE may use it to predict the throughput it will get in the new technology 50 to 200 m-sec prior to handover. The prediction can inform the Transport Control Protocol (TCP) layer or the application layer of the upcoming handover and the throughput expected after handover so that the user application receives the best quality of service. This paper is an extended version of the paper presented at IEEE Sarnoff Symposium 2015 [1]. It extends the paper with expanded foundational knowledge and explanation of the results and their implications. In this paper we: • propose that there is a way to predict the unobservable quality metrics in the new cell prior to commencement of the handover. This is achieved by 1) a prediction mechanism and 2) a signaling mechanism. In this paper we focus on the prediction mechanism. • propose that the observable metric (“pilot” quality) is predicted with prediction error below 9% with prediction step sizes of 200 m-sec. • show that the throughput metric (we choose bits/physical-resourceblock = β) can be predicted with error below 8% with prediction horizon of 200 m-sec.