Author(s): Bălan Irina | Sas Bart | Jansen Thomas | Moerman Ingrid | Spaey Kathleen | Demeester Piet
Journal: EURASIP Journal on Wireless Communications and Networking
ISSN 1687-1472
Volume: 2011;
Issue: 1;
Start page: 98;
Date: 2011;
Original page
Keywords: LTE | Self-Organization | handover | operator policy | PDP
ABSTRACT
Abstract This article introduces an enhanced version of previously developed self-optimizing algorithm that controls the handover (HO) parameters of a long-term evolution base station in order to diminish and prevent the negative effects that can be introduced by HO (radio link failures, HO failures and ping-pong HOs) and thus improve the overall network performance. The default algorithm selects the best hysteresis and time-to-trigger combination based on the current network status. The enhancement proposed here aims to maximize the gain provided by the algorithm by improving its convergence time. The effects of this enhancement have been studied in a rural scenario setting and compared to the original algorithm; the results show a clear improvement, faster convergence, and better network performance, because of the enhancement.
Journal: EURASIP Journal on Wireless Communications and Networking
ISSN 1687-1472
Volume: 2011;
Issue: 1;
Start page: 98;
Date: 2011;
Original page
Keywords: LTE | Self-Organization | handover | operator policy | PDP
ABSTRACT
Abstract This article introduces an enhanced version of previously developed self-optimizing algorithm that controls the handover (HO) parameters of a long-term evolution base station in order to diminish and prevent the negative effects that can be introduced by HO (radio link failures, HO failures and ping-pong HOs) and thus improve the overall network performance. The default algorithm selects the best hysteresis and time-to-trigger combination based on the current network status. The enhancement proposed here aims to maximize the gain provided by the algorithm by improving its convergence time. The effects of this enhancement have been studied in a rural scenario setting and compared to the original algorithm; the results show a clear improvement, faster convergence, and better network performance, because of the enhancement.
