Technique for order of preference by similarity to ideal solution based predictive handoff for heterogeneous networks

This study presents an efficient handoff algorithm for heterogeneous networks comprising macrocells, microcells, picocells, and femtocells. The proposed algorithm is based on call admission control (CAC) for selecting target base station (BS) from a list of neighbouring candidate BSs, as well as using the technique for order of preference by similarity to ideal solution (TOPSIS) as decision method. The introduced algorithm takes into account multiple criteria including measured received signal strength (RSS) and signal-to-interference-plus noise ratio (SINR), predicted RSS and SINR, and number of free resource blocks of neighbouring BSs. With the use of TOPSIS, neighbouring BSs are ordered based on their priority determined using the five mentioned criteria. Selection of target BS from ordered list is then performed using CAC. Also, coverage expansion is used to retain the connection when there is not any BS for handoff. In addition, in order to predict the RSS and SINR samples, the logistic smooth transition autoregressive model is used. Performance of the proposed algorithm is evaluated in terms of ping-pong rate, outage probability, and throughput. The results indicate efficiency of the proposed algorithm in reducing number of unnecessary handoffs while significantly increasing throughput and decreasing connection dropping when compared with conventional algorithms.