Classification of VHD algorithms

There are various ways to classify VHD algorithms. I have chosen to divide VHD algorithms into four groups based on the handover decision criteria used and the methods used to process these.

RSS based algorithms: RSS is used as the main handover decision criterion in this group. Various strategies have been developed to compare the RSS of the current point of attachment with that of the candidate point of attachment. In RSS based horizontal handover

decision strategies are classified into the following six subcategories: relative RSS, relative RSS with threshold, relative RSS with hysteresis, relative RSS with hysteresis and threshold, and prediction techniques. For VHD, relative RSS is not applicable, since the RSS from different types of networks can not be compared directly due to the disparity of the technologies involved. For example, separate thresholds for each network. Furthermore, other network parameters such as bandwidth are usually combined with RSS in the VHD process.

Bandwidth based algorithms: Available bandwidth for a mobile terminal is the main criterion in this group. In some algorithms, both bandwidth and RSS information are used in the decision process. Depending on whether RSS or bandwidth is the main criterion considered, an algorithm is classified either as RSS based or bandwidth based.

Cost function based algorithms: This class of algorithms combine metrics such as monetary cost, security, bandwidth and power consumption in a cost function, and the handover decision is made by comparing the result of this function for the candidate networks. Different weights are assigned to different input metrics depending on the network conditions and user preferences.

Combination algorithms: These VHD algorithms attempt to use a richer set of inputs than the others for making handover decisions. When a large number of inputs are used, it is usually very difficult or impossible to develop analytical formulations of handover decision processes. Due to this reason, researchers apply machine learning techniques to formulate the processes. A literature survey reveals that fuzzy logic and artificial neural networks based techniques are popular choices. Fuzzy logic systems allow human experts’ qualitative thinking to be encoded as algorithms to improve the overall efficiency. Examples of applying this approach into VHD can be found. If there is a comprehensive set of input-desired output patterns available, artificial neural networks can be trained to create handover decision algorithms. It is also possible to create adaptive versions of these algorithms. By using continuous and real-time learning processes, the systems can monitor their performance and modify their own structure to create highly effective handover decision algorithms.