Adaptive Knn-based algorithm for network selection in next-generation networks

Abstract

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users’ experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods.

A machine learning access network selection in a heterogeneous wireless environment

“…These values are presented in Table 4. 17 In order to evaluate the efficacy of our proposition and make a comparison with conventional MADM methods (SWM, TOPSIS, WEW, and AHP), we carried out three scenarios: VoIP, video calls, and best effort.…”

“…After setting up all the inputs for Algorithm 1, which include the weight vector and the benchmark vector 3, we can proceed to utilize Table 2 in order to generate QoS values for all the involved RATs. These values are presented in Table 4 17 . In order to evaluate the efficacy of our proposition and make a comparison with conventional MADM methods (SWM, TOPSIS, WEW, and AHP), we carried out three scenarios: VoIP, video calls, and best effort.…”

“…A machine learning approach based on a modified version of the K‐ nearest neighbour (KNN) algorithm is proposed in Reference 17 The idea was to create a vector of the best possible QoS for a user, allowing a smooth performance for each possible service (VoIP, video, and best‐effort services). The obtained results show that their solution surpasses the MADM methods and eliminates the rank reversal problem.…”