Enhanced Handoff Decision Making for Application-Aware Environment by Using Blended Approach

Abstract

Seamless integration of the new and existing networks is required to create next generation wireless networks capable of providing wide range of services coupled with uninterrupted connectivity and high speeds offering unprecedented quality of service to users. Smooth and proficient handoff techniques are important to achieve optimal network performance in heterogeneous wireless environments. This paper proposes Multiple Criteria Decision Making (MCDM) techniques of both non-fuzzy (Technique for Order of Performance by Similarity to Ideal Solution (TOPSIS)) and fuzzy (Fuzzy TOPSIS (FTOPSIS)) type for network ranking. The proposed algorithm uses priority ranking to assign weights to six parameters by using Fuzzy Analytic Hierarchy Process (FAHP). Further, TOPSIS and FTOPSIS techniques are compared for various traffic classes and used for network selection in the prevalent network conditions. The results show that contingent on the traffic classes, the FTOPSIS selects the ideal network with diminished ping-pong rate and call blocking probability as compared to TOPSIS while accounting for the uncertain environment.

Distributed Reinforcement Learning–based Seamless Multi-Connectivity Solution for Heterogeneous 5GNR and Wi-Fi Networks

“…The proposed solution is applied to enhance wireless coverage and network capacity, and energy efficiency. Similarly, a seamless communication solution is proposed in [17,18] to enhance the quality of service (QoS) of heterogeneous wireless networks (HWNs) involving various applications using licensed spectrum. However, the solutions proposed in [16][17][18] challenges when scaling up to a larger network or a more complex environment, especially since it relies on the use of a single radio spectrum band, which may be insufficient, causing service outages as user requests increase.…”

“…Similarly, a seamless communication solution is proposed in [17,18] to enhance the quality of service (QoS) of heterogeneous wireless networks (HWNs) involving various applications using licensed spectrum. However, the solutions proposed in [16][17][18] challenges when scaling up to a larger network or a more complex environment, especially since it relies on the use of a single radio spectrum band, which may be insufficient, causing service outages as user requests increase. Furthermore, an approach based on utility-based handover decisions involves selecting the network with a lower load is proposed in [19] to provide better performance for vehicular network, particularly in terms of throughput and handover rate.…”

“…It provides a feedback to the agent, influencing its decisionmaking process throughout the system operation (lines 8-13). Once the Q-values are computed, the agent can use them to choose the action with the highest Q-value, indicating the optimal choice in that particular state (lines [14][15][16][17][18]. This selection process, based on maximizing the Q-values, allows the agent to make more effective and informed decisions.…”

NLADSS: Design of Connectivity as a Service (CaaS) Model using Node-Level Augmentation & Dynamic Sleep Scheduling for Heterogeneous Wireless Network Handoffs

“…These algorithms are further extended by the work in [12][13][14], wherein security-based models, fuzzy analytic hierarchy process, and secure handoff for mobile-based cloud deployments is proposed. These models assist in improving security during handoffs, thereby reducing attack probability, and increasing overall QoS of the heterogeneous network.…”