An integrated AHP‐ELECTRE and deep reinforcement learning methods for handover performance optimization in an LTE‐A networks

Abstract

Emblematic growth of telecommunications networks has also produced high data rates and low user equipment (UE) latency. Also, the third generation partnership project launched long term evolution (LTE) and LTE advance (A), which supports different types of network traffic including video, voice, and so on. A handover is needed to provide semantic interloper ability is the process to move the UE power from serving evolved node B (eNB) to the subsequent eNB without interruption. Consequently, high quality of service (QoS), reliability relies on a smooth execution. If eNBs cannot be selected from available eNBs in an optimized manner to transfer during UE mobility, the criteria for quality of service can be violated. In order to accommodate QoS requires to be more configured for smooth synchronization, eNB range, and trigger points. This article aims to automate handover by means of sophisticated program based on the analytical hierarchy process (AHP) with ELECTRE and deep reinforcement (DR) learning methods. Network performance is scrutinized and simulation is utilized to visualize the proposed scheme. The experiments demonstrated that the proposed system helps to reduce the handover failure rate and handover ping‐pong. Thus, the proposed AHP‐ELECTRE‐DR‐learning method has achieved better results in delay, throughput, fairness index, and packet loss than the existing works like AHP‐TOPSIS‐Q‐learning for handover optimization in LTE‐A, auto tuning optimization for reducing frequent handovers and handover failure in LTE‐A, and particle swarm optimization with adaptive neuro‐fuzzy inference system for LTE and LTE‐A networks, respectively.

A Novel Epidemic-Based Video Diffusion Strategy Using Awareness of Sociality and Mobility in Wireless Networks

“…The frequent interaction and the close social relationships between users can speed up the diffusion process and promotes the diffusion scale. Increases in speed and scale of diffusion trigger a run on a bank for the bandwidth resources, which results in an overload of networks [11][12][13][14][15]. Different levels of network congestion caused by the violent squeezing of bandwidth resources bring long delays and high packet loss rate (PLR), which reduces the quality of experience (QoE) of users such as distortion or discontinuity of videos [16][17][18][19].…”