A Probabilistic Handover Based Energy Consumption Optimization Method for Mobile Terminals

Abstract

The invention discloses an energy consumption optimization method for a mobile terminal based on probability handover. The energy consumption optimization method comprises the steps that in a process of moving in a heterogeneous network of a mobile terminal user, when the mobile terminal user is located in the coverage area of a WiFi network, the WiFi network is used for transmitting data; when the mobile terminal user moves into an area without the WiFi network coverage, the user selectively uses a cellular network to transmit data at a certain probability, wherein the selection probability ensures the minimum energy consumption of the user terminal, and the user selection probability is related to a total data size to be transmitted by each user, a tolerable delay time, the length of a line segment between a movement starting point of the user and a destination, a WiFi network density, a coverage radius of the WiFi network and a user density; due to the handover based on probability, a transmission task can be completed within the tolerable time, and the purpose of minimizing the energy consumption is realized; and compared with the existing seamless handover data transmission scheme, the energy consumption optimization method disclosed by the invention can be used for reducing the energy consumption of data transmission to a large extent.

Description

technical field [0001] The invention belongs to the field of mobile communication, and more specifically relates to a method for optimizing energy consumption of a mobile terminal based on probability switching. Background technique [0002] With the rapid development of the mobile Internet, the popularization of mobile devices and the increase in network speed brought about by 4G networks, mobile network data has shown a blowout growth, and the energy consumed by mobile network data transmission has also reached an immeasurable level. [0003] In a heterogeneous network composed of a WiFi (Wireless Fidelity) network and a cellular network, the WiFi network has higher bandwidth and lower power than the cellular network, but its coverage is limited. Handover between multiple users will increase switching energy consumption; multiple users are under the same cellular base station, and multi-user competition for limited bandwidth also leads to low network transmission efficienc...

AI Technical Summary

Problems solved by technology

[0003] In a heterogeneous network composed of a WiFi (Wireless Fidelity) network and a cellular network, the WiFi network has higher bandwidth and lower power than the cellular network, but its coverage is limited. Switching between them will increase switching energy consumption; multiple users are under the same cellular base station, and multiple users compete for limited bandwidth, which also leads to low network transmission efficiency, and the energy consumption of mobile terminals will increase sharply

[0004] Due to the uncertainty of WiFi deployment, it is impossible to accurately know the specific deployment locations of all WiFi in a large area. The existing technology predicts the time when WiFi will appear through user behavior, and decides to choose a cellular network in an area without WiFi or wait for the next time based on the prediction results. With the advent of WiFi networks, this solution is not universal; some solutions consider using the Markov chain to select the network with the best network status to achieve the purpose of energy saving, but they do not consider the status of multiple users. The bandwidth is related to other users, and the existing switching will consume more transmission energy due to poor network quality caused by user bandwidth competition

[0005] The mobile network status will fluctuate greatly due to network congestion or the movement of mobile devices. Choosing different timings for network transmission has a great impact on the transmission energy consumption of users. The existing solutions for reducing transmission energy consumption in heterogeneous networks are lacking. Consideration of Cellular Network Conditions and Random Mobility of Users in Current Environment

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