LTE (long term evolution) resource scheduling method

Abstract

The invention discloses an LTE (long term evolution) resource scheduling method. A neighbor node list with semi-diameter of R is maintained for each UE (user equipment) in the network and composed of two fields including UE_ID (identity) and UE_RS-SINR (signal to interference and noise ratio), to be specific, the UE_ID is a neighbor UE identity, and the UE_RS-SINR is a reference signal to noise ratio of the neighbor UE; if the reference signal to noise ratio of one target UE is smaller than a threshold value, a neighbor UE with the maximum reference signal to noise ratio is selected as a relay UE, the UE_ID of the relay UE is packaged in an MAC (media access control) layer and then reported to a base station, downlink data of the base station is transmitted through a link IEEE 802.11, and the data are transmitted from the relay UE to the target UE during scheduling of the base station.

Description

technical field [0001] The invention relates to the technical field of communication, in particular to an IEEE 802.11 technology-assisted LTE resource scheduling method. Background technique [0002] The LTE network can provide a downlink rate of more than 100M, and can meet the requirements of various services. With the increasing demand of mobile users for high-speed data services, LTE networks have begun large-scale construction and application. Currently common LTE scheduling algorithms include maximum carrier-to-interference ratio scheduling algorithm (MAX C / I), round robin scheduling algorithm (RR), proportional fair scheduling algorithm (RF), and dynamic scheduling algorithm. [0003] Various factors such as capacity, fairness and inter-cell interference need to be considered comprehensively in the above scheduling algorithms. The capacity of LTE is related to the channel quality between eNodeB (base station) and UE (user equipment). If the channel quality is good, ...

AI Technical Summary

Problems solved by technology

The capacity of LTE is related to the channel quality between eNodeB (base station) and UE (user equipment). If the channel quality is good, the transmission rate will be high, otherwise, the transmission rate will be low.

The maximum load-to-interference ratio scheduling algorithm fully considers the correlation between network capacity and channel quality; the round-robin scheduling algorithm focuses on the fairness of user scheduling; proportional fairness takes into account the advantages of both, but it still cannot make full use of high-quality channels to improve the overall network quality

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