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Wireless prediction resource distribution method based on rough information

A resource allocation and wireless technology, applied in wireless communication, electrical components, etc., can solve problems such as the difficulty of predicting resource allocation methods, and achieve the effects of improving network throughput, improving QoS, and reducing solution complexity

Active Publication Date: 2018-11-02
BEIHANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Therefore, the existing predictive resource allocation methods are difficult to implement

Method used

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  • Wireless prediction resource distribution method based on rough information
  • Wireless prediction resource distribution method based on rough information
  • Wireless prediction resource distribution method based on rough information

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Experimental program
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Embodiment

[0078] scene such as figure 2 As shown, among them, there are N in the system b cells, and the radius of each cell is R b , there is a base station in the center of each cell, and the height of the base station is h b , equipped with N t root antenna. The large-scale channel gain in each frame of the prediction window is regarded as constant, and the small-scale channel gain in each time slot of each frame is regarded as constant.

[0079] Resource allocation and transmission are performed in units of time slots; multiple users move within the system and may initiate VoD service requests.

[0080] At the beginning of the prediction window, the prediction node first predicts the cell-level information, including the median of large-scale gains in each cell within the prediction window Average request arrival rate of VoD service λ m , the average access time of users requesting VoD services and the average available bandwidth of each base station in the prediction wind...

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Abstract

The invention discloses a wireless prediction resource distribution method based on rough information, and belongs to the technical field of the wireless communication. The method comprises the following steps: a prediction node responsible for predicting information is firstly user for predicting information related to the prediction resource for a future period window at a multi-cell system, andthe information comprises user moving trail and each base station network load and like; and then information is converted into a large-scale channel threshold of each user and an available bandwidththreshold of each base station to notify to each base station, the base station performs resource distribution on a user requesting the non-real-time service in the coverage range in real time, moreresources are distributed to the user with the current large-scale channel gain greater than the large-scale channel threshold and with the maximum data rate, wherein the available bandwidth, be aboutto the base station, of the user is less than the available bandwidth threshold, and the transmission is performed so as to achieve the aim of improving the system throughput and the user quality ofservice (QoS).

Description

technical field [0001] The invention relates to a wireless prediction resource allocation method based on rough information, and belongs to the technical field of wireless communication. Background technique [0002] In order to support the explosive growth of mobile communication service demands, one of the main technologies in the 5G cellular network is to improve spectrum efficiency through network densification. Although improving spectral efficiency is an effective method, long-term observations have shown that due to the temporal and spatial variation of mobile communication traffic, the utilization rate of network resources in base stations is very low. [0003] The dynamic nature of mobile communication services stems from user behaviors that have long been considered random. However, the rise of big data analysis in recent years shows that user behavior is predictable. For example, network load and user trajectories can be predicted by machine learning methods, se...

Claims

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Application Information

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IPC IPC(8): H04W72/04H04W72/08
CPCH04W72/53H04W72/542
Inventor 郭佳佘昌洋杨晨阳
Owner BEIHANG UNIV