Static and dynamic combined millimeter wave beam resource allocation and optimization method

Abstract

The invention discloses a static and dynamic combined millimeter wave beam resource allocation and optimization method, and belongs to the field of wireless communication. Aiming at a static hot spot region scene, a distributed multi-base-station multi-user cooperation framework based on a matching theory is applied, a bilateral matching utility function capable of dynamically sensing the return capability of millimeter wave base stations and beam interference in the base stations and among the base stations is constructed, and efficient distribution of millimeter wave multi-beams is realized on the premise of low calculation complexity. A theoretical convex upper bound and a theoretical convex lower bound of the optimal transmitting power are acquired by derivation with an optimization strategy of the millimeter wave beam transmitting power, and network throughput is optimized. for a dynamic vehicle network scene, in a V2I stage, a low-complexity beam selection scheme is provided to realize preliminary caching of vehicle contents in a coverage range of a base station. In the V2V stage, a transmitting and receiving vehicle cooperation method based on coalition games is provided, and efficient distribution of differentiated content is achieved.

Description

technical field [0001] The invention relates to millimeter wave beam resource allocation technology in the field of wireless communication, in particular to a millimeter wave beam resource allocation and optimization method combining static and dynamic. Background technique [0002] In recent years, the rapid increase in the number of smart terminals and the continuous emergence of new services have made wireless communication face various challenges, but it has also brought greater opportunities and broader application scenarios. According to the forecast of the ITU-R (International Telecommunication Union-Radiocommunication Sector) project alliance, by 2030, the global mobile communication traffic will reach 5zettabytes per month, and the personal data rate will reach 100Gbps. However, only relying on spectrum resources below 6 GHz can no longer meet the urgent needs of new services such as virtual reality, augmented reality, and tactile Internet for large bandwidth and hi...

AI Technical Summary

Problems solved by technology

[0008] First, for scenarios in static hotspot areas, most current resource allocation schemes only consider the ideal fiber backhaul deployment, and do not consider the geographical limitations of fiber deployment

The communication architecture of the wireless backhaul has not been considered, and the allocation problem of the mmWave base station under the limited capacity of the backhaul link cannot be solved

[0009] Second, the impact of the differentiated backhaul capabilities of multiple mmWave base stations on the traffic load balance between the backhaul and access links is not fully considered, resulting in two situations: 1) When the backhaul capability of the mmWave base station is much larger than that of the base station When the data rates of all current access links are combined, it will cause waste of backhaul spectrum resources and low resource utilization; 2) For millimeter-wave base stations with good quality access links, poor backhaul capabilities will not be able to satisfy current access users data rate requirements, resulting in traffic overload

Therefore, the number of failed access links increases, which further leads to a decrease in network spatial multiplexing gain and network throughput

[0010] Third, in the millimeter-wave multi-beam transmission scenario, the beam interference within the base station and between base stations has not been fully modeled and analyzed, especially when the millimeter-wave base station and users are densely distributed, the inter-beam interference is more obvious

[0011] Fourth, for dynamic vehicle networks, existing methods only optimize millimeter-wave resource allocation in millimeter-wave V2I or V2V modes, and have not considered the joint optimization of communication links inside and outside the coverage of millimeter-wave base stations.

In addition, the content needs of vehicle differentiation cannot be met

From the perspective of computational efficiency, the existing centralized resource allocation algorithm has the disadvantage of high computational complexity, which cannot be applied to vehicle networks with highly dynamic changes in network topology.

[0012] Fifth, the traditional centralized resource allocation algorithm has the problem of large amount of calculation and high complexity, resulting in low actual calculation efficiency

[0013] However, in the static hotspot scenario, the existing resource allocation methods only aim at the ideal situation where the capacity of the backhaul link is not limited, and do not solve the actual allocation problem under the limited backhaul of the mmWave base station, and do not consider the multiple mmWave Differentiated backhaul capabilities between base stations

At the same time, when millimeter-wave base stations and users are densely distributed, severe inter-beam interference will lead to a decrease in network performance gain. However, the existing resource allocation methods only consider beam interference within millimeter-wave base stations or between base stations, and lack the ability to target mm-wave Complete analysis of multi-beam interference;

[0014] Furthermore, in the dynamic vehicle network, the existing mmWave resource allocation methods only consider V2I (Vehicle to Infrastructure) or V2V (Vehicle to Vehicle) transmission alone, and have not yet achieved end-to-end optimization of content distribution and transmission, and at the same time cannot satisfy vehicle differences. In addition, the traditional centralized resource allocation algorithm has the disadvantage of high computational complexity, and cannot cope with the huge amount of calculation caused by multi-beam multi-user interference cancellation, and the practical application scalability is not high

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