5G Technology Optimization Based On Location Awareness

Compared with past mobile communication systems,the fifth-generation(5G)system will serve wireless applications with rapidly changing content and thousands of mobile terminals.The demand for high-speed transmission and the lack of spectrum resources will bring tremendous pressure to 5G mobile communication systems.In order to alleviate the problem of spectrum resource tension,many scholars put thier emphasis on the cognitive radio technology.Cognitive radio technology allows the primary users and secondary users to dynamically access the spectrum to achieve efficient utilization of spectrum resources.In addition,5G system spectrum resources can also be migrated to higher frequencies to obtain rich bandwidth resources.Therefore,millimeter wave technology has been included in the research of 5G system as well.This thesis optimizes the power allocation in cognitive radio and millimeter wave beam training based on location awareness.This thesis first introduces the characteristics of cognitive radio system and specifically analyzes spectrum sensing and spectrum sharing methods.Then we introduce the characteristics of the Massive Multi-Input Multi-Output(Massive MIMO)system and the research status of beamforming and beam training.Then we introduce the wireless localization technology and analyze the square position error bound(SPEB),which can be used as a criterion for estimating localization error.In the cognitive radio system,this thesis proposes a location-aware power allocation algorithm,which fully considers the trade-off between localization performance and localization interference.We seek to joint localization and transmission power in order to maximize the transmission rate of secondary user under the power budget,localization performance and primary users' outage constraints.The proposed optimization problem is nonconvex and highly coupled,which is challenging to solve.To simplify the problem,we apply a algorithm based on concave-convex procedure(CCCP).The simulation results show that the algorithm can quickly complete power allocation and effectively improve the communication rate.Relying on antenna arrays and beamforming technology,millimeter wave can propagate in the form of extremely narrow beams to compensate for transmission losses and reduce inter-signal interference.However,the beam misalignment will cause signal gain attenuation even link outage.For the initial access phase,we propose a fast beam training algorithm based on location awareness.In this thesis,we first select several beam codebooks as candidate codebooks based on the location information.Then we complete the best codebook selection based on Thompson Sampling.Considering the localization error,this thesis calculates the distribution of the true location based on the equivalent Fisher Information Matrix.The simulation results show that the beam training algorithm proposed in this thesis can quickly select the best beam codebook.