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Research On Energy Efficiency Of Massive MIMO Communication System

Posted on:2021-01-26Degree:MasterType:Thesis
Country:ChinaCandidate:R Q ZhangFull Text:PDF
GTID:2428330620965136Subject:Information and Communication Engineering
Abstract/Summary:PDF Full Text Request
In the past few decades,the number of wireless devices that can access the Internet has increased significantly.Currently,wireless networks can connect and manage billions of devices.Each device used will basically install a series of applications,such as office software,WeChat,video client and so on.It is usually necessary to connect the network to meet the needs of online use,so high throughput is required.As a result,the throughput of the wireless system needs to be continuously expanded to meet the needs of the number of wireless devices.Since the energy consumption problem is widespread in wireless communication system,the development of green communication is a new way to solve this problem.Therefore,the future wireless system must meet three key elements:(1)has high throughput;(2)can provide services to many users at the same time;(3)uses less energy-efficient consumption.At present,massive multiple input multiple output(MIMO)technology can meet the above requirements.The main advantage of this technology is that a large number of collocated or distributed antennas can be equipped at the cell base station to meet the user's needs in the same time domain and frequency domain.When the antenna at the base station is a massive antenna array,the channel vector between the user and the base station is nearly orthogonal to each other,and linear processing is completely feasible.And because of its multiplexing gain and array gain performance,it can achieve huge throughput and reduce the energy efficiency of the system.In addition,it is possible to provide a unified and high-quality service to all users through a simple power control scheme.The research on massive MIMO system involves many aspects,and many experts and scholars at home and abroad have done some research work.Therefore,this paper mainly analyzes and studies the energy efficiency of massive MIMO communication system.Firstly,the phenomenon of antenna hardware damage in the transmission antenna of massive MIMO system is introduced.The uplink and downlink are respectively modeled and analyzed,and the effects of hardware damage are modeled as sending and receiving distortion noise.The effect of hardware damage on each antenna is equivalent to an independent zero-mean Gaussian noise,and its variance is proportional to the signal power on the antenna.Then,the system capacity under hardware damage is analyzed and simulated,and the influence of hardware damage on the system is very obvious,which provides some reference for related research.Secondly,the energy efficiency of uplink in massive MIMO system is studied.Jensen's inequality,Wishart matrix distribution and other mathematical tools are used toobtain the expression of the user's reachable rate on the uplink.On the basis of obtaining the accessibility rate and combining with the system power consumption model,the system energy efficiency optimization model is determined.Since the energy efficiency model of the system is a fractional expression,it needs to be transformed and then solved.In the process of fractional conversion,Dinkelbach's fractional optimization theory and Lagrangian function are introduced to obtain the optimal solution of the system's energy efficiency,and the transmission power and the number of base station antennas corresponding to the optimal solution are obtained.Finally,the energy efficiency optimization of downlink in massive MIMO system is studied.The number of antenna selection scheme,transmission power optimization scheme and number of users selection scheme are put forward.And some simulation analyses are carried out.It is concluded that the system energy efficiency has an optimal solution under certain constraints.
Keywords/Search Tags:massive MIMO, hardware impairment, system capacity, energy efficiency model, fractional optimization
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