Research On Codebook Design And Capacity Optimization For Massive MIMO

Along with the popularization of smart devices and the development of broadband mobility,people put forward higher requirements on availability and reliability of mobile communication systems.Multiple-input multiple-output(MIMO)is recognized as the best approach to achieve the above goals.In the practical system,however,due to the severe inter-cell interference and higher data transmission requirements,the research work focuses on the Massive MIMO technology.Compared with the traditional MIMO,Massive MIMO technology can achieve more diversity gain,and significantly increase the system capacity and energy efficiency by adopting large scale antenna array.Besides,it can not only reduce the computational complexity of the interference elimination effectively,but also achieve high-speed and high stability of the data transmission.Therefore,this thesis focuses on the following innovative content based on the Massive MIMO system:Firstly,in order to reduce system feedback overhead caused by the increasing of antennas in limited feedback pre-coding of frequency division multiplexing system,this thesis proposes a sub-codebook selection algorithm based on the design method of multi-level codebook.On the basis of the superposition of codebook,this algorithm assigns multiple codebooks to both transmitter and receiver,which breaks the limits of a single codebook and theoretically build a method of cyclic traversal.When the code is selected,these codebooks are recycled by the receiver,which is equivalent to the total number of code in the full feedback codebook.It improves the performance of the system under the same feedback overhead.Through the simulation experiment,it is verified that the sub-codebook selection algorithm has a certain performance gain compared with a single codebook and can effectively reduce the system's feedback overhead.Secondly,due to channel spatial correlation and accurate channel estimation are affected by the increasement of antennas,in the meantime,current capacity analysis has high complexity in the practical system.Thus,the system capacity of Massive MIMO is analyzed under different channel states with characteristics of channel hardening and Rand Matrix Theory(RMT).The simulation results show that the approximate capacity is almost the same as that of the precise system,and the computational complexity is reduced in the meantime,which lays the foundation for the following system optimization.Finally,the energy efficiency optimization problem is further studied based on the approximate capacity of the Massive MIMO system under imperfect correlation channel.In order to solve it,an optimization algorithm for combined transmit power and pilot power is discussed in the thesis.It compares with the other two energy efficiency optimization algorithms through the simulation experiment.The simulation results show that the energy efficiency of the proposed method is superior to that of the other two algorithms.