Energy Efficient Design For Hybrid Precoding In Massive MIMO System

Requirements for the mobile communication effectiveness and reliability are getting higher and higher.5G requires capacity increased by 1000 times while energy consumption stays constant.To meet this demand,massive MIMO and millimeter-wave have become the key technology in 5G.In the case of massive MIMO and millimeter-wave,the full-digital precoding technology for MIMO is unrealistic due to the high cost of hardware and a large amount of energy consumption cost by a large number of RF chains and phase shifters.This paper studies the energy efficiency of massive MIMO hybrid precoding technology.First,this thesis establishes an energy efficiency model which includes computational power model.As the base station needs to handle a large amount of data traffic at the baseband,the power used for computing in massive MIMO systems grows rapidly.This paper establishes a computational power model that includes linear processing power,channel estimation power,and channel coding power.Further,we have built an energy efficiency model that includes computational power.As far as we know,this is the first hybrid precoding study that takes into account both the communication power and the computation power.Then,this paper proposes an energy efficiency joint optimization algorithm on the partially-connected structure under the assumption that there is no interference between the users in a single cell.In order to maximize the energy efficiency of multi-user massive MIMO system,we obtain the upper bound of massive MIMO system energy efficiency and use it as the approximation target to find the local optimal solution of baseband precoding matrix and radio frequency precoding matrix.Finally,this paper assumes that there is interference among users in a single cell,and an optimization algorithm of massive MIMO energy efficiency is proposed.In addition,considering the cost is an important factor in the industry,this paper establishes a cost efficiency evaluation model,including achievable rate,hardware cost,and energy cost,and compares the cost efficiency between the partially-connected and fully-connected structure.The simulation results show that the proposed algorithm can effectively improve the energy efficiency and cost efficiency of the massive MIMO communication system.