| Since Multiple Input Multiple Output(MIMO)technology was proposed,it has attracted more and more attention because it can increase the spatial degrees of freedom and enhance the performance of mobile communication system.However,with the rapid development of the intelligent terminal and its applications,the huge data traffic generated by the massive network terminal has brought great challenges to the existing mobile communication network.Conventional MIMO technology has been gradually unable to meet the demand.Massive MIMO technology as an extension of the traditional MIMO technology,inherited the advantages of traditional MIMO technology,but also greatly improves the spectrum efficiency for the system,significantly reduces the energy consumption,hardware cost and air interface delay,etc.,to become one of the most popular technologies for the next generation mobile communication system.But the current Massive MIMO technology is practical poorer,there are still a lot of problems need to be solved.Among them,with the increase of the number of antennas and of users,the overhead for the downlink training and feedback required by the Massive MIMO system will increase dramatically.It is a major problem that how to reduce these costs in the practical application for Massive MIMO technology.Joint Spatial Division and Multiplexing(JSDM)downlink transmission scheme can effectively reduce the overhead in channel estimation of Massive MIMO system operating in Frequency Division Duplex(FDD)transmission mode,whose main idea is to utilize the spatial correlation of the channel to partitioning users within the cell into groups,and then to precoding in two stages for each user groups respectively.Thereinto,the user grouping is the first step in the implementation of JSDM scheme,and the traditional JSDM scheme is carried out in a single cell scenario,so this thesis focuses on the research of JSDM user grouping technology and multi cell interference coordination technology.In the existing JSDM user grouping algorithms,K-means grouping algorithm is simple,effective and widely used.But the algorithm chosen initial centers of user groups randomly,it has great influence on final group result,and easy to generate local optimal solution.To solve this problem,this thesis proposes an improved K-means user grouping algorithm,using the maximum distance method to select the initial value,and measuring the differences of users with weighted likelihood function,which is to replace the Euclidean distance function in the original algorithm.After several iterations,the algorithm has convergence,and the users can be fully group.However,in the multi cell scenario,the improved grouping algorithm cannot suppress inter cell interference.Therefore,this thesis proposes a JSDM interference coordination scheme for multi cell.The scheme exploits channel null space precoding design principle based on JSDM scheme,which the useful signals of each user in the cellular system was mapped to the rest of the all users channel null space,so that it can effectively suppress the multi cell interference.By modifying the corresponding module in the existing simulation platform of Long Term Evolution(LTE)system,the performance of the improved user grouping algorithm and interference coordination scheme proposed in this thesis are verified and analyzed in depth.The simulation results show that,in the Massive MIMO network scenario,JSDM grouping algorithm can effectively solve the local optimization causedby the traditional K-means algorithm with random initialization,and the grouping is more thorough,so it also improves the performance of JSDM transmission scheme almost 20%.In addition,the null space interference coordination method based on the JSDM scheme can effectively suppress inter cell interference,and the maximum cell edge 5%spectral efficiency and the overall throughput of the system are improved around 2.2 times,respectively. |
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