Research On Interference Suppresion Technologies For Massive MIMO Systems

Faced with the explosive growth of data traffic and the sharp increase in user scale,future mobile communication systems have put forward higher requirements in terms of system capacity,peak link transmission rate and delay.Massive Multiple-Input Multiple-Output(Massive MIMO)has become a hotspot in recent years because it can effectively improve data transmission rate and spectrum efficiency.With the development of mobile communication from 4G(the Fourth Generation)to 5G(the Fifth Generation),the number of antennas has increased dramatically.Massive MIMO technology can further meet system capacity and communication quality based on traditional multi-antenna technology,and is regarded as one of the most potential physical layer key technology for 5G mobile communication.However,a large number of studies have shown that with the increase of the number of Massive MIMO antennas,pilot contamination will be more serious,especially when the number of antennas tends to infinity,pilot contamination is the main constraint of the performance of Massive MIMO systems.Therefore,this thesis focuses on the pilot contamination in the interference of Massive MIMO system,the mainly studies are summarized as follows:(1)Considering that the pilot contamination can be suppressed by reducing the number of reuses of pilot sequences,a dynamic pilot allocation scheme based on pilot multiplexing coefficients in Massive MIMO systems is proposed.The scheme divides the cell into a central area and an edge area based on the cell radius coefficient,and introduces a concept of pilot multiplexing coefficients to identif y the number of times of using each pilot in the current cell.When the pilot sequence is allocated to the target cell,the allocation scheme is adjusted according to the dynamic variation of the pilot multiplexing coefficient to minimize the pilot multiplexing times,so as to reduce the pilot contamination.The simulation results show that the proposed algorithm can improve the uplink reachable rate of the system,and the gain becomes more obvious with the increase of the number of antennas,which is more suitable for Massive MIMO systems.(2)Consider reducing the pilot contamination from the spatial dimension,using the Direction of Arrival(DOA)to distinguish between target user and interfering users to achieve pilot contamination suppression.In order to accurately estimate the DOA of the signal,this thesis proposes a DOA estimation algorithm based on predictive iterative sear-ch.Considering that the target user's DOA is sparsely distributed in the spatial dimension,the algorithm uses the compressed iterative-based predictive iterative search estimation algorithm to estimate the target user's DOA.The simulation results show that the proposed algorithm can improve the gain of the target user and reduce the pilot contamination.As the number of antennas increases,the gain of the algorithm increases.In the user motion scenario,the running time of the proposed algorithm is 20%of that of the traditional one,the algorithm complexity is significantly reduced.(3)Considering that pilot contamination can be suppressed by reducing the power of interfering users,a scheme for reducing Massive MIMO pilot contamination using power control is proposed.The non-cooperative game Koskie-Gajic power control algorithm is combined with the previously proposed predictive iterative search DOA estimation algorithm to reduce the pilot contamination generated by reducing the user's transmit power under the same link transmission effect.The simulation results show that the proposed algorithm can achieve the same transmission effect as the average power algorithm with lower transmit power,the combined algorithm can effectively suppress pilot contamination and achieve higher target user's gain.