Research On Pilot Contamination Mitigation Algorithms In Massive MIMO Systems

With the development of wireless communication networks and the emergence of various emerging services,the number of mobile users and mobile devices has increased rapidly,and the demand for mobile data traffic has exploded.Massive MIMO technology uses its antenna array gain to support spatial multiplexing transmission for more users,which can greatly improve spectral efficiency and energy efficiency of communication systems.Massive MIMO technology has become one of the key technologies of 5 G by virtue of many advantages,but due to the limited number of orthogonal pilots,users in neighboring cells multiplexing non-orthogonal pilots between cells will cause interference,affecting accurate channel estimation and resulting in pilot contamination problems.The topic of this thesis is supported by the enterprise scientific research cooperation project:"Key technology research in 5G wireless air interface".This thesis focuses on the pilot contamination mitigation of massive MIMO systems,and proposes a pilot allocation based pilot contamination mitigation algorithm,and a joint power control and pilot allocation based pilot contamination mitigation algorithm.1)This thesis reviews the current research status of pilot contamination mitigation in massive MIMO systems.Firstly,the system model,the uplink and downlink transmission process,the advantages and existing problems of the massive MIMO systems are expounded.Secondly,the causes and the impacts on the system performance of the pilot contamination problem are analyzed.Finally,the research status of pilot contamination mitigation technology in massive MIMO systems is reviewed.2)Aiming at the problem that the existing interference-based pilot allocation algorithms in massive MIMO systems does not consider the cumulative interference,a pilot allocation algorithm based on hypergraph coloring is proposed.The proposed algorithm uses the large-scale fading factor to construct the interference relationship between users,and divides the interference relationships between users into two types:independent interference and cumulative interference.The cumulative interference strength is defined as the minimum achievable rate when users in neighboring cells use the same pilot,and the two interference relationships are further modeled as edges and hyperedges in the hypergraph respectively,thereby converting the pilot allocation problem into hypergraph coloring problem.Then the hypergraph coloring algorithm is proposed to realize the pilot allocation;the simulation results show that compared with the traditional graph coloring algorithm,the proposed algorithm not only improves the massive MIMO system uplink sum rate and mitigates the pilot contamination,but also improves the minimum achievable rate of users in the system.3)On the basis of pilot allocation,in order to further mitigate pilot contamination,considering the influence of pilot transmission power on system performance in massive MIMO systems,a joint power control and pilot allocation based pilot contamination mitigation algorithm is proposed.First,an optimization problem model that maximizes the system sum rate is given.Considering that the optimization problem is a combinatorial optimization problem and the optimal solution cannot be obtained,this thesis decomposes the original optimization problem into the pilot allocation sub-problem and the power control sub-problem.This thesis uses a distributed Q learning algorithm to solve the pilot allocation sub-problem with given power.In the case of given pilot allocation,the power control sub-problem is transformed into a convex optimization problem by successive convex approximation algorithm.Then the algorithms of two sub-questions loop iteratively to obtain the approximate optimal solution of the original optimization problem.The simulation results verify that the proposed algorithm has good convergence.Compared with the pilot allocation algorithms based on hypergraph coloring,the Q-learning based pilot allocation algorithm improves the system uplink sum rate,and verifies that the proposed algorithm can further improve the performance of the pilot learning algorithm based on Q learning by combining power control,and effectively mitigate pilot contamination.