User Selection for 5G IoT System with Correlated Channel under the Condition of Limited Feedback
In order to solve the problem of existing user scheduling algorithms being unable to reach the upper bound of user capacity in a coherent environment of multiple input multiple output(MIMO)channels,a new user selection method is proposed.Existing algorithms are mostly based on the assumption that MIMO channels are incoherent,but coherent channel could be existed in practice.Meanwhile,in the multi-user MIMO system,the user can only feedback part of channel state information to the base station,which results in insufficient consideration of the residual inter-user interference.Firstly,the influence of the Internet of things(IoT)channel coherence in the fifth generation of mobile communications system(5G)on the upper limit of user capacity and transmission rates are analyzed,and the user capacity elasticity in this case is indicated.Then,the low-complexity transmission rate under limited feedback is deduced,and the codeword selection criterion based on maximizing the user's achievable data rate is designed.For multi-user MIMO limited feedback systems with channel coherence,a user selection method based on reinforcement learning is proposed.The proposed selection method can avoid recalculating the achievable rate in each cycle,and the times for calculating the rate are only related to the number of scheduled users.The experimental results show that when the system is in the coherent channel environment,the proposed algorithm can schedule more users,so as to improve the system throughput.
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