Research On Access Control Strategy In LAA

With the development of mobile communication and ever-increasing use equipment and applications,the existing spectrum resources have been unable to meet the increasing demand for user communication.Particularly,licensed spectrum resources have become very crowded.In order to further improve the overall capacity of a cellular network,enhance the user data transmission experience,and meet users,requirements for data transmission rate and delay performance,3 GPP proposes a Licensed-Assisted Access(LAA)technology to address the problem of current spectrum scarcity.LAA technology extends the Long Term Evolution(LTE)working spectrum to unlicensed bands and enables the LTE licensed spectrum to assist the data transmission on the unlicensed spectrum.However,the us'e of unlicensed spectrum will interfere other wireless communication systems,such as WiFi,currently operating in the same band.Meanwhile,LAA networks deployed by different operator in unlicensed spectrum also interfere with each other when there is data transmission.For this reason,3 GPP standardizes a listen-before-talk(LBT)mechanism to enable fair deployment of resources among different sending nodes in the same unlicensed band.However,in practical data transmission,the radio environment and the traffic load change continuously.And the evolved nodeB(eNB)buffer also has a maximum length limitation due to some real-time service requirement.The existing LBT exponential backoff strategy cannot flexibly adjust the contention window size according to the service scenario,and the coexistence performance and fairness can still be improved.Therefore,this thesis mainly studies the LAA access control strategy in the unlicensed band coexistence scenario.The main contribution of this thesis is as follows:First,based on the LAA wireless channel access procedure proposed in 3GPP Release-13,a Markov model of LAA contention window adaptation strategy with static backoff and exponential backoff procedure are established,respectively.According to the state transition equations,the steady-state probability of different Markov states is calculated.Furthermore,based on these probabilities,the successful transmission probability,system throughput and system waiting time of LAA and WiFi systems can also be obtained.Finally,according to the simulation results,the coexistence performances of LAA-LAA and LAA-WiFi scenarios in the unlicensed band are analyzed.Second,according to the former established Markov model and coexistence performance analysis,the exponential backoff strategy in the existing protocols cannot dynamically adjust the contention window size based on the traffic differentiation between LAA eNBs,and the WiFi transmission is greatly disturbed by LAA system.Thus,this thesis proposes a dynamic contention window adjustment strategy based on Q-learning algorithm in reinforcement learning and simulated annealing algorithm(SAQ algorithm).Particularly,during the interaction between LAA eNBs and the environment,the contention window size of each eNB is dynamically adjusted according to the changing traffic load to achieve the predefined LAA target throughput.In the strategy exploration stage,the simulated annealing algorithm is introduced to further enhance the whole learning efficiency.After several learning interactions,the algorithm converges to the optimal contention window adaptation strategy.Finally,simulation results validate that the proposed strategy not only satisfies the traffic differentiation among different LAA eNBs,but also provides more transmission opportunities for WiFi system to improve the coexistence fairness.Third,considering real-time requirements of practical system service,the maximum buffer length of each eNB needs to be constrained.This thesis proposes a dynamic contention window adjustment strategy based on constrained stochastic game theory.A non-cooperative stochastic game model is constructed based on the LAA channel access procedure.A set of linear programming equations is introduced to optimize both LAA individual and cooperative throughput under certain buffer length constraint.An iterative algorithm is adopted to obtain the optimal solution of the linear programming.It has been proved that the optimal solution is the Nash equilibrium of the stochastic game,which is also the optimal contention window adaptation strategy.Finally,simulation results reveal that the proposed contention window adjustment strategy can improve the LAA individual and cooperative throughput while guaranteeing LAA real-time transmission performance.