为解决当多个用户通过一个公共信道与其他用户进行通信时必须采用多址技术的问题。在详细阐述了时隙ALOHA系统和连续时间ALOHA系统以及非坚持CSMA(Carrier Sense Multiple Access)控制协议的分析的同时,分别得出了3种系统的平均成功队...为解决当多个用户通过一个公共信道与其他用户进行通信时必须采用多址技术的问题。在详细阐述了时隙ALOHA系统和连续时间ALOHA系统以及非坚持CSMA(Carrier Sense Multiple Access)控制协议的分析的同时,分别得出了3种系统的平均成功队长和系统吞吐量的数学表达式,并通过仿真实验验证了理论分析的正确性。展开更多
The fifth generation （5G） wireless communication is currently a hot research topic and wireless communication systems on high speed railways （HSR） are important applications of 5G technologies. Existing stud- ies ...The fifth generation （5G） wireless communication is currently a hot research topic and wireless communication systems on high speed railways （HSR） are important applications of 5G technologies. Existing stud- ies about 5G wireless systems on high speed railways （HSR） often utilize ideal channel parameters and are usually based on simple scenarios. In this paper, we evaluate the down- link throughput of 5G HSR communication systems on three typical scenarios including urban, cutting and viaduct with three different channel estimators. The channel parameters of each scenario are generated with tapped delay line （TDL） models through ray-tracing sim- ulations, which can be considered as a good match to practical situations. The channel estimators including least square （LS）, linear minimum mean square error （LMMSE）, and our proposed historical information based ba- sis expansion model （HiBEM）. We analyze the performance of the HiBEM estimator in terms of mean square error （MSE） and evaluate the system throughputs with different channel estimates over each scenario. Simulation results are then provided to corroborate our proposed studies. It is shown that our HiBEM estimator outperforms other estimators and that the sys-tem throughput can reach the highest point in the viaduct scenario.展开更多
In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver u...In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.展开更多
文摘为解决当多个用户通过一个公共信道与其他用户进行通信时必须采用多址技术的问题。在详细阐述了时隙ALOHA系统和连续时间ALOHA系统以及非坚持CSMA(Carrier Sense Multiple Access)控制协议的分析的同时,分别得出了3种系统的平均成功队长和系统吞吐量的数学表达式,并通过仿真实验验证了理论分析的正确性。
基金supported by the National Natural Science Foundation of China(Grant Nos.61522109,61671253,61571037and 91738201)the Fundamental Research Funds for the Central Universities(No.2016JBZ006)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20150040and BK20171446)the Key Project of Natural Science Research of Higher Education Institutions of Jiangsu Province(No.15KJA510003)
文摘The fifth generation （5G） wireless communication is currently a hot research topic and wireless communication systems on high speed railways （HSR） are important applications of 5G technologies. Existing stud- ies about 5G wireless systems on high speed railways （HSR） often utilize ideal channel parameters and are usually based on simple scenarios. In this paper, we evaluate the down- link throughput of 5G HSR communication systems on three typical scenarios including urban, cutting and viaduct with three different channel estimators. The channel parameters of each scenario are generated with tapped delay line （TDL） models through ray-tracing sim- ulations, which can be considered as a good match to practical situations. The channel estimators including least square （LS）, linear minimum mean square error （LMMSE）, and our proposed historical information based ba- sis expansion model （HiBEM）. We analyze the performance of the HiBEM estimator in terms of mean square error （MSE） and evaluate the system throughputs with different channel estimates over each scenario. Simulation results are then provided to corroborate our proposed studies. It is shown that our HiBEM estimator outperforms other estimators and that the sys-tem throughput can reach the highest point in the viaduct scenario.
基金supported by the Key Research and Development Program of China(No.2022YFC3005401)Key Research and Development Program of China,Yunnan Province(No.202203AA080009,202202AF080003)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0482).
文摘In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.
