A higher quality of service （QoS） is provided for ad hoc networks through a multi-channel and slotted random multi-access （MSRM） protocol with two-dimensional probability. For this protocol, the system time is slo...A higher quality of service （QoS） is provided for ad hoc networks through a multi-channel and slotted random multi-access （MSRM） protocol with two-dimensional probability. For this protocol, the system time is slotted into a time slot with high channel utilization realized by the choice of two parameters p1 and p2, and the channel load equilibrium. The protocol analyzes the throughput of the MSRM protocol for a load equilibrium state and the throughput based on priority. Simulations agree with the theoretical analysis. The simulations also show that the slotted-time system is better than the continuous-time system.展开更多
Recently,the increasing demand of radio spectrum for the next generation communication systems due to the explosive growth of applications appetite for bandwidths has led to the problem of spectrum scarcity.The potent...Recently,the increasing demand of radio spectrum for the next generation communication systems due to the explosive growth of applications appetite for bandwidths has led to the problem of spectrum scarcity.The potential approaches among the proposed solutions to resolve this issue are well explored cognitive radio(CR)technology and recently introduced non-orthogonal multiple access(NOMA)techniques.Both the techniques are employed for efficient spectrum utilization and assure the significant improvement in the spectral efficiency.Further,the significant improvement in spectral efficiency can be achieved by combining both the techniques.Since the CR is well-explored technique as compared to that of the NOMA in the field of communication,therefore it is worth and wise to implement this technique over the CR.In this article,we have presented the frameworks of NOMA implementation over CR as well as the feasibility of proposed frameworks.Further,the differences between proposed CR-NOMA and conventional CR frameworks are discussed.Finally,the potential issues regarding the implementation of CR-NOMA are explored.展开更多
This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols w...This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.展开更多
为了解决物联网信道资源有限的问题以及提高物联网系统的信息时效性,考虑了包括一个主用户(primary user,PU)和两个次用户(secondary user,SU)节点的多接入认知无线电(CR)物联网系统模型。在PU工作状态和SU数据队列稳定的约束下,分别分...为了解决物联网信道资源有限的问题以及提高物联网系统的信息时效性,考虑了包括一个主用户(primary user,PU)和两个次用户(secondary user,SU)节点的多接入认知无线电(CR)物联网系统模型。在PU工作状态和SU数据队列稳定的约束下,分别分析了第一个SU节点在先来先服务(first come first served,FCFS)、后来先服务(last come last served,LCLS)以及包丢弃队列下的平均信息年龄(age of information,AoI),推导了在阈值策略下第二个SU节点的平均AoI。然后,提出了使第一个SU平均AoI最小化,并且第二个SU的平均AoI低于给定阈值的优化问题。优化问题的约束条件是凸的,但所得到的目标函数是非凸的,故引入了一种次优技术,利用双层凸优化算法得到最优解。仿真结果给出了所考虑优化算法在不同系统参数下的性能,该算法在不同系统参数和多天线影响下的性能表现良好。后续工作可以考虑扩展到两个以上次用户的CR物联网系统。展开更多
Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning method...Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.展开更多
This article presents a new multichannel medium access control (MAC) protocol to solve the exposed-terminal (ET) problem for efficient channel sharing in multi-hop wireless networks. It uses request-to-send and clear-...This article presents a new multichannel medium access control (MAC) protocol to solve the exposed-terminal (ET) problem for efficient channel sharing in multi-hop wireless networks. It uses request-to-send and clear-to-send (RTS/CTS) dialogue on a common channel and flexibly opts for conflict-free traffic channels to carry out the data packet transmission on the basis of a new channel selection scheme. The acknowledgment (ACK) packet for the data packet transmission is sent back to the sender over another ...展开更多
The MultiCarrier Code Division Multiple Access (MC-CDMA) scheme is promising for relieving capacity limit problems of Direct Sequence (DS-) CDMA systems due to serious InterChip Interference (ICI) and MultiUser Interf...The MultiCarrier Code Division Multiple Access (MC-CDMA) scheme is promising for relieving capacity limit problems of Direct Sequence (DS-) CDMA systems due to serious InterChip Interference (ICI) and MultiUser Interference (MUI) in high-data-rate wireless communication systems. In this paper, the Uniform Linear Array (ULA) is applied to the base station of macrocellular MC-CDMA systems in a frequency-selective fading channel environment. A joint space-frequency multiuser symbol sequence detector is developed for all active users within one macrocell without space-frequency channel estimation. Simultaneously, Directions-Of-Arrivals (DOAs) of all active users can also be estimated. By dividing the ULA into two identical overlapping subarrays, a specific auxiliary matrix is constructed, which includes both symbol sequence and DOA information of all active users. Then, based on the subspace method, performing the eigen decomposition on such auxiliary matrix, the closed-form solution of symbol sequences and DOAs for all active users can be obtained. In comparison with schemes based on channel estimation, our algorithm need not explicitly estimate the space-frequency channel for each active user,so it has lower computation complexity. Extensive computer simulations demonstrate the overall performance of this novel scheme.展开更多
基金Supported by the National Natural Science Foundation of China(Nos. 60362001 and F0424104)the Natural Science Foundationof Yunnan Province (No. 2004F0011R)
文摘A higher quality of service （QoS） is provided for ad hoc networks through a multi-channel and slotted random multi-access （MSRM） protocol with two-dimensional probability. For this protocol, the system time is slotted into a time slot with high channel utilization realized by the choice of two parameters p1 and p2, and the channel load equilibrium. The protocol analyzes the throughput of the MSRM protocol for a load equilibrium state and the throughput based on priority. Simulations agree with the theoretical analysis. The simulations also show that the slotted-time system is better than the continuous-time system.
文摘Recently,the increasing demand of radio spectrum for the next generation communication systems due to the explosive growth of applications appetite for bandwidths has led to the problem of spectrum scarcity.The potential approaches among the proposed solutions to resolve this issue are well explored cognitive radio(CR)technology and recently introduced non-orthogonal multiple access(NOMA)techniques.Both the techniques are employed for efficient spectrum utilization and assure the significant improvement in the spectral efficiency.Further,the significant improvement in spectral efficiency can be achieved by combining both the techniques.Since the CR is well-explored technique as compared to that of the NOMA in the field of communication,therefore it is worth and wise to implement this technique over the CR.In this article,we have presented the frameworks of NOMA implementation over CR as well as the feasibility of proposed frameworks.Further,the differences between proposed CR-NOMA and conventional CR frameworks are discussed.Finally,the potential issues regarding the implementation of CR-NOMA are explored.
基金This research project was funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No(43-PRFA-P-58).
文摘This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.
文摘为了解决物联网信道资源有限的问题以及提高物联网系统的信息时效性,考虑了包括一个主用户(primary user,PU)和两个次用户(secondary user,SU)节点的多接入认知无线电(CR)物联网系统模型。在PU工作状态和SU数据队列稳定的约束下,分别分析了第一个SU节点在先来先服务(first come first served,FCFS)、后来先服务(last come last served,LCLS)以及包丢弃队列下的平均信息年龄(age of information,AoI),推导了在阈值策略下第二个SU节点的平均AoI。然后,提出了使第一个SU平均AoI最小化,并且第二个SU的平均AoI低于给定阈值的优化问题。优化问题的约束条件是凸的,但所得到的目标函数是非凸的,故引入了一种次优技术,利用双层凸优化算法得到最优解。仿真结果给出了所考虑优化算法在不同系统参数下的性能,该算法在不同系统参数和多天线影响下的性能表现良好。后续工作可以考虑扩展到两个以上次用户的CR物联网系统。
文摘Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.
基金National Natural Science Foundation of China (10577005, 60532030)National Outstanding Youth Science Foundation of China (60625102)NSBS Program of Beijing University of Aeronau-tics and Astronautics, China (221235)
文摘This article presents a new multichannel medium access control (MAC) protocol to solve the exposed-terminal (ET) problem for efficient channel sharing in multi-hop wireless networks. It uses request-to-send and clear-to-send (RTS/CTS) dialogue on a common channel and flexibly opts for conflict-free traffic channels to carry out the data packet transmission on the basis of a new channel selection scheme. The acknowledgment (ACK) packet for the data packet transmission is sent back to the sender over another ...
基金Partially supported by the National Natural Science Foundation(No.69872029)and the Research Fund for Doctoral Program of Higher Education(No.19990690808)of China
文摘The MultiCarrier Code Division Multiple Access (MC-CDMA) scheme is promising for relieving capacity limit problems of Direct Sequence (DS-) CDMA systems due to serious InterChip Interference (ICI) and MultiUser Interference (MUI) in high-data-rate wireless communication systems. In this paper, the Uniform Linear Array (ULA) is applied to the base station of macrocellular MC-CDMA systems in a frequency-selective fading channel environment. A joint space-frequency multiuser symbol sequence detector is developed for all active users within one macrocell without space-frequency channel estimation. Simultaneously, Directions-Of-Arrivals (DOAs) of all active users can also be estimated. By dividing the ULA into two identical overlapping subarrays, a specific auxiliary matrix is constructed, which includes both symbol sequence and DOA information of all active users. Then, based on the subspace method, performing the eigen decomposition on such auxiliary matrix, the closed-form solution of symbol sequences and DOAs for all active users can be obtained. In comparison with schemes based on channel estimation, our algorithm need not explicitly estimate the space-frequency channel for each active user,so it has lower computation complexity. Extensive computer simulations demonstrate the overall performance of this novel scheme.
