无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR (signal to interference plus noise ratio)模型准确地描述了无线信号传播的固有特性,能够...无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR (signal to interference plus noise ratio)模型准确地描述了无线信号传播的固有特性,能够真实反映无线信号之间的干扰,提出一种在动态无线网络中基于SINR模型的常数近似因子的在线分布式链路调度算法(OLD_LS).在线的意思是指,在算法执行的过程中任意节点可以随时加入网络,也可以随时离开网络.节点任意加入网络或者从网络中离开体现了无线网络的动态变化的特性. OLD_LS算法把网络区域划分为多个正六边形,局部化SINR模型的全局干扰.设计动态网络下的领导者选举算法(LE),只要网络节点的动态变化速率小于1/ε, LE就可以在O(log n+log R)ε≤5(1-21-α/2)/6,α表示路径损耗指数, n是网络节点的规模, R是最长链路的长度.根据文献调研,所提算法是第1个用于动态无线网络的在线分布式链路调度算法.展开更多
The improvements of anti-jamming performance of modem radar seeker are great threats to military targets. To protect the target from detection and estimation, the novel signal-to-interference-plus-noise ratio （SINR）...The improvements of anti-jamming performance of modem radar seeker are great threats to military targets. To protect the target from detection and estimation, the novel signal-to-interference-plus-noise ratio （SINR）-based and mutual information （Ml）-based jamming design techniques were proposed. To interfere with the target detection, the jamming was designed to minimize the S1NR of the radar seeker. To impair the estimation performance, the mutual information between the radar echo and the random target impulse response was used as the criterion. The spectral of optimal jamming under the two criteria were achieved with the power constraints. Simulation results show the effectiveness of the jamming techniques. SINR and MI of the SINR-based jamming, the MI-based jamming as well as the predefined jamming under the same power constraints were compared. Furthermore, the probability of detection and minimum mean-square error （MMSE） were also utilized to validate the jamming performance. Under the jamming power constraint of I W, the relative decrease of the probability of detection using S1NR-based optimal jamming is about 47%, and the relative increase of MMSE using Ml-based optimal jamming is about 8%. Besides, two useful jamming design principles are concluded which can be used in limited jamming power situations.展开更多
在LTE(Long Term Evolution)的预编码技术中,预编码矩阵存在着使用酉阵和非酉阵的选择。本文分析了酉阵预编码的输出SINR的形式,提出了一种简化计算输出SINR计算的方法,计算4个矩阵(8个矢量)的输出SINR操作复杂度下降到原始方法的18%。...在LTE(Long Term Evolution)的预编码技术中,预编码矩阵存在着使用酉阵和非酉阵的选择。本文分析了酉阵预编码的输出SINR的形式,提出了一种简化计算输出SINR计算的方法,计算4个矩阵(8个矢量)的输出SINR操作复杂度下降到原始方法的18%。同时分析了非酉阵预编码时的输出SINR相比于使用酉阵时的损失。仿真结果表明,非酉阵预编码相比于酉阵预编码在输出SINR上会有损失,这种损失从统计上可以用线性关系近似描述。展开更多
We consider the Signal-to-Interference plus Noise Ratio(SINR) balancing problem in-volving joint beamfoming and power allocation in the Cognitive Radio(CR) network,wherein the Single-Input Multi-Output Multiple Access...We consider the Signal-to-Interference plus Noise Ratio(SINR) balancing problem in-volving joint beamfoming and power allocation in the Cognitive Radio(CR) network,wherein the Single-Input Multi-Output Multiple Access Channels(SIMO-MAC) are assumed.Subject to two sets of constraints:the interference temperature constraints of Primary Users(PUs) and the peak power constraints of Cognitive Users(CUs),a low-complexity joint beamforming and power allocation algo-rithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection(SDMCPA-CP) for SINR balancing is proposed.Compared with the existing algorithm,the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions signifi-cantly,especially when large numbers of PUs and CUs are active,while still providing the optimal balanced SINR level for all the CUs.展开更多
In this paper, we present a theoretical analysis of the output signal-to-interference-plus-noise ratio (SINR) for eigen-space beamformers so as to investigate the performance degradation caused by large pointing error...In this paper, we present a theoretical analysis of the output signal-to-interference-plus-noise ratio (SINR) for eigen-space beamformers so as to investigate the performance degradation caused by large pointing errors. For the sake of reducing such performance loss, a robust scheme, which consists of two cascaded signal processors, is proposed for adaptive beamformers. In the first stage, an algorithm possessing time efficiency is developed to adjust the direc-tion-of-arrival (DOA) estimate of the desired source. Based the achieved DOA estimate, the second stage provides an eigenspace beamformer combined with the spatial derivative constraints (SDC) to further mitigate the cancellation of the desired signal. Analysis and numerical results have been conducted to verify that the proposed scheme yields a better robustness against pointing errors than the conventional approaches.展开更多
This paper derives an approximate formula for probability density function(PDF) of received signal-to-interference-and-noise ratio(SINR) at user terminal when matched filter(MF) is adopted at a base station(BS).This d...This paper derives an approximate formula for probability density function(PDF) of received signal-to-interference-and-noise ratio(SINR) at user terminal when matched filter(MF) is adopted at a base station(BS).This distribution of SINR can be used to make an analysis of average sum-rate,outage probability,and symbol error rate of massive MIMO downlink with MF at BS.From simulation,it is found that the derived approximate analytical expression for PDF of SINR is consistent with the simulated exact PDF from the definition of SINR in medium-scale and large-scale MIMO systems.展开更多
In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improv...In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.展开更多
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,China
文摘The improvements of anti-jamming performance of modem radar seeker are great threats to military targets. To protect the target from detection and estimation, the novel signal-to-interference-plus-noise ratio （SINR）-based and mutual information （Ml）-based jamming design techniques were proposed. To interfere with the target detection, the jamming was designed to minimize the S1NR of the radar seeker. To impair the estimation performance, the mutual information between the radar echo and the random target impulse response was used as the criterion. The spectral of optimal jamming under the two criteria were achieved with the power constraints. Simulation results show the effectiveness of the jamming techniques. SINR and MI of the SINR-based jamming, the MI-based jamming as well as the predefined jamming under the same power constraints were compared. Furthermore, the probability of detection and minimum mean-square error （MMSE） were also utilized to validate the jamming performance. Under the jamming power constraint of I W, the relative decrease of the probability of detection using S1NR-based optimal jamming is about 47%, and the relative increase of MMSE using Ml-based optimal jamming is about 8%. Besides, two useful jamming design principles are concluded which can be used in limited jamming power situations.
文摘在LTE(Long Term Evolution)的预编码技术中,预编码矩阵存在着使用酉阵和非酉阵的选择。本文分析了酉阵预编码的输出SINR的形式,提出了一种简化计算输出SINR计算的方法,计算4个矩阵(8个矢量)的输出SINR操作复杂度下降到原始方法的18%。同时分析了非酉阵预编码时的输出SINR相比于使用酉阵时的损失。仿真结果表明,非酉阵预编码相比于酉阵预编码在输出SINR上会有损失,这种损失从统计上可以用线性关系近似描述。
基金Supported by the National Basic Research Program (973) of China (No. 2009CB320400)the National High-Tech Research and Development Program (863) of China (No. 2009AA01Z243)+1 种基金the National Science Fundation of China (No. 61072044)the Natural Science Fundation of Jiangsu Province (BK2009056)
文摘We consider the Signal-to-Interference plus Noise Ratio(SINR) balancing problem in-volving joint beamfoming and power allocation in the Cognitive Radio(CR) network,wherein the Single-Input Multi-Output Multiple Access Channels(SIMO-MAC) are assumed.Subject to two sets of constraints:the interference temperature constraints of Primary Users(PUs) and the peak power constraints of Cognitive Users(CUs),a low-complexity joint beamforming and power allocation algo-rithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection(SDMCPA-CP) for SINR balancing is proposed.Compared with the existing algorithm,the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions signifi-cantly,especially when large numbers of PUs and CUs are active,while still providing the optimal balanced SINR level for all the CUs.
文摘In this paper, we present a theoretical analysis of the output signal-to-interference-plus-noise ratio (SINR) for eigen-space beamformers so as to investigate the performance degradation caused by large pointing errors. For the sake of reducing such performance loss, a robust scheme, which consists of two cascaded signal processors, is proposed for adaptive beamformers. In the first stage, an algorithm possessing time efficiency is developed to adjust the direc-tion-of-arrival (DOA) estimate of the desired source. Based the achieved DOA estimate, the second stage provides an eigenspace beamformer combined with the spatial derivative constraints (SDC) to further mitigate the cancellation of the desired signal. Analysis and numerical results have been conducted to verify that the proposed scheme yields a better robustness against pointing errors than the conventional approaches.
基金Supported by the National Natural Science Foundation of China(No.61271230,61301107)the Fundamental Research Funds for the Central Universities(No.30920130122004)Open Research Fund of National Mobile Communications Research Laboratory,Southeast University(No.2013D02)
文摘This paper derives an approximate formula for probability density function(PDF) of received signal-to-interference-and-noise ratio(SINR) at user terminal when matched filter(MF) is adopted at a base station(BS).This distribution of SINR can be used to make an analysis of average sum-rate,outage probability,and symbol error rate of massive MIMO downlink with MF at BS.From simulation,it is found that the derived approximate analytical expression for PDF of SINR is consistent with the simulated exact PDF from the definition of SINR in medium-scale and large-scale MIMO systems.
文摘In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.
