Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced ...Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user(PU), which may lead to low signalto-noise-ratio at cognitive radio receivers(CRRs). Consequently, sum rate of cognitive users(CUs) may fall short of the theoretical maximum through IA. To solve this problem,we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.展开更多
In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channe...In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channel. Therefore,methods for the calculation of equivalent SINR of a random fading channel are very necessary.In this paper,an enhanced algorithm on the exponential effective SINR mapping( EESM) model for random fading channels was proposed. First, the optimal adjustment parameters of typical channel fading models including extended pedestrian A( EPA)model,extended vehicular A( EVA) model and extended typical urban( ETU) model were obtained by simulation. Then the proposed solution was used to actualize channel classification according to the maximum multipath delay and the average power of the random channel. The solution can determine the typical channel closest to random channel for obtaining the optimal adjustment value of EESM. The evaluation results indicate that the proposed one can improve the whole system throughput significantly and meanwhile the accuracy of the link prediction algorithm is also guaranteed.展开更多
针对高频地波雷达(High frequency surface wave radar,HFSWR)在探测中产生的回波数据,传统的人工识别和分类方法存在工作量大、效率低和主观性强等问题,本研究在分析一阶海杂波、电离层杂波和射频干扰的回波数据特性的基础上,创新性地...针对高频地波雷达(High frequency surface wave radar,HFSWR)在探测中产生的回波数据,传统的人工识别和分类方法存在工作量大、效率低和主观性强等问题,本研究在分析一阶海杂波、电离层杂波和射频干扰的回波数据特性的基础上,创新性地提出了基于YOLOv5识别模型的HFSWR杂波和干扰识别分类方法。该方法旨在帮助研究人员在海量实验数据中快速筛选出符合其科学研究需求的数据集,从而提高研究效率和数据准确性。在具体实施过程中,通过采用批量实测距离-多普勒(Range-Doppler,RD)谱数据对所提出模型进行训练和分析,使该方法能够在频域范围内对杂波和干扰进行有效识别。本研究以该识别分类算法为核心,进一步基于Python语言设计了一款地波雷达智能杂波和干扰识别分类软件。经过严格的批量实测数据测试验证,该软件能够满足设计需求,具有良好的可靠性,极大地提高了研究人员筛选有效实测数据的工作效率,为科学研究工作提供了有力的技术支撑。展开更多
Space-based Automatic Dependent Surveillance-Broadcast(ADS-B)technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability.However,the space-based ADS-B system faces n...Space-based Automatic Dependent Surveillance-Broadcast(ADS-B)technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability.However,the space-based ADS-B system faces new problems such as extremely low Signal-toNoise Ratio(SNR)and serious co-channel interference,which result in long update intervals.To minimize the position message update interval at an update probability of 95%with full coverage constraint,this paper presents an optimization model of digital multi-beamforming for space-based ADS-B.Then,a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy.The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam.Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods,and DECCG_A&A is superior in convergence to the Genetic Algorithm(GA)as well as the coevolution algorithms using other grouping strategies.Overall,the proposed optimization model and method can significantly reduce the update interval,thus improving the surveillance performance of space-based ADS-B for air traffic control.展开更多
基金supported by National Natuvertexesral Science Foundation of China under Grant 61201233 61271262 and 61701043
文摘Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user(PU), which may lead to low signalto-noise-ratio at cognitive radio receivers(CRRs). Consequently, sum rate of cognitive users(CUs) may fall short of the theoretical maximum through IA. To solve this problem,we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.
基金Institute of Nonlinear Science of Donghua University,China
文摘In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channel. Therefore,methods for the calculation of equivalent SINR of a random fading channel are very necessary.In this paper,an enhanced algorithm on the exponential effective SINR mapping( EESM) model for random fading channels was proposed. First, the optimal adjustment parameters of typical channel fading models including extended pedestrian A( EPA)model,extended vehicular A( EVA) model and extended typical urban( ETU) model were obtained by simulation. Then the proposed solution was used to actualize channel classification according to the maximum multipath delay and the average power of the random channel. The solution can determine the typical channel closest to random channel for obtaining the optimal adjustment value of EESM. The evaluation results indicate that the proposed one can improve the whole system throughput significantly and meanwhile the accuracy of the link prediction algorithm is also guaranteed.
文摘针对高频地波雷达(High frequency surface wave radar,HFSWR)在探测中产生的回波数据,传统的人工识别和分类方法存在工作量大、效率低和主观性强等问题,本研究在分析一阶海杂波、电离层杂波和射频干扰的回波数据特性的基础上,创新性地提出了基于YOLOv5识别模型的HFSWR杂波和干扰识别分类方法。该方法旨在帮助研究人员在海量实验数据中快速筛选出符合其科学研究需求的数据集,从而提高研究效率和数据准确性。在具体实施过程中,通过采用批量实测距离-多普勒(Range-Doppler,RD)谱数据对所提出模型进行训练和分析,使该方法能够在频域范围内对杂波和干扰进行有效识别。本研究以该识别分类算法为核心,进一步基于Python语言设计了一款地波雷达智能杂波和干扰识别分类软件。经过严格的批量实测数据测试验证,该软件能够满足设计需求,具有良好的可靠性,极大地提高了研究人员筛选有效实测数据的工作效率,为科学研究工作提供了有力的技术支撑。
文摘Space-based Automatic Dependent Surveillance-Broadcast(ADS-B)technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability.However,the space-based ADS-B system faces new problems such as extremely low Signal-toNoise Ratio(SNR)and serious co-channel interference,which result in long update intervals.To minimize the position message update interval at an update probability of 95%with full coverage constraint,this paper presents an optimization model of digital multi-beamforming for space-based ADS-B.Then,a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy.The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam.Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods,and DECCG_A&A is superior in convergence to the Genetic Algorithm(GA)as well as the coevolution algorithms using other grouping strategies.Overall,the proposed optimization model and method can significantly reduce the update interval,thus improving the surveillance performance of space-based ADS-B for air traffic control.
