With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A pe...With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A performance-driven Joint Radar-target and Communication-user Assignment,along with Power and Subchannel Allocation(JRCAPSA)strategy,is proposed for an RCC network.The optimization model aims to minimize the sum of weighted Bayesian Cramer-Rao Lower Bounds(BCRLBs)of target state estimates for radar purpose.This is subject to constraints such as the Communication Data Rate(CDR)for communication purpose,the total power budget in each RCC system,assignment relationships,and the number of available subchannels.Considering that such a problem falls into the realm of Mixed Integer Programming(MIP),a Three-stage Iteratively Augment-based Optimization Method(TIAOM)is developed.The Communication-User Assignment(CUA),Communication Subchannel Allocation(SCA),and Radar-Target Assignment(RTA)feasible solution domains are iteratively expanded based on their importance,leading to the efficient acquisition of a suboptimal solution.Simulation results show the outperformance of the proposed JRCAPSA strategy,compared to the other benchmarks and the OPTI toolbox.The results also imply that the Bayesian Cramer-Rao Lower Bound(BCRLB)is a more stringent optimization metric for the achieved Mean Square Error(MSE),compared to Mutual Information(MI)and Signal-to-Interference-Noise Ratio(SINR).展开更多
The resource allocation technique is of great significance in achieving frequency spectrum coexistence in Joint Radar-Communication(JRC) systems, by which the problem of radio frequency spectrum congestion can be well...The resource allocation technique is of great significance in achieving frequency spectrum coexistence in Joint Radar-Communication(JRC) systems, by which the problem of radio frequency spectrum congestion can be well alleviated. A Robust Joint Frequency Spectrum and Power Allocation(RJFSPA) strategy is proposed for the Coexisting Radar and Communication(CRC)system. Specifically, we consider the uncertainty of target Radar Cross Section(RCS) and communication channel gain to formulate a bi-objective optimization model. The joint probabilities that the Cramér-Rao Lower Bound(CRLB) of each target satisfying the localization accuracy threshold and the Communication Data Ratio(CDR) of each user satisfying the communication threshold are simultaneously maximized, under the constraint of the total power budget. A Three-Stage Alternating Optimization Method(TSAOM) is proposed to obtain the Best-Known Pareto Subset(BKPS) of this problem, where the frequency spectrum, radar power, and communicator power are allocated using the greedy search and standard convex optimization methods, respectively. Simulation results confirm the effectiveness of the proposed RJFSPA strategy, compared with the resource allocation methods in a uniform manner and that ignores the uncertainties. The efficiency of the TSAOM is also verified by the comparison with the exhaustive search-based method.展开更多
Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of ...Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of managing finite transmit and receive antennas and transmit power systematically to enhance detection performance.To tackle the multidimensional resource optimization challenge,we introduce a Cooperative Transmit-Receive Antenna Selection and Power Allocation(CTRSPA)strategy.It employs a perception-action cycle that incorporates uncertain external support information to optimize worst-case detection performance with multiple targets.First,we derive a closed-form expression that incorporates uncertainty for the noncoherent integration squared-law detection probability using the Neyman-Pearson criterion.Subsequently,a joint optimization model for antenna selection and power allocation in CFAR detection is formulated,incorporating practical radar resource constraints.Mathematically,this represents an NPhard problem involving coupled continuous and Boolean variables.We propose a three-stage method—Reformulation,Node Picker,and Convex Power Allocation—that capitalizes on the independent convexity of the optimization model for each variable,ensuring a near-optimal result.Simulations confirm the approach's effectiveness,efficiency,and timeliness,particularly for large-scale radar networks,and reveal the impact of threat levels,system layout,and detection parameters on resource allocation.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62071482,62471485,62471348)Shaanxi Association of Science and Technology Youth Talent Support Program Project,China(No.20230137)+1 种基金Innovative Talents Cultivate Program for Technology Innovation Team of ShaanXi Province,China(No.2024RS-CXTD-08)Youth Talent Lifting Project of the China Association for Science and Technology(No.2021-JCJQ-QT-018)。
文摘With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A performance-driven Joint Radar-target and Communication-user Assignment,along with Power and Subchannel Allocation(JRCAPSA)strategy,is proposed for an RCC network.The optimization model aims to minimize the sum of weighted Bayesian Cramer-Rao Lower Bounds(BCRLBs)of target state estimates for radar purpose.This is subject to constraints such as the Communication Data Rate(CDR)for communication purpose,the total power budget in each RCC system,assignment relationships,and the number of available subchannels.Considering that such a problem falls into the realm of Mixed Integer Programming(MIP),a Three-stage Iteratively Augment-based Optimization Method(TIAOM)is developed.The Communication-User Assignment(CUA),Communication Subchannel Allocation(SCA),and Radar-Target Assignment(RTA)feasible solution domains are iteratively expanded based on their importance,leading to the efficient acquisition of a suboptimal solution.Simulation results show the outperformance of the proposed JRCAPSA strategy,compared to the other benchmarks and the OPTI toolbox.The results also imply that the Bayesian Cramer-Rao Lower Bound(BCRLB)is a more stringent optimization metric for the achieved Mean Square Error(MSE),compared to Mutual Information(MI)and Signal-to-Interference-Noise Ratio(SINR).
基金Supported by the National Natural Science Foundation of China(No.62071482)Shaanxi Association of Science and Technology Youth Talent Support Program Project,China(No.20230137)+1 种基金the Innovative Talents Cultivate Program for Technology Innovation Team of ShaanXi Province,China(No.2024RS-CXTD-08)the Youth Talent Lifting Project of the China Association for Science and Technology(No.2021-JCJQ-QT-018).
文摘The resource allocation technique is of great significance in achieving frequency spectrum coexistence in Joint Radar-Communication(JRC) systems, by which the problem of radio frequency spectrum congestion can be well alleviated. A Robust Joint Frequency Spectrum and Power Allocation(RJFSPA) strategy is proposed for the Coexisting Radar and Communication(CRC)system. Specifically, we consider the uncertainty of target Radar Cross Section(RCS) and communication channel gain to formulate a bi-objective optimization model. The joint probabilities that the Cramér-Rao Lower Bound(CRLB) of each target satisfying the localization accuracy threshold and the Communication Data Ratio(CDR) of each user satisfying the communication threshold are simultaneously maximized, under the constraint of the total power budget. A Three-Stage Alternating Optimization Method(TSAOM) is proposed to obtain the Best-Known Pareto Subset(BKPS) of this problem, where the frequency spectrum, radar power, and communicator power are allocated using the greedy search and standard convex optimization methods, respectively. Simulation results confirm the effectiveness of the proposed RJFSPA strategy, compared with the resource allocation methods in a uniform manner and that ignores the uncertainties. The efficiency of the TSAOM is also verified by the comparison with the exhaustive search-based method.
基金supported by the National Natural Science Foundation of China(Nos.62071482 and 62471348)the Shaanxi Association of Science and Technology Youth Talent Support Program Project,China(No.20230137)+1 种基金the Innovative Talents Cultivate Program for Technology Innovation Team of Shaanxi Province,China(No.2024RS-CXTD-08)the Youth Innovation Team of Shaanxi Universities,China。
文摘Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of managing finite transmit and receive antennas and transmit power systematically to enhance detection performance.To tackle the multidimensional resource optimization challenge,we introduce a Cooperative Transmit-Receive Antenna Selection and Power Allocation(CTRSPA)strategy.It employs a perception-action cycle that incorporates uncertain external support information to optimize worst-case detection performance with multiple targets.First,we derive a closed-form expression that incorporates uncertainty for the noncoherent integration squared-law detection probability using the Neyman-Pearson criterion.Subsequently,a joint optimization model for antenna selection and power allocation in CFAR detection is formulated,incorporating practical radar resource constraints.Mathematically,this represents an NPhard problem involving coupled continuous and Boolean variables.We propose a three-stage method—Reformulation,Node Picker,and Convex Power Allocation—that capitalizes on the independent convexity of the optimization model for each variable,ensuring a near-optimal result.Simulations confirm the approach's effectiveness,efficiency,and timeliness,particularly for large-scale radar networks,and reveal the impact of threat levels,system layout,and detection parameters on resource allocation.
