Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity an...Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.展开更多
In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use par...In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.展开更多
Due to its opportunistic spectrum sharing capability, cognitive radio （CR） has been proposed as a fundamental solution to alleviate the contradiction between spectrum scarcity and inefficient utilization of licensed...Due to its opportunistic spectrum sharing capability, cognitive radio （CR） has been proposed as a fundamental solution to alleviate the contradiction between spectrum scarcity and inefficient utilization of licensed spectrum. In CR system （CRS）, to efficiently utilize the spectrum resource, one important issue is to allocate the sensing and transmission duration reasonably. In this paper, the evaluation metric of energy efficiency, which represented the total number of bits that were delivered with per joule of energy consumed, is adopted to evaluate the proposed scheme. We study a joint design of energy efficient sensing and transmission durations to maximize energy efficiency capacity （EEC） of CRS. The tradeoff between EEC and sensing and transmission durations are formulized as an optimization problem under constraints on target detection probability of secondary users （SUs） and toleration interference threshold of primary users （PUs）. To obtain the optimal solution, optimizing sensing duration and transmission duration will be first performed separately. Then, a joint optimization iterative algorithm is proposed to search the optimal pair of sensing and transmission durations. Analytical and simulation results show that there exists a unique duration pair where the EEC is maximized, and that the EEC of the proposed joint optimization algorithm outperforms that of existed algorithms. Furthermore, the simulation results also reveal that the performance of the proposed low complexity iterative algorithm is comparable with that of the exhaustive search scheme.展开更多
For a future scenario where everything is connected,cognitive technology can be used for spectrum sensing and access,and emerging coding technologies can be used to address the erasure of packets caused by dynamic spe...For a future scenario where everything is connected,cognitive technology can be used for spectrum sensing and access,and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios.Machine learning technologies are being increasingly used in the implementation of smart networks.In this paper,after an overview of several key technologies in the cognitive spectrum collaboration,a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning,and the effectiveness of the algorithm is verified by simulations,thus providing a feasible research direction for the realization of cognitive spectrum collaboration.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61102066)China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No.Y201119890)
文摘Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.
基金Project supported by the National Fundamental Research (Grant Nos.2009CB3020402,2010CB731803)the National Natural Science Foundation of China (Grant Nos.60702046,60832005,60972050,60632040)the Natural High-Technology Research and Development Program of China (Grant Nos.2007AA01Z267,2009AA01Z248,2009AA011802)
文摘In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.
基金supported by the National Natural Science Foundation of China (61001116)the National Science and Technology Major Project (2012ZX03003006)
文摘Due to its opportunistic spectrum sharing capability, cognitive radio （CR） has been proposed as a fundamental solution to alleviate the contradiction between spectrum scarcity and inefficient utilization of licensed spectrum. In CR system （CRS）, to efficiently utilize the spectrum resource, one important issue is to allocate the sensing and transmission duration reasonably. In this paper, the evaluation metric of energy efficiency, which represented the total number of bits that were delivered with per joule of energy consumed, is adopted to evaluate the proposed scheme. We study a joint design of energy efficient sensing and transmission durations to maximize energy efficiency capacity （EEC） of CRS. The tradeoff between EEC and sensing and transmission durations are formulized as an optimization problem under constraints on target detection probability of secondary users （SUs） and toleration interference threshold of primary users （PUs）. To obtain the optimal solution, optimizing sensing duration and transmission duration will be first performed separately. Then, a joint optimization iterative algorithm is proposed to search the optimal pair of sensing and transmission durations. Analytical and simulation results show that there exists a unique duration pair where the EEC is maximized, and that the EEC of the proposed joint optimization algorithm outperforms that of existed algorithms. Furthermore, the simulation results also reveal that the performance of the proposed low complexity iterative algorithm is comparable with that of the exhaustive search scheme.
基金This work was supported by the National Natural Science Foundation of China(No.61790553)Shenzhen Science and Technology Plan Projects(No.JCYJ20180306170614484)Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX04).
文摘For a future scenario where everything is connected,cognitive technology can be used for spectrum sensing and access,and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios.Machine learning technologies are being increasingly used in the implementation of smart networks.In this paper,after an overview of several key technologies in the cognitive spectrum collaboration,a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning,and the effectiveness of the algorithm is verified by simulations,thus providing a feasible research direction for the realization of cognitive spectrum collaboration.
