This paper utilizes uniquely decodable codes[UDCs]in an M-to-1 free-space optical[FSO]system.Benefiting from UDCs’nonorthogonal nature,the sum throughput is improved.We first prove that the uniquely decodable propert...This paper utilizes uniquely decodable codes[UDCs]in an M-to-1 free-space optical[FSO]system.Benefiting from UDCs’nonorthogonal nature,the sum throughput is improved.We first prove that the uniquely decodable property still holds,even in optical fading channels.It is further discovered that the receiver can extract each source’s data from superimposed symbols with only one processing unit.According to theoretical analysis and simulation results,the throughput gain is up to the normalized UDC’s sum rate in high signal-to-noise ratio cases.An equivalent desktop experiment is also implemented to show the feasibility of the UDC-FSO structure.展开更多
This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the recei...This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure.On the contrary,a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements.Then,the spectrum occupancy is determined directly from the reconstructed support vector.To carry out this process,a data-driven methodology is utilized to obtain the mini-mum number of necessary samples required for support reconstruction,and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels.Following this phase,an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision.Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance.Furthermore,the remainder of the sensing time can be employed by secondary users for data transmission,thus leading to the enhancement of the total throughput of the CR network.展开更多
基金supported in part by the National Natural Science Foundation of China(No.62101527)in part by the Funding Program of Innovation Labs by CIOMP。
文摘This paper utilizes uniquely decodable codes[UDCs]in an M-to-1 free-space optical[FSO]system.Benefiting from UDCs’nonorthogonal nature,the sum throughput is improved.We first prove that the uniquely decodable property still holds,even in optical fading channels.It is further discovered that the receiver can extract each source’s data from superimposed symbols with only one processing unit.According to theoretical analysis and simulation results,the throughput gain is up to the normalized UDC’s sum rate in high signal-to-noise ratio cases.An equivalent desktop experiment is also implemented to show the feasibility of the UDC-FSO structure.
文摘This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure.On the contrary,a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements.Then,the spectrum occupancy is determined directly from the reconstructed support vector.To carry out this process,a data-driven methodology is utilized to obtain the mini-mum number of necessary samples required for support reconstruction,and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels.Following this phase,an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision.Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance.Furthermore,the remainder of the sensing time can be employed by secondary users for data transmission,thus leading to the enhancement of the total throughput of the CR network.
