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OFDM Relay Cognitive Radio Multiple Antennas Resource Allocation Full Duplex Spectrum Sensing Synchronization Spectrum Sharing Interference Cancellation Channel Estimation Feedback Heterogeneous Networks Bi-directional Energy Harvesting Stochastic Geometry HetNet relay networks FBMC Equalization channel capacity TVWS CDMA interference in-band full-duplex system Duplex MIMO Ultra Low Power C-V2V 5G Reliability SC-FDMA interference suppression D-TDD CLI indoor positioning reinforcement learning RSRP weighting - Computation offloading smart factory Cell-free multi-access edge computing estimated position overlapping —Device-to-device (D2D) estimated position updating mMIMO control overhead hybrid NR positioning Femtocell Rat-dependent positioning frame structure Zigbee body area networks channel estimation error Handoff CoMP User grouping power uncertainty ultra-dense small cell network mode selection antenna arrays 5G mobile communication UFMC resource block management inter user interference WVAN health care partial overlap GFDM Dynamic TDD Multi-user Receiver Number of training blocks Uplink SCMA system V2X Vehicular communication cross-link interference LTE-TDD FS-NOMA Location-based user fairness Mode 3 QR Factorization Metaheuristics P-NOMA non-orthogonal multiple access dynamic HetNet spectrum partitioning and 5G networks. massive connectivity non-orthogonal multiple access (NOMA) overloading DQN OTDOA distributed mode Communication range resource selection maximum likelihood method Resource sharing Power allocation packet delay
|Title :||Stochastic Multichannel Sensing for Cognitive Radio Systems: Optimal Channel Selection for Sensing with Interference Constraints|
|Authors :||Gosan Noh, Jemin Lee, and Daesik Hong|
|Conference :||IEEE Conference Vehicular Technology|
|Abstract :||This paper considers the problem of sensing and transmission strategy of multiple parallel channels owned by the primary user, referred as stochastic multichannel sensing. The trafﬁc parameters follow the Markovian trafﬁc assumption and are not identically distributed among the channels. In order to obtain the optimal probabilities of channel selection for sensing,we formulate a maximization problem for the secondary user throughput with interference constraints to the primary user.
The solution to the problem is obtained via linear programming.Numerical results show that the proposed stochastic sensing achieves higher normalized effective throughput and lower average collision probability than the conventional deterministic sensing in a non-identical trafﬁc environment. Additionally, the proposed method greatly reduces computational overheads and memory space.
Noh, G.; Jemin Lee; Daesik Hong; , "Stochastic Multichannel Sensing for Cognitive Radio Systems: Optimal Channel Selection for Sensing with Interference Constraints," Vehicular Technology Conference Fall (VTC 2009-Fall), 2009 IEEE 70th , vol., no., pp.1-5, 20-23 Sept. 2009