Machine Learning and Cognitive Technology for Intelligent Wireless Networks
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Summary
The state-of-the-art of cognitive technology is described, covering spectrum sensing and access approaches that may enhance spectrum utilization and curtail energy consumption and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments.
- Type
- article
- Published
- 2017-10-30
- Cited by
- 8
- References
- 262
- Access
- Open access
- OpenAlex
- https://openalex.org/W2766549693
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:27890308
Keywords
Reconfigurability, Computer science, Wireless, Cognitive radio, Cognitive network
References
- Distributed Detection and Data Fusion
- Optimal Resource Allocation and EE-SE Trade-Off in Hybrid Cognitive Gaussian Relay Channels
- An Introduction to Signal Detection and Estimation
- Estimating probabilities in recommendation systems
- Optimal Energy Allocation for Wireless Communications With Energy Harvesting Constraints
- Distributed Resource Allocation for Relay-Aided Device-to-Device Communication Under Channel Uncertainties: A Stable Matching Approach
- Mutual interference in OFDM-based spectrum pooling systems
- Spectrum sensing in cognitive radio with robust principal component analysis
- A Truthful QoS-Aware Spectrum Auction with Spatial Reuse for Large-Scale Networks
- Interference-Limited Resource Optimization in Cognitive Femtocells With Fairness and Imperfect Spectrum Sensing
- Radio resource allocation for full-duplex OFDMA networks using matching theory
- Aggregation Aware Spectrum Assignment in Cognitive Ad-hoc Networks
- Lessons Learned from an Extensive Spectrum Occupancy Measurement Campaign and a Stochastic Duty Cycle Model
- Estimation of Primary User Parameters in Cognitive Radio Systems via Hidden Markov Model
- Partially-Distributed Resource Allocation in Small-Cell Networks
- Multi-Channel Sensing and Access Game: Bayesian Social Learning with Negative Network Externality
- Cooperative Spectrum Sensing in Cognitive Radio Networks with Weighted Decision Fusion Schemes
- Spectrum Sensing for Digital Primary Signals in Cognitive Radio: A Bayesian Approach for Maximizing Spectrum Utilization
- Energy and throughput efficient cooperative spectrum sensing in cognitive radio sensor networks
- Cooperative Spectrum Sharing: A Contract-Based Approach
Cited by
- A Comprehensive Survey on Blind Source Separation for Wireless Adaptive Processing: Principles, Perspectives, Challenges and New Research Directions
- A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges
- A Blind Spectrum Sensing Method Based on Deep Learning
- Deep Learning in Mobile and Wireless Networking: A Survey
- Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine
- Intelligent Software-Defined Network for Cognitive Routing Optimization using Deep Extreme Learning Machine Approach
- AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management
- Machine learning for cooperative spectrum sensing and sharing: A survey
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