Spectrum inference in cognitive radio networks: Algorithms and applications
Spectrum inference, also known as spectrum prediction in the literature, is a promising
technique of inferring the occupied/free state of radio spectrum from already …
technique of inferring the occupied/free state of radio spectrum from already …
Spectrum occupancy measurements: A survey and use of interference maps
In order to provide meaningful data about spectrum use, occupancy measurements
describing the utilization rate of a specific frequency band should be conducted over a …
describing the utilization rate of a specific frequency band should be conducted over a …
Distributive dynamic spectrum access through deep reinforcement learning: A reservoir computing-based approach
Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to
share radio spectrum among different networks. As a secondary user (SU), a DSA device …
share radio spectrum among different networks. As a secondary user (SU), a DSA device …
Kernel-based learning for statistical signal processing in cognitive radio networks: Theoretical foundations, example applications, and future directions
Kernel-based learning (KBL) methods have recently become prevalent in many engineering
applications, notably in signal processing and communications. The increased interest is …
applications, notably in signal processing and communications. The increased interest is …
Mobility and intruder prior information improving the barrier coverage of sparse sensor networks
The barrier coverage problem in emerging mobile sensor networks has been an interesting
research issue due to many related real-life applications. Existing solutions are mainly …
research issue due to many related real-life applications. Existing solutions are mainly …
Robust spectrum sensing with crowd sensors
This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end
personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where …
personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where …
Deep-reinforcement-learning-based spectrum resource management for industrial Internet of Things
The Industrial Internet of Things (IIoT) has attracted tremendous interest from both industry
and academia as it can significantly improve production efficiency and system intelligence …
and academia as it can significantly improve production efficiency and system intelligence …
Specsense: Crowdsensing for efficient querying of spectrum occupancy
We describe an end-to-end platform called SpecSense to support large scale spectrum
monitoring. SpecSense crowdsources spectrum monitoring to low-cost, low-power …
monitoring. SpecSense crowdsources spectrum monitoring to low-cost, low-power …
Exploring indoor white spaces in metropolises
It is a promising vision to exploit white spaces, that is, vacant VHF and UHF TV channels, to
meet the rapidly growing demand for wireless data services in both outdoor and indoor …
meet the rapidly growing demand for wireless data services in both outdoor and indoor …
Multiband spectrum sensing in cognitive radio networks with secondary user hardware limitation: Random and adaptive spectrum sensing strategies
Hardware limitation at the secondary user (SU) terminal makes multiband (wideband)
spectrum sensing more challenging. This paper considers spectrum sensing under SU …
spectrum sensing more challenging. This paper considers spectrum sensing under SU …