Results 311 to 320 of about 4,727,301 (347)

Explainable AI for Spectrum Sensing

2025 34th International Conference on Computer Communications and Networks (ICCCN)
In conventional paradigms of machine learning (ML) and deep learning (DL), models are trained as ’black boxes’ on task-specific datasets prior to deployment. This poses various challenges to the application of AI for spectral adaptation. First, we cannot ensure the reliability of the model, since we do not know how they correlate signal features with ...
Varun Magotra, Sirani M. Perera, Arjuna Madanayake, Houbing Herbert Song   +3 more
openaire   +1 more source

Machine Learning-Enabled Cooperative Spectrum Sensing for Non-Orthogonal Multiple Access

IEEE Transactions on Wireless Communications, 2020
In this paper, multiple machine learning-enabled solutions are adopted to tackle the challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms
Zhenjiang Shi, Wei Gao, Shangwei Zhang, Jiajia Liu, N. Kato   +4 more
semanticscholar   +1 more source

Privacy-preserving crowdsourced spectrum sensing

IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, 2016
Crowdsourced spectrum sensing has great potential in improving current spectrum database services. Without strong incentives and location privacy protection in place, however, mobile users will be reluctant to act as mobile crowdsourcing workers for spectrum sensing tasks.
Xiaocong Jin, Yanchao Zhang
openaire   +2 more sources

Classification of Business Scenarios for Spectrum Sensing

SSRN Electronic Journal, 2010
Because of ever growing use of wireless applications and inflexibilities in the current way spectrum is allocated, spectrum is becoming more and more scarce. One of the methods to overcome this is spectrum sensing. In spectrum sensing research, use case analysis is often used to determine challenges and opportunities for this technology.
Barrie, Matthias, Delaere, Simon, Ballon, Pieter   +2 more
openaire   +2 more sources

Progression on spectrum sensing for cognitive radio networks: A survey, classification, challenges and future research issues

Journal of Network and Computer Applications, 2019
It is widely believed that the advances of networking technologies will reshape the future of telecommunication system. The continuous growth in data traffic originated by mobile users will be witnessed very serious problem in future. By 2021, the number
M. Gupta, Krishan Kumar
semanticscholar   +1 more source

Activity Pattern Aware Spectrum Sensing: A CNN-Based Deep Learning Approach

IEEE Communications Letters, 2019
In cognitive radio, most spectrum sensing algorithms are model-based and their detection performance relies heavily on the accuracy of the assumed statistical model.
Jiandong Xie, Chang Liu, Ying-Chang Liang, Jun Fang   +3 more
semanticscholar   +1 more source

A Reliable Energy Efficient Dynamic Spectrum Sensing for Cognitive Radio IoT Networks

IEEE Internet of Things Journal, 2019
The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded.
James Adu Ansere, Guangjie Han, Hao Wang, Chang Choi, Celimuge Wu   +4 more
semanticscholar   +1 more source

Spectrum Sensing for Dynamic Spectrum Access of TV Bands

2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2007
In this paper we address the issue of spectrum sensing in cognitive radio based wireless networks. Spectrum sensing is the key enabler for dynamic spectrum access as it can allow secondary networks to reuse spectrum without causing harmful interference to primary users. Here we propose a set of integrated medium access control (MAC) and physical layer (
Carlos Cordeiro 0001, Monisha Ghosh, Dave Cavalcanti 0001, Kiran S. Challapali   +3 more
openaire   +1 more source

Cooperative Spectrum Sensing

2014
The main objective of this chapter is to provide a detailed technical insight into latest key aspects of cooperative spectrum sensing. We focus on fusion strategies, quantization enhancements, effect of imperfect reporting channel, cooperative spectrum sensing scheduling, and utilizing cooperatively sensed data via Radio Environment Map (REM).
H. Birkan Yilmaz, Salim Eryigit, Tuna Tugcu   +2 more
openaire   +1 more source

Compressive Spectrum Sensing

2019
In this chapter, the authors discuss how compressive sensing can be used in wideband spectrum sensing in cognitive radio systems. Compressive sensing helps decrease the complexity and processing time and allows for higher data rates to be used, since it makes it possible for the signal to be sampled at rates lower than the Nyquist rate and still be ...
Said E. El-Khamy, Mina B. Abd el-Malek, Sara H. Kamel   +2 more
openaire   +1 more source