Review on Spectrum Sensing Techniques for Wireless Networks
DOI:
https://doi.org/10.24113/ijoscience.v8i2.476Keywords:
Spectrum Sensing, Wireless Network, Collaborative Optimization, Spectrum Access.Abstract
The demand for bandwidth is expanding in lockstep with the advancement of wireless communication technologies, and as a result, wireless spectrum resources are becoming more limited. The main principle of cognitive radio is dynamic spectrum access, which has been highlighted as a possible solution for spectrum shortage. Spectrum sensing is thought to be a popular solution for spectrum shortage caused by a high number of sensors, especially in Internet of Things (IoT) technologies. Nonetheless, the Internet of Things faces significant spectrum sensing problems yet to be addressed. To be used in complex and scalable IoT systems, traditional spectrum sensing methods must be properly adjusted. The purpose of this study is to provide an introduction of spectrum sensing for Internet of Things and its various architectural configurations. We present a comprehensive list of spectrum sensing issues for IoT devices. In the deployment of smart networks, machine learning and deep learning technologies are becoming more popular.
Downloads
Metrics
References
M. Agiwal, A. Roy, and N. Saxena, ‘‘Next generation 5G wireless networks: A comprehensive survey,’’ IEEE Commun. Surveys Tuts., vol. 18, no. 3, pp. 1617–1655, 3rd Quart., 2016.
A. Gupta and E. R. K. Jha, ‘‘A survey of 5G network: Architecture and emerging technologies,’’ IEEE Access, vol. 3, pp. 1206–1232, Jul. 2015.
J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, ‘‘Internet of Things (IoT): A vision, architectural elements, and future directions,’’ Future Generat. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, 2013.
V. Gazis, ‘‘A survey of standards for machine-to-machine and the Internet of Things,’’ IEEE Commun. Surveys Tuts., vol. 19, no. 1, pp. 482–511, 1st Quart., 2016.
A. Ghanbari, A. Laya, J. Alonso-Zarate, and J. Markendahl, ‘‘Business development in the Internet of Things: A matter of vertical cooperation,’’ IEEE Commun. Mag., vol. 55, no. 2, pp. 135–141, Feb. 2017.
D. Hortelano, T. Olivares, M. C. Ruiz, C. Garrido, and V. López, ‘‘From sensor networks to Internet of Things. Bluetooth low energy, a standard for this evolution,’’ Sensors, vol. 17, no. 2, p. 372, Feb. 2017.
L. Atzori, A. Iera, and G. Morabito, ‘‘The Internet of Things: A survey,’’ Comput. Netw., vol. 54, no. 15, pp. 2787–2805, Oct. 2010.
A.Whitmore, A. Agarwal, and L. D. Xu, ‘‘The Internet of Things— A survey of topics and trends,’’ Inf. Syst. Frontiers, vol. 17, no. 2, pp. 261–274, Apr. 2015.
Afzal et al., ‘‘The cognitive Internet of Things: A unified perspective,’’ Mobile Netw. Appl., vol. 20, no. 1, pp. 72–85, Feb. 2015.
Gupta, V., Beniwal, N.S., Singh, K.K. et al. Optimal cooperative spectrum sensing for 5G cognitive networks using evolutionary algorithms. Peer-to-Peer Netw. Appl. 14, 3213–3224 (2021). https://doi.org/10.1007/s12083-021-01159-6
F. F. Digham, M. S. Alouini, and M. K. Simon, On the energy detection of unknown signals over fading channels, IEEE Trans. Commun., vol. 55, no. 1, pp. 21–24, 2007.
S. Rajendran, W. Meert, D. Giustiniano, V. Lenders, and S. Pollin, Deep learning models for wireless signal classification with distributed low-cost spectrum sensors, IEEE Trans. Cogn. Commun. Netw., vol. 4, no. 3, pp. 433– 445, 2018.
G. Ganesan and Y. Li, Cooperative spectrum sensing in cognitive radio networks, in Proc. 1st IEEE Int. Symp. New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, USA, 2005, pp. 137–143
F. Akyildiz, W. Y. Lee, M. C. Vuran, and S. Mohanty, NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey, Comput. Networks, vol. 50, no. 13, pp. 2127–2159, 2006.
Q. Zhao and B. M. Sadler, A survey of dynamic spectrum access, IEEE Signal Process. Mag., vol. 24, no. 3, pp. 79– 89, 2007
Y. Cassuto and A. Shokrollahi, Online fountain codes with low overhead, IEEE Trans. Inf. Theory, vol. 61, no. 6, pp. 3137–3149, 2015
X. L. Xu, Y. Zeng, Y. L. Guan, and L. Yuan, BATS code with unequal error protection, presented at 2016 IEEE Int. Conf. Communication Systems (ICCS), Shenzhen, China, 2016, pp. 1–6
Y. Cui, L. Wang, X. Wang, H. Y. Wang, and Y. N. Wang, FMTCP: A fountain code-based multipath transmission control protocol, IEEE/ACM Trans. Netw., vol. 23, no. 2, pp. 465–478, 2015
M. Nazzal, O. Hasekio?lu, A. R. Ekti, A. Görçin and H. Arslan, "Compressed Spectrum Sensing Using Sparse Recovery Convergence Patterns through Machine Learning Classification," 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019, pp. 1-6, doi: 10.1109/PIMRC.2019.8904321.
X. Wang, S. Ekin and E. Serpedin, "Joint Spectrum Sensing and Resource Allocation in Multi-Band-Multi-User Cognitive Radio Networks," in IEEE Transactions on Communications, vol. 66, no. 8, pp. 3281-3293, Aug. 2018, doi: 10.1109/TCOMM.2018.2807432.
L. Zhang, J. Tan, Y. Liang, G. Feng and D. Niyato, "Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks," in IEEE Transactions on Wireless Communications, vol. 18, no. 6, pp. 3281-3294, June 2019, doi: 10.1109/TWC.2019.2912754.
Y. Xu, P. Cheng, Z. Chen, Y. Li and B. Vucetic, "Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach," in IEEE Transactions on Signal Processing, vol. 66, no. 21, pp. 5634-5647, 1 Nov.1, 2018, doi: 10.1109/TSP.2018.2870379.
Kaur, A., Sharma, S. & Mishra, A. An Efficient Opposition Based Grey Wolf Optimizer for Weight Adaptation in Cooperative Spectrum Sensing. Wireless Pers Commun 118, 2345–2364 (2021). https://doi.org/10.1007/s11277-021-08129-4
Ramesh Sekaran, Surya Narayana Goddumarri, Suresh Kallam, Manikandan Ramachandran, Rizwan Patan, Deepak Gupta, “5G Integrated Spectrum Selection and Spectrum Access using AI-based Frame work for IoT based Sensor Networks”, Computer Networks, Volume 186, 2021, 107649, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2020.107649.
Mokhtar, R.A., Saeed, R.A., Alhumyani, H. et al. Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing. Cluster Comput (2021). https://doi.org/10.1007/s10586-021-03363-8
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Sunil Khoja, Anoop Tiwari
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
