Qiang Fan
ABSTRACT
Dynamic spectrum access (DSA) is a promising technology to increase the spectrum efficiency of Internet of Things (IoT) networks, where the traffic demand grows up dramatically recently. In this paper, an intelligent DSA-assisted IoT network is introduced, where we investigate the spectrum sensing through neuromorphic computing (NC) and spectrum access through genetic algorithm (GA)-based power allocation. To be specific, we apply the NC's unconventional computing architectures that exploit and harness the intrinsic dynamics for computation, and thus provide increased functionality with better spectrum sensing performance requiring significantly lower size, weight, and power budgets. Furthermore, we design a GA algorithm to intelligently search the desirable transmission power for multiple IoT devices sharing the same channel to enhance the capacity of the highly dynamic DSA-assisted IoT network. Extensive simulation results have demonstrated the benefits of NC and GA compared to other baseline algorithms and methodologies.
- R. Atat, L. Liu, H. Chen, J. Wu, H. Li, and Y. Yi. 2017. Enabling cyber-physical communication in 5G cellular networks: challenges, spatial spectrum sensing, and cyber-security. IET Cyber-Physical Systems: Theory Applications 2, 1 (2017), 49--54.Google Scholar
Cross Ref
- H. Chang, L. Liu, H. Song, A. Pidwerbetsky, A. Berlinsky, J. Ashdown, K. Turck, and Y. Yi. 2019. Maximizing System Throughput in D2D Networks Using Alternative DC Programming. In Proc. IEEE GLOBECOM. 1--6.Google Scholar
- H. Chang, H. Song, Y. Yi, J. Zhang, H. He, and L. Liu. 2019. Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach. IEEE Internet Things J. 6, 2 (2019), 1938--1948.Google ScholarCross Ref
- H. Chen, L. Liu, H. S. Dhillon, and Y. Yi. 2019. QoS-Aware D2D Cellular Networks With Spatial Spectrum Sensing: A Stochastic Geometry View. IEEE Transactions on Communications 67, 5 (2019), 3651--3664.Google ScholarCross Ref
- H. Chen, L. Liu, T. Novlan, J. D. Matyjas, B. L. Ng, and J. Zhang. 2016. Spatial Spectrum Sensing-Based Device-to-Device Cellular Networks. IEEE Transactions on Wireless Communications 15, 11 (2016), 7299--7313.Google ScholarDigital Library
- M. Chiang et al. 2007. Power Control By Geometric Programming. IEEE Trans. Wireless Commun. 6, 7 (Jul. 2007), 2640--2651.Google ScholarDigital Library
- Qiang Fan and Nirwan Ansari. 2016. Green energy aware user association in heterogeneous networks. In Proc. of IEEE Wireless Communications and Networking Conference (WCNC'2016). Doha, Qatar.Google ScholarDigital Library
- Kian Hamedani, Lingjia Liu, Shiya Liu, Haibo He, and Yang Yi. 2020. Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020. AAAI Press, 1292--1299.Google Scholar
- Y. He and S. Dey. 2013. Power Allocation for Secondary Outage Minimization in Spectrum Sharing Networks with Limited Feedback. IEEE Transactions on Communications 61, 7 (2013), 2648--2663.Google ScholarCross Ref
- Damien B Jourdan and Olivier L de Weck. 2004. Layout optimization for a wireless sensor network using a multi-objective genetic algorithm. In 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring, Vol. 5. 2466--2470.Google ScholarCross Ref
- X. Kang. 2013. Optimal Power Allocation for Bi-Directional Cognitive Radio Networks with Fading Channels. IEEE Wireless Commun. Lett. 2, 5 (2013), 567--570.Google ScholarCross Ref
- L. Li, L. Liu, J. Bai, H. Chang, H. Chen, J. D. Ashdown, J. Zhang, and Y. Yi. early access, 2020. Accelerating Model Free Reinforcement Learning with Imperfect Model Knowledge in Dynamic Spectrum Access. IEEE Internet of Things Journal (early access, 2020).Google Scholar
- O. Naparstek and K. Cohen. 2019. Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access. IEEE Transactions on Wireless Communications 18, 1 (2019), 310--323.Google ScholarDigital Library
- Thomas Warren Rondeau. 2007. Application of artificial intelligence to wireless communications. Ph.D. Dissertation. Virginia Tech.Google Scholar
- B. Shang and L. Liu. 2020. Machine Learning Meets Point Process: Spatial Spectrum Sensing in User-Centric Networks. IEEE Wireless Communications Letters 9, 1 (2020), 34--37.Google ScholarCross Ref
- H. Song et al. 2019. Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum Access Networks. In IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 774--779.Google Scholar
- Darrell Whitley. 1994. A genetic algorithm tutorial. Statistics and computing 4, 2 (1994), 65--85.Google Scholar
- Qiong Wu et al. 2019. Trajectory protection schemes based on a gravity mobility model in IoT. Electronics 8, 2 (2019), 148.Google ScholarCross Ref
- Wei Yu, G. Ginis, and J. M. Cioffi. 2002. Distributed multiuser power control for digital subscriber lines. IEEE J. Sel. Areas Commun. 20, 5 (Jun. 2002), 1105--1115.Google Scholar
- S. M. Zafaruddin, I. Bistritz, A. Leshem, and D. Niyato. 2019. Distributed Learning for Channel Allocation Over a Shared Spectrum. IEEE J. Sel. Areas Commun. 37, 10 (2019), 2337--2349.Google ScholarCross Ref
Index Terms
- Intelligent DSA-assisted clustered IoT networks: neuromorphic computing meets genetic algorithm
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