[en] Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Khan, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Nguyen, Tu N.
Jameel, Furqan
Jamshed, Muhammad Ali
Pervaiz, Haris
Javed, Muhammad Awais
Jantti, Riku
External co-authors :
yes
Language :
English
Title :
Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks
Alternative titles :
[en] Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks
Publication date :
October 2022
Journal title :
IEEE Transactions on Intelligent Transportation Systems
ISSN :
1558-0016
Publisher :
Institute of Electrical and Electronics Engineers, New-York, United States - New York
W. U. Khan, F. Jameel, T. Ristaniemi, S. Khan, G. A. S. Sidhu, and J. Liu, "Joint spectral and energy efficiency optimization for downlink NOMA networks, " IEEE Trans. Cognit. Commun. Netw., vol. 6, no. 2, pp. 645-656, Jun. 2020.
F. Jameel, W. U. Khan, N. Kumar, and R. Jantti, "Efficient power-splitting and resource allocation for cellular V2X communications, " IEEE Trans. Intell. Transp. Syst., vol. 22, no. 6, pp. 3547-3556, Jun. 2021.
W. U. Khan, X. Li, A. Ihsan, M. A. Khan, V. G. Menon, and M. Ahmed, "NOMA-enabled optimization framework for next-generation small-cell IoV networks under imperfect SIC decoding, " IEEE Trans. Intell. Transp. Syst., early access, Jun. 29, 2021, doi: 10.1109/TITS.2021.3091402.
M. A. Javed and S. Zeadally, "AI-empowered content caching in vehicular edge computing: Opportunities and challenges, " IEEE Netw., vol. 35, no. 3, pp. 109-115, May 2021.
Z. Ali, W. Farooq, W. U. Khan, M. Qureshi, and G. A. S. Sidhu, "Artificial intelligence techniques for rate maximization in interference channels, " Phys. Commun., vol. 47, Aug. 2021, Art. no. 101294.
F. Jameel, U. Javaid, W. U. Khan, M. N. Aman, H. Pervaiz, and R. Jäntti, "Reinforcement learning in blockchain-enabled IIoT networks: A survey of recent advances and open challenges, " Sustainability, vol. 12, no. 12, p. 5161, Jun. 2020.
U. M. Malik, M. A. Javed, S. Zeadally, and S. U. Islam, "Energy efficient fog computing for 6G enabled massive IoT: Recent trends and future opportunities, " IEEE Internet Things J., early access, Mar. 22, 2021, doi: 10.1109/JIOT.2021.3068056.
M. Z. Khan, M. Rahim, M. A. Javed, F. Ghabban, O. Ameerbakhsh, and I. Alfadli, "A D2D assisted multi-hop data dissemination protocol for inter-UAV communication, " Int. J. Commun. Syst., vol. 34, no. 11, May 2021, Art. no. e4857.
I. A. Elgendy, W.-Z. Zhang, H. He, B. B. Gupta, and A. A. A. El-Latif, "Joint computation offloading and task caching for multi-user and multitask MEC systems: Reinforcement learning-based algorithms, " Wireless Netw., vol. 27, no. 3, pp. 2023-2038, Apr. 2021.
M. S. E. Shahabadi, H. Tabrizchi, M. K. Rafsanjani, B. B. Gupta, and F. Palmieri, "A combination of clustering-based under-sampling with ensemble methods for solving imbalanced class problem in intelligent systems, " Technol. Forecasting Social Change, vol. 169, Aug. 2021, Art. no. 120796.
U. Farooq, M. W. Shabir, M. A. Javed, and M. Imran, "Intelligent energy prediction techniques for fog computing networks, " Appl. Soft Comput., vol. 111, Nov. 2021, Art. no. 107682. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1568494621006037
M. Rahim et al., "An intelligent content caching protocol for connected vehicles, " Trans. Emerg. Telecommun. Technol., vol. 32, no. 4, Apr. 2021, Art. no. e4231, doi: 10.1002/ett.4231.
M. Rahim, M. A. Javed, A. N. Alvi, and M. Imran, "An efficient caching policy for content retrieval in autonomous connected vehicles, " Transp. Res. A, Policy Pract., vol. 140, pp. 142-152, Oct. 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0965856420306893
M. A. Naeem, T. N. Nguyen, R. Ali, K. Cengiz, Y. Meng, and T. Khurshaid, "Hybrid cache management in IoT-based named data networking, " IEEE Internet Things J., early access, Apr. 23, 2021, doi: 10.1109/JIOT.2021.3075317.
F. Jameel, S. Zeb, W. U. Khan, S. A. Hassan, Z. Chang, and J. Liu, "NOMA-enabled backscatter communications: Toward battery-free IoT networks, " IEEE Internet Things Mag., vol. 3, no. 4, pp. 95-101, Dec. 2020.
Z. Ali et al., "Fair power allocation in cooperative cognitive systems under NOMA transmission for future IoT networks, " Alexandria Eng. J., vol. 61, no. 1, pp. 575-583, 2022.
W. U. Khan et al., "Energy efficiency maximization for beyond 5G NOMA-enabled heterogeneous networks, " Peer Peer Netw. Appl., pp. 1-15, Jun. 2021.
S. Zeadally, M. A. Javed, and E. B. Hamida, "Vehicular communications for ITS: Standardization and challenges, " IEEE Commun. Stand. Mag., vol. 4, no. 1, pp. 11-17, Dec. 2020.
R. Long, H. Guo, L. Zhang, and Y.-C. Liang, "Full-duplex backscatter communications in symbiotic radio systems, " IEEE Access, vol. 7, pp. 21597-21608, 2019.
F. Jameel, T. Ristaniemi, I. Khan, and B. M. Lee, "Simultaneous harvest-and-transmit ambient backscatter communications under Rayleigh fading, " EURASIP J. Wireless Commun. Netw., vol. 2019, no. 1, p. 166, Dec. 2019.
M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, "Artificial neural networks-based machine learning for wireless networks: A tutorial, " IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 3039-3071, 4th Quart., 2019.
P. Do, T. H. V. Phan, and B. B. Gupta, "Developing a Vietnamese tourism question answering system using knowledge graph and deep learning, " ACM Trans. Asian Low-Resource Lang. Inf. Process., vol. 20, no. 5, pp. 1-18, Jun. 2021, doi: 10.1145/3453651.
M. Hammad, M. H. Alkinani, B. B. Gupta, and A. A. A. El-Latif, "Myocardial infarction detection based on deep neural network on imbalanced data, " Multimedia Syst., pp. 1-13, Jan. 2021.
C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo, "Machine learning paradigms for next-generation wireless networks, " IEEE Wireless Commun., vol. 24, no. 2, pp. 98-105, Apr. 2017.
R. I. Ansari et al., "A new dimension to spectrum management in IoT empowered 5G networks, " IEEE Netw., vol. 33, no. 4, pp. 186-193, Jul. 2019.
W. U. Khan, J. Liu, F. Jameel, V. Sharma, R. Jantti, and Z. Han, "Spectral efficiency optimization for next generation NOMA-enabled IoT networks, " IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 15284-15297, Dec. 2020.
M. Matinmikko-Blue, S. Yrjola, V. Seppanen, P. Ahokangas, H. Ham-mainen, and M. Latva-aho, "Analysis of spectrum valuation approaches: The viewpoint of local 5G networks in shared spectrum bands, " in Proc. IEEE Int. Symp. Dyn. Spectr. Access Netw. (DySPAN), Oct. 2018, pp. 1-9.
A. R. Syed, K. L. A. Yau, H. Mohamad, W. Hashim, and N. Ramli, "Channel selection in multi-hop cognitive radio network using reinforcement learning: An experimental study, " in Proc. Int. Conf. Frontiers Commun., Netw. Appl. (ICFCNA-Malaysia), 2014, pp. 10-16.
S. Tubachi, M. Venkatesan, and A. V. Kulkarni, "Predictive learning model in cognitive radio using reinforcement learning, " in Proc. IEEE Int. Conf. Power, Control, Signals Instrum. Eng. (ICPCSI), Sep. 2017, pp. 564-567.
T. K. Vu, M. Bennis, M. Debbah, M. Latva-Aho, and C. S. Hong, "Ultra-reliable communication in 5G mmWave networks: A risk-sensitive approach, " IEEE Commun. Lett., vol. 22, no. 4, pp. 708-711, Apr. 2018.
Y. Guo, F. R. Yu, J. An, K. Yang, Y. He, and V. C. M. Leung, "Buffer-aware streaming in small-scale wireless networks: A deep reinforcement learning approach, " IEEE Trans. Veh. Technol., vol. 68, no. 7, pp. 6891-6902, Jul. 2019.
T. K. Vu, C.-F. Liu, M. Bennis, M. Debbah, and M. Latva-Aho, "Path selection and rate allocation in self-backhauled mmWave networks, " in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Apr. 2018, pp. 1-6.
T. K. Vu, M. Bennis, M. Debbah, and M. Latva-Aho, "Joint path selection and rate allocation framework for 5G self-backhauled mm-Wave networks, " IEEE Trans. Wireless Commun., vol. 18, no. 4, pp. 2431-2445, Apr. 2019.
S. Nie, Z. Fan, M. Zhao, X. Gu, and L. Zhang, "Q-learning based power control algorithm for D2D communication, " in Proc. IEEE 27th Annu. Int. Symp. Pers., Indoor, Mobile Radio Commun. (PIMRC), Sep. 2016, pp. 1-6.
S. Sharma and B. Singh, "Cooperative reinforcement learning based adaptive resource allocation in V2 V communication, " in Proc. 6th Int. Conf. Signal Process. Integr. Netw. (SPIN), Mar. 2019, pp. 489-494.
Y. Qiu, Z. Ji, Y. Zhu, G. Meng, and G. Xie, "Joint mode selection and power adaptation for D2D communication with reinforcement learning, " in Proc. IEEE ISWCS, Aug. 2018, pp. 1-6.
A. Moussaid, W. Jaafar, W. Ajib, and H. Elbiaze, "Deep reinforcement learning-based data transmission for D2D communications, " in Proc. 14th Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMob), Oct. 2018, pp. 1-7.
F. Jameel, W. U. Khan, M. A. Jamshed, H. Pervaiz, Q. Abbasi, and R. Jantti, "Reinforcement learning for scalable and reliable power allocation in SDN-based backscatter heterogeneous network, " in Proc. IEEE INFOCOM Conf. Comput. Commun. Workshops (INFOCOM WKSHPS), Jul. 2020, pp. 1069-1074.
F. Jameel, W. U. Khan, S. T. Shah, and T. Ristaniemi, "Towards intelligent IoT networks: Reinforcement learning for reliable backscatter communications, " in Proc. IEEE Globecom Workshops (GC Wkshps), Dec. 2019, pp. 1-6.
T. T. Anh, N. C. Luong, D. Niyato, Y.-C. Liang, and D. In Kim, "Deep reinforcement learning for time scheduling in RF-powered backscatter cognitive radio networks, " 2018, arXiv:1810.04520.
A. Rahmati and H. Dai, "Reinforcement learning for interference avoidance game in RF-powered backscatter communications, " 2019, arXiv:1903.03600.
Y. Hu, P. Wang, Z. Lin, M. Ding, and Y.-C. Liang, "Machine learning based signal detection for ambient backscatter communications, " in Proc. IEEE Int. Conf. Commun. (ICC), May 2019, pp. 1-6.
S. Ma, Y. Zhu, G. Wang, and R. He, "Machine learning aided channel estimation for ambient backscatter communication systems, " in Proc. IEEE Int. Conf. Commun. Syst. (ICCS), Dec. 2018, pp. 67-71.
X. Wen, S. Bi, X. Lin, L. Yuan, and J. Wang, "Throughput maximization for ambient backscatter communication: A reinforcement learning approach, " in Proc. IEEE 3rd Inf. Technol., Netw., Electron. Autom. Control Conf. (ITNEC), Mar. 2019, pp. 997-1003.
W. U. Khan, J. Liu, F. Jameel, M. T. R. Khan, S. H. Ahmed, and R. Jantti, "Secure backscatter communications in multi-cell NOMA networks: Enabling link security for massive IoT networks, " in Proc. IEEE INFOCOM Conf. Comput. Commun. Workshops (INFOCOM WKSHPS), Jul. 2020, pp. 213-218.
T. Hong, C. Liu, and M. Kadoch, "Machine learning based antenna design for physical layer security in ambient backscatter communications, " Wireless Commun. Mobile Comput., vol. 2019, pp. 1-10, Jan. 2019.
X. Fan, F. Wang, F. Wang, W. Gong, and J. Liu, "When RFID meets deep learning: Exploring cognitive intelligence for activity identification, " IEEE Wireless Commun., vol. 26, no. 3, pp. 19-25, Jun. 2019.
Q. Zhang, Y.-C. Liang, and H. V. Poor, "Intelligent user association for symbiotic radio networks using deep reinforcement learning, " 2019, arXiv:1905.04041.
W. U. Khan, E. Lagunas, A. Mahmood, S. Chatzinotas, and B. Ottersten, "Integration of backscatter communication with multi-cell NOMA: A spectral efficiency optimization under imperfect SIC, " 2021, arXiv:2109.11509.
M. Ahmed, W. U. Khan, A. Ihsan, X. Li, J. Li, and T. A. Tsiftsis, "Backscatter sensors communication for 6G low-powered NOMA-enabled IoT networks under imperfect SIC, " 2021, arXiv:2109.12711.
W. U. Khan, M. A. Javed, T. N. Nguyen, S. Khan, and B. M. Elhalawany, "Energy-efficient resource allocation for 6G backscatter-enabled NOMA IoV networks, " IEEE Trans. Intell. Transp. Syst., early access, Sep. 21, 2021, doi: 10.1109/TITS.2021.3110942.
X. Li et al., "Physical layer security of cognitive ambient backscat-ter communications for green Internet-of-Things, " IEEE Trans. Green Commun. Netw., vol. 5, no. 3, pp. 1066-1076, Sep. 2021.
W. U. Khan, X. Li, M. Zeng, and O. A. Dobre, "Backscatter-enabled NOMA for future 6G systems: A new optimization framework under imperfect SIC, " IEEE Commun. Lett., vol. 25, no. 5, pp. 1669-1672, May 2021.
A. Ihsan, W. Chen, and W. U. Khan, "Energy-efficient backscatter aided uplink NOMA roadside sensor communications under channel estimation errors, " 2021, arXiv:2109.05341.
W. U. Khan, F. Jameel, N. Kumar, R. Jantti, and M. Guizani, "Backscatter-enabled efficient V2X communication with non-orthogonal multiple access, " IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1724-1735, Feb. 2021.
F. Jameel et al., "Multi-tone carrier backscatter communications for massive IoT networks, " in Wireless-Powered Backscatter Communications for Internet of Things. New York, NY, USA: Springer, 2021, pp. 39-50.
W. U. Khan, N. Imtiaz, and I. Ullah, "Joint optimization of NOMA-enabled backscatter communications for beyond 5G IoT networks, " Internet Technol. Lett., vol. 4, no. 2, Mar. 2021, Art. no. e265.
F. Jameel et al., "Time slot management in backscatter systems for large-scale IoT networks, " in Wireless-Powered Backscatter Communications for Internet of Things. New York, NY, USA: Springer, 2021, pp. 51-65.
W. U. Khan, Z. Ali, A. U. Khan, and G. A. S. Sidhu, "Secure backscatter-enabled NOMA system design in 6G era, " Internet Technol. Lett., p. e307, Jul. 2021.
F. Amato, C. W. Peterson, B. P. Degnan, and G. D. Durgin, "Tunneling RFID tags for long-range and low-power microwave applications, " IEEE J. Radio Freq. Identif., vol. 2, no. 2, pp. 93-103, Jun. 2018.
R. Duan, E. Menta, H. Yiǧitler, R. Jäntti, and Z. Han, "Hybrid beam-former design for high dynamic range ambient backscatter receivers, " 2019, arXiv:1901.05323.
Q. Ye, B. Rong, Y. Chen, M. Al-Shalash, C. Caramanis, and J. G. Andrews, "User association for load balancing in heterogeneous cellular networks, " IEEE Trans. Wireless Commun., vol. 12, no. 6, pp. 2706-2716, Jun. 2013.
S. Boyd, S. P. Boyd, and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
W. Yu and R. Lui, "Dual methods for nonconvex spectrum optimization of multicarrier systems, " IEEE Trans. Commun., vol. 54, no. 7, pp. 1310-1322, Jul. 2006.
S. Gortzen and A. Schmeink, "Optimality of dual methods for discrete multiuser multicarrier resource allocation problems, " IEEE Trans. Wireless Commun., vol. 11, no. 10, pp. 3810-3817, Oct. 2012.
S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, "An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel, " IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4331-4340, Sep. 2011.
N. Wang, C. He, T. A. Gulliver, and V. K. Bhargava, "Generalized queue-aware resource management and scheduling for wireless communications, " IEEE Access, vol. 3, pp. 1298-1312, 2015.
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, "Statistical and machine learning forecasting methods: Concerns and ways forward, " PLoS ONE, vol. 13, no. 3, Mar. 2018, Art. no. e0194889.
X. Peng, K. Ota, and M. Dong, "Edge computing based traffic analysis system using broad learning, " in Proc. IEEE AICON. Harbin, China: Springer, 2019, pp. 238-251.
N. Huin, J. Leguay, S. Martin, P. Medagliani, and S. Cai, "Routing and slot allocation in 5G hard slicing, " in Proc. Int. Netw. Optim. Conf. (INOC). France: Univ. Avignon, 2019, pp. 72-77.
C. Liu, B. Natarajan, and H. Xia, "Small cell base station sleep strategies for energy efficiency, " IEEE Trans. Veh. Technol., vol. 65, no. 3, pp. 1652-1661, Mar. 2016.
