Maximizing energy efficiency in 6G cognitive radio network

Umar Ghafoor , Adil Masood Siddiqui

›› 2025, Vol. 11 ›› Issue (5) : 1356 -1369.

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›› 2025, Vol. 11 ›› Issue (5) :1356 -1369. DOI: 10.1016/j.dcan.2025.06.008
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Maximizing energy efficiency in 6G cognitive radio network

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Abstract

The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation. Sixth-Generation (6G) networks, utilizing Cognitive Radio (CR) technology within CR Network (CRN), can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment (PME) is inactive or served by a Primary Base Station (PrBS). Secondary Mobile Equipment (SME) accesses this spectrum through a Secondary Base Station (SrBS) using opportunistic access, i.e., spectrum sensing. Hybrid Multiple Access (HMA), combining Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), can enhance Energy Efficiency (EE). Additionally, SME Clustering (SMEC) reduces inter-cluster interference, enhancing EE further. Despite these advancements, the integration of CR technology, HMA, and SMEC in CRN for better bandwidth utilization and EE remains unexplored. This paper introduces a new CR- assisted SMEC-based Downlink HMA (CR-SMEC-DHMA) method for 6G CRN, aimed at jointly optimizing SME admission, SME association, sum rate, and EE subject to imperfect sensing, collision, and Quality of Service (QoS). A novel optimization problem, formulated as a non-linear fractional programming problem, is solved using the Charnes-Cooper Transformation (CCT) to convert into a concave optimization problem, and an 𝜖-optimal Outer Approximation Algorithm (OAA) is employed to solve the concave optimization problem. Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA, surpassing the performance of current OMA- enabled CRN, NOMA-enabled CRN, SMEC-OMA enabled CRN, and SMEC-NOMA enabled CRN methods, with 𝜖-optimal results obtained at 𝜖 = 10−3, while satisfying Performance Measures (PMs) including SME admission in SMEC, SME association with SrBS, SME-channel opportunistic allocation through spectrum sensing, sum rate and overall EE within the 6G CRN.

Keywords

6G / CRN / HMA / SMEC / Energy efficiency / Charnes-cooper transformation / Outer approximation algorithm

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Umar Ghafoor, Adil Masood Siddiqui. Maximizing energy efficiency in 6G cognitive radio network. , 2025, 11(5): 1356-1369 DOI:10.1016/j.dcan.2025.06.008

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References

[1]

C. Bazelon, P. Sanyal, R. Taylor, S. Peretz, M. Sullivan, N. Selfe,P. Christenson, Forecasting wireless broadband capacity shortfalls, Available at SSRN 4528778.

[2]

H.F. Alhashimi, M.N. Hindia, K. Dimyati, E.B. Hanafi, N. Safie, F. Qamar, K. Azrin, Q.N. Nguyen,A survey on resource management for 6g heterogeneous networks: current research, future trends, and challenges, Electronics 12 (3) (2023) 647.

[3]

A. Taneja, S. Rani, A. Alhudhaif, D. Koundal, E.S. Gündüz, An optimized scheme for energy efficient wireless communication via intelligent reflecting surfaces, Expert Syst. Appl. 190 (2022) 116106.

[4]

A. Usman, I. Ozturk, A. Hassan, S.M. Zafar, S. Ullah, The effect of ict on energy consumption and economic growth in South Asian economies: an empirical analysis, Telemat. Inform. 58 (2021) 101537.

[5]

J. Zagdanski,P. Castells, The impact of spectrum policy on carbon emissions.

[6]

M. Abdullah, H.Z. Khan, U. Fakhar, A.N. Akhtar, S. Ansari, Satellite synergy: navi- gating resource allocation and energy efficiency in iot networks, J. Netw. Comput. Appl. 230 (2024) 103966.

[7]

U. Fakhar, H.Z. Khan, Z. Tariq, M. Ali, A.N. Akhtar, M. Naeem, A. Wakeel, Radio resource allocation for energy efficiency maximization in satellite-terrestrial inte- grated networks, Ad Hoc Netw. 138 (2023) 103001.

[8]

J. Lorincz, Z. Klarin, D. Begusic, Advances in improving energy efficiency of fiber-wireless access networks: a comprehensive overview, Sensors 23 (4) (2023) 2239.

[9]

C.-X. Wang, X. You, X. Gao, X. Zhu, Z. Li, C. Zhang, H. Wang, Y. Huang, Y. Chen, H. Haas, et al., On the road to 6G: Visions, requirements, key technologies, and testbeds, IEEE Commun. Surv. Tutor. 25 (2) (2023) 905-974.

[10]

J.S.P. Singh, Apc: adaptive power control technique for multi-radio multi-channel cognitive radio networks, Wirel. Pers. Commun. 122 (4) (2022) 3603-3632.

[11]

N.A. Alhaj, M.F. Jamlos, S.A. Manap, S. Abdelsalam, A.A. Bakhit, R. Mamat, M.A. Jamlos, M.S.M. Gismalla, M. Hamdan, Integration of hybrid networks, AI ultra massive-MIMO,THz frequency, and FBMC modulation toward 6G requirements: A review, IEEE Access 12 (2023) 483-513.

[12]

B. Clerckx, Y. Mao, Z. Yang, M. Chen, A. Alkhateeb, L. Liu, M. Qiu, J. Yuan, V.W. Wong, J. Montojo, Multiple access techniques for intelligent and multi-functional 6g: tutorial, survey, and outlook, arXiv preprint, arXiv:2401.01433.

[13]

S. Alraih, I. Shayea, M. Behjati, R. Nordin, N.F. Abdullah, A. Abu-Samah, D. Nandi, Revolution or evolution? Technical requirements and considerations towards 6g mo- bile communications, Sensors 22 (3) (2022) 762.

[14]

Y. Zeng, Y. Ge, X. Tan, Z. Ji, Z. Zhang, X. You, C. Zhang, A Deep-Learning-Aided Message Passing Detector for MIMO SC-FDMA, IEEE Trans. Veh. Technol. PP (2024) 1-5.

[15]

J. Liu, Z. Jiang, K. Lin, A Robust Reliable Low-power High Throughput Data Collec- tion Wireless Sensor Network, 2024.

[16]

M. Rezaie, M. Dosaranian-Moghadam, H. Bakhshi, M.H. Bibalan, Achievable rates and resource allocation for cdma-based overlay cognitive radio with rf energy har- vesting, IEEE Syst. J. 17 (1) (2022) 1137-1145.

[17]

D. Lin, K. Wang, T. Wang, Z. Ding, Uplink Data Rate Maximization in Multi-cell BackCom NOMA Systems, IEEE Open J. Commun. Soc. 5 (2024) 526-539.

[18]

M. Ghous, A.K. Hassan, Z.H. Abbas, G. Abbas, A. Hussien, T. Baker, Cooperative power-domain noma systems: an overview, Sensors 22 (24) (2022) 9652.

[19]

S.S. Rana, G. Verma, O. Sahu, Machine learning based improved user-pairing and power allocation with imperfect-successive interference cancellation for downlink noma-uav system, Int. J. Wirel. Inf. Netw. (2024) 1-8.

[20]

Himanshi V. Nandal, Analyzing the performance and wireless network capacity of noma: study of the impact of oma and noma on 5g network,in: International Con- ference on Data Science and Applications, Springer, 2023, pp. 283-307.

[21]

S.M. Hamedoon, J.N. Chattha, M. Bilal, Towards intelligent user clustering tech-niques for non-orthogonal multiple access: a survey, EURASIP J. Wirel. Commun. Netw. 2024 (1) (2024) 7.

[22]

R. Hashemi, H. Beyranvand, M.R. Mili, A. Khalili, H. Tabassum, D.W.K. Ng, Energy efficiency maximization in the uplink delta-oma networks, IEEE Trans. Veh. Technol. 70 (9) (2021) 9566-9571.

[23]

W.U. Khan, E. Lagunas, A. Mahmood, Z. Ali, M. Asif, S. Chatzinotas, B. Ottersten, Integration of NOMA with Reflecting Intelligent Surfaces: A Multi-cell Optimiza- tion with SIC Decoding Errors, IEEE Trans. Green Commun. Netw. 7 (3) (2023) 1554-1565.

[24]

J. He, D. Wang, Y. Chen, Energy efficiency optimization of d2d communications with swipt and noma, in: Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022), vol. 12586, SPIE, 2023, pp. 21-33.

[25]

N.K. Breesam, W.A. Al-Hussaibi, F.H. Ali, I.M. Al-Musawi, Efficient resource allo- cation for wireless-powered mimo-noma communications, IEEE Access 10 (2022) 130302-130313.

[26]

X. Zhao, F. Liu, Y. Zhang, S. Chen, J. Gan, Energy-efficient power allocation for full-duplex device-to-device underlaying cellular networks with noma, Electronics 12 (16) (2023) 3433.

[27]

D. Rayaroth, V.B. Kumaravelu, H.S. John Kennedy, K. Bagadi, F.R.C. Soria,Differen- tial evolution optimized non-orthogonal multiple access for sum rate maximization, Eng. Proc. 59 (1) (2023) 101.

[28]

S. Devipriya, J. Martin Leo Manickam, B. Victoria Jancee, Energy-efficient semi- supervised learning framework for subchannel allocation in non-orthogonal multiple access systems, ETRI J. 45 (6) (2023) 963-973.

[29]

X. Wei, H. Al-Obiedollah, K. Cumanan, Z. Ding, O.A. Dobre, Energy efficiency max- imization for hybrid tdma-noma system with opportunistic time assignment, IEEE Trans. Veh. Technol. 71 (8) (2022) 8561-8573.

[30]

S. Gopinath, K.V. Kumar, P. Elayaraja, A. Parameswari, S. Balakrishnan, M. Thirup- pathi, Sceer: secure cluster based efficient energy routing scheme for wireless sensor networks, Mater. Today Proc. 45 (2021) 3579-3584.

[31]

H. Zhang, H. Zhang, J. Dong, V.C. Leung, et al., Energy efficient user clustering and hybrid precoding for terahertz mimo-noma systems, in: ICC 2020-2020 IEEE International Conference on Communications (ICC), IEEE, 2020, pp. 1-5.

[32]

K. Lee, Energy-efficient secure communications for wireless-powered cognitive radio networks, Sensors 21 (23) (2021) 8040.

[33]

J. Liao, H. Yu, W. Jiang, R. Lin, J. Wang, Optimal resource allocation method for energy harvesting based underlay cognitive radio networks, PLoS ONE 18 (1) (2023) e0279886.

[34]

R. Amin, M. Fraz, M.M.A. Muslam, M. Hussain, J. Xie, Smart sensing enabled dy- namic spectrum management for cognitive radio networks, Front. Comput. Sci. 5 (2023) 1271899.

[35]

X. Wang, X. Wang, J. Ge, Z. Liu, Y. Ma, X. Li, Reconfigurable intelligent surface- assisted secure communication in cognitive radio systems, Energies 17 (2) (2024) 515.

[36]

S. Sodagari, Real-time scheduling for cognitive radio networks, IEEE Syst. J. 12 (3) (2017) 2332-2343.

[37]

A. Goldsmith, Wireless Communications, Cambridge University Press, 2005.

[38]

J. Li, T. Gao, B. He, W. Zheng, F. Lin, Power allocation and user grouping for noma downlink systems, Appl. Sci. 13 (4) (2023) 2452.

[39]

Z. Xie, P. Chen, Y. Fang, Q. Chen, Polarization-Aided Coding for Nonorthogonal Multiple Access, IEEE Internet Things J. 11 (17) (2024) 27894-27903.

[40]

S. Chilakala, M.S.S. Ram, Optimization of cooperative secondary users in cognitive radio networks, Eng. Sci. Technol. Int. J. 21 (5) (2018) 815-821.

[41]

K. Arshid, Z. Jianbiao, I. Hussain, M.S. Pathan, M. Yaqub, A. Jawad, R. Munir, F. Ahmad, Energy efficiency in cognitive radio network using cooperative spectrum sensing based on hybrid spectrum handoff, Egypt. Inform. J. 23 (4) (2022) 77-88.

[42]

R. Fletcher, S. Leyffer, Solving mixed integer nonlinear programs by outer approxi- mation, Math. Program. 66 (1-3) (1994) 327-349.

[43]

M.A. Duran, I.E. Grossmann, An outer-approximation algorithm for a class of mixed- integer nonlinear programs, Math. Program. 36 (3) (1986) 307-339.

[44]

C.A. Floudas, P.M. Pardalos, Encyclopedia of Optimization, Springer Science & Busi- ness Media, 2008.

[45]

H.Z. Khan, M. Ali, M. Naeem, I. Rashid, A.M. Siddiqui, M. Imran, S. Mumtaz, Joint admission control cell association, power allocation and throughput maximization in decoupled 5g heterogeneous networks, Telecommun. Syst. (2020) 1-14.

[46]

C.A. Floudas, E.N. Pistikopoulos, Non-linear and mixed-integer optimization. Fun- damentals and applications, J. Glob. Optim. 12 (1) (1998) 108.

[47]

A.H. Land, A.G. Doig, An automatic method for solving discrete programming prob- lems, in: 50 Years of Integer Programming 1958-2008, Springer, 2010, pp. 105-132.

[48]

A. Charnes, W.W. Cooper, Programming with linear fractional functionals, Nav. Res. Logist. Q. 9 (3-4) (1962) 181-186.

[49]

H.Z. Khan, M. Ali, I. Rashid, A. Ghafoor, M. Naeem, A.A. Khan, A.M. Saddiqui, Re- source allocation for energy efficiency optimization in uplink-downlink decoupled 5g heterogeneous networks, Int. J. Commun. Syst. 34 (14) (2021) e4925.

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