Energy Efficiency Improvement in Dynamic Orthogonal and Non-Orthogonal Multiple Access Uplink Networks

Document Type : Original Article

Authors

1 Kashan university

2 Communication group - Department of Electrical and Computers Engineering - University of Kashan - Kashan - Iran

3 Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran

Abstract

One proposed approach in fifth-generation wireless communication to support more users is the dynamic use of orthogonal and non-orthogonal multiple access schemes. In this research, a dynamic orthogonal and non-orthogonal multiple access system is proposed to maximize the energy efficiency (EE), and several schemes are presented for allocation of sub-channels and power. Due to the complexity of the proposed resource allocation problem and its non-convexity property, it is not possible to find a global solution. Hence, the main problem is divided into two sub-problems which are sub-channels and power allocation.  In the first step, the sub-channel allocation problem is solved and its output is sub-channel allocation for all active users and determining the access mode for each sub-carrier. The second step is the power allocation sub-problem which is converted into a quasi-convex sub-problem by using the difference of convex functions algorithm iteratively, and ultimately. Then, the bisection method is applied for solving the quasi-convex sub-problem. Also, the KKT equations are provided for the feasibility problem of the bisection method. Finally, in the simulation section, the maximum EE versus the maximum power for each user is calculated. Besides, the impact of user’s presence at the cell edge on the EE is discussed. According to the simulation results, our proposed resource allocation approach can improve the sum rate and EE of the system compared to the heuristic approach of the previous literature.

Keywords


[1]     P. Sciences, Y. Liu, M. Elkashlan, and Z. Qin, “Nonorthogonal multiple access for 5G and beyond,” Proc. IEEE, vol. 105, no. 12, pp. 2347–2381, 2017.
[2]     M. Masoudi et al., “Green mobile networks for 5G and beyond,” IEEE Access, vol. 7, pp. 107270–107299, 2019.
[3]     M. Moltafet, P. Azmi, and N. Mokari, “Power minimization in 5G heterogeneous cellular networks,” Iran. Conf. Electr. Eng., pp. 234–238, 2016.
[4]     F. Fang, H. Zhang, J. Cheng, S. Member, and V. C. M. Leung, “Energy-efficient resource allocation for downlink non-orthogonal multiple access network,” IEEE Trans. Commun., vol. 64, no. 9, pp. 3722–3732, 2016.
[5]     G. Liu, R. Wang, H. Zhang, S. Member, W. Kang, and T. Tsiftsis, “Super-modular game-based user scheduling and power allocation for energy-efficient NOMA network,” IEEE Trans. Wirel. Commun., vol. 17, no. 6, pp. 3877–3888, 2018.
[6]     M. Zeng, A. Yadav, O. A. Dobre, and H. V. Poor, “Energy-efficient power allocation for uplink NOMA,” 2018 IEEE Glob. Commun. Conf., pp. 1–6, 2018.
[7]     A. J. Muhammed, Z. Ma, P. D. Diamantoulakis, S. Member, L. Li, and G. K. Karagiannidis, “Energy-efficient resource allocation in multicarrier NOMA systems with fairness,” IEEE Trans. Commun., vol. 67, no. 12, pp. 8639–8654, 2019.
[8]     S. Fu, F. Fang, L. Zhao, Z. Ding, and X. Jian, “Joint transmission scheduling and power allocation in non-orthogonal multiple access,” IEEE Trans. Commun., vol. 67, no. 11, pp. 8137–8150, 2019.
[9]     M. Zeng, A. Yadav, O. A. Dobre, and H. V. Poor, “Energy-efficient power allocation for hybrid multiple Access systems,” 2018 IEEE Int. Conf. Commun. Work. (ICC Work., pp. 1–5, 2018.
[10]   W. U. Khan et al., “Joint spectral and energy efficiency optimization for downlink NOMA networks,” IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 2, pp. 1–12, 2020.
[11]   M. Baghani, S. Parsaeefard, M. Derakhshani, and W. Saad, “Dynamic non-orthogonal multiple access ( NOMA ) and orthogonal multiple access ( OMA ) in 5G wireless networkss,” IEEE Trans. Commun., vol. 67, no. 9, pp. 6360–6373, 2019.
[12]   S. Boyd, L. Xiao, A. Mutapic, and J. Mattingley, “Sequential convex programming,” Stanford University, Stanford, 2007.
[13]   S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004.
[14]   C.-Y. Chi, W.-C. Li, and L. Chia-Hsiang, Convex Optimization for Signal Processing and Communications. CRC Press, 2017.
[15]   A. L. Yuille and A. Rangarajan, “The Concave-Convex Procedure,” MIT Press Journals, vol. 15, no. 4, pp. 915–936, 2003.