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Energy efficient MIMO–NOMA aided IoT network in b5g communications

Publication Type : Journal Article

Source : Computer Networks

Url :

Campus : Amaravati

School : School of Engineering

Year : 2022

Abstract : To accelerate future intelligent wireless systems, we designed an energy-efficient Massive multiple-input-multiple-output (MIMO)- non-orthogonal multiple access (NOMA) aided internet of things (IoT) network in this paper to support the massive number of distributed users and IoT devices with seamless data transfer and maintain connectivity between them. Massive MIMO has been identified as a suitable technology to implement the energy efficient IoT network in beyond 5G (B5G) communications due to its distinct characteristics with large number of antennas. However, to provide fast data transfer and maintain hyper connectivity between the IoT devices in B5G communications will bring the challenge of energy deficiency. Hence, we considered a massive MIMO–NOMA aided IoT network considering imperfect channel state information and practical power consumption at the transmitter. The far users of the base stations are selected to investigate the power consumption and quality of service. Then, calculate the power consumption which is non-convex function and non-deterministic polynomial problem. To solve the above problem, fractional programming properties are applied which converted polynomial problem into the difference of convex function. And then we employed the successive convex approximation technique to represent the non-convex to convex function. Effective iterative based branch and the reduced bound process are utilized to solve the problem. Numerical results observe that our implemented approach surpasses previous standard algorithms on the basis of convergence, energy-efficiency and user fairness.

Cite this Research Publication : Rajak, S., Selvaprabhu, P., Chinnadurai, S., Hosen, A. S., Saad, A., & Tolba, A. (2022). Energy efficient MIMO–NOMA aided IoT network in b5g communications. Computer Networks, 216, 109250

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