The optimal allocation of time and energy resources is characterized in a Wireless Powered Communication Network (WPCN) with Non-Orthogonal Multiple Access (NOMA). We consider two different formulations; in the first one (max-sum), the sum-throughput of all users is maximized. In the second one (max-min), and targeting fairness among users, we consider maximizing the min-throughput of all users. Under the above two formulations, two NOMA decoding schemes are studied, namely, Low Complexity Decoding (LCD) and Successive Interference Cancellation Decoding (SICD). Due to the non-convexity of three of the studied optimization problems, we consider an approximation approach, in which the non-convex optimization problem is approximated by a convex optimization problem, which satisfies all the constraints of the original problem. The approximate convex optimization problem can then be solved iteratively. The results show a trade-off between maximizing the sum throughout and achieving fairness through maximizing the minimum throughput.

Towards optimal resource allocation in wireless powered communication networks with non-orthogonal multiple access

Biason A.;ElBatt T.;Zorzi M.
2019

Abstract

The optimal allocation of time and energy resources is characterized in a Wireless Powered Communication Network (WPCN) with Non-Orthogonal Multiple Access (NOMA). We consider two different formulations; in the first one (max-sum), the sum-throughput of all users is maximized. In the second one (max-min), and targeting fairness among users, we consider maximizing the min-throughput of all users. Under the above two formulations, two NOMA decoding schemes are studied, namely, Low Complexity Decoding (LCD) and Successive Interference Cancellation Decoding (SICD). Due to the non-convexity of three of the studied optimization problems, we consider an approximation approach, in which the non-convex optimization problem is approximated by a convex optimization problem, which satisfies all the constraints of the original problem. The approximate convex optimization problem can then be solved iteratively. The results show a trade-off between maximizing the sum throughout and achieving fairness through maximizing the minimum throughput.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3414139
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