Optimal Power Allocation for Downlink NOMA Heterogeneous Networks to Improve Sum Rate and Outage Probability

Trankatwar, Sachin; Wali, Prashant

doi:10.1109/indiscon54605.2022.9862842

Cited by 2 publications

(2 citation statements)

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“…Thus, the worst-case time computation of 𝛼 𝑚 is 𝑀 3 . Since the inner loop (line 5 to line 15) repeats 𝑀 times, the inner loop's worst-case complexity is 𝑀 4 . Assume that the total number of iterations required for algorithm convergence is 𝑇 .…”

Section: Solution Of Multi-objective Optimization Problemmentioning

confidence: 99%

“…Non-orthogonal multiple access (NOMA) has been recognized as one of the important enabling elements to satisfy such a demand. 1,2,3,4 . The fundamental idea of NOMA is that several users are served in a single time-frequency block, in contrast to the orthogonal multiple access (OMA) method, which serves a single user in each resource block 2,5,6,7,8 .…”

Section: Introductionmentioning

confidence: 99%

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Power Allocation for Joint Sum Rate and Fairness Optimization in Downlink NOMA Networks

Trankatwar

Wali

2023

Preprint

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Non-Orthogonal Multiple Access (NOMA) is an essential enabling technology that is expected to help future broadband wireless networks meet their higher system throughput requirements. However, in addition, NOMA should also aim to provide a desired trade-off between system throughput and user fairness, as fairness is an equally important aspect that should go hand in hand with system throughput. In order to achieve such a desired trade-off, in this paper, we derive optimal power allocation (PA) coefficients at the NOMA transmitter, by formulating and solving a joint sum rate and fairness optimization problem. To the best of our knowledge, such a work is missing in the literature. Additionally, along with the usual transmitter power budget and Quality of Service (QoS) constraints, we also consider the minimum transmit power gap between users, that is required for successful signal decoding of a user in a SIC receiver, a constraint ignored at large in the literature. The weighted sum method is used to convert the joint objective optimization problem into a single-objective optimization problem to make it analytically solvable and also provide the desired trade-off between the conflicting objectives. As part of the validation process, we present simulation results and also compare the performance of the derived system with a system that considers only the sum rate objective.

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Section: Solution Of Multi-objective Optimization Problemmentioning

confidence: 99%