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Maximizing throughput and energy efficiency in 6G based on phone user clustering enabled UAV assisted downlink hybrid multiple access HetNet

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Abstract

The surge in technology is driving demands for real-time interactive applications and high-speed transmissions, necessitating improved network throughput and energy efficiency (EE) for immersive experiences. The rise in industrial automation has led to higher connectivity needs, straining fifth-generation networks. Sixth-generation networks aim to address these demands, potentially maximizing throughput and EE through enhanced coverage. This paper introduces innovative techniques like phone user clustering-based downlink hybrid multiple access in unmanned aerial vehicle-assisted heterogeneous networks (HetNets) to jointly optimize phone user (PU) admission, cell association, throughput, and EE while ensuring PU fair association with cell (PUFAC) and quality of service (QoS), i.e., minimum rate requirement of PUs. An outer approximation algorithm solves the mixed integer non-linear programming (MINLP) optimization problem arising from the transformation of the concave fractional programming optimization problem using the Charnes-Cooper transformation. The method’s effectiveness is assessed, showcasing its superiority over existing macro-cell-only networks and HetNets concerning throughput, EE, PU admission, PU-cell association, PUFAC, and QoS.

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  1. National University of Sciences and Technology, Islamabad, Pakistan

    Umar Ghafoor

  2. Government College University, Faisalabad, Pakistan

    Tahreem Ashraf

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A author wrote main manuscript, prepared figures, and conduct simulations. A and B authors reviewed the manuscript.

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Appendices

Appendix A

Fractional programing and Charnes Cooper transformation

Fractional programme (FP) contains objective function as a ratio of two nonlinear functions generally. A FP is defined as

$$\begin{aligned} \begin{aligned}&\max _{t \in S} \frac{j(t)}{k(t)}\\ \text {subject to}&\\&C1: g_{n}(t)\le 0 \\ \end{aligned} \end{aligned}$$
(42)

where j(t), k(t) and \(g_{n}(t)\) (where \(n= 1,2,...,N\)) are defined on set \(S \subset R^{t}\), having real values. In (42), if j(t) is positive and concave, k(t) is positive and convex, assuming S is convex set, then FP is called CFP. CCT [68] use following variable transformations to reduce a CFP to a concave programme.

$$\begin{aligned}{} & {} w=\frac{t}{k(t)} \end{aligned}$$
(43)
$$\begin{aligned}{} & {} \quad z=\frac{1}{k(t)} \end{aligned}$$
(44)

The equivalent concave problem for (42) can be written as

$$\begin{aligned} \begin{aligned}&\max _{\frac{w}{z} \in S} z j_{o}\frac{w}{z}\\ \text {subject to}&\\&C1: zk(\frac{w}{z})=1, \\&C2: zg_{n}(\frac{w}{z})\le 0, \forall \hspace{4pt} n= 1,2,3,...,N. \\\\ \end{aligned} \end{aligned}$$
(45)

Problem in (42) can have optimal solution if and only if problem in (45) have optimal solution.

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Ghafoor, U., Ashraf, T. Maximizing throughput and energy efficiency in 6G based on phone user clustering enabled UAV assisted downlink hybrid multiple access HetNet. Telecommun Syst 85, 563–590 (2024). https://doi.org/10.1007/s11235-024-01101-0

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