Radio resource allocation for heterogeneous traffic in GFDM‐NOMA heterogeneous cellular networks

Mokdad, Ali H.; Azmi, Paeiz; Mokari, Nader

doi:10.1049/iet-com.2016.0011

Cited by 30 publications

(26 citation statements)

References 46 publications

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“…By considering a finite set of transmit power and other variables of optimization problem, the objective becomes bounded and it coverages to a local solution. [26][27][28]…”

Section: Alternative Methodsmentioning

confidence: 99%

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Throughput maximization in poisson aerial base station–based networks with coverage probability and power density constraints

Saeedi

Azizi

Mokari

2018

Trans Emerging Tel Tech

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In this paper, we propose a new scheme called dynamic power‐dynamic density at which our goal is to maximize the throughput of aerial base station–based networks with coverage probability and power density constraints. The corresponding optimization problem is nonconvex and intractable. To tackle this issue, we propose an iterative algorithm based on the well‐known alternative method. In this method, we decompose the optimization problem into two nonconvex subproblems, namely, power allocation and density finding. Then, in order to provide a low complex and high speed algorithm, these subproblems are transformed into geometric programming forms, which can be solved by existing convex tools. Moreover, the convergence of the proposed iterative scheme is proved and it is studied from the computational complexity perspective. To investigate the performance of the proposed algorithm with existing schemes, we consider two other schemes, namely, dynamic power‐fixed density and dynamic density‐fixed power. Based on simulation results, the throughput of aerial base stations in our scheme is approximately 27% more than that of the two existing schemes. Furthermore, the trade‐off between power and density for different network parameters is studied.

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“…By considering a finite set of transmit power and other variables of optimization problem, the objective becomes bounded and it coverages to a local solution. [26][27][28]…”

Section: Alternative Methodsmentioning

confidence: 99%

“…Therefore, the objective function increases after each iteration i or stays constant. By considering a finite set of transmit power and other variables of optimization problem, the objective becomes bounded and it coverages to a local solution …”

Section: Solutionmentioning

confidence: 99%

Throughput maximization in poisson aerial base station–based networks with coverage probability and power density constraints

Saeedi

Azizi

Mokari

2018

Trans Emerging Tel Tech

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“…In order to improve the BER in a PD‐NOMA–based system, a transmit power limitation is considered, which can be mathematically expressed as

\sum_{i \in {scriptM}_{f}, {(||, h_{i, f}^{n})}^{2} > {(||, h_{m, f}^{n})}^{2}} ρ_{m, f}^{n} ρ_{i, f}^{n} p_{i, f}^{n} \leq ρ_{m, f}^{n} p_{m, f}^{n}, \forall m, n, f .

…”

Section: System Models and Problem Formulationsmentioning

confidence: 99%

“…In order to improve the BER in a PD-NOMA-based system, a transmit power limitation is considered, which can be mathematically expressed as 17,33 ∑…”

Section: • Ber Constraintmentioning

confidence: 99%

Optimal radio resource allocation to achieve a low BER in PD‐NOMA–based heterogeneous cellular networks

Moltafet

Azmi

Javan

et al. 2019

Trans Emerging Tel Tech

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In this paper, the problem of downlink radio resource allocation for a power‐domain nonorthogonal multiple‐access–based heterogeneous cellular network is investigated. We propose two scenarios: (1) resource allocation with exclusive subcarrier allocation and (2) resource allocation considering intercell interference. The aim of this paper is to obtain joint subcarrier and power allocation for the two considered system models by addressing the maximization of the users' sum rate subject to the bit error rate constraint, the successive interference cancellation constraint, subcarrier constraints, and the transmit power restriction of each base station. To solve the corresponding optimization problems, an alternating method is applied by decoupling the proposed problems into two subproblems, namely, subcarrier allocation problem and power allocation problem, where they are solved by using the mesh adaptive direct search algorithm and different successive convex approximation methods, respectively. Moreover, to evaluate the optimality gap of the alternating method, the proposed problems are solved using an optimal method, namely, monotonic optimization with high complexity as a benchmark. As the results show, with the bit error rate constraint, the link‐level performance, at the cost of a decreasing system sum rate, is improved.

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“…In [18], joint power and channel allocation for PD-NOMA in 5G downlink cellular systems by considering one BS is developed. In [19], [20], the radio resource allocation for HCNs based on PD-NOMA is studied. In [22], robust radio resource allocation for a cellular system based on PD-NOMA is investigated.…”

Section: Introductionmentioning

confidence: 99%

Cross-Layer Energy Efficient Resource Allocation in PD-NOMA Based H-CRANs: Implementation via GPU

Mokdad

Azmi

Mokari

et al. 2019

IEEE Trans. on Mobile Comput.

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In this paper, we propose a cross layer energy efficient resource allocation and remote radio head (RRH) selection algorithm for heterogeneous traffic in power domainnon-orthogonal multiple access (PD-NOMA) based heterogeneous cloud radio access networks (H-CRANs). The main aim is to maximize the EE of the elastic users subject to the average delay constraint of the streaming users and the constraints, RRH selection, subcarrier, transmit power and successive interference cancellation. The considered optimization problem is non-convex, NP-hard and intractable. To solve this problem, we transform the fractional objective function into a subtractive form. Then, we utilize successive convex approximation approach. Moreover, in order to increase the processing speed, we introduce a framework for accelerating the successive convex approximation for low complexity with the Lagrangian method on graphics processing unit. Furthermore, in order to show the optimality gap of the proposed successive convex approximation approach, we solve the proposed optimization problem by applying an optimal method based on the monotonic optimization. Studying different scenarios show that by using both PD-NOMA technique and H-CRAN, the system energy efficiency is improved.Index Terms-Heterogeneous traffic, PD-NOMA, remote radio head selection, graphics processing unit.(RRHs) where one of the RRHs is a high power node (HPN) and the others are low power nodes (LPNs). Instead of the processing that is distributed at the base stations (BSs) in the HCN, a centralized signal processing is applied in the BBU pool which reduces the manufacturing and operating cost. Moreover, a cooperation between different RRHs is permitted due to the centralized signal processing, thus spectrum efficiency and link reliability are improved. The RRHs compress and forward the received signals from the user to the BBU pool via high bandwidth and low latency fiber links [2]. Therefore, H-CRANs improve the users quality of service (QoS), the spectral efficiency (SE) of the system and increase the network architecture flexibility. Moreover, H-CRANs decrease the power consumption of the system, and PD-NOMA technique improves the system throughput, SE, and energy efficiency (EE) of the fifth generation (5G) cellular communication systems. In order to cover the advantages of H-CRAN and PD-NOMA technique at the same time, we consider PD-NOMA based H-CRAN system.Due to the enormous increase in mobile data traffic and the complexity of the proposed technologies including PD-NOMA and H-CRAN, a high computational processing is needed where the conventional methods can not tackle this issue. Therefore, we seek toward a new processing method which accelerates the processing time. Graphics Processing Unit (GPU), due to the advantage of its massive number of cores and its parallelism directives, handles the works with parallel data [3]- [7]. Accelerating applications and simulations with using GPUs has turned out to be progressively wellknown from 2006 [8].OpenACC is an open GPU d...

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Radio resource allocation for heterogeneous traffic in GFDM‐NOMA heterogeneous cellular networks

Cited by 30 publications

References 46 publications

Throughput maximization in poisson aerial base station–based networks with coverage probability and power density constraints

Throughput maximization in poisson aerial base station–based networks with coverage probability and power density constraints

Optimal radio resource allocation to achieve a low BER in PD‐NOMA–based heterogeneous cellular networks

Cross-Layer Energy Efficient Resource Allocation in PD-NOMA Based H-CRANs: Implementation via GPU

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