Joint Subcarrier Assignment With Power Allocation for Sum Rate Maximization of D2D Communications in Wireless Cellular Networks

Kai, Caihong; Li, Hui; Xu, Lei; Li, Yuzhou; Jiang, Tao

doi:10.1109/tvt.2019.2903815

Cited by 62 publications

(55 citation statements)

References 37 publications

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“…A less complex and simpler pairing algorithm named as inverse popularity pairing order is given in [13]. Kai et al [41] proposed a heuristic resource allocation algorithm for uplink as well as downlink D2D. However, the power of each CU and DUE is fixed to the maximum value.…”

Section: Related Workmentioning

confidence: 99%

“…Kai et al . [41] proposed a heuristic resource allocation algorithm for uplink as well as downlink D2D. However, the power of each CU and DUE is fixed to the maximum value.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the complexity of SQP is equal to

O false(false(false(M / N false) + 1 false)^{3} N false)

[11]. The worst case complexity of the interior point method is given as

γ^{0.5} false(γ + θ false) θ^{2}

[41] where,

γ

is the number of inequality constraints and

θ

is the number of variables. Hence, the complexity of the same problem can be derived as

O false(false(false(M / N false) + 2 false)^{0.5} false(false(2 M / N false) + 3 false) false(false(M / N false) + 1 false)^{2} false)

.…”

Section: Channel Assignment and Power Optimisationmentioning

confidence: 99%

“…(b) Phase 2: The complexity of our d.c. programming based power allocation is O(((M /N) + 1) 3 ) while the complexity of bisection method based power allocation in [8] is O(log 2 (1/ϵ)MN).Moreover, the complexity of SQP is equal to O(((M /N) + 1) 3 N)[11]. The worst case complexity of the interior point method is given as γ 0.5 (γ + θ)θ 2[41] where, γ is the number of inequality constraints and θ is the number of variables. Hence, the complexity of (M /N) + 2) 0.5 ((2M /N) + 3)((M /N) + 1) 2 ).…”

mentioning

confidence: 99%

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Cluster oriented resource allocation and power optimisation for D2D network in cellular communications

Gour

Tyagi

2020

IET Networks

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This work proposes a cluster oriented channel assignment and difference of two convex functions (d.c.) programming based power optimisation algorithm for the downlink device-to-device (D2D) communication underlaying cellular networks. The authors' objective is to maximise D2D throughput while protecting the performance of existing cellular users (CUs) whose channel is reused among multiple D2D pairs, by imposing a minimum rate requirement constraint on each CU. The joint channel and power optimisation problem is a mixed integer non-linear programming problem that is NP-hard to solve. Therefore, a three stage solution is proposed: cluster formation to minimise the interference among the D2D pairs followed by an optimal channel assignment using the Hungarian algorithm and then an iterative power optimisation algorithm based on d.c. programming. Moreover, the transmission power of base station and D2D users is optimised on the same channel while considering the mutual interference among D2D pairs. Numerical results verify the effectiveness of the proposed resource allocation scheme in terms of the D2D sum rate and the number of successful transmission of D2D users. Moreover, the iterative power optimisation algorithm shows a fast convergence behaviour. In addition to this, the energy efficiency is also analysed with respect to various parameters.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Kai et al . [41] proposed a heuristic resource allocation algorithm for uplink as well as downlink D2D. However, the power of each CU and DUE is fixed to the maximum value.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the complexity of SQP is equal to

O false(false(false(M / N false) + 1 false)^{3} N false)

[11]. The worst case complexity of the interior point method is given as

γ^{0.5} false(γ + θ false) θ^{2}

[41] where,

γ

is the number of inequality constraints and

θ

is the number of variables. Hence, the complexity of the same problem can be derived as

O false(false(false(M / N false) + 2 false)^{0.5} false(false(2 M / N false) + 3 false) false(false(M / N false) + 1 false)^{2} false)

.…”

Section: Channel Assignment and Power Optimisationmentioning

confidence: 99%

mentioning

confidence: 99%

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Cluster oriented resource allocation and power optimisation for D2D network in cellular communications

Gour

Tyagi

2020

IET Networks

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show abstract

“…The aforementioned prior works mainly focus on resource allocation separately, while allocating power among subchannels depends on the channel condition of each subchannel, and to the best of our knowledge, the subchannel and power allocation should be jointly designed. Based on the subject of this study, the joint subcarrier and power allocation are most studied in the literature [22][23][24]. Furthermore, to address the traditional MU-OFDM, researchers have reported a few joint resource allocation schemes.…”

Section: Related Workmentioning

confidence: 99%

Joint optimal resource allocation schemes for downlink cooperative cellular networks over orthogonal frequency division multiplexing carriers

2020

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Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

Gopal,

2024

Int J Communication

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SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

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Joint Subcarrier Assignment With Power Allocation for Sum Rate Maximization of D2D Communications in Wireless Cellular Networks

Cited by 62 publications

References 37 publications

Cluster oriented resource allocation and power optimisation for D2D network in cellular communications

Cluster oriented resource allocation and power optimisation for D2D network in cellular communications

Joint optimal resource allocation schemes for downlink cooperative cellular networks over orthogonal frequency division multiplexing carriers

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

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