Joint Resource Allocation and Power Control Algorithm for Cooperative D2D Heterogeneous Networks

Gao, Hongyuan; Zhang, Shibo; Su, Yumeng; Diao, Ming

doi:10.1109/access.2019.2895975

Cited by 45 publications

(36 citation statements)

References 30 publications

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“…It is noticed from the above literature survey that most of the research work for resource allocation in D2D is dedicated to uplink. A few works like [5,6,[28][29][30][31][32] and more, have proposed resource allocation for downlink D2D. Since uplink and downlink traffic pattern is more likely to be symmetric in future cellular networks [5] and CUs near to the BS may suffer from strong interference due to the near-far effect [6].…”

Section: Related Workmentioning

confidence: 99%

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.

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Section: Related Workmentioning

confidence: 99%

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

Gour

Tyagi

2020

IET Networks

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“…According to the theory of SSA and quantum computing [21][22][23][24][25], the GO-QSSA is proposed in this paper. e GO-QSSA not only takes advantage of salps' swarm intelligence but also designs brand new mathematical equations to overcome the drawbacks of SSA.…”

Section: Approximation For Nakagami-m Quantile Functionmentioning

confidence: 99%

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

Gao

Hou

Zhang

et al. 2019

Mathematical Problems in Engineering

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With the further research in communication systems, especially in wireless communication systems, a statistical model called Nakagami-mdistribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining received faded envelopes. Therefore, the Nakagami-mquantile function plays an important role in numerical calculations and theoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and theoretical analyses because Nakagami-mquantile function has no exact closed-form expression. In order to obtain the closed-form expression that is able to fit the curve of Nakagami-mquantile function as well as possible, we adopt the method of curve fitting in this paper. An efficient expression for approximating the Nakagami-mquantile function is proposed first and then a novel heuristic optimization algorithm—generalized opposition-based quantum salp swarm algorithm (GO-QSSA)—which contains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in quantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm intelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm algorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and SSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results show that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one order of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient.

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“…Radar and communication systems have been widely studied as two independent entities [1]. In general, target localization [2]- [4] is one of the main functions of the traditional radar system (TRS) whereas the traditional communication system is used to share information with other derives [5]. However, considering the increasing strain on limited spectral resources and the demand for hardware unification, it is necessary to integrate radar system with the communication system and solve the problems of the generalization and rational use of the system resources [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Cramer-Rao Bounds of Localization Estimation for Integrated Radar and Communication System

Tian

2020

IEEE Access

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Instead of only considering the radar estimation error in the traditional radar system (TRS), for the integrated radar and communication system (IRCS), we investigate the Cramer-Rao bound (CRB) of the localization estimation, which is influenced by both radar estimation error and communication transmission error. The functions of radar and communication are operated simultaneously by embedding the communication symbols into the multicarrier radar waveforms. Firstly, we derive the CRB of time/direction of arrival (TOA/DOA) estimation. To minimize the estimation error, we maximize the signal-to-interferenceplus-noise ratio (SINR) of radar by iteratively optimizing the radar transmit and receive beamformers (with the constraint of available transmit power). Then, the CRB of localization estimation is derived using hybrid TOA/DOA measurement. The local CRBs from different IRCSs are fused according to the linear fusion rule at the fusion center (FC). Finally, numerical results demonstrate that the additional estimation errors for the IRCS are mainly determined by the channel conditions of communication and available transmit power; the estimation accuracy for both IRCS and TRS can be improved through the iterative transmit and receive beamforming (ITRB) technique. INDEX TERMS Cramer-Rao bound, localization estimation, multicarrier waveform, beamforming, integrated radar and communication system.

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Joint Resource Allocation and Power Control Algorithm for Cooperative D2D Heterogeneous Networks

Cited by 45 publications

References 30 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

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

Cramer-Rao Bounds of Localization Estimation for Integrated Radar and Communication System

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