Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

Peng, Mugen; Yu, Yu-Ling; Xiang, Haige; Poor, H. Vincent

doi:10.1109/tmm.2016.2535722

Cited by 111 publications

(56 citation statements)

References 26 publications

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“…Moreover, the energy consumption of air conditioning is avoided. Therefore, we suppose that the power consumption in the fiber links and the circuit power consumption are fixed to constant values [2], [34]- [37]. Thus, as mentioned before, the total power consumption of the system consists of three parts: 1) the power consumption of the fiber links where the power consumption of each LPN RRH and HPN RRH fiber links are equal to P L f and P H f , respectively, 2) the power consumption at RRHs where the power consumption at each RRH m is equal to η m k∈K n∈N ρ (n) m,k p (n) m,k where η m is the efficiency of the power amplifier in each RRH and 3) the circuit power consumption for each LPN RRH m and HPN RRH is equal to P L c and P H c , respectively [2].…”

Section: A System Modelmentioning

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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Section: A System Modelmentioning

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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“…For the non-coherent transmission case in (18), each UE's sum rate is divided among the serving candidate of RRHs, while for the coherent transmission case in (20), each RRH has to transmit the signals with each UE's sum rate. Hence, for the case with stringent fronthaul capacity limit, the non-coherent transmission may be the better option.…”

Section: Problem Formulationmentioning

confidence: 99%

The Non-Coherent Ultra-Dense C-RAN Is Capable of Outperforming Its Coherent Counterpart at a Limited Fronthaul Capacity

Pan

Ren

Elkashlan

et al. 2018

IEEE J. Select. Areas Commun.

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The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit-beamforming used is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase-synchronization is required. By exploiting the classic successive interference cancellation (SIC) technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1norm technique to approximate the l0-norm in the fronthaul capacity constraints as the weighted power constraints. A lowcomplexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the non-coherent one in the low fronthaul capacity regime, which is the case in ultra-dense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed.Index Terms-Ultra-dense networks (UDN), C-RAN, fronthaul capacity, pilot reuse, robust design.C. Pan, M. Elkashlan, and A. Nallanathan are with the Queen Mary

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“…The indicator function in (22) can be equivalently expressed as an 0 -norm of a scalar, which is the number of nonzero entries in a vector. This equivalent expression allows us approximately transform a nonconvex 0 -norm optimization objective into a convex reweighted 1 -norm [24]. Therefore, the indicator function 1 D n v k (t) 2 2 can be written as follows:…”

Section: Uplink Beamforming Design Algorithmmentioning

confidence: 99%

Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications

Peng

Xiang

et al. 2017

IEEE Access

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To alleviate the burdens on the fronthaul and reduce the transmit latency, the device-to-device (D2D) communication is presented in cloud radio access networks (C-RANs). Considering dynamic traffic arrivals and time-varying channel conditions, the resource allocation in C-RANs with D2D is formulated into a stochastic optimization problem, which is aimed at maximizing the overall throughput subject to network stability, interference, and fronthaul capacity constraints. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem is transformed into a delay-aware optimization problem, which is a mixed-integer nonlinear programming problem and can be decomposed into three subproblems: mode selection, uplink beamforming design, and power control. An optimization solution that consists of a modified branch and bound method as well as a weighted minimum mean square error approach has been developed to obtain the close-to-optimal solution. Simulation results validate that the D2D can improve throughput, decrease latency, and alleviate the burdens of the constrained fronthaul in C-RANs. Furthermore, an average throughput-delay tradeoff can be achieved by the proposed solution.Index Terms-Cloud radio access networks (C-RANs), radio resource allocation, device-to-device (D2D)

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Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

Cited by 111 publications

References 26 publications

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

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

The Non-Coherent Ultra-Dense C-RAN Is Capable of Outperforming Its Coherent Counterpart at a Limited Fronthaul Capacity

Resource Allocation in Cloud Radio Access Networks With Device-to-Device Communications

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