Load balancing by dynamic BBU-RRH mapping in a self-optimised Cloud Radio Access Network

Khan, Muhammad Zahid; Sabir, Firas; Al-Raweshidy, H.S.

doi:10.1109/ict.2017.7998268

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…To solve the problem formulated equivalent to the classical bin-packing problem, they propose a heuristic genetic algorithm (HeuGA), which combines the FF algorithm with GA algorithm. In [44], the authors investigate the problem of dynamic BBU-RRH mapping and formulate it as a linear integer-based constrained optimization problem with the aim of achieving load balancing among BBUs and avoiding unnecessary handovers, which is solved by an Estimation Distribution Discrete Particle Swarm Optimization (EDDPSO) algorithm.…”

Section: B Computing Resource Allocation In Cranmentioning

confidence: 99%

Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning

Wang

2022

IEEE Open J. Commun. Soc.

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Due to the greatly increased bandwidth of 5G networks compared with that of 4G networks, the power consumption brought by baseband signal processing of 5G networks is much higher, which inevitably raises the operation expenditures. Cloud Radio Access Network (CRAN) is widely adopted in 5G networks, which splits the traditional base stations into Remote Radio Heads (RRHs) and Baseband Units (BBUs), which are equipped with computing resource for baseband signal processing. The number of required BBUs varies due to the fluctuation of wireless traffic of RRHs. Hence, fixed computing resource allocation might waste power. This paper investigates energy-efficient dynamic computing resource allocation in CRAN by predicting the wireless traffic of RRHs and allocating computing resource based on the prediction results aiming at using fewest BBUs to minimize power consumption. For wireless traffic prediction, a novel method based on two-dimensional CNN LSTM model with temporal aggregation is proposed. By treating the wireless traffic data as images, this model could extract spatial correlation from these data to improve accuracy. Moreover, the problem of dynamic computing resource allocation in CRAN is formulated as an offline four-constraint bin packing problem, considering both uplink and downlink baseband signal processing capacities of BBUs and Common Public Radio Interface (CPRI) bandwidths. For solving this problem, a Multi-start Simulated Annealing (MSA) algorithm is proposed. Simulation results demonstrate that the proposed method for wireless traffic prediction could outperform the state-of-the-art deep learning models. In addition, the proposed MSA algorithm could achieve lower power consumption than the state-of-the-art heuristic algorithms.

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Section: B Computing Resource Allocation In Cranmentioning

confidence: 99%

Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning

Wang

2022

IEEE Open J. Commun. Soc.

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“…At this problem, the [22] used the mixedinteger linear programming to prove the NP-hard, and they also proposed the approximate algorithm to select strategy. In addition to the above literature [23,24], also consider other issues in C-RAN, such as energy consumption, information security, network performance, and spectrum allocation.…”

Section: Related Workmentioning

confidence: 99%

An efficient spectrum scheduling mechanism using Markov decision chain for 5G mobile network

et al. 2021

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Recently, the 5G as the next-generation network is a popular research and discussed widely. The architecture of 5G is a heterogeneous network, and it can support more networked types, like the ultra-dense network, traditional cellular network, and Machine to Machine communication. Although the high frequency and larger bandwidth have been using in 5G, resource allocation is still a critical issue that needs to be discussed and solved. Consider the spectrum resource is limited, but almost all users hope that equipment can get a better quality of services. Therefore, how to manage the spectrum resource and allocation is a big problem. Consider the fast-growing devices and traffic in the future; hence, task scheduling for UEs to reduce energy consumption will be focused on. To solve resource allocation and minimise energy consumption, the Markov decision chain is proposed to be used to predict the channel state. The modified particle swarm optimization (MPSO) is also used in this paper to find the best task scheduling. The simulation will be used to verify the performance of the mechanism that is used and compare it with PSO and first-in-firstservice (FIFS). The result shows the method used can be scheduled for the task efficiently.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…By splitting base station (BS) hardware into a remote radio head (RRH) and a baseband unit, the C-RAN also increases flexibility and enables mobile network operations to be more dynamic than network operations without the C-RAN. In a C-RAN, resources can be dynamically allocated and redistributed across a given geographical area while being adjusted to a time-varying traffic load, and the C-RAN thus benefits from the statistical multiplexing gain achieved by adapting it to traffic fluctuations [9]. In traditional RANs, baseband capacity is statically assigned to each cell, meaning that the resources are allocated regardless of the users' movements.…”

Section: Introductionmentioning

confidence: 99%

The Heuristic for Hardware Dimensioning Considering Tidal Effect

Falcão

Vieira²,

Pereira

et al. 2020

JCIS

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The recent increase in the volume of services and applications, in addition to the accelerated growth in demand for wireless access, represent significant challenges for the fifth generation (5G) of mobile networks. The daily large-scale migration of people to urban centres is another aspect of this trend, as it entails what is known as the "tidal effec". This effect leads to natural fluctuations in traffic throughout the day and makes it difficult to conduct network dimensioning, control, and management, thus resulting in the inefficient use of network resources. A heuristic with two approaches for provisioning resources (one based on the aggregate throughput and the other on the number of connected users) is proposed in this paper. This is based on data extracted from the mobile subscriber movement in the current network architecture, where a database with geolocation information from the city of New York is used. The aim of this heuristic is to meet the imminent network demands of the future in light of the expected lack of available hardware resources in future mobile networks. Our results suggest that the network provisioning strategy meet the requirements of traffic variability by reducing the number of active antennas by 13% and the network-blocking probability by 3.7%, as well as by maximizing the efficiency of the baseband unit (BBU) and quantifying the small cells (SCs) needed to meet network demands.

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Load balancing by dynamic BBU-RRH mapping in a self-optimised Cloud Radio Access Network

Cited by 9 publications

References 9 publications

Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning

Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning

An efficient spectrum scheduling mechanism using Markov decision chain for 5G mobile network

The Heuristic for Hardware Dimensioning Considering Tidal Effect

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