Dynamic Resource Allocation in C-RAN with Real-Time Traffic and Realistic Scenarios

Guerra-Gómez, Rolando; Boqué, Sílvia Ruiz; García-Lozano, Mario; Bonafe, Joan Olmos

doi:10.1109/wimob.2019.8923310

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…This scenario was first defined in [20], and it has been widely used as a common platform to test the performance of different radio resource optimization algorithms [21], [22]. [18], [19].…”

Section: C-ran Platform Descriptionmentioning

confidence: 99%

“…The model provides accurate and realistic spatial channel proper-ties, and it is suitable for evaluating massive MIMO and advanced beamforming techniques, and also for realistic path-loss modeling in case of the device to device (D2D) and vehicles to vehicles (V2V) communications. RRHs to BBU pool connections (fronthaul links) are established by minimizing the delay, being the advantages and inconveniences of this assumption discussed in [18].…”

Section: C-ran Platform Descriptionmentioning

confidence: 99%

“…Moreover, ACC r,t represents the allocated computational capacity to the RRH r at time t. The DRM allocates the resources available at the BBU pool to manage each RRH with service priority as well as QoS constraints. It assigns the resources solving the optimization problem presented in [18], [19] and detailed in (8). subject to…”

Section: A Resource Demand Estimationmentioning

confidence: 99%

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Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN

et al. 2020

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Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at the baseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overall Quality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). In this paper, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). To further improve, two new strategies are proposed and tested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-ACES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectively.

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Section: C-ran Platform Descriptionmentioning

confidence: 99%

Section: C-ran Platform Descriptionmentioning

confidence: 99%