Throughput-Aware RRHs Clustering in Cloud Radio Access Networks

Salhab, Nazih; Rahim, Rana; Langar, Rami

doi:10.1109/giis.2018.8635647

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…The impact of all the above parameters on the system cost was compared while comparing the proposed method with the optimal ILP by using CPLEX and the nearest-first scheme. In [63], the proposed heuristic for solving the k-MCKP-based clustering technique was simulated via Monte Carlo simulation in MATLAB. Then, the performance was compared with the optimal solution and two no-clustering approaches (no-clustering upper bound and no-clustering lower bound) in terms of the end-user throughput, spectral efficiency, and execution time.…”

Section: Evaluation Techniques For Rrh Clustering Methodsmentioning

confidence: 99%

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Resource Management in Cloud Radio Access Network: Conventional and New Approaches

Rodoshi

Kim

Choi

2020

Sensors

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Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.

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Section: Evaluation Techniques For Rrh Clustering Methodsmentioning

confidence: 99%

“…Salhab et al investigated a throughput-aware RRH clustering problem with the objective of maximizing the throughput for end-users by maintaining multiple constraints on BBU resources [63]. A two-stage approach was proposed.…”

Section: Throughput-aware Rrh Clusteringmentioning

confidence: 99%

Resource Management in Cloud Radio Access Network: Conventional and New Approaches

Rodoshi

Kim

Choi

2020

Sensors

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“…Problem Formulation and Resolution: Based on our previous work in [20], we formalize the "Admission Control" problem as D-dimensional Multiple-Choice Knapsack problem that is constrained by multiple limits stipulated by each slice type. The problem can be formalized as follows.…”

Section: Admission Controllermentioning

confidence: 99%

“…To solve this NP-hard problem, we can use our polynomial time heuristic algorithm proposed in our previous work in [20].…”

Section: Admission Controllermentioning

confidence: 99%

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Machine Learning Based Resource Orchestration for 5G Network Slices

Salhab

Rahim

Langar

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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5G will serve heterogeneous demands in terms of data-rate, reliability, latency, and efficiency. Mobile operators shall be able to serve all of these requirements using shared network infrastructure's resources. To this end, we propose in this paper a framework for resource orchestration for 5G network slices implementing four Quality of Service pillars. Starting from traffic classification, demands are marked so that they are best served by dedicated logical virtual networks called Network Slices (NSs). To optimally serve multiple NSs over the same physical network, we then implement a new dynamic slicing approach of network resources exploiting Machine Learning (ML). Indeed, as demands change dynamically, a mere recursive optimization leading to progressive convergence towards an optimum slice is not sufficient. Consequently, we need an initial well-informed slicing decision of physical resources from a total available resource pool. Moreover, we formalize both admission control and slice scheduler modules as Knapsack problems. Using our 5G experimental prototype based on OpenAirInterface (OAI), we generate a realistic dataset for evaluating ML based approaches as well as two baselines solutions (i.e. static slicing and uninformed random slicing-decisions). Simulation results show that using regression trees as an ML based approach for both classification and prediction, outperform other alternative solutions in terms of prediction accuracy and throughput.

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