Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic
In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the...
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doaj-c5ba4c80e0f44d4ead494c4120ad2b562021-09-25T23:40:50ZengMDPI AGApplied Sciences2076-34172021-09-01118559855910.3390/app11188559Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random TrafficIskanter-Alexandros Chousainov0Ioannis Moscholios1Panagiotis Sarigiannidis2Michael Logothetis3Department Informatics & Telecommunications, University Peloponnese, 221 00 Tripolis, GreeceDepartment Informatics & Telecommunications, University Peloponnese, 221 00 Tripolis, GreeceDepartment Electrical & Computer Engineering, University W. Macedonia, 501 00 Kozani, GreeceDepartment Electrical & Computer Engineering, University Patras, 265 04 Patras, GreeceIn this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters.https://www.mdpi.com/2076-3417/11/18/8559cloud-radio accessclustertime congestionquasi-randomproduct formconvolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Iskanter-Alexandros Chousainov Ioannis Moscholios Panagiotis Sarigiannidis Michael Logothetis |
spellingShingle |
Iskanter-Alexandros Chousainov Ioannis Moscholios Panagiotis Sarigiannidis Michael Logothetis Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic Applied Sciences cloud-radio access cluster time congestion quasi-random product form convolution |
author_facet |
Iskanter-Alexandros Chousainov Ioannis Moscholios Panagiotis Sarigiannidis Michael Logothetis |
author_sort |
Iskanter-Alexandros Chousainov |
title |
Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic |
title_short |
Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic |
title_full |
Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic |
title_fullStr |
Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic |
title_full_unstemmed |
Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic |
title_sort |
multiservice loss models in single or multi-cluster c-ran supporting quasi-random traffic |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-09-01 |
description |
In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters. |
topic |
cloud-radio access cluster time congestion quasi-random product form convolution |
url |
https://www.mdpi.com/2076-3417/11/18/8559 |
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