Resource Allocation and Admission Control in OFDMA-Based Cloud-RAN

Lyazidi, Mohammed Yazid; Aitsaadi, Nadjib; Langar, Rami

doi:10.1109/glocom.2016.7842217

Cited by 22 publications

(17 citation statements)

References 8 publications

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“…By considering this constraint, an important issue is associating RRHs and subcarriers to users such that the best performance is achieved. Resource allocation problem for C‐RAN has been studied in some related works …”

Section: Introductionmentioning

confidence: 99%

“…Resource allocation problem for C-RAN has been studied in some related works. [8][9][10] Due to the importance of delay QoS requirement, a considerable amount of literature has been investigated resource allocation problems to meet this essential requirement in the C-RAN architecture. [11][12][13] One well-known approach for delay QoS guarantees is to impose average delay constraint.…”

Section: Introductionmentioning

confidence: 99%

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Joint uplink and downlink delay‐aware resource allocation in C‐RAN

Tohidi

Bakhshi

Parsaeefard

2019

Trans Emerging Tel Tech

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This work considers two‐way communication between each pair of users with highly delay‐aware applications. We formulate a joint uplink and downlink resource allocation problem in a cloud radio access network. Assuming average end‐to‐end (E2E) delay of each user pair and practical limitation such as maximum transmit power, we maximize the total throughput of all pair of users in the cloud radio access network. In this setup, we consider that each user can be connected to at most one remote radio head and a limited capacity fronthaul link between each remote radio head and baseband unit. To present the resource allocation problem in a more tractable manner, we replace the E2E delay limitation with its equivalent throughput‐based formulation. Due to inherent NP‐hard and nonconvex nature of the proposed problem, we apply successive convex approximation to reach a two‐step iterative algorithm where, in each step, a specific set of optimization variable derived while other variables are fixed. The problem of each step is transformed into the standard geometric programming via the arithmetic‐geometric mean approximation. Simulation results reveal that our proposed joint uplink‐downlink resource allocation algorithm outperforms a case that uplink and downlink resources are allocated separately in terms of total throughput and outage probability of E2E delay, ie, a chance that E2E delay does not hold.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint uplink and downlink delay‐aware resource allocation in C‐RAN

Tohidi

Bakhshi

Parsaeefard

2019

Trans Emerging Tel Tech

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“…Cloud-RAN (C-RAN) is an attractive solution to MNO [2]. It saves CAPEX by leveraging the benefits of Base Stations (BSs) components' distribution and functionalities' split in eNodeB (eNB).…”

Section: Introductionmentioning

confidence: 99%

Throughput-Aware RRHs Clustering in Cloud Radio Access Networks

Salhab¹,

Rahim²,

Langar³

2018

2018 Global Information Infrastructure and Networking Symposium (GIIS)

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Cloud-Radio Access Network (C-RAN) is an attractive solution to Mobile Network Operators. Firstly, C-RAN leverages the effect of pooling multiple Baseband Units (BBUs) to offer centralized processing resources while hosting them on cloud. This results in multiple benefits ranging from statistical multiplexing gains, to energy efficiency. Secondly, C-RAN allows deploying Remote Radio Heads (RRHs) in proximity of end-users allowing exploiting Inter-Cell Interference Cancellation (ICIC) to maximize throughput by coordinating multiple RRHs. In this context, we propose, in this paper, a new throughput-aware RRHs clustering method for C-RAN that maximizes the throughput for end-users, while meeting multiple constrained resources on BBUs. Our approach consists of two stages: First, individual throughput value and requirements of each RRH are calculated taking into account the Signal-to-Interference-plus-Noise Ratio (SINR) values and the distance between RRHs and users. Then, they are included into a k-dimensional Multiple-Choice Knapsack Problem (k-MCKP) subject to several constraints in terms of required resources in order to form RRHs clusters that maximize the global throughput. Simulation results demonstrate the good performance of our proposal in terms of end-users throughput, spectral efficiency and execution time, when compared with the optimal solution and the basic strategy using no-clustering scenarios.

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“…For instance, the optimization issues of latencies in fronthaul networks includes the study of functional splits and Remote Radio Heads (RRHs) deployment [3]. Other C-RAN related studies encompass end-users admission control regarding fronthaul links capacity and quality-of-service requirements [4]; Baseband Units (BBUs) virtualization [5]; development of dynamic resource sharing mechanisms [6]; and flexible BBU-RRH mappings [7].…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Novel Optimization Framework for C-RAN BBU Selection Based on Resiliency and Price

Lyazidi

Giupponi²,

Mangues‐Bafalluy³

et al. 2017

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

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As Mobile Network Operators (MNOs) are shifting towards Cloud-Radio Access Network (C-RAN), they have to upgrade their infrastructure to not only support higher processing capacities but also to be more resilient. We consider the problem where a MNO is faced with the choice of selecting virtualized Baseband Units (BBUs) from various cloud service providers, that are each characterized with distinct failure probabilities and prices. We propose to solve the BBU selection problem, formulated as an Integer Linear Program (ILP) subject to BBU capacity and virtualization cost using the Branch-and-Price algorithm. We present several schemes depicting which optimization goal the MNO can foster the most: BBU processing power minimization, resiliency, traffic handling or all. Simulation results demonstrate the good performance of our algorithm to solve the BBU selection problem for all schemes, while also emphasizing the advantages of a particular one that can realize more than 10% in virtualization cost savings.

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Resource Allocation and Admission Control in OFDMA-Based Cloud-RAN

Cited by 22 publications

References 8 publications

Joint uplink and downlink delay‐aware resource allocation in C‐RAN

Joint uplink and downlink delay‐aware resource allocation in C‐RAN

Throughput-Aware RRHs Clustering in Cloud Radio Access Networks

A Novel Optimization Framework for C-RAN BBU Selection Based on Resiliency and Price

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