Dynamic resource allocation for Cloud-RAN in LTE with real-time BBU/RRH assignment

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

doi:10.1109/icc.2016.7511580

Cited by 49 publications

(46 citation statements)

References 7 publications

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“…Furthermore, they have not been designed to deal with finite-horizon scheduling problems, where user transmission requests dynamically arrive and have to be scheduled within a deadline. The scheduling problem for C-RANs over a finite horizon has been considered in [22,23]. However, the solution in [22] only focuses on the BBU pool and does not consider the power consumption of the RRHs, while [23] does not account for CoMP transmissions, and the power consumption is restricted to the transmission power only.…”

Section: Related Workmentioning

confidence: 99%

Power-Efficient Resource Allocation in C-RANs With SINR Constraints and Deadlines

Marotta

Both

DaSilva

et al. 2019

IEEE Trans. Veh. Technol.

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In this paper, we address the problem of powerefficient resource management in Cloud Radio Access Networks (C-RANs). Specifically, we consider the case where Remote Radio Heads (RRHs) perform data transmission, and signal processing is executed in a virtually centralized Base-Band Units (BBUs) pool. Users request to transmit at different time instants; they demand minimum signal-to-noise-plus-interference ratio (SINR) guarantees, and their requests must be accommodated within a given deadline. These constraints pose significant challenges to the management of C-RANs and, as we will show, considerably impact the allocation of processing and radio resources in the network. Accordingly, we analyze the power consumption of the C-RAN system, and we formulate the power consumption minimization problem as a weighted joint scheduling of processing and power allocation problem for C-RANs with minimum SINR and finite horizon constraints. The problem is a Mixed Integer Non-Linear Program (MINLP), and we propose an optimal offline solution based on Dynamic Programming (DP). We show that the optimal solution is of exponential complexity, thus we propose a suboptimal greedy online algorithm of polynomial complexity. We assess the performance of the two proposed solutions through extensive numerical results. Our solution aims to reach an appropriate trade-off between minimizing the power consumption and maximizing the percentage of satisfied users. We show that it results in power consumption that is only marginally higher than the optimum, at significantly lower complexity.

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Section: Related Workmentioning

confidence: 99%

Power-Efficient Resource Allocation in C-RANs With SINR Constraints and Deadlines

Marotta

Both

DaSilva

et al. 2019

IEEE Trans. Veh. Technol.

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“…In average load case, only some of the available BBUs need to be active U act m for supporting the total network load, while the remaining U id m = U m − U act m are considered idle and can go into sleep mode for energy consumption optimization. As already mentioned, one possible way of calculating the optimum BBU-RRH mapping is by solving a Knapsack problem [10]. In this context we consider the ideal minimum number of active BBUs defined, similarly to U m , as…”

Section: A Power Modelmentioning

confidence: 99%

“…power consumption minimization). Among the possible strategies, [8] models the problem as a bin-packing problem, [9] by using graph coloring, [10] as a Knapsack In the literature, it is common opinion that SC offloading by itself will not be sufficient for enabling the forecasted traffic and that mmWave bandwidth extension will be necessary. On the other hand, many works provide strategies for improving the spectrum utilization efficiency by means of opportunistic (Cognitive Radio) or cooperative spectrum sharing.…”

Section: Introductionmentioning

confidence: 99%

“…[12] studied the benefits of jointly deploying a new shared network, [13] investigated by means of non-cooperative game theory the feasible infrastructure sharing advantages thanks to base stations switching-off. On the other hand, works concerned with C-RAN are mainly focused on the BBU optimization in single operator case [8], [10], [11]. [9] considers the two operators case but with separate spectrum licenses.…”

Section: Introductionmentioning

confidence: 99%

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Cooperation incentives for multi-operator C-RAN energy efficient sharing

Vincenzi¹,

Antonopoulos²,

Kartsakli³

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Abstract-Facing the increasing energy demands associated with the perspective of fifth generation (5G) wireless networks, the Mobile Network Operators (MNOs) are motivated to gradually convert their traditional Radio Access Network (RAN) infrastructure to more flexible and power efficient centralized architectures, i.e., Cloud-RAN (C-RAN). Apart from their promising benefits in terms of management and network optimization, these new architectures further enable the sharing of spectrum and network elements, such as the Remote Radio Heads (RRHs) and the Baseband Units (BBUs), among multiple operators. In this paper, we introduce a novel scheme based on coalitional game theory to identify the potential room for cooperation among different MNOs that provide service to the same area. The proposed scheme sets the rules for profitable collaboration and identifies the core formation conditions (i.e., pricing) for various scenarios with different market and spectrum shares among three operators. Our results show that i) cooperation among subcoalitions of MNOs is always beneficial, yielding both higher revenues and enhanced Quality of Service (QoS) for the end users, and ii) the cooperation of all operators (grand coalition) is profitable for given user pricing in different scenarios.

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“…In order to facilitate the emergence of such ultra lowlatency and high-reliability applications, this paper develops a novel cloud-based radio access network (CRAN) system [8], [9] as a new cloud architecture for future mobile network operators (MNOs). This flexible cloud radio network (FRAN) for Tactile Internet (TI) adaptively uses user-provided networks [10] to provide an efficient framework for ultra low-latency communications.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Network Slicing for Flexible Radio Access in Tactile Internet

Shafigh

Glisic

Lorenzo

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Abstract-Tactile Internet (TI) will generate a variety of 5G-enabled use cases with different requirements for latency, throughput and reliability. In this paper, we propose a flexible cloud-based radio access network (FRAN) for TI, where traffic of user equipments (UEs) can be temporary offloaded from the operator-provided networks to user-provided networks if needed. Such a concept enables dynamic network slicing (DNS) where the network architecture is temporally augmented with slices of infrastructure borrowed from user provided network. FRAN is able to support Tactile applications without any basic change to the hardware/software infrastructure in the network. We model DNS system as a two-layer/slice traffic-aware resource allocation framework, where every layer uses a separate two-step matching game in order to serve Tactile users (TUs) and low-priority users (LUs). We propose a subgame Nash stable and two-sided exchange stable concepts as solutions of the proposed two-layer traffic-aware resource allocation. Our numerical results show that network operators and UEs (either TUs or LUs) significantly benefit from deploying the FRAN rather than conventional cloudbased radio access networks (CRANs).

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Dynamic resource allocation for Cloud-RAN in LTE with real-time BBU/RRH assignment

Cited by 49 publications

References 7 publications

Power-Efficient Resource Allocation in C-RANs With SINR Constraints and Deadlines

Power-Efficient Resource Allocation in C-RANs With SINR Constraints and Deadlines

Cooperation incentives for multi-operator C-RAN energy efficient sharing

Dynamic Network Slicing for Flexible Radio Access in Tactile Internet

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