Energy-Efficient Resource Allocation in Heterogeneous Cloud Radio Access Networks via BBU Offloading

Amani, Nahid; Pedram, Hossein; Taheri, Hassan; Parsaeefard, Saeedeh

doi:10.1109/tvt.2018.2882466

Cited by 29 publications

(29 citation statements)

References 22 publications

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“…In scenario II, we assume an equal but disjoint delay constraint for each uplink and downlink part of one E2E transmission between each pair of users. Such scenario leads to disjoint resource allocation for each pair of users separately in uplink and downlink sessions with two septate delay constraints for uplink and downlink transmission …”

Section: Simulation Resultsmentioning

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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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: Simulation Resultsmentioning

confidence: 99%

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

Tohidi

Bakhshi

Parsaeefard

2019

Trans Emerging Tel Tech

Self Cite

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“…Also, RRHs relay information to the cloud decoder for further processing which gives rise to the issues related to distributed compression and decoding specifically in multiantenna systems. Problems related to the CRAN uplink have been vastly investigated in literature including [44]- [46], [52], [56]- [58], [61], [65], [68], [69], [71]- [73], [75], [84], [89], [90]. The downlink design in a CRAN is also important as the RRHs transmit the received signals from the BBUs to users.…”

Section: B Resource Allocation Parametersmentioning

confidence: 99%

“…Also, the energy efficiency of H-CRAN has been investigated in [83], where an energy harvesting solution has been presented that minimizes the grid power consumption. In [84], the energy efficiency of H-CRAN has been improved by switching off underutilized BBUs and offloading traffic to low power femtocell APs. Joint access and fronthaul resource allocation in H-CRAN with dual connectivity in millimeter wave and microwave bands has been investigated to maximize the energy efficiency of the system in [88].…”

Section: A Objectivesmentioning

confidence: 99%

A comprehensive survey on resource allocation for CRAN in 5G and beyond networks

Ejaz

Sharma

Saadat

et al. 2020

Journal of Network and Computer Applications

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The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non-Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions.

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“…The fractional objective function in P1 and the C1 and C2 constraints turn the problem P1 into a mixed integer non-convex fractional programming problem. The objective function in P1 can transform to the subtractive linear form [16,17] and solved according to the Dinkelbach theorem [18]. The relaxed problem can be represented by: In the cellular D2D system, we assume that the resource allocation policy is centrally coordinated by the base station.…”

Section: Iterative Algorithm For Resource Allocationmentioning

confidence: 99%

Energy Efficient Power Domain Non-Orthogonal Multiple Access Based Cellular Device-to-Device Communication for 5G Networks

2020

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This paper examines the resource block and power allocation in the power domain non-orthogonal multiple access (PD-NOMA) based cellular device-to-device (D2D) systems. To improve the energy efficiency of the D2D systems and to manage the mutual interference level as well as the quality of service (QoS) requirement of cellular users, different power level is applied to the D2D users sharing the same resource blocks (RBs) to the legacy users. It is essential to design an efficient resource block and power allocation method for PD-NOMA based cellular D2D systems which guarantee the successive interference cancellation (SIC) order in the power allocation solution. In this paper, we propose an iterative algorithm of resource block and power allocation for cellular D2D system which incorporates the SIC aware geometric water filling (GWF) method in the power allocation solution. It is shown that the proposed SIC aware geometric water filling achieves higher energy efficiency compared to iterative water-filling (IWF) power allocation and the GWF based orthogonal multiple access (OMA) method.

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Energy-Efficient Resource Allocation in Heterogeneous Cloud Radio Access Networks via BBU Offloading

Cited by 29 publications

References 22 publications

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

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

A comprehensive survey on resource allocation for CRAN in 5G and beyond networks

Energy Efficient Power Domain Non-Orthogonal Multiple Access Based Cellular Device-to-Device Communication for 5G Networks

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