QoS and Energy Efficient Resource Allocation in Uplink SC-FDMA Systems

Triantafyllopoulou, Dionysia; Kollias, Konstantinos; Moessner, Klaus

doi:10.1109/twc.2015.2399916

Cited by 26 publications

(25 citation statements)

References 25 publications

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“…Previous works on energy-efficient resource allocation (RA) in SC-FDMA based HetNets [18][19][20][21][22][23][24][25][26][27][28] investigated different aspects of RA, eg, some of the works studied only PA, [18][19][20][21][22][23][24] which take transmit power minimization as EE or only subchannel assignment (SA), [25][26][27] which take bits/joule as EE. Dechene and Shami 18 studied the RA with synchronous hybrid automatic repeat request constraints, which focuses on improving the user's EE provided that the users avoid automatic repeat request blocking.…”

Section: Related Workmentioning

confidence: 99%

Energy‐efficient resource allocation and RRH association in multitier 5G H‐CRANs

Ali

Ahmad

Khan

2018

Trans Emerging Tel Tech

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The rapid growth of connected devices and multimedia applications intensify the needs of high data rate and energy‐efficient networks. Multitier heterogeneous cloud radio access networks have been presented as a capable candidate for fifth‐generation networks for much higher data rate and energy efficiency (EE) than fourth generation. Therefore, in this paper, we consider the EE maximization in the uplink of a single‐carrier frequency division multiple access–based multitier heterogeneous cloud radio access network. We formulate a joint subchannel assignment, power allocation, and remote radio head association problem for EE maximization. Single‐carrier frequency division multiple access introduces subchannel exclusivity and subchannel adjacency constraints. These constraints, respectively, ensure the allocation of a subchannel to a single user at a time and the allocation of multiple subchannels to a user only if they are adjacent. In addition, we put constraints on each user's minimum data rate and maximum transmit power. Due to the simultaneous consideration of these four constraints, the solution of the formulated problem becomes harder. Therefore, a three‐step algorithm is developed, which solves this problem in an iterative manner. Simulation results verify that our proposed algorithm significantly improve the EE of the network.

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Section: Related Workmentioning

confidence: 99%

Energy‐efficient resource allocation and RRH association in multitier 5G H‐CRANs

Ali

Ahmad

Khan

2018

Trans Emerging Tel Tech

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“…where G u m,k is the binary entry in the mth row and kth column of matrix G u , which indicates that whether group k contains subchannel m or not. The constraint (18) guarantees that any 2 subchannel groups assigned to 2 different users have no subchannel in common and the constraint (19) reflects the assignment of at most 1 group to each user. The constraints (18)- (20) combinedly ensure that each subchanel is assigned to a single user and the multiple subchannel assigned to each user are contiguous.…”

Section: Optimal Energy-efficient Resource Allocation Through Transfomentioning

confidence: 99%

“…The authors of the study also provide game‐theory–based resource allocation frameworks for multiservice SC‐FDMA uplink network in the works of Tsiropoulou et al In other studies, the authors have proposed Invasive Weeds Optimization (meta‐heuristic algorithm) based energy‐efficient subcarrier allocation with statistical QoS constraint for uplink SC‐FDMA. In 1 study, the authors have investigated a subchannel allocation algorithm to enhance the EE and QoS of the uplink SC‐FDMA network. The authors in previous study propose 2 dynamic relay selection and subband allocation schemes for improving the EE of opportunistic‐relay–assisted SC‐FDMA where the EE is expressed in terms of energy reduction gain and the energy consumption gain …”

Section: Introductionmentioning

confidence: 99%

Optimal energy‐efficient resource allocation in uplink SC‐FDMA networks

Orakzai

Ahmad

Khan

et al. 2017

Trans. Emerging Tel. Tech.

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Energy efficiency (EE) is currently the key performance evaluation metric for wireless networks. This paper considers the maximization of EE of an uplink wireless network that uses single‐carrier frequency division multiple access. The EE metric considered is bits/Joule, and a joint power and subchannel allocation problem is formulated to maximize this EE metric under constraints on the minimum achieved data rate of each user, the maximum transmit power budget of each user, and the exclusive as well as the consecutive allocation of single‐carrier frequency division multiple access subchannels among the users. This problem is a nonlinear and combinatorial optimization problem, and its optimal solution is prohibitively difficult. To make the problem tractable and find its optimal solution, it is transformed into an equivalent binary integer programming problem, which is a standard set partitioning problem. This approach named as the optimal energy‐efficient (OPT‐EE) algorithm finds optimal solution with significantly reduced computational complexity compared to the solution of the original problem. In addition, a heuristic suboptimal energy‐efficient approach is also investigated, which has acceptable performance with much less computational complexity compared to the OPT‐EE algorithm. Simulation results demonstrate the performance of OPT‐EE and heuristic suboptimal energy‐efficient algorithms and their comparison with the available work in the literature.

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“…τ m is combined with D m n ð Þ to adjust the priority sequence according to different fee classes. UEs paying higher fees are given a higher τ m , with τ m ∈ (0, 1), such that it is given higher priority according to (13). With β m ∈ (0, 1), the function of β m is to provide more resources to UEs associated with higher fees whenever they are scheduled for transmission.…”

Section: Proposed Scheme For Differentiating Qosmentioning

confidence: 99%

“…The existence of parties seeking superior levels of service makes it necessary for telecommunications operators to provide options with regard to QoS. Most previous studies focused on the adjustment of QoS levels according to the type of traffic [5,[12][13][14], rather than customers/UEs. In this study, we present a revision of the AAG-2 scheme referred to as AAG-D, which adjusts QoS levels for different classes of UE in terms of throughput and packet delay.…”

Section: Introductionmentioning

confidence: 99%

Differentiating and Scheduling LTE Uplink Traffic Based on Exponentially Weighted Moving Average of Data Rate

et al. 2016

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The importance of uplink resource allocation in ensuring the Quality of Service (QoS) of guaranteed bit rate (GBR) bearers has led to the development of numerous resource allocation schemes. The criteria used in the development of such schemes include maximizing system throughput or fairness and the ability to take transmission power or user priority into account. After accepting a GBR bearer, the eNodeB (eNB) must allocate sufficient resource blocks (RBs) to guarantee the requested QoS. In order to achieve high utilization of radio resource, eNB also must allocate remaining RBs to non-GBR bearers. The QoS is generally specified by the data rate (throughput) or packet delay; however, the 3rd Generation Partnership Project (3GPP) has not specified the means by which data rates for a GBR bearer are to be measured. In this paper, we define the measurement of data rates based on the exponentially weighted moving average (EWMA), thereby enabling the eNB to perform scheduling tasks with greater accuracy. We also present an AAG-2 (Allocate As Granted-version 2) scheme capable of supporting the QoS for GBR bearers, while enabling the efficient allocation of RBs to non-GBR bearers. The existence of parties seeking superior levels of service makes it necessary for telecommunications operators to provide options with regard to QoS. This study proposes a revision of the AAG-2 scheme referred to as AAG-D to accommodate such demands. Simulation results demonstrate the efficacy of the new scheme in achieving goals related to throughput and average packet delay.

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QoS and Energy Efficient Resource Allocation in Uplink SC-FDMA Systems

Cited by 26 publications

References 25 publications

Energy‐efficient resource allocation and RRH association in multitier 5G H‐CRANs

Energy‐efficient resource allocation and RRH association in multitier 5G H‐CRANs

Optimal energy‐efficient resource allocation in uplink SC‐FDMA networks

Differentiating and Scheduling LTE Uplink Traffic Based on Exponentially Weighted Moving Average of Data Rate

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