Energy-Efficient Downlink Resource Allocation in Heterogeneous OFDMA Networks

Yang, Kai; Martin, Steven; Quadri, Dominique; Wu, Jinsong; Feng, Gang

doi:10.1109/tvt.2016.2624022

Cited by 63 publications

(59 citation statements)

References 40 publications

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“…Moreover, this approach does not guarantee the rate requirement of FUs. Without considering the co‐tier and cross‐tier interference, a branch and bound algorithm is proposed for EERA in OFDMA HetNets in the work of Yang et al…”

Section: Related Workmentioning

confidence: 99%

“…For the sake of comparison, we plot the performance of the following algorithms: Nominal EERA (N‐EERA): Nominal EERA corresponds to the optimization problem where parameters R f , u s are assumed to be perfectly known at femtocells. Distributionally robust EERA (DR‐EERA): This approach corresponds to problem where the effect of uncertainty is taken into account. Energy‐efficient algorithm proposed in the work of Yang et al (EERA): To demonstrate the superiority of our nominal and distributionally robust algorithms, we compare them with the energy‐efficient paradigm proposed in the work of Yang et al In this method, co‐tier and cross‐tier interference threshold constraints are ignored. Capacity maximization resource allocation: This scheme is the resource allocation problem defined in Equation with the objective to maximize the capacity of the network. …”

Section: Performance Evaluationmentioning

confidence: 99%

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Slow adaptive energy‐efficient resource allocation in OFDMA HetNets considering demand uncertainty

Vaezpour

Dehghan

2017

Trans Emerging Tel Tech

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In this paper, we consider the downlink of a hybrid heterogeneous network with femtocells overlaid on a macrocell. Considering the rate requirements of femtocell users and interference thresholds for both co‐tier and cross‐tier users, we formulate the joint power and subchannel allocation problem to maximize the energy efficiency in orthogonal frequency‐division multiple access–based open‐access 2‐tier heterogeneous networks. Due to user's mobility and fluctuating data rate demand, data rate requirements of users are subject to uncertainty, which is ignored in previous works. Toward this end, we study the demand uncertainty by an error model in which femtocells do not have knowledge about the exact distributions and instead assume that each user demand follows a probability distribution function, which belongs to a set of distributions with specific properties (ie, mean and variance). Since these statistics are obtained from historical data, we also take into account the moment uncertainty. Under this error model, the distributionally robust energy‐efficient optimization problem is formulated, which is intractable. To make it tractable, we present the problem in an equivalent form by using the duality theory and then propose decomposition techniques to divide the problem into a fractional programming and a semi‐infinite subproblem. Moreover, we provide optimization methods to solve each one. Our extensive experimental results highlight the effectiveness of our robust solution in uncertain environments, examine the cost of robustness, and demonstrate the convergence of our proposed algorithm. As shown by simulations, our approach provides robustness while incurring minor loss (less than 5%) in optimality.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Slow adaptive energy‐efficient resource allocation in OFDMA HetNets considering demand uncertainty

Vaezpour

Dehghan

2017

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Yang et al investigate energy‐efficient downlink resource allocation in heterogeneous orthogonal frequency division multiple access (OFDMA) network and formulate the EE maximization problem as a mixed‐integer nonlinear fractional programming problem with a nonconcave nonlinear objective function and nonlinear constraints. It reduces the computational complexity of the network and increased the EE of the network.…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficient resource allocation for device‐to‐device communication through noncooperative game theory

Mohammed

Sreenivasan

Ravishankar

et al. 2020

Int J Communication

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Summary Wireless network with high data rate applications has seen a rapid growth in recent years. This improved quality of service (QoS) leads to huge energy consumption in wireless network. Therefore, in order to have an energy‐efficient resource allocation in cellular system, a device‐to‐device (D2D) communication is the key component to improve the QoS. In this paper, we propose a noncooperative game (NCG) theory approach for resource allocation to improve energy efficiency (EE) of D2D pair. A three‐tier network with macrocell base station (MBS), femtocell base station (FBS), and D2D pair is considered, which shares the uplink resource block. A resource allocation strategy with constraints is arrived, which maintains minimum throughput for each user in the network. The proposed resource allocation strategy optimizes the EE of D2D pair in the three‐tier network, which achieves Nash equilibrium (NE) and Pareto optimality (PO). Simulation results validate that EE is uniform and optimum for all D2D pair, which converges to NE when channel is static and it converges to PO when the channel is dynamic.

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“…Many works investigated interference mitigation problem in two‐tier HetNets using small cell RA or ABS at the MBS or the SBSs . In orthogonal frequency division multiple access (OFDMA) HetNets, downlink RA was studied to maximize the energy efficiency, where each resource block can only be assigned to one user to avoid interference between users . In Soret and Pedersen, authors investigated transmit power allocation to mitigate the intercell interference together with the user association optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Many works investigated interference mitigation problem in two-tier HetNets using small cell RA [4][5][6][8][9][10][11][12][13][14][15][16][17] or ABS at the MBS [18][19][20][21][22][23] or the SBSs. 24 In orthogonal frequency division multiple access (OFDMA) HetNets, downlink RA was studied to maximize the energy efficiency, where each resource block can only be assigned to one user to avoid interference between users.…”

Section: Introductionmentioning

confidence: 99%

Distributed optimization of enhanced intercell interference coordination and resource allocation in heterogeneous networks

Zhou

Sun

et al. 2019

Int J Communication

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Summary In two‐tier heterogeneous networks (HetNets), the cross‐tier interference caused by spectrum sharing between macrocell and small cells poses obstacle to ideal overall network performance. Both enhanced intercell interference coordination (eICIC) at the macrocell base station (MBS) and resource allocation (RA) at small cell base station (SBS) have been considered as efficient approaches to mitigate the cross‐tier interference. In this paper, we propose a distributed optimization framework to jointly optimize the eICIC and RA schemes at different tiers. In specific, an enhanced almost blank subframe (ABS) scheme is performed by the MBS to encourage SBSs to adopt hybrid access policy and admit the offloaded macrocell users (MUs) so as to guarantee their QoS requirements. To model the intricate interaction between two tiers, we formulate the joint optimization problem as a one‐leader multiple‐follower Stackelberg game. We prove the existence of Stackelberg equilibrium (SE) and obtain the optimal strategies for both tiers. Numerical results are presented to show that the proposed framework with hybrid access policy converges to a unique SE and utilities for both tiers is maximized, which creates a win‐win situation.

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Energy-Efficient Downlink Resource Allocation in Heterogeneous OFDMA Networks

Cited by 63 publications

References 40 publications

Slow adaptive energy‐efficient resource allocation in OFDMA HetNets considering demand uncertainty

Slow adaptive energy‐efficient resource allocation in OFDMA HetNets considering demand uncertainty

Energy‐efficient resource allocation for device‐to‐device communication through noncooperative game theory

Distributed optimization of enhanced intercell interference coordination and resource allocation in heterogeneous networks

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