Radio Resource Allocation in SC-FDMA Uplink with Resource Adjacency Constraints

Lima, F. Rafael M.; Maciel, Tarcísio F.; Cavalcanti, Francisco R. P.

doi:10.14209/jcis.2016.24

Cited by 8 publications

(30 citation statements)

References 27 publications

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“…The per‐service minimum satisfaction concept was proposed as a system‐level metric in the work of Furuskär and used in many other works in downlink OFDMA. In the work of Lima et al two distinct RRA problems in uplink SC‐FDMA in a multiservice scenario were proposed: the constrained rate maximization (CRM) and unconstrained rate maximization problems that consist in maximizing the total data rate with and without per‐service minimum satisfaction requirements, respectively. Besides considering the adjacency constraint, Lima et al also proposed an optimal and heuristic solution for each problem .…”

Section: State‐of‐the‐art and Main Contributionsmentioning

confidence: 99%

“…In the work of Lima et al two distinct RRA problems in uplink SC‐FDMA in a multiservice scenario were proposed: the constrained rate maximization (CRM) and unconstrained rate maximization problems that consist in maximizing the total data rate with and without per‐service minimum satisfaction requirements, respectively. Besides considering the adjacency constraint, Lima et al also proposed an optimal and heuristic solution for each problem . However, transmit power allocation is not optimized in that study.…”

Section: State‐of‐the‐art and Main Contributionsmentioning

confidence: 99%

“…Fortunately, accordingly to the work of Lima et al, we can linearize the constraint as follows. Basically, we can add a new optimization variable (slack variable) and replace constraint by two sets of linear constraints:

\sum_{p \in scriptP} \sum_{m \in scriptM} r_{m} \cdot x_{j, p, m} \geq ρ_{j} \cdot t_{j}, \forall j \in scriptJ .

\sum_{j \in scriptJ} ρ_{j} \geq k_{s}, \forall s \in scriptS,

where ρ j is a selection variable that assumes the value 1 if UE j is selected to be satisfied and 0 otherwise.…”

Section: Characterization Of the Optimal Solutionmentioning

confidence: 99%

“…RRA algorithms are accountable for controlling the scarce resources in wireless systems such as frequency chunks and transmit power. Also, several objectives can be achieved through efficient use of RRA algorithms, for example, improved spectral and EE, fairness, and quality‐of‐service (QoS) fulfillment …”

Section: Introductionmentioning

confidence: 99%

“…This makes the resource allocation in SC‐FDMA quite different from OFDMA where these constraints do not exist. Therefore, RRA in SC‐FDMA uplink imposes an additional difficulty when trying to satisfy QoS requirements …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Maximizing energy efficiency in SC‐FDMA uplink with QoS guarantees and user satisfaction

Braga

Lima

Maciel

et al. 2019

Trans Emerging Tel Tech

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Despite the advent of fifth-generation mobile networks in the next years, the Long-Term Evolution system still have an important role in the mobile communication scenario. Moreover, the development of solutions that fulfill the demand of new multimedia applications as well as provide an efficient use of the energy resources is one of the drivers of future networks. Therefore, in this article, we deal with the total energy efficiency maximization problem subject to minimum satisfaction constraints per service and quality of service in the uplink of Long-Term Evolution using single-carrier frequency-division multiple access. Although the formulated problem is a nonlinear combinatorial optimization problem, we obtain the optimal solution through algebraic manipulations and by using an iterative method. Motivated by the high computational complexity to obtain the optimal solution, we propose a heuristic solution with lower computational complexity. Realistic system-level simulations indicate that the proposed solution presents good performance regarding both outage rate and energy efficiency.Trans Emerging Tel Tech. 2019;30:e3674.wileyonlinelibrary.com/journal/ett

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Section: State‐of‐the‐art and Main Contributionsmentioning

confidence: 99%

Section: State‐of‐the‐art and Main Contributionsmentioning

confidence: 99%

\sum_{p \in scriptP} \sum_{m \in scriptM} r_{m} \cdot x_{j, p, m} \geq ρ_{j} \cdot t_{j}, \forall j \in scriptJ .

\sum_{j \in scriptJ} ρ_{j} \geq k_{s}, \forall s \in scriptS,

where ρ j is a selection variable that assumes the value 1 if UE j is selected to be satisfied and 0 otherwise.…”

Section: Characterization Of the Optimal Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Maximizing energy efficiency in SC‐FDMA uplink with QoS guarantees and user satisfaction

Braga

Lima

Maciel

et al. 2019

Trans Emerging Tel Tech

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Energy‐efficient radio resource allocation for dual‐hop relay‐assisted orthogonal frequency division multiple access systems with quality of service provisioning

Saraiva

Lima

Pessoa

et al. 2021

Trans Emerging Tel Tech

Self Cite

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Besides the conventional spectral efficiency metric, energy efficiency (EE) has been adopted as one of the new mandatory performance metrics for green future mobile networks. In this sense, in this article we study energy-efficient radio resource allocation for dual-hop orthogonal frequency division multiple access relay networks considering multiple relays and users. Resource allocation in our scenario is comprised of relay selection, subcarrier pairing, subcarrier assignment, and transmit power allocation. Particularly, in the context of EE with quality of service constraints, three different problems, namely, power consumption minimization, global EE maximization, and minimum individual EE maximization are addressed. For tackling them, first, we demonstrate a property that exploits the use of the decode and forward protocol employed in the relays and we show how it can be applied to simplify the investigated problems. Although some of these problems are nonconvex due to the fractional objective functions and integer optimization domain, optimal solutions are provided using generalized fractional programming theory to transform the fractional objective functions to the subtractive forms. Then, by employing Dinkelbach or bisection methods, we propose iterative algorithms where a parametric problem is solved in each iteration until reaching convergence. By introducing a penalty function to handle integer variables and applying majorization-minimization approach, we make the parametric problem convex and we propose suboptimal iterative solutions with reduced computational cost. Regarding obtained simulation results, we discuss the performance of the involved solutions in terms of global EE and fairness with respect to different parameters of the system.

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Strong inequalities and a branch-and-price algorithm for the convex recoloring problem

Campêlo

Freire

Moura

et al. 2022

European Journal of Operational Research

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Radio Resource Allocation in SC-FDMA Uplink with Resource Adjacency Constraints

Cited by 8 publications

References 27 publications

Maximizing energy efficiency in SC‐FDMA uplink with QoS guarantees and user satisfaction

Maximizing energy efficiency in SC‐FDMA uplink with QoS guarantees and user satisfaction

Energy‐efficient radio resource allocation for dual‐hop relay‐assisted orthogonal frequency division multiple access systems with quality of service provisioning

Strong inequalities and a branch-and-price algorithm for the convex recoloring problem

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