Power and Subcarrier Allocation Strategies for Energy-Efficient Uplink OFDMA Systems

Souza, Álvaro Ricieri Castro e; Amazonas, José Roberto de Almeida; Abrão, Taufik

doi:10.1109/jsac.2016.2600409

Cited by 13 publications

(5 citation statements)

References 29 publications

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“…where p k,n is the transmit power of the k-th user on the n-th subcarrier, and ν k describes the imperfections of the channel and modulation [26], which can be expressed as…”

Section: Signal Modelmentioning

confidence: 99%

“…Since the proposed transmissive RMS multi-antenna system is equipped with a single receiving antenna, we consider orthogonal frequency division multiple access (OFDMA) for uplink multiuser communications. For a multi-carrier system, the subcarrier allocation of this access scheme is more flexible, and it can bring higher frequency diversity gain [26], [27]. There are many works to optimize the power consumption, spectrum-and energy-efficiency of the network through joint design of OFDMA network configuration [28]- [35].…”

Section: Introductionmentioning

confidence: 99%

“…There are many works to optimize the power consumption, spectrum-and energy-efficiency of the network through joint design of OFDMA network configuration [28]- [35]. For uplink communication of a singlecell OFDMA system, Souza et al solved energy efficiency maximization by joint optimizing power allocation and subcarrier allocation for all mobile users [26]. For uplink OFDMA based cloud radio access network (C-RAN), Liu et al jointly optimized power allocation and fronthaul quantization design on the subcarrier to maximize the system throughput with practical uniform scalar quantization [28].…”

Section: Introductionmentioning

confidence: 99%

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Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems

Li¹,

Chen²,

Lu³

et al. 2021

Preprint

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In this paper, a novel uplink communication for the transmissive reconfigurable metasurface (RMS) multi-antenna system with orthogonal frequency division multiple access (OFDMA) is investigated.Specifically, a transmissive RMS-based receiver equipped with a single receiving antenna is first proposed, and a far-near field channel model based on planar waves and spherical waves is given. Then, in order to maximize the system sum-rate of uplink communications, we formulate a joint optimization problem over subcarrier allocation, power allocation and RMS transmissive coefficient design. Due to the coupling of optimization variables, the optimization problem is non-convex, so it is challenging to solve it directly. In order to tackle this problem, the alternating optimization (AO) algorithm is used to decouple the optimization variables and divide the problem into two sub-problems to solve.First, the problem of joint subcarrier allocation and power allocation is solved via the Lagrangian dual decomposition method. Then, the RMS transmissive coefficient design can be obtained by applying difference-of-convex (DC) programming, successive convex approximation (SCA) and penalty function methods. Finally, the two sub-problems are iterated alternately until convergence is achieved. Numerical simulation results verify that the proposed algorithm has good convergence performance and can improve system sum-rate compared with other benchmark algorithms.

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“…where p k,n is the transmit power of the k-th user on the n-th subcarrier, and ν k describes the imperfections of the channel and modulation [26], which can be expressed as…”

Section: Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems

Li¹,

Chen²,

Lu³

et al. 2021

Preprint

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“…The main limitations of [18] are the dependence of the proposed solution on the initial values of the dual variables and step size of the LDD algorithm to provide a fast convergence. In [19], the same problem is studied in an uplink scenario and a procedure to calculate those initial values is provided. In [20], three different metrics of EE maximization are studied in a multi-cell scenario: the ratio between the sum rate and the total power consumption, the weighted sum of the energy efficiencies achieved on each Resource Block (RB) and the exponentially-weighted product of the energy efficiencies achieved on each RB.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…The proposed solution to solve the convex problem is based on DKB and LDD algorithms. However, the works [18], [19], [20], [21], [22] do not model QoS constraints and multiservice scenario. In [23], the EE maximization problem considering QoS requirements in a multi-cell OFDMA scenario is studied.…”

Section: A Literature Reviewmentioning

confidence: 99%

Resource Allocation for Energy Efficiency and QoS Provisioning

Maurício

Lima

Abrão³

et al. 2019

JCIS

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In this paper, we formulate and solve two Energy Efficiency (EE) problems, namely the Power Minimization Problem (PMP) and the Maximization of Energy Efficiency Problem (MEEP), for a wireless system using power and frequency resource allocation considering Quality of Service (QoS) requirements and multiple services. Despite those problems are nonlinear, they can be converted into Integer Linear Problems (ILPs). Therefore, the optimal solution for both PMP and MEEP can be obtained by well-known methods. Additionally, we propose two fast suboptimal algorithms as to avoid the high computational complexity of obtaining optimal solution for MEEP. Our results show that the MEEP has a better trade-off between transmitted data rate and power saving than the PMP solution. Moreover, the suboptimal algorithms present good performance compared to the optimal solution for moderated loads but with a much lower computational complexity, thus achieving a remarkable trade-off between performance and computational complexity.

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Deep Reinforcement Learning Based Subcarrier Allocation With End‐To‐End Implicit CSI Acquisition in Multiuser OFDM Systems

Zhang,

Zhang

2024

Trans Emerging Tel Tech

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In multiuser orthogonal frequency division multiplexing (OFDM) systems, subcarrier allocation is a key means for improving the system capacity. During subcarrier allocation process, the channel state information (CSI) plays a key role. Recently, implicit CSI is considered as a possible alternative to replace explicit CSI. But under the condition of implicit CSI, the existing subcarrier allocation algorithms using explicit CSI need to be redesigned. In this article, subcarrier allocation with implicit CSI acquisition in frequency‐division duplex (FDD) OFDM systems is investigated. To adapt to the implicit CSI, a corresponding deep reinforcement learning (DRL) based proportional fairness (PF) subcarrier allocation algorithm is proposed to maximize the long term PF utility. Simulation results show that compared with the subcarrier allocation algorithms with explicit CSI acquisition such as LS/MMSE channel estimation and individual neural network (NN) based channel estimation and feedback, the proposed scheme achieves higher system sum rate and user fairness and gets close to the traditional PF algorithm with perfect CSI, which demonstrates the effectiveness of the proposed subcarrier allocation scheme with implicit CSI acquisition.

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Power and Subcarrier Allocation Strategies for Energy-Efficient Uplink OFDMA Systems

Cited by 13 publications

References 29 publications

Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems

Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems

Resource Allocation for Energy Efficiency and QoS Provisioning

Deep Reinforcement Learning Based Subcarrier Allocation With End‐To‐End Implicit CSI Acquisition in Multiuser OFDM Systems

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