Energy-efficient resource allocation in multi-user OFDMA systems

Abstract-Energy Efficiency (EE) is becoming increasingly important for wireless communications and has caught more attention due to steadily rising energy costs and environmental concerns. Recently, a new network architecture known as Massive Multiple-Input Multiple-Output (MIMO) has been proposed with the remarkable potential to achieve huge gains in EE with simple linear processing. In this paper, a power allocation algorithm is proposed for EE to achieve the optimal EE in Massive MIMO. Based on the simplified expression, we develop a new algorithm to compute the optimal power allocation algorithm and it has been compared with the existing scheme from the previous literature. An improved water filling algorithm is proposed and embedded in the power allocation algorithm to maximize EE and Spectral Efficiency (SE). The numerical analysis of the simulation results indicates an improvement of 40% in EE and 50% in SE at the downlink transmission, compared to the other existing schemes. Furthermore, the results revealed that SE does not influence the EE enhancement after using the proposed algorithm as the number of Massive MIMO antenna at the Base Station (BS) increases.

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“…where the function   BS P max, 0  confines the range of pk as per (14). Using (3), the SINR of ZF can be written as follows …”

Section: A Energy Efficiency Water Filling Power Allocation Algorithmmentioning

confidence: 99%

“…The work presented in [14] developed a resource allocation of EE to maximize the EE for a multi-user OFDMA. However, an energy consumption is increasing, even though the authors in [15] showed that MIMO techniques are effective in improving capacity and SE of wireless systems.…”

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confidence: 99%

Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

Shahida

Nordin²,

Ismail³

2017

International Journal of Electronics and Telecommunications

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“…Because of this, we first present a low-complexity channel assignment protocol. Then, we analyze the power allocation problem in (15) and propose a power allocation procedure that has constant complexity. We finish this section by comparing the complexity of our resource allocation protocol with a particular one in the literature that maximizes the EE of OFDMA transmissions from a base station.…”

Section: Analysis and Solutionmentioning

confidence: 99%

“…It should be noted that, of the work that consider both subchannel and power allocation, Xiong et al [14] had the solution with the lowest computational complexity. Reference [15] uses the same algorithm as in [12] for power allocation, but their subchannel assignment algorithm has a complexity that is at least a few orders of magnitude greater than the algorithm of Xiong et al [14].…”

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confidence: 99%

Energy-Efficient Frequency and Power Allocation for Cognitive Radios in Television Systems

Illanko

Naeem

Anpalagan

et al. 2016

IEEE Systems Journal

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Energy-efficient resource allocation for cognitive radios operating in television systems (TV white spaces) presents a unique challenge compared with other cognitive radios because the interference constraint is for the whole frequency band rather than per carrier. This paper presents a subchannel and power allocation protocol that maximizes the energy efficiency (EE) of transmissions from a cognitive base station operating in the TV white spaces. The system model conforms to the IEEE 802.22 standard, and the proposed two-step solution to the EE maximization problem satisfies users' minimum rate requirements and keeps the interference to the primary users in the neighboring areas below a specified threshold. The first step of the protocol is a near-optimal but low-complexity subchannel assignment. This is followed by an optimal power allocation procedure that is obtained by analyzing the Karush-Kuhn-Tucker conditions. The computational complexity of the resulting resource allocation protocol is the same as that of the least complex resource allocation protocol for orthogonal frequency division multiple access (OFDMA) downlinks in the literature. Simulation results show that our protocol achieves higher EE compared with a modified and improved version of the OFDMA protocol from the literature.

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“…The algorithm stops when it reaches a local optimum, at which none of the neighbouring solutions leads to any improvement. Many other two-stage algorithms have been proposed recently, including sorting-and-assignment algorithms [11,12], subcarrier-and-power allocation algorithms [13] and userand-rate allocation algorithms [14]. The aforementioned heuristic algorithms, however, achieve loose gaps from the optimum, or their complexities are hard to predict as the number of iterations needed is a tuning parameter.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient network-flow-based algorithm for multiuser multicarrier systems

Joung

Yuan

et al. 2012

IET Networks

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The resource allocation problem of minimising transmission power with per user rate constraint is studied for energy efficiency of multiuser multicarrier systems. The authors introduce an algorithm that deploys a flow-based decomposition strategy, called the network-flow-based algorithm (NFBA), to circumvent the NP-hard complexity of the resource allocation problem. The authors prove global optimality of the algorithm for the case of a flat-fading channel. For the general frequency selective channel, the flow size is adjusted adaptively to minimise the transmission power. Also, a compact integer programming formulation is developed to provide tight lower and upper bounds of the minimum power. Numerical results show that the authors' proposed NFBA achieves near-optimal performance with polynomial complexity even for large-scale systems.

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Energy-efficient resource allocation in multi-user OFDMA systems

Cited by 10 publications

References 16 publications

Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

Energy-Efficient Frequency and Power Allocation for Cognitive Radios in Television Systems

Energy efficient network-flow-based algorithm for multiuser multicarrier systems

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