Energy Efficient Power Allocation Algorithm for Downlink Massive MIMO with MRT Precoding

Zhao, Long; Zhao, Hui; Hu, Fanglong; Zheng, Kan; Zhang, Jingxing

doi:10.1109/vtcfall.2013.6692185

Cited by 59 publications

(51 citation statements)

References 12 publications

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“…In this section, we simulate the EE power allocation scheme for Massive MIMO. The proposed power allocation is compared with the existing scheme reported in [16], [20] and [21]. The main parameters used in the simulation are listed in Table II.…”

Section: B Energy Efficiency Proposed Power Allocation Algorithmmentioning

confidence: 99%

“…However, there is no power allocation scheme considered in both studies. Therefore, the authors in [11]- [16] introduced power allocation strategies to improve EE that can be described as the successfully delivered information bits per unit energy consumption. Authors [11] investigated the impact of circuit power consumption on the EE in Massive MIMO.…”

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confidence: 99%

“…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. In [16], an EE power allocation has been proposed for the Massive MIMO system with Maximum Ratio Transmission (MRT) precoding and the EE and SE are improved when the number of antennas at BS 80 NOOR SHAHIDA M. K, ROSDIADEE NORDIN AND MAHAMOD ISMAIL increases and hence reduce power consumption. However, the precoding can not eliminate inter-user interference at each User Terminal (UT).…”

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confidence: 99%

“…In this paper, we develop an EE power allocation using an improved water filling algorithm (IWF) for Massive MIMO and compare against the work reported in [16], [20] and [21] because to determine the power allocation algorithm with different power levels for the users'. The contributions of this paper are listed as follows: (i) both the number of antenna arrays at BS and the transmit power at the user are adjusted to maximize the EE, and (ii) we proposed a novel power allocation algorithm and compared with the another power allocation scheme from the previous works in [16], [20] and [21]. The simulation results show that it is possible to maximize EE by using the proposed power allocation compared with the work proposed in [20], [21].…”

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confidence: 99%

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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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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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Section: B Energy Efficiency Proposed Power Allocation Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…However, these algorithms focus only on optimizing the objective function, without considering the computational complexity, latency and overhead constraints. Complexity problems have partially been addressed in [8], [9] where the authors focus their effort on improving the performance of low complexity linear precoders such as the Conjugate Beamforming (CB). It is interestingly shown that such simple precoders can achieve considerable gains in SE and EE when coupled together with an interference wise power allocation.…”

Section: Introductionmentioning

confidence: 99%

Angular Based Beamforming and Power Allocation Framework in a Multi-User Millimeter-Wave Massive MIMO System

Shehata

Hélard

Crussière

et al. 2018

2018 IEEE 87th Vehicular Technology Conference (VTC Spring)

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Abstract-Millimeter-Wave (mmWave) systems offer extremely large Band-Width (BW) and highly Line-of-Sight (LOS) dominant channels. Many beamforming and power allocation techniques have recently emerged to leverage such channel characteristics in order to enhance the sum capacity and the coverage for multi-user transmissions. However, most of the existing algorithms are practically limited due to full channel knowledge and high complexity requirements. In this paper we introduce a novel, low complexity, angular based beamforming and power allocation framework, that requires the knowledge of the main contributive Directions of Arrival/Departure (DoAs/DoDs) of the propagation channel only. Exploiting the fact that propagation conditions are highly driven by the geometrical structure of the channel in mmWave scenarios, our method relies on the estimation of the leakage caused by each User Equipment (UE) on all the other UEs, approximated from the DoAs contributions. We prove with the simulation results that this approach, in addition to be practically plausible, can enhance the network Spectral Efficiency (SE) alongside with maintaining acceptable data rates for the cell edge UEs.

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Secure energy efficient power allocation in massive multiple‐input multiple‐output systems with an eavesdropper using cell division technique

Gharagezlou

Pourrostam

Nangir

2021

Int J Communication

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Summary In this paper, we study energy‐efficient power allocation (PA) to secure the confidential information of massive multiple‐input multiple‐output (MIMO) systems in the presence of an eavesdropper. First a training scheme is presented in the uplink using the MMSE method. Based on the estimated channel state information (CSI), the base station (BS) encrypts the confidential information. Next, the ergodic secret rate is obtained at the downlink. In this paper, in order to maximize the ergodic secrecy rate, the allocation of common power in the uplink and downlink is proposed by considering the constraints such as the transmit power constraint in the uplink, the transmit power constraint in the downlink, and the minimum ergodic secret rate constraint. The objective function in the proposed scenario is a nonconvex problem, which first becomes a convex problem and then, by using the Lagrange dual function method, the problem becomes an unconstraint problem. We also divide the cell into two areas using the cell division method. A practical energy‐efficient PA algorithm is presented which deals with the adverse effect of the eavesdropper. Simulations are carried out to demonstrate the effectiveness of the proposed methods.

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Energy Efficient Power Allocation Algorithm for Downlink Massive MIMO with MRT Precoding

Cited by 59 publications

References 12 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

Angular Based Beamforming and Power Allocation Framework in a Multi-User Millimeter-Wave Massive MIMO System

Secure energy efficient power allocation in massive multiple‐input multiple‐output systems with an eavesdropper using cell division technique

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