Antenna selection and power allocation for energy efficient MIMO systems

Eskandari, Mahdi; Doost-Hoseini, Ali Mohamad; Jung, Jaehoon; Lee, Inkyu

doi:10.1109/jcn.2018.000087

Cited by 16 publications

(18 citation statements)

References 23 publications

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“…They proposed an efficient algorithm and the bisectiongradient-assisted interior point as a practical solution to solve an EE maximization problem. The authors in [13] proposed the use of circuit power and channel conditions in a closed form to maximize EE. The authors in [14] analyzed quality of service issues to maximize EE in a single-cell massive MIMO system.…”

Section: Related Workmentioning

confidence: 99%

“…This involves a large number of multiplexed users sharing the same sub-channel using the maximum power whilst minimizing data rates. Reference [13] used an iterative numerical algorithm to maximize EE in a point-topoint MIMO system. Reference [13] also applied singular value decomposition as optimal solution in a closed-form to optimize EE in terms of occupied active transmission antennas, circuit power, and noise power.…”

Section: Related Workmentioning

confidence: 99%

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Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

et al. 2020

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Singular value decomposition is highly essential to achieve a higher performance in signal processing using massive multiple-input multiple-output (MIMO) systems. This paper aims to provide a solution to control power allocation problem identified as an essential metric in a massive MIMO system that maximizes energy efficiency (EE). The network performance was evaluated by measuring circuit power consumption to maximize EE. The computational efficiency to maximize EE power allocation is very important to fifth generation networks (5G). The study aims to maximize the non-convex EE in a downlink (DL) massive MIMO system using a proposed energy-efficient low-complexity algorithm (EELCA) that guarantees optimal power allocation solution based on Newton's methods and joint user's association based on the Lagrange's decomposition method. An optimal power allocation solution in closed form to decrease the complexity of the power subject to both the maximum power and minimum data rate constrained systems was derived. Then, the unconstrained EE power allocation to solve the unconstrained optimal power was used to select the optimal power allocation by computing a root of the first derivative of the EE based on differentiating the instantaneous power allocation to maximize EE was formulated. Simulation results showed that the proposed EELCA with a total transmitted power allocation provided maximum EE for a large number of antennas at the base station (BS), Generally, non-linear schemes outperformed linear schemes. Finally, the large cost of circuit power consumption increased at the BS due to the large loss of radio frequency (RF) chains at every antenna when the signals were transmitted to all users. The maximum EE = 5.9 Mbits/joule when the number of distributed users, K = 33, with (p c , M) = (1000mW, 200). The proposed low complexity algorithm provides the better result EE based on a training channel for a number of distributed users.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

et al. 2020

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“…. ,M}, |A| denotes the size of set A. P DAC ,P filt , P mix , and P sta are the power consumption of digital to analog converter, filter, mixer, and the static power, respectively [13].…”

Section: The Maximum Ee In Separated Ce and A Feedback Modelmentioning

confidence: 99%

“…In practice, the total power consumption of the BS contains not only the transmitting power of the power amplifier (PA) but also power consumption caused by circuit dissipation and signal processing. Meanwhile, activating more transmit antennas enables a higher diversity gain, while the corresponding RF chains consume more circuit and signal processing power [13]. Unlike the existing power allocation schemes that maximize the throughput, the studied scheme maximizes EE by allocating both transmitting power of each sub-channel in reconstructed MIMO architecture and antenna subset selection for transmission, according to the improved CSI feedback and the circuit power consumption.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

Feng

Wang

et al. 2020

Electronics

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This paper proposes a dynamic resource allocation scheme to maximize the energy efficiency (EE) for Massive MIMO Systems. The imperfect channel estimation (CE) and feedback are explicitly considered in the EE maximization problem, which aim to optimize the power allocation, the antenna subset selection for transmission, and the pilot assignment. Assuming CE error to be bounded for the complex-constrained Cramer–Rao Bound (CRB), theoretical results show that the lower bound is directly proportional to its number of unconstrained parameters. Utilizing this perspective, a separated and bi-directional estimation is developed to achieve both low CRB and low complexity by exploiting channel and noise spatial separation. Exploiting global optimization procedure, the optimal resource allocation can be transformed into a standard convex optimization problem. This allows us to derive an efficient iterative algorithm for obtaining the optimal solution. Numerical results are provided to demonstrate that the outperformance of the proposed algorithms are superior to existing schemes.

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“…In [6] exchange the data by usage of antennas to introduced multi pair communication system. To eliminates the inter-pair interference [7] by the approach in the imperfect channel information the energy efficiency and spectral efficiency does not consider . optimum energy efficiency is determine by the factors circuit power and noise power.…”

Section: In Multiple Input Multiple Output -Non Orthogonalmentioning

confidence: 99%

Energy Efficient Fractional Particle Swarm Optimization Based Power Allocation in MIMO-NOMA System

Khaleelahmed¹,

Venkateswararao²

2019

IJITEE

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Non-Orthognal Multiple Access (NOMA) is a key technology used for improving the achievable rate in Multiple Input Multiple Output (MIMO) wireless networks of the next generation. In MIMO-NOMA systems the Energy Efficiency (EE) needs to be improved by a Fractional Particle Swarm Optimization Algorithm (FPSO) based on user ordering. To recommend capable energy and power allocation in the platform efficiently, the proposed optimization algorithm prioritizes the users based on satisfying the quality of service (QoS) and maximum power constraints. The FPSO algorithm prioritizes the users in optimal way by using objective function. The simulated results are analyze using the assessment metrics, like Bit Error Rate (BER), achievable rate, energy and spectral power. The performance of the FPSO-based power allocation approach is showing the higher spectral power, energy, achievable rate are 113.1915dB, 19.4898dB, 81.19153Mbps and lower BER of 0.0000152 respectively.

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Antenna selection and power allocation for energy efficient MIMO systems

Cited by 16 publications

References 23 publications

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

Energy Efficient Fractional Particle Swarm Optimization Based Power Allocation in MIMO-NOMA System

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