Energy Efficiency Optimization of Massive MIMO System with Uplink Multi-Cell Based on Imperfect CSI with Power Control

Zhang, Jie; Deng, Haixiao; Li, Youzhen; Zhu, Zaoxing; Liu, Gang; Liu, Hongmei

doi:10.3390/sym14040780

Cited by 12 publications

(13 citation statements)

References 23 publications

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“…To sum up, the reduction in the overall EE is due to two main reasons, the first is the power consumption of the additional antennas (as the number of antennas increases, the total power consumption of the BS also rises), and the second is the increased complexity of the signal processing (as the number of antennas increases, the amount of data that needs to be processed also rises). Our results are consistent with the published results in the literature [7,54].…”

Section: Symbolsupporting

confidence: 94%

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

Sabaawi,

Almasaoodi,

El Gaily

et al. 2023

IET Networks

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Devising efficient optimisation methods has been a subject of great research attention since current evolving trends in communication networks, machine learning, and other cutting‐edge systems that need a fast and accurate optimised computational model. Classical computers became incapable of handling new optimisation problems posed by newly emerging trends. Quantum optimisation algorithms appear as alternative solutions. The existing bottleneck that restricts the use of the newly developed quantum strategies is the limited qubit size of the available quantum computers (the size of the most recent universal quantum computer is 433 qubits). A new quantum genetic algorithm (QGA) is proposed that handles the presented problem. A quantum extreme value searching algorithm and quantum blind computing framework are utilised to extend the search capabilities of the GA. The quantum genetic strategy is exploited to maximise energy efficiency at full spectral efficiency of massive multiple‐input, multiple‐output (M‐MIMO) technology as a toy example for pointing out the efficiency of the presented quantum strategy. The authors run extensive simulations and prove how the presented quantum method outperforms the existing classical genetic algorithm.

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Section: Symbolsupporting

confidence: 94%

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

Sabaawi,

Almasaoodi,

El Gaily

et al. 2023

IET Networks

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“…To illustrate the power consumption model impact on EE, four widely used power models are compared including model 1 [10], model 2 [9], model 3 [23], and model 4 we adopt. As model 1 only takes radio frequency (RF) chains into account, model 2 includes the power consumption of UEs.…”

Section: Simulation Parameters 52 Analysis Of Simulation Resultsmentioning

confidence: 99%

Energy Efficiency Optimization Based on Power Allocation in Massive MIMO Downlink Systems

Deng

et al. 2022

Symmetry

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To solve the energy efficiency (EE) optimization in a multi-cell (MU) massive multiple-input multiple-output (MIMO) downlink system, of which channels are of symmetry in the Time-Division Duplex (TDD) protocol, we utilize a spatially correlated channel model and adopt the minimum mean-squared error (MMSE) estimator to tailor linear precoding vectors. Then, we derive the expression of downlink spectral efficiency (SE), taking interference into account. Subsequently, we establish the EE optimization function, which is defined as the average capacity divided by power consumption. In an interference-limited scenario, the EE optimization is of high complexity to solve globally as it is not jointly concave. To this end, we propose the Dinkelbach-like power allocation algorithm to obtain a suboptimal solution. We transform the EE problem in a fractional form into a subtractive optimization form called an auxiliary subproblem. Then, we relax the sub-problem to a concave problem by initializing the interference and omitting the dynamic power term about throughput. Lastly, we solve iteratively the Karush–Kuhn–Tucker (KKT) conditions by bisection search. Consequently, we obtain a sub-solution with modest complexity. The simulation results justify the rationality of the Dinkelbach-like algorithm and demonstrate that the proposal outperforms the reference schemes and effectively improves the performance metrics EE and SE.

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“…In this designed model, the optimization is performed in three parts in a massive MIMO system, which are the number of base station antennas

E

, power allocation for all

u

‐users (transmit power)

ω

, and beamforming vectors

italicVT

. At first, the optimization of transmit power

ξ

is done with the ASS‐CO algorithm in a massive MIMO system, 39,40 to maximize the resource efficiency measures by solving the “spectral efficiency and energy efficiency” functions. The “solution encoding for power allocation” is shown in Figure 4.…”

Section: Enhancing “Spectral and Energy Efficiency In Massive Mimo” B...mentioning

confidence: 99%

Energy and spectral efficiency improvement using improved shark smell‐coyote optimization for massive MIMO system

Byreddy

Логашанмугам

2022

Int J Communication

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Massive multiple-input multiple-output (MIMO) can considerably enhance the "spectral efficiency and energy efficiency" since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large-scale radio frequency (RF) chains represent the large-scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high "spectral efficiency and energy efficiency" owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding "energy efficiency and spectral efficiency." Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize "energy efficiency and spectral efficiency" when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell-coyote optimization (ASS-CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS-CO Algorithm. The proposed ASS-CO algorithm-based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS-WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding "spectral efficiency and energy efficiency" is accomplished over the traditional systems.

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Energy Efficiency Optimization of Massive MIMO System with Uplink Multi-Cell Based on Imperfect CSI with Power Control

Cited by 12 publications

References 23 publications

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

Energy Efficiency Optimization Based on Power Allocation in Massive MIMO Downlink Systems

Energy and spectral efficiency improvement using improved shark smell‐coyote optimization for massive MIMO system

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