Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

Ibrahim, Salah; Mabrook, M. Mourad; Rahouma, Kamel H.; Hussein, Aziza I.

doi:10.1007/s11082-021-03507-5

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…Each UE's uplink (UL) spectral efficiency is determined according to equation (1), as derived in [15]:…”

Section: Tradeoff Between Energy Efficiency and Spectral Efficiency (...mentioning

confidence: 99%

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Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Salah,

Rahouma,

Hussein

et al. 2022

International Journal of Electronics and Telecommunications

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On fifth-generation wireless networks, a potential massive MIMO system is used to meet the ever-increasing request for high-traffic data rates, high-resolution streaming media, and cognitive communication. In order to boost the trade-off between energy efficiency (EE), spectral efficiency (SE), and throughput in wireless 5G networks, massive MIMO systems are essential. This paper proposes a strategy for EE 5G optimization utilizing massive MIMO technology. The massive MIMO system architecture would enhance the trade-off between throughput and EE at the optimum number of working antennas. Moreover, the EE-SE tradeoff is adjusted for downlink and uplink massive MIMO systems employing linear precoding techniques such as Multiple -Minimum Mean Square Error (M-MMSE), Regularized Zero Forcing (RZF), Zero Forcing (ZF), and Maximum Ratio (MR). Throughput is increased by adding more antennas at the optimum EE, according to the analysis of simulation findings. Next, utilizing M MMSE instead of RZF and ZF, the suggested trading strategy is enhanced and optimized. The results indicate that M-MMSE provides the best tradeoff between EE and throughput at the determined optimal ratio between active antennas and active users equipment's (UE).

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“…Each UE's uplink (UL) spectral efficiency is determined according to equation (1), as derived in [15]:…”

Section: Tradeoff Between Energy Efficiency and Spectral Efficiency (...mentioning

confidence: 99%

“…These metrics account for the power to transmit UL and DL signals and pilot sequences. Therefore, ETP is estimated according to equation ( 14): ETPj = ETP for pilots + ETP UL+ ETP DL (14) The equations (15)(16)(17)…”

Section: Energy Efficiency -Throughput (Ee-tr) Tradeoff In M-mimomentioning

confidence: 99%

Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Salah,

Rahouma,

Hussein

et al. 2022

International Journal of Electronics and Telecommunications

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“…This configuration increases the channel capacity and data transmission speed of the network by utilizing multiple signal paths and supporting a large number of users simultaneously. Because of the advantages it offers over point‐to‐point MIMO 35 communication, mMIMO is expected to be utilized in both 5G and B5G networks. In single‐carrier transmission, a higher data transmission rate requires a wider bandwidth, as indicated by the Nyquist sampling rate, which is equal to half the symbol rate in samples per second.…”

Section: Introductionmentioning

confidence: 99%

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

Shankar

2023

Int J Communication

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SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

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“…Dikmen & Kulac [4] examine power allocation algorithms for MIMO systems. Salah et al [5] propose an adaptive optimization technique focusing on maximizing Energy Efficiency in adaptive massive MIMO networks.…”

Section: Introductionmentioning

confidence: 99%

Maximizing Signal to Interference Noise Ratio for Massive MIMO: A Stochastic Neurodynamic Approach

Tassouli,

Lisser

2023

Lecture Notes in Computer Science

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In this paper, we consider the problem of maximizing the worst user signal to interference noise ratio (SINR) for massive multiple input multiple output (MaMIMO). We reformulate the nonlinear optimization model as a joint chance-constrained geometric program. We propose a neurodynamic approach to solve the obtained problem. Our numerical results indicate that our approach outperforms the state-of-art convex approximations used to solve joint chance-constrained geometric problems.

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Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

Cited by 10 publications

References 21 publications

Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

Maximizing Signal to Interference Noise Ratio for Massive MIMO: A Stochastic Neurodynamic Approach

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