scholarly journals Partial Cooperation Based on Dynamic Transmit Antennas for Two-Hop Massive MIMO Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Jing Yang ◽  
Chunhua Zhu ◽  
Xinying Guo ◽  
Weidong Yang ◽  
Jiankang Zhang
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Base Station ◽  
Achievable Rate ◽  
Error Performance ◽  
Transmit Antenna Selection ◽  
Green Communication ◽  
Partial Cooperation ◽  
Coded Cooperation

As the ever-increasing attention to green communication, energy efficiency has become an imperative metric in the emerging massive multiple-input multiple-output (MIMO) systems. In order to maximize the energy efficiency, transmit antenna selection has been widely concerned by researchers. In this paper, we investigate the coded cooperation transmission for dynamic transmit antennas aided two-hop massive MIMO systems. Explicitly, we propose a rate-less codes aided cooperation scheme for reducing the implementation complexity in the broadcast phase, compared to the fixed-rate coded cooperation scheme. Furthermore, we develop a partial cooperation scheme in the cooperative phase in order to avoid the low achievable rate caused by the full cooperation, especially when the source-to-relay (S-R) channels are poor. Finally, the number of transmit antennas at the base station (BS) is optimized through theoretical analysis in a metric of maximizing the energy efficiency. Moreover, we also analyze the achievable error performance for different modulations. Our simulation results demonstrate the effectiveness of the proposed scheme.

scholarly journals Enhanced Transmit-Antenna Selection Schemes for Multiuser Massive MIMO Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Byung-Jin Lee ◽  
Sang-Lim Ju ◽  
Nam-il Kim ◽  
Kyung-Seok Kim
Keyword(s):  
Computational Complexity ◽  
Radio Frequency ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Base Station ◽  
Base Stations ◽  
Transmit Antenna Selection ◽  
Selection Scheme

Massive multiple-input multiple-output (MIMO) systems are a core technology designed to achieve the performance objectives defined for 5G wireless communications. They achieve high spectral efficiency, reliability, and diversity gain. However, the many radio frequency chains required in base stations equipped with a high number of transmit antennas imply high hardware costs and computational complexity. Therefore, in this paper, we investigate the use of a transmit-antenna selection scheme, with which the number of required radio frequency chains in the base station can be reduced. This paper proposes two efficient transmit-antenna selection (TAS) schemes designed to consider a trade-off between performance and computational complexity in massive MIMO systems. The spectral efficiency and computational complexity of the proposed schemes are analyzed and compared with existing TAS schemes, showing that the proposed algorithms increase the TAS performance and can be used in practical systems. Additionally, the obtained results enable a better understanding of how TAS affects massive MIMO systems.

A Comparison of Norm Based Antenna Selection and Random Antenna Selection with Regard to Energy Efficiency in Wireless System with Large Number of Users

2020 ◽  
Vol 10 (2) ◽  
pp. 189-196
Author(s):  
Ashu Taneja ◽  
Nitin Saluja
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Transmit Antenna Selection ◽  
Wireless System ◽  
Multiple Input ◽  
Input Multiple Output

Background: The paper considers the wireless system with large number of users (more than 50 users) and each user is assigned large number of antennas (around 200) at the Base Station (BS). Objective: The challenges associated with the defined system are increased power consumption and high complexity of associated circuitry. The antenna selection is introduced to combat these problems while the usage of linear precoding reduces computational complexity. The literature suggests number of antenna selection techniques based on statistical properties of signal. However, each antenna selection technique suits well to specific number of users. Methods: In this paper, the random antenna selection is compared with norm-based antenna selection. It is analysed that the random antenna selection leads to inefficient spectral efficiency if the number of users are more than 50 in Multi-User Multiple-Input Multiple Output (MU-MIMO) system. Results: The paper proposes the optimization of Energy-Efficiency (EE) with random transmit antenna selection for large number of users in MU-MIMO systems. Conclusion: Also the computation leads to optimization of number of transmit antennas at the BS for energy efficiency. The proposed algorithm results in improvement of the energy efficiency by 27% for more than 50 users.

scholarly journals Enhancing the Energy Efficiency of mmWave Massive MIMO by Modifying the RF Circuit Configuration

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4356
Author(s):  
Peerapong Uthansakul ◽  
Arfat Ahmad Khan
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Base Station ◽  
Optimal System ◽  
Hybrid Architectures ◽  
System Parameters ◽  
Power And Energy ◽  
Circuit Configuration ◽  
Rf Circuit

Hybrid architectures are used in the Millimeter wave (mmWave) Massive MIMO systems, which use a smaller number of RF chains and reduces the power and energy consumption of the mmWave Massive MIMO systems. However, the majority of the hybrid architectures employs the conventional circuit configuration by connecting each of the RF chains with all the transmitting antennas at the base station. As a result, the conventional circuit configuration requires a large number of phase shifters, combiners, and low-end amplifiers. In this paper, we modify the RF circuit configuration by connecting each of the RF chains with some of the transmitting antennas of mmWave Massive MIMO. Furthermore, the hybrid analogue/digital precoders and decoders along with the overall circuit power consumptions are modelled for the modified RF circuit configuration. In addition, we propose the alternating optimization algorithm to enhance the optimal energy efficiency and compute the optimal system parameters of the mmWave Massive MIMO system. The proposed framework provides deeper insights of the optimal system parameters in terms of throughput, consumed power and the corresponding energy efficiency. Finally, the simulation results validate the proposed framework, where it can be seen that the proposed algorithm significantly reduces the power and energy consumptions, with a little compromise on the system spectral gain.

scholarly journals Low Spatial Peak-to-Average Power Ratio Transmission for Improved Energy Efficiency in Massive MIMO Systems

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5534
Author(s):  
Sina Rezaei Aghdam ◽  
Thomas Eriksson
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Null Space ◽  
Average Power ◽  
Realistic Model ◽  
Base Station ◽  
Power Ratio ◽  
Power Efficient ◽  
The Impact

A significant portion of the operating power of a base station is consumed by power amplifiers (PAs). Much of this power is dissipated in the form of heat, as the overall efficiency of currently deployed PAs is typically very low. This is because the structure of conventional precoding techniques typically results in a relatively high variation in output power at different antennas in the array, and many PAs are operated well below saturation to avoid distortion of the transmitted signals. In this work, we use a realistic model for power consumption in PAs and study the impact of power variation across antennas in the array on the energy efficiency of a massive MIMO downlink system. We introduce a family of linear precoding matrices that allow us to control the spatial peak-to-average power ratio by projecting a fraction of the transmitted power onto the null space of the channel. These precoding matrices preserve the structure of conventional precoders; e.g., they suppress multiuser interference when used together with zeroforcing precoding and bring advantages over these precoders by operating PAs in a more power-efficient region and reducing the total radiated distortion. Our numerical results show that by controlling the power variations between antennas in the array and incorporating the nonlinearity properties of PA into the precoder optimization, significant gains in energy efficiency can be achieved over conventional precoding techniques.

scholarly journals Energy Efficiency Augmentation in Massive MIMO Systems through Linear Precoding Schemes and Power Consumption Modeling

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rao Muhammad Asif ◽  
Jehangir Arshad ◽  
Mustafa Shakir ◽  
Sohail M. Noman ◽  
Ateeq Ur Rehman
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Antenna Arrays ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Mimo System ◽  
Base Station ◽  
Total Power ◽  
Minimum Mean Squared Error

Massive multiple-input multiple-output or massive MIMO system has great potential for 5th generation (5G) wireless communication systems as it is capable of providing game-changing enhancements in area throughput and energy efficiency (EE). This work proposes a realistic and practically implementable EE model for massive MIMO systems while a general and canonical system model is used for single-cell scenario. Linear processing schemes are used for detection and precoding, i.e., minimum mean squared error (MMSE), zero-forcing (ZF), and maximum ratio transmission (MRT/MRC). Moreover, a power dissipation model is proposed that considers overall power consumption in uplink and downlink communications. The proposed model includes the total power consumed by power amplifier and circuit components at the base station (BS) and single antenna user equipment (UE). An optimal number of BS antennas to serve total UEs and the overall transmitted power are also computed. The simulation results confirm considerable improvements in the gain of area throughput and EE, and it also shows that the optimum area throughput and EE can be realized wherein a larger number of antenna arrays at BS are installed for serving a greater number of UEs.

scholarly journals Towards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems

2021 ◽  
pp. 1-1
Author(s):  
A. Papazafeiropoulos ◽  
H. Q. Ngo ◽  
P. Kourtessis ◽  
S. Chatzinotas ◽  
J. M. Senior
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Optimal Energy

An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

2021 ◽  
Author(s):  
Seyedeh Samira Moosavi ◽  
Paul Fortier
Keyword(s):  
Massive Mimo ◽  
Clustering Algorithm ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Principal Component ◽  
Base Station ◽  
Location Estimation ◽  
Similar Data ◽  
High Coverage ◽  
Single Antenna

Abstract Currently, localization in distributed massive MIMO (DM-MIMO) systems based on the fingerprinting (FP) approach has attracted great interest. However, this method suffers from severe multipath and signal degradation such that its accuracy is deteriorated in complex propagation environments, which results in variable received signal strength (RSS). Therefore, providing robust and accurate localization is the goal of this work. In this paper, we propose an FP-based approach to improve the accuracy of localization by reducing the noise and the dimensions of the RSS data. In the proposed approach, the fingerprints rely solely on the RSS from the single-antenna MT collected at each of the receive antenna elements of the massive MIMO base station. After creating a radio map, principal component analysis (PCA) is performed to reduce the noise and redundancy. PCA reduces the data dimension which leads to the selection of the appropriate antennas and reduces complexity. A clustering algorithm based on K-means and affinity propagation clustering (APC) is employed to divide the whole area into several regions which improves positioning precision and reduces complexity and latency. Finally, in order to have high precise localization estimation, all similar data in each cluster are modeled using a well-designed deep neural network (DNN) regression. Simulation results show that the proposed scheme improves positioning accuracy significantly. This approach has high coverage and improves average root-mean-squared error (RMSE) performance to a few meters, which is expected in 5G and beyond networks. Consequently, it also proves the superiority of the proposed method over the previous location estimation schemes.

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