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Number of results: 6
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Abstract

Hybrid precoding techniques are lately involved a lot of interest for millimeter-wave (mmWave) massive MIMO systems is due to the cost and power consumption advantages they provide. However, existing hybrid precoding based on the singular value decomposition (SVD) necessitates a difficult bit allocation to fit the varying signal-to-noise ratios (SNRs) of altered sub-channels. In this paper, we propose a generalized triangular decomposition (GTD)-based hybrid precoding to avoid the complicated bit allocation. The development of analog and digital precoders is the reason for the high level of design complexity in analog precoder architecture, which is based on the OMP algorithm, is very non-convex, and so has a high level of complexity. As a result, we suggest using the GTD method to construct hybrid precoding for mmWave mMIMO systems. Simulated studies as various system configurations are used to examine the proposed design. In addition, the archived findings are compared to a hybrid precoding approach in the classic OMP algorithm. The proposed Matrix Decomposition’s simulation results of signal-to-noise ratio vs spectral efficiencies.
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Authors and Affiliations

Sammaiah Thurpati
1
P. Muthuchidambaranathan
1

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, India
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Abstract

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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Authors and Affiliations

Ibrahim Salah
1
Kamel Hussein Rahouma
2 3
Aziza I. Hussein
4
ORCID: ORCID
Mohamed M. Mabrook
5 1
ORCID: ORCID

  1. CCE Department, Faculty of Engineering, Nahda University, Beni-Suef, Egypt
  2. Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt
  3. Faculty of Computer Science, Nahda University, Beni-Suef, Egypt
  4. Electrical & Computer Eng. Dept., Effat University, Jeddah, KSA
  5. Faculty of Navigation Science & Space Technology, Beni-Suef University, Beni-Suef, Egypt
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Abstract

In a massive multiple-input multiple-output (MIMO) system, a large number of receiving antennas at the base station can simultaneously serve multiple users. Linear detectors can achieve optimal performance but require large dimensional matrix inversion, which requires a large number of arithmetic operations. Several low complexity solutions are reported in the literature. In this work, we have presented an improved two-dimensional double successive projection (I2D-DSP) algorithm for massive MIMO detection. Simulation results show that the proposed detector performs better than the conventional 2D-DSP algorithm at a lower complexity. The performance under channel correlation also improves with the I2D-DSP scheme. We further developed a soft information generation algorithm to reduce the number of magnitude comparisons. The proposed soft symbol generation method uses real domain operation and can reduce almost 90% flops and magnitude comparisons.
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Authors and Affiliations

Sourav Chakraborty
1
Nirmalendu Bikas Sinha
2
Monojit Mitra
3

  1. Department of Electronics and Communication Engineering, Cooch Behar Government Engineering College, Coochbehar,India
  2. Principal, Maharaja Nandakumar Mahavidyalaya, Purba Medinipore, India
  3. Department of Electronics and Telecommunication, Engineering, IIEST Shibpur, Howrah, India
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Abstract

Massive multiple-input-multiple-output (MIMO) and beamforming are key technologies, which significantly influence on increasing effectiveness of emerging fifth-generation (5G) wireless communication systems, especially mobile-cellular networks. In this case, the increasing effectiveness is understood mainly as the growth of network capacity resulting from better diversification of radio resources due to their spatial multiplexing in macro- and micro-cells. However, using the narrow beams in lieu of the hitherto used cell-sector brings occurring interference between the neighboring beams in the massive-MIMO antenna system, especially, when they utilize the same frequency channel. An analysis of this effect is the aim of this paper. In this case, it is based on simulation studies, where a multi-elliptical propagation model and standard 3GPP model are used. We present the impact of direction and width of the neighboring beams of 5G new radio gNodeB base station equipped with the multi-beam antenna system on the interference level between these beams. The simulations are carried out for line-of-sight (LOS) and non-LOS conditions of a typical urban environment.

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Authors and Affiliations

Jan M. Kelner
Cezary Ziółkowski
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Abstract

The blustery growth of high data rate applications leads to more energy consumption in wireless networks to satisfy service quality. Therefore, energy-efficient communications have been paid more attention to limited energy resources and environmentally friendly transmission functioning. Countless publications are present in this domain which focuses on intensifying network energy efficiency for uplink-downlink transmission. It is done either by using linear precoding schemes, by amending the number of antennas per BS, by power control problem formulation, antenna selection schemes, level of hardware impairments, and by considering cell-free (CF) Massive-MIMO. After reviewing these techniques, still there are many barriers to implement them practically. The strategies mentioned in this review show the performance of EE under the schemes as raised above. The chief contribution of this work is the comparative study of how Massive MIMO EE performs under the background of different methods and architectures and the solutions to few problem formulations that affect the EE of network systems. This study will help choose the best criteria to improve EE of Massive MIMO while formulating a newer edition of testing standards. This survey provides the base for interested readers in energy efficient Massive MIMO.
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Authors and Affiliations

Ritu Singh Phogat
1
Rutvij Joshi
2

  1. Gujarat Technological University,Ahmedabad, India
  2. Parul University, Vadodara, India
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Abstract

With the advent of massive MIMO and mmWave, Antenna selection is the new frontier in hybrid beamforming employed in 5G base stations. Tele-operators are reworking on the components while upgrading to 5G where the antenna is a last-mile device. The burden on the physical layer not only demands smart and adaptive antennas but also an intelligent antenna selection mechanism to reduce power consumption and improve system capacity while degrading the hardware cost and complexity. This work focuses on reducing the power consumption and finding the optimal number of RF chains for a given millimeter wave massive MIMO system. At first, we investigate the power scaling method for both perfect Channel State Information (CSI) and imperfect CSI where the power is reduced by ��/���� and ��/√���� respectively. We further propose to reduce the power consumption by emphasizing on the subdued resolution of Analog-to-Digital Converters (ADCs) with quantization awareness. The proposed algorithm selects the optimal number of antenna elements based on the resolution of ADCs without compromising on the quality of reception. The performance of the proposed algorithm shows significant improvement when compared with conventional and random antenna selection methods.

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Authors and Affiliations

Abdul Haq Nalband
Mrinal Sarvagya
Mohammed Riyaz Ahmed

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