Extended Coprime Array Configuration Generating Large-Scale Antenna Co-Array in Massive MIMO System

The conventional direction of arrival (DOA) estimation algorithm is not effective with the tremendous complexity due to the large-scale array antennas in a massive multiple-input multiple-output (MIMO) system. A new frame structure for downlink transmission is presented. Then, codebook-aided (C-aided) algorithms are proposed based on this frame structure that can fully exploit the priori information under channel codebook feedback mechanism. An oriented angle range is scoped through the codebook feedback, which is drastically beneficial to reduce computational burden for DOA estimation in massive MIMO systemss. Compared with traditional DOA estimation algorithms, our proposed C-aided algorithms are computationally efficient and meet the demand of future green communication. Simulations show the estimation effectiveness of C-aided algorithms and advantage for decrement of computational cost.

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An Improved Multicell MMSE Channel Estimation in a Massive MIMO System

International Journal of Antennas and Propagation ◽

10.1155/2014/387436 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 14

Author(s):

Ke Li ◽

Xiaoqin Song ◽

M. Omair Ahmad ◽

M. N. S. Swamy

Keyword(s):

Channel Estimation ◽

Interference Cancellation ◽

Massive Mimo ◽

Large Scale ◽

Mimo System ◽

Computational Cost ◽

Spectrum Efficiency ◽

Performance Simulation ◽

Estimation Scheme ◽

Joint Channel

Massive MIMO is a promising technology to improve both the spectrum efficiency and the energy efficiency. The key problem that impacts the throughput of a massive MIMO system is the pilot contamination due to the nonorthogonality of the pilot sequences in different cells. Conventional channel estimation schemes cannot mitigate this problem effectively, and the computational complexity is increasingly becoming larger in views of the large number of antennas employed in a massive MIMO system. Furthermore, the channel estimation is always carried out with some ideal assumptions such as the complete knowledge of large-scale fading. In this paper, a new channel estimation scheme is proposed by utilizing interference cancellation and joint processing. Highly interfering users in neighboring cells are identified based on the estimation of large-scale fading and then included in the joint channel processing; this achieves a compromise between the effectiveness and efficiency of the channel estimation at a reasonable computational cost, and leads to an improvement in the overall system performance. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

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Signal Detection Algorithms for Massive MIMO

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1049-1050.2063 ◽

2014 ◽

Vol 1049-1050 ◽

pp. 2063-2068

Author(s):

Xiao Tian Wang ◽

Long Xiang Yang

Keyword(s):

Signal Detection ◽

Massive Mimo ◽

Large Scale ◽

Mimo System ◽

Base Station ◽

Mobile Systems ◽

Detection Algorithms ◽

Fifth Generation ◽

Base Station Antennas ◽

Large Scale Antenna Systems

massive MIMO (also known as Large-Scale Antenna Systems),which is one of the key technologies for the fifth generation (5G) mobile systems, brings huge improvements in spectral efficiency and energy efficiency through the use of a large excess of antennas for base station. This paper analyses and simulates the performances of several signal detection algorithms under the massive MIMO system model. The results show that when the number of base station antennas is considerably larger than the number of users, even the simple signal detection algorithms can achieve good system performance.

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Evaluation of different quantization resolution levels on the BER performance of massive MIMO systems under different operating scenarios

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i3.pp1493-1500 ◽

2021 ◽

Vol 23 (3) ◽

pp. 1493

Author(s):

Hayder Khaleel AL-Qaysi ◽

Tahreer Mahmood ◽

Khalid Awaad Humood

Keyword(s):

Massive Mimo ◽

Large Scale ◽

Influence Factor ◽

Mimo Systems ◽

Mimo System ◽

Base Station ◽

Analog To Digital ◽

Simulation Performance ◽

Hardware Costs ◽

Ber Performance

The massive MIMO system is one of the main technologies in the fifth generation (5G) of telecommunication systems, also recognized as a highly large-scale system. Constantly in massive MIMO systems, the base station (BS) is provided with a large number of antennas, and this large number of antennas need high-quantization resolution levels analog-to-digital converters (ADCs). In this situation, there will be more power consumption and hardware costs. This paper presents the simulation performance of a suggested method to investigate and analyze the effects of different quantization resolution levels of ADCs on the bit error rate (BER) performance of massive MIMO system under different operating scenarios using MATLAB software. The results show that the SNR exceeds 12 dB accounts for only 0.001% of BER signals when the number of antennas 60 with low quantization a 2 bits’ levels ADCs, approximately. But when the antenna number rises to 300, the SNR exceeds 12 dB accounts for almost 0.01% of BER transmitted signals. Comparably with the BER performance of high quantization, 4 bits-quantization resolution levels ADCs with the same different antennas have a slight degradation. Therefore, the number of antennas is a very important influence factor.

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Time-shifted Pilot-based Scheduling with Adaptive Optimization for Pilot Contamination Reduction in Massive MIMO

Journal of Telecommunications and Information Technology ◽

10.26636/jtit.2020.145020 ◽

2020 ◽

Vol 4 ◽

pp. 10-21

Author(s):

Ambala Pradeep Kumar ◽

Tadisetty Srinivasulu

Keyword(s):

Massive Mimo ◽

Communication Systems ◽

Large Scale ◽

Mimo System ◽

Base Station ◽

Superior Performance ◽

Optimal Time ◽

Pilot Contamination ◽

Adaptive Optimization ◽

User Grouping

Massive multiple-input multiple-output (MIMO) is considered to be an emerging technique in wireless communication systems, as it offers the ability to boost channel capacity and spectral efficiency. However, a massive MIMO system requires huge base station (BS) antennas to handle users and suffers from inter-cell interference that leads to pilot contamination. To cope with this, time-shifted pilots are devised for avoiding interference between cells, by rearranging the order of transmitting pilots in different cells. In this paper, an adaptive-elephant-based spider monkey optimization (adaptive ESMO) mechanism is employed for time-shifted optimal pilot scheduling in a massive MIMO system. Here, user grouping is performed with the sparse fuzzy c-means (Sparse FCM) algorithm, grouping users based on such parameters as large-scale fading factor, SINR, and user distance. Here, the user grouping approach prevents inappropriate grouping of users, thus enabling effective grouping, even under the worst conditions in which the channel operates. Finally, optimal time-shifted scheduling of the pilot is performed using the proposed adaptive ESMO concept designed by incorporating adaptive tuning parameters. The efficiency of the adaptive ESMO approach is evaluated and reveals superior performance with the highest achievable uplink rate of 43.084 bps/Hz, the highest SINR of 132.9 dB, and maximum throughput of 2.633 Mbps

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Adaptive Power Control Using Large Scale Antenna of the Massive MIMO System in the Mobile Communication

KSII Transactions on Internet and Information Systems ◽

10.3837/tiis.2015.08.018 ◽

2015 ◽

Vol 9 (8) ◽

pp. 3068-3078 ◽

Cited By ~ 1

Keyword(s):

Power Control ◽

Mobile Communication ◽

Massive Mimo ◽

Large Scale ◽

Mimo System ◽

Adaptive Power

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Deep Learning Based Analog Beamforming Design for Millimetre Wave Massive MIMO System

10.21203/rs.3.rs-661112/v1 ◽

2021 ◽

Author(s):

Rajdeep Singh Sohal ◽

Vinit Grewal ◽

Jaipreet Kaur ◽

Maninder Lal Singh

Keyword(s):

Deep Learning ◽

Massive Mimo ◽

Large Scale ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Design Procedure ◽

Base Station ◽

Small Scale ◽

Millimetre Wave ◽

Input Multiple Output

Abstract Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of ABF network that accepts input as channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple input multiple output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

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