scholarly journals Signal Reconstruction with Semidefinite Relaxation Optimization in Uplink Massive MIMO Systems

2020 ◽  
Author(s):  
Guo Li
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Base Station ◽  
Semidefinite Relaxation ◽  
Coherent Detection ◽  
Decision Matrix ◽  
Single Antenna

Abstract In this paper, we consider a single group massive MIMO system, which consists of one receiver (base station) deployed with a large number of antennas and multiple users with each having single antenna. In the uplink transmissions from users to base station, we focus on an efficient signal reconstruction of all the users' transmitted symbols. We build a statistic decision matrix based on the received signals, whose eigenvectors and eigenvalues constitute the key components of our signal reconstruction algorithm. Then, an optimization problem is formulated and we convert this problem to two sub-problems: obtaining the optimal weights by solving Semidefinite Relaxation (SDR) optimization and obtaining the optimal rotation phases. Finally, an iterative algorithm framework is proposed for multi-user signal reconstruction. Numerical results are also carried out to verify the bit-error-ratio (BER) performance compared with the several coherent detection schemes and the noncoherent transmission schemes.

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.

scholarly journals On the Capacity and Transmission Techniques of Massive MIMO Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mohamed Abdul Haleem
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Transmission Rate ◽  
Mimo System ◽  
Coupling Factor ◽  
Base Station ◽  
Power Sharing ◽  
System Capacity ◽  
Bit Rate ◽  
Channel Coupling

A massive MIMO wireless system is a multiuser MISO system where base stations consist of a large number of antennas with respect to number of user devices, each equipped with a single antenna. Massive MIMO is seen as the way forward in enhancing the transmission rate and user capacity in 5G wireless. The potential of massive MIMO system lies in the ability to almost always realize multiuser channels with near zero mutual coupling. Coupling factor reduces by 1/2 for each doubling of transmit antennas. In a high bit rate massive MIMO system with m base station antennas and n users, downlink capacity increases as log2⁡m bps/Hz, and the capacity per user reduces as log2⁡n bps/Hz. This capacity can be achieved by power sharing and using signal weighting vectors aligned to respective 1×m channels of the users. For low bit rate transmission, time sharing achieves the capacity as much as power sharing does. System capacity reduces as channel coupling factor increases. Interference avoidance or minimization strategies can be used to achieve the available capacity in such scenarios. Probability distribution of channel coupling factor is a convenient tool to predict the number of antennas needed to qualify a system as massive MIMO.

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

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.

scholarly journals Research and Implementation of Rateless Spinal Codes Based Massive MIMO System

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Liangliang Wang ◽  
Xiang Chen ◽  
Hongzhou Tan
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Software Radio ◽  
Block Size ◽  
Coherence Time ◽  
Achievable Rate ◽  
Rateless Codes ◽  
Fixed Rate ◽  
Single Antenna

The potential performance gains promised by massive multi-input and multioutput (MIMO) rely heavily on the access to accurate channel state information (CSI), which is difficult to obtain in practice when channel coherence time is short and the number of mobile users is huge. To make the system with imperfect CSI perform well, we propose a rateless codes-aided massive MIMO scheme, with the aim of approaching the maximum achievable rate (MAR) as well as improving the achieved rate over that based on the fixed-rate codes. More explicitly, a recently proposed family of rateless codes, called spinal codes, are applied to massive MIMO systems, where the spinal codes bring the benefit of approximately achieving the MAR with sufficiently large encoding block size. In addition, the multilevel puncturing and dynamic block-size allocation (MPDBA) scheme is proposed, where the block sizes are determined by user MAR to curb the average retransmission delay for successfully decoding the messages, which further enhances the system retransmission efficiency. Multilevel puncturing, which is MAR dependent, narrows the gap between the system MAR and the related achieved rate. Theoretical analysis is provided to demonstrate that spinal codes with the MPDBA can guarantee the system retransmission efficiency as well as achieved rate, which are also verified by numerical simulations. Finally, a simplified but comparable MIMO testbed with 2 transmit antennas and 2 single-antenna users, based on NI Universal Software Radio Peripheral (USRP) and LabVIEW communication toolkits, is built up to demonstrate the effectiveness of our proposal in realistic wireless channels, which is easy to be extended to massive MIMO scenarios in future.

scholarly journals Performance Analysis of 3D Massive MIMO Cellular Systems with Collaborative Base Station

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xingwang Li ◽  
Lihua Li ◽  
Ling Xie ◽  
Xin Su ◽  
Ping Zhang
Keyword(s):  
Lower Bound ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Base Station ◽  
Cellular Systems ◽  
Small Scale ◽  
Pilot Contamination ◽  
3D Mimo ◽  
Sum Rate

Massive MIMO have drawn considerable attention as they enable significant capacity and coverage improvement in wireless cellular network. However, pilot contamination is a great challenge in massive MIMO systems. Under this circumstance, cooperation and three-dimensional (3D) MIMO are emerging technologies to eliminate the pilot contamination and to enhance the performance relative to the traditional interference-limited implementations. Motivated by this, we investigate the achievable sum rate performance of MIMO systems in the uplink employing cooperative base station (BS) and 3D MIMO systems. In our model, we consider the effects of both large-scale and small-scale fading, as well as the spatial correlation and indoor-to-outdoor high-rise propagation environment. In particular, we investigate the cooperative communication model based on 3D MIMO and propose a closed-form lower bound on the sum rate. Utilizing this bound, we pursue a “large-system” analysis and provide the asymptotic expression when the number of antennas at the BS grows large, and when the numbers of antennas at transceiver grow large with a fixed ratio. We demonstrate that the lower bound is very tight and becomes exact in the massive MIMO system limits. Finally, under the sum rate maximization condition, we derive the optimal number of UTs to be served.

scholarly journals Experimental Analysis of Concentrated versus Distributed Massive MIMO in an Indoor Cell at 3.5 GHz

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1646
Author(s):  
Jesús R. Pérez ◽  
Óscar Fernández ◽  
Luis Valle ◽  
Abla Bedoui ◽  
Mohamed Et-tolba ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Access Network ◽  
Base Station ◽  
Mimo Channel ◽  
Power Imbalance ◽  
Channel Measurements

This paper presents a measurement-based comparison between distributed and concentrated massive multiple-input multiple-output (MIMO) systems, which are called D-mMIMO and C-mMIMO systems, in an indoor environment considering a 400 MHz bandwidth centered at 3.5 GHz. In both cases, we have considered an array of 64 antennas in the base station and eight simultaneously active users. The work focuses on the characterization of both schemes in the up-link, considering the analysis of the sum capacity, the total spectral efficiency (SE) achievable by using the zero forcing (ZF) combining method, as well as the user fairness. The effect of the power imbalance between the different transmitters or user terminal (UT) locations, and thus, the benefits of performing an adequate power control are also investigated. The differences between the C-mMIMO and D-mMIMO channel performances are explained through the observation of the structure of their respective measured channel matrices through parameters such as the condition number or the power imbalance between the channels established by each UT. The channel measurements have been performed in the frequency domain, emulating a massive MIMO system in the framework of a time-domain duplex orthogonal frequency multiple access network (TDD-OFDM-MIMO). The characterization of the MIMO channel is based on the virtual array technique for both C-mMIMO and D-mMIMO systems. The deployment of the C-mMIMO and D-MIMO systems, as well as the distribution of users in the measurement environment, has been arranged as realistically as possible, avoiding the movement of people or machines.

scholarly journals Grouping-Based Channel Estimation and Tracking for Millimeter Wave Massive MIMO Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Rui Yin ◽  
Xin Zhou ◽  
Wei Qi ◽  
Celimuge Wu ◽  
Yunlong Cai
Keyword(s):  
Computational Complexity ◽  
Channel Estimation ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Closed Form Solution ◽  
Network Capacity ◽  
Base Station ◽  
Estimation Accuracy

Although the millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping-based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low-complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple-signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed-form solution for updating the step-size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

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.

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