scholarly journals Deep Learning-Based Symbol-Level Precoding for Large-Scale Antenna System

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Changxu Xie ◽  
Huiqin Du ◽  
Xialing Liu
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Back Propagation ◽  
Symbol Error Rate ◽  
Antenna System ◽  
Alternating Direction ◽  
Input Multiple Output ◽  
Deep Learning Network ◽  
Optimum Solution

In this work, we consider a multiple input multiple-output system with large-scale antenna array which creates unintended multiuser interference and increases the power consumption due to the large number of radio frequency (RF) chains. The antenna selective symbol level precoding design is developed by minimizing the symbol error rate (SER) with limits of available RF chains. The ℓ 0 -norm constrained nonconvex problem can be approximated as ℓ 1 -minimization, which is further solved by alternating direction method of multipliers (ADMM) approach. The basic ADMM scheme is mapped into iterative construction process where the optimum solution is obtained by taking deep learning network as building block. Moreover, because that the standard ADMM algorithm is sensitive to the selection of hyperparameters, we further introduce the back propagation process to train the parameters. Simulation results show that the proposed deep learning ADMM scheme can achieve significantly low SER performance with small activated subset of transmit antennas.

scholarly journals Limited Feedback Precoding for Massive MIMO

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xin Su ◽  
Jie Zeng ◽  
Jingyu Li ◽  
Liping Rong ◽  
Lili Liu ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Limited Feedback ◽  
Antenna System ◽  
Base Station ◽  
Array Antenna ◽  
Codebook Design ◽  
Input Multiple Output ◽  
Scenarios Simulation

The large-scale array antenna system with numerous low-power antennas deployed at the base station, also known as massive multiple-input multiple-output (MIMO), can provide a plethora of advantages over the classical array antenna system. Precoding is important to exploit massive MIMO performance, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a new avenue of codebook design based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular antenna array, which is preferable for the antenna deployment of massive MIMO. Although the feedback overhead is quite limited, the codebook design can provide an effective solution to support multiple users in different scenarios. Simulation results demonstrate that our proposed codebook outperforms the previously known codebooks remarkably.

scholarly journals Discreteness and group sparsity aware detection for uplink overloaded MU-MIMO systems

2020 ◽  
Vol 9 ◽  
Author(s):  
Ryo Hayakawa ◽  
Ayano Nakai-Kasai ◽  
Kazunori Hayashi
Keyword(s):  
Signal Detection ◽  
Optimization Problem ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Symbol Error Rate ◽  
Base Station ◽  
Detection Methods ◽  
Group Sparsity ◽  
Alternating Direction ◽  
Input Multiple Output

This paper proposes signal detection methods for frequency domain equalization (FDE) based overloaded multiuser multiple input multiple output (MU-MIMO) systems for uplink Internet of things (IoT) environments, where a lot of IoT terminals are served by a base station having less number of antennas than that of IoT terminals. By using the fact that the transmitted signal vector has the discreteness and the group sparsity, we propose a convex discreteness and group sparsity aware (DGS) optimization problem for the signal detection. We provide an optimization algorithm for the DGS optimization on the basis of the alternating direction method of multipliers (ADMM). Moreover, we extend the DGS optimization into weighted DGS (W-DGS) optimization and propose an iterative approach named iterative weighted DGS (IW-DGS), where we iteratively solve the W-DGS optimization problem with the update of the parameters in the objective function. We also discuss the computational complexity of the proposed IW-DGS and show that we can reduce the order of the complexity by using the structure of the channel matrix. Simulation results show that the symbol error rate (SER) performance of the proposed method is close to that of the oracle zero forcing (ZF) method, which perfectly knows the activity of each IoT terminal.

scholarly journals Multisegment Mapping Network for Massive MIMO Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yongzhi Yu ◽  
Jianming Wang ◽  
Limin Guo
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Activation Function ◽  
Symbol Error Rate ◽  
Mimo Detection ◽  
Residual Structure ◽  
Detection Algorithms ◽  
Input Multiple Output

The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.

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

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.

scholarly journals A Graphene-Assembled Film Based MIMO Antenna Array with High Isolation for 5G Wireless Communication

2021 ◽  
Vol 11 (5) ◽  
pp. 2382
Author(s):  
Rongguo Song ◽  
Xiaoxiao Chen ◽  
Shaoqiu Jiang ◽  
Zelong Hu ◽  
Tianye Liu ◽  
...  
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Multiple Input Multiple Output ◽  
Frequency Selective Surface ◽  
Antenna System ◽  
Antenna Element ◽  
Mimo Antenna ◽  
High Isolation ◽  
Input Multiple Output ◽  
Alternative Material

With the development of 5G, Internet of Things, and smart home technologies, miniaturized and compact multi-antenna systems and multiple-input multiple-output (MIMO) antenna arrays have attracted increasing attention. Reducing the coupling between antenna elements is essential to improving the performance of such MIMO antenna system. In this work, we proposed a graphene-assembled, as an alternative material rather than metal, film-based MIMO antenna array with high isolation for 5G application. The isolation of the antenna element is improved by a graphene assembly film (GAF) frequency selective surface and isolation strip. It is shown that the GAF antenna element operated at 3.5 GHz has the realized gain of 2.87 dBi. The addition of the decoupling structure improves the isolation of the MIMO antenna array to more than 10 dB and corrects the antenna radiation pattern and operating frequency. The isolation between antenna elements with an interval of 0.4λ is above 25 dB. All experimental results show that the GAF antenna and decoupling structure are efficient devices for 5G mobile communication.

scholarly journals Low-complexity sparse-aware multiuser detection for large-scale MIMO systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Rong Ran ◽  
Hayoung Oh
Keyword(s):  
Large Scale ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Finite Alphabet ◽  
Multiuser Detectors ◽  
Input Multiple Output ◽  
Error Detector ◽  
Significant Performance

AbstractSparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

scholarly journals Mutual Coupling Reduction of DRA for MIMO Applications

2019 ◽  
Vol 8 (1) ◽  
pp. 75-81
Author(s):  
N. Al Shalaby ◽  
S. G. El-Sherbiny
Keyword(s):  
Mutual Coupling ◽  
Dielectric Material ◽  
Ground Plane ◽  
Multiple Input Multiple Output ◽  
Antenna System ◽  
Parasitic Element ◽  
Mimo Antenna ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Element Method

In this paper, A multiple input Multiple Output (MIMO) antenna using two Square Dielectric Resonators (SDRs) is introduced. The mutual coupling between the two SDRAs is reduced using two different methods; the first method is based on splitting a spiral slot in the ground plane, then filling the slot with dielectric material, "E.=2.2". The second method is based on inserting a copper parasitic element, having the same shape of the splitted Spiral, between the two SDRAs.  The effect of replacing the copper parasitic element with Carbon nanotubes (CNTs) parasitic element "SOC12 doped long-MWCNT BP" is also studied. The antenna system is designed to operate at 6 GHz. The analysis and simulations are carried out using finite element method (FEM). The defected ground plane method gives a maximum isolation of l8dB at element spacing of 30mm (0.6λo), whereas the parasitic element method gives a maximum isolation of 42.5dB at the same element spacing.

scholarly journals Location-Aided Uplink Transmission for User-Centric Cell-Free Massive MIMO Systems: A Fairness Priority Perspective

2022 ◽  
Author(s):  
Chen Wei ◽  
Kui Xu ◽  
Zhexian Shen ◽  
Xiaochen Xia ◽  
Wei Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Access Point ◽  
Estimation Accuracy ◽  
Minimum Mean Squared Error ◽  
Input Multiple Output ◽  
User Centric ◽  
Assignment Scheme

Abstract In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme based on a novel proposed metric representing inter-user interference to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which the normalized local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while the normalized maximum ratio (MR) combining is adopted for the remaining APs. A new closed-form SE expression using the normalized MR combining is derived and a novel metric to indicate the UE fairness is also proposed. Moreover, the max-min fairness (MMF) power control algorithm is utilized to further ensure uniformly good service to the UEs. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. Furthermore, although the proposed location-aided uplink combining scheme is not always the best in terms of the per-UE SE, it can provide the more fairness among UEs and can achieve a good trade-off between the average SE and computational complexity.

scholarly journals Unification of Broadband and Broadcast Mobile Services using Cognitive Radio

2021 ◽  
pp. 71-75
Author(s):  
V. Annapoorani ◽  
S. Sureshkumar ◽  
Srisaravanapathimurugesan ◽  
M. Manoj ◽  
K. Prabhu
Keyword(s):  
Cognitive Radio ◽  
Dynamic Spectrum Access ◽  
Large Scale ◽  
High Reliability ◽  
Multiple Input Multiple Output ◽  
Future Generation ◽  
Research Areas ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Overall Performance

The networks in future generation uses the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are located as a preferred paradigm to bring up with spectrum functionality traumatic conditions. CRS addresses the ones troubles via dynamic spectrum access. However, the precept traumatic conditions faced through manner of manner of the CR pertain to accomplishing spectrum overall performance. At the end, spectrum overall performance improvement models based on spectrum sensing and sharing models have attracted quite a few research hobby in modern-day years, which incorporates CR mastering models, network densification architectures, and Massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper deals with a survey of modern CR spectrum overall improvement performance models and techniques which helps ultra-high reliability with low latency communications which might be resilient to surges in web page site visitors and competition for spectrum. These models and techniques, mainly speaks about permit a big form of functionality beginning from extra superb mobiliary broadband to large-scale Internet of Things (IoT) type communications. It also provides a research correlation for many of the regular periods of a spectrum block, as well as the realistic statistics rate, the models which are used in this paper are applicable in an ultra-high frequency band. This study provides a super compare of CRs and direction for future investigations into newly identified 5G research areas, such as in business enterprise and academia.

High spatiotemporal radar observation of the polar summer mesosphere using MAARSY in a MIMO configuration

2021 ◽  
Author(s):  
Ralph Latteck ◽  
Jorge Chau ◽  
Miguel Urco ◽  
Juha Vierinen ◽  
Victor Avsarkisov
Keyword(s):  
Large Scale ◽  
Middle Atmosphere ◽  
Multiple Input Multiple Output ◽  
Horizontal Resolution ◽  
Neutral Wind ◽  
Stratified Turbulence ◽  
Turbulent Dissipation ◽  
Radar Echoes ◽  
Multiple Input ◽  
Input Multiple Output

<p>Atmospheric structures due to gravity waves, turbulence, Kelvin Helmholtz instabilities, etc. in the mesosphere are being studied with a varying of ground-based and satellite-based instruments. At scales less than 100 km, they are mainly studied with airglow imagers, lidars, and radars. Typical radar observations have not been able to resolve spatial and temporal ambiguities due to the strength of radar echoes, the size of the system, and/or the nature of the atmospheric irregularities. In this work we observed spatially and temporally resolved structures of PMSE with unprecedented horizontal resolution, using the improved radar imaging accuracy of the Middle Atmosphere Alomar Radar System (MAARSY) with the aid of a multiple-input multiple output (MIMO) technique. The studies are performed in both the brightness of the mesospheric echoes and their Doppler velocities. The resolutions achieved are less than 1 km in the horizontal direction, less than 300m in altitude, and less than 1 minute in time, in an area of ~15km x 15km around 85km of altitude. We present a couple of wavelike monochromatic events, one drifting with the background neutral wind, and one propagating against the neutral wind. Horizontal wavelengths, periods, and vertical and temporal coverage of the events are described and discussed. A theory of stratified turbulence is employed in the present study. In particular, it is shown that the structure that propagates with the background wind is a large-scale turbulent KHI event.  Some important turbulence characteristics, such as a turbulent dissipation rate, buoyancy Reynolds number, and Froude number, support our conclusion.</p>

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