Large Number Of Antennas Research Articles

Implementation of 5G New Radio Secondary Synchronization on FPGA

: 5th Generation New Radio (NR) cellular technology operates at low latency, and higher bandwidths, and makes use of a large number of antennas. NR communication is compatible with a wide range of machine- and human-centered applications. The cell search procedure plays a crucial role in establishing a synchronized connection with the base station (gNodeB) when the user equipment (UE) is enabled, or in the on state. The procedure for cell search finds the physical cell identity of the base station with primary synchronization and secondary synchronization for 3rd Generation Partnership Project (3GPP) 5G NR standards. This paper focuses on identifying the secondary synchronization of Physical Cell Identity (PCI) in the 3rd Generation Partnership Project (3GPP) 5G NR system. Field Programmable Gate Arrays are reprogrammable, flexible, reconfigurable computing circuits and simple to design at very peak frequencies. This suggested architecture employs Secondary Synchronization Signal detection and aims for low power consumption and high speed. The secondary synchronization block has been designed and executed on the Xilinx Zynq FPGA board. Index Terms: 5G New Radio, FPGA, Cell search, Physical Cell Identity, Primary and Secondary synchronization (SS).

The Robustness of Favorable Propagation in Massive MIMO to Location and Phase Errors

The impact of random errors (implementation inaccuracies) in element locations and beamforming phases on favorable propagation (FP) in massive multiple-input multiple-output (MIMO) line-of-sight (LOS) channels is studied. For arbitrary array geometry and under independent (possibly non-Gaussian) errors, the FP property is shown to hold for the perturbed array as long as it holds for the unperturbed one. This means that small errors do not have catastrophic impact on the FP, even for a large number of antennas. The negative impact of random errors is to slow down the convergence to the asymptotic value so that more antennas are needed under random errors to achieve the same low inter-user interference as without errors. For large but finite number of antennas, the distribution and an analytically-tractable approximation of the inter-user interference power are obtained. Practical design guidelines are given that quantify the accuracy level needed to make the impact of random errors negligible. The analytical results are validated via numerical simulations and are in agreement with measurement-based studies.

An Ordered QR Decomposition based Signal Detection Technique for Uplink Massive MIMO System

Signal detection turns out to be a critical challenge in massive MIMO (m-MIMO) system due to the deployment of large number of antennas at the base station. Although, the mini mum mean square error (MMSE) is one of the popular signal detection method, but, it requires matrix inversion with cubic complexity. In order to reduce computational complexity, sev eral suboptimal signal detection methods were proposed such as Gauss-Seidel, successive over relaxation, Jacobi, Richardson methods. Although, these methods provide low complexity but their performance are limited to MMSE method. In this paper, we have proposed two signal detection techniques namely QR decompositions (QRD) and ordered QRD (OQRD). Finally, the performances of proposed signal detection methods are com pared with various conventional methods in terms of symbol er ror rate (SER) and computational complexity. The simulation re sults validate that the proposed methods outperform the MMSE method with substantially lower computational complexity.

Thinned Smart Antenna of a Semi-circular Dipole Array for Massive MIMO Systems

Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that can provide high-speed multimedia services in 5G wireless networks using sub-6 GHz and millimeter wave bands. The massive MIMO (MMIMO) installs array antennas in the base stations, using hundreds of transceivers with other RF modules. One of the drawbacks of the MMIMO system is its huge power consumption, and the beamforming network with RF modules for a large number of antennas is the main contributor to the power consumption. In this paper, a novel beamforming method is proposed for the low power consumption of an MMIMO system. The proposed thinned smart antenna (TSA) of a semi-circular array produces a secure beam toward the user’s terminal with reduced interference. By thinning the antenna array, some of the antenna elements are kept off, resulting in less power consumption, while the array pattern remains the same as a fully populated array with a reduced side lobe level (SLL). The sub-6 GHz band of 5 GHz is used for the design of thinned array antennas. The genetic algorithm (GA) is used to determine the array sequence in thinning, and the adaptive signal processing algorithms least mean square (LMS), recursive least square (RLS), and sample matrix inversion (SMI) are used for the beamforming of the TSA, and the corresponding algorithms are GA-LMS, GA-RLS, and GA-SMI. The power saving of 40% to 55% is achieved using TSA. The maximum SLL reductions of 13 dB, 12 dB, and 14 dB are achieved for TSA using GA-LMS, GA-RLS, and GA-SMI algorithms, respectively.

3-D Millimeter Wave Fast Imaging Technique Based on 2-D SISO/MIMO Array

In this article, a novel three-dimensional (3-D) imaging method based on the range decomposing algorithm (RDA) is proposed for millimeter wave imaging. We combined it with binomial theory and we derive the theoretical formulation of RDA applied to single-input–single-output (SISO)/multiple-input–multiple-output (MIMO) array; meanwhile, its computational complexity and computational error are analyzed. Compared to the classical Fourier algorithm, such as the range migration algorithm (RMA) and the phase shift migration (PSM), the proposed algorithm can replace the time-consuming interpolation and accumulation operations with reasonable approximations and transformations offering a more efficient approach, while maintaining the image quality. In addition, a method based on RDA which is applicable to the transformation between MIMO and SISO, is proposed to further enhance the processing efficiency. Proof-of-principle simulation using echo data collected from a large number of antennas, verifies that the proposed algorithm has higher efficiency. In order to better verify the feasibility of the proposed algorithm, a scanning prototype located in the millimeter wave band is designed. The experimental results of different targets demonstrate that the proposed algorithm achieves significantly higher reconstruction efficiency when compared to the traditional algorithms.

Robust Precoding for HF Skywave Massive MIMO

In this paper, we investigate the robust precoding with imperfect channel state information (CSI) for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. Starting with a sparse beam based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization (MM). Furthermore, we develop a low-complexity BDRP design with an ergodic sum-rate upper bound, simplifying the MM based design algorithm. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

RIS Partitioning Based Scalable Beamforming Design for Large-Scale MIMO: Asymptotic Analysis and Optimization

In next-generation wireless networks, reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems are foreseeable to support a large number of antennas at the transceiver as well as a large number of reflecting elements at the RIS. To fully unleash the potential of RIS, the phase shifts of RIS elements should be carefully designed, resulting in a high-dimensional non-convex optimization problem that is hard to solve with affordable computational complexity. In this paper, we address this scalability issue by partitioning RIS into sub-surfaces, so as to optimize the phase shifts in sub-surface levels to reduce complexity. Specifically, each sub-surface employs a linear phase variation structure to anomalously reflect the incident signal to a desired direction, and the sizes of sub-surfaces can be adaptively adjusted according to channel conditions. We formulate the achievable rate maximization problem by jointly optimizing the transmit covariance matrix and the RIS phase shifts. Under the RIS partitioning framework, the RIS phase shifts optimization reduces to the manipulation of the sub-surface sizes, the phase gradients of sub-surfaces, as well as the common phase shifts of sub-surfaces. Then, we characterize the asymptotic behavior of the system with an infinitely large number of transceiver antennas and RIS elements. The asymptotic analysis provides useful insights on the understanding of the fundamental performance-complexity tradeoff in RIS partitioning design. We show that in the asymptotic domain, the achievable rate maximization problem has a rather simple form with an explicit physical meaning of optimization variables. We develop an efficient algorithm to find an approximately optimal solution to the asymptotic problem via a one-dimensional (1D) grid search. Moreover, we discuss the insights and impacts of the asymptotic result on finite-size system design. By applying the asymptotic result to a finite-size system with necessary modifications, we show by numerical results that the proposed design achieves a favorable tradeoff between system performance and computational complexity.

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment.

Analysis of MIMO methods for 5G and subsequent technologies: advantages and disadvantages

Formulation of the problem. The global bandwidth shortage in the wireless communications sector has prompted the study and research of wireless access technology, known as mass Multiple Input/output (MIMO) technology. Mass MIMO is one of the key technologies for next-generation networks, which combines antennas in both the transmitter and receiver to provide high spectral and energy efficiency using relatively simple processing. Gaining a better understanding of the massive MIMO system to overcome the fundamental challenges of this technology is vital for the successful deployment of 5G and higher networks to implement various intelligent sensor system applications. Thus, in this paper, fully digital, analog and hybrid structures are analyzed and the method of multilevel massive MIMO transmission is described in detail. Purpose. Analysis of the main technologies needed to meet the demand for data transmission expected for 5G and 6G networks, with the allocation of transmission methods necessary for the mass implementation of MIMO. Practical significance. Evaluation of the MIMO system bandwidth, hardware and computational complexity, as well as energy efficiency, it is shown that massive MIMO circuits can rely on asymptotic linear independence when designing signal processing, given the large number of antennas used. Classical MIMO methods are not able to meet the technical requirements, so hybrid architectures use a combination of analog and digital areas to use the spatial resolution provided by a large number of antenna elements, but at the same time the number of energy-consuming and expensive radio frequency circuits remains within reasonable limits, and computational complexity is controlled. It becomes clear that there is no single structure/algorithm that would provide the best compromise between complexity and performance in all possible scenarios, rather, there is a need to adapt them to the characteristics of the application and channel when designing the system. As for optimizing the energy efficiency of the RF power supply, this is achievable using solutions such as multilayer massive MIMO.

Low‐complexity unsupervised learning‐based hybrid precoding for massive MIMO systems

AbstractHybrid precoding can significantly reduce the number of required radio frequency (RF) chains in massive MIMO systems. However, due to the large number of antennas and the high‐dimensional channel, the existing hybrid precoding algorithms usually require very high complexity. In this paper, we propose a convolutional neural network (CNN)‐based hybrid precoding scheme by utilizing unsupervised learning to reduce both the computational complexity and space complexity. Specifically, we first design a low‐complexity CNN structure to achieve the analog precoder, where the size of the convolution kernel and the number of channels are reduced by the further improved inception network. Then, we obtain the digital precoder by the classical zero‐forcing (ZF) algorithm. Simulation results show that, the proposed CNN‐based hybrid precoding can approach the sum‐rate performance of the classical two‐stage hybrid precoding but has much lower complexity.

Secure Linear Precoding in Overloaded MU-MIMO Wireless Networks

We investigate signaling and linear precoding designs for secure downlink communications in a multiuser MIMO (MU-MIMO) network. The network is tapped by an external eavesdropper (Eve) who is equipped with a large number of antennas. In addition to the transmission of several (unicast) information signals to downlink users (Bobs), the transmitter (Alice) generates friendly jamming (FJ), aiming to prevent Eve from decoding Alice’s signals. To mitigate both multiuser interference (MUI) and FJ on Bobs, MU-MIMO systems often rely on zero-forcing (ZF) precoders. A condition for the application of such precoders is that the network must be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">underloaded</i> , i.e., Alice has more antennas than all Bobs combined. For <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overloaded</i> scenarios, ZF-based precoding cannot guarantee nullification of both MUI and FJ at legitimate receivers. Accordingly, we propose a combined signaling-and-precoding scheme that can also be applied to an overloaded MU-MIMO network. In an underloaded scenario, our technique is shown to impose a more stringent antenna requirement on Eve than a classical ZF-based precoding, i.e., Eve must utilize more antennas if she is to successfully eavesdrop on Alice’s transmissions. Although our scheme is comparable to antenna selection (AS) approaches in terms of Eve’s antenna requirement, it incurs much less delay and complexity by avoiding frequent on/off switching of the RF chains at Bobs. For underloaded scenarios, we provide security analysis that establishes the conditions under which our scheme is superior to the ZF scheme. Using computer simulations, we validate the advantages of our design and contrast it with ZF and AS schemes in terms of the symbol error rate, the EVM, and the achievable rate in both underloaded and overloaded scenarios.

Performance Evaluation of Full Array, Sub-Array and Overlapped-Sub-Array Hybrid Beamforming Architectures for Massive Mimo Systems

The technological advancement in wireless communication, promises high data rate for end users. This has led to the possibility of smart cities, inter connected vehicles, and virtual reality applications. One of the recent technologies in wireless communication is massive MIMO where large number of antennas are deployed at the transmitter or receiver. This is possible due to the use of mmWave in wireless communication. With massive MIMO, beamforming technique can be employed in the communication system. Beamforming is the ability of communication system to direct power to the intended users and to cancel power at non-intended users and thus significantly improving communication system performance. Digital beamforming was initially used in MIMO systems. However, for massive MIMO systems it leads to high power consumption due to large number of dedicated radio frequency chain in each antenna. To address this challenge, hybrid beamforming techniques were introduced. There are three architectures for hybrid beamforming: Fully array architecture (FAA), overlapped sub-array architecture (OSA) and sub-array architecture (SAA). This paper has analysed three performance parameters of the mentioned hybrid beamforming architectures. The simulation results show that, FAA architecture has high performance in outage probability and spectral efficiency. However, its energy efficiency is lower compared to OSA and SAA. Specifically, SAA has the highest energy efficiency in comparison to FAA and OSA. It can also be observed that, with only 25% increased number of elements in OSA, the energy efficiency can be slightly lower compared to SAA, while achieving appreciable spectral efficiency performance with respect to FAA. Additionally, this work has derived an outage probability expression, which has not been covered in most of the studies. This study gives an insight of selecting the best architecture based on the performance requirement.

End-to-End Deep Learning for Multipair Two-Way Massive MIMO with PA Impairments

Multipair (MP) massive multiple-input–multiple-output (MIMO) two-way relaying system is a promising solution that is able to provide excellent spectral efficiency performance. It consists in multiple pairs ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2K$</tex-math></inline-formula> ) of single-antenna user terminals that exchange information through of a relay using a large number of antennas <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{t}\gg 2K$</tex-math></inline-formula> . The relay is equipped with a large number of energy-efficient power amplifiers (PAs) and ensure the multipair two-way forwarding. In this article, we first present an analytical study focused on the effects of PAs on the considered system over block fading Rayleigh channel. We derive closed-form expressions for the symbol error rate. Then, we develop a new efficient distributed learning approach for MP two-way massive MIMO. Specifically, we design the MP two-way massive MIMO system by leveraging the concept of the autoencoder, whereas the end-to-end communication system is designed using one deep neural network (NN). Interestingly, the proposed scheme, referred to as AE-MP-mMIMO, is suited for varying block fading Rayleigh channel scenarios. Indeed, we propose to adopt one stage precoder/decoder for each UE and two-stage precoding scheme for the relay: 1) an NN-Tx is used to address the PA nonlinearity and 2) a linear zero-forcing precoder is adopted to remove the multiuser interference. The NN-Tx and the UE's precoder/decoder are trained off-line and when the channel varies, only the zero-forcing precoder changes on-line. Numerical simulations show the capability of the proposed approach to achieve competitive performance with a significantly lower complexity compared to existing literature.

Near-Field Rainbow: Wideband Beam Training for XL-MIMO

Wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) plays an important role in boosting the data rate for 6G networks. Because of the huge bandwidth and the large number of antennas, wideband XL-MIMO introduces a significant near-field beam split effect, where beams at different frequencies are focused on different locations. This effect results in a severe array gain loss, and existing works mainly consider to compensate for this loss by utilizing time-delay (TD) beamforming. This paper demonstrates that despite degrading the array gain, the near-field beam split effect can also contribute to the fast near-field beam training. Specifically, we first reveal the controllable near-field beam split effect. This effect indicates that TD beamforming can control the degree of the near-field beam split effect, i.e., beams at different frequencies can flexibly occupy the desired location range. Due to the similarity with the dispersion of natural light caused by a prism, we also call this effect as “near-field rainbow”. Then, by taking advantage of the near-field rainbow, a fast wideband beam training scheme is proposed to generate beams focusing on multiple locations at multiple frequencies with the help of TD beamforming. Finally, simulation results demonstrate that the proposed scheme is able to realize efficient near-field beam training with low training overheads.

Capacity Analysis of Holographic MIMO Channels With Practical Constraints

Holographic Multiple-Input and Multiple-Output (MIMO) is envisioned as a promising technology to realize unprecedented spectral efficiency by integrating a large number of antennas into a compact space. Most research on holographic MIMO is based on isotropic scattering environments, and the antenna gain is assumed to be unlimited by deployment space. However, the channel might not satisfy isotropic scattering because of generalized angle distributions, and the antenna gain is limited by the array aperture in reality. In this letter, we aim to analyze the holographic MIMO channel capacity under practical angle distribution and array aperture constraints. First, we calculate the spectral density for generalized angle distributions by introducing a wavenumber domain-based method. And then, the capacity under generalized angle distributions is analyzed and two different aperture schemes are considered. Finally, numerical results show that the capacity is obviously affected by angle distribution at high signal-to-noise ratio (SNR) but hardly affected at low SNR, and the capacity will not increase infinitely with antenna density due to the array aperture constraint.

Optimization of the Compressive Measurement Matrix in a Massive MIMO System Exploiting LSTM Networks

Massive multiple-input multiple-output (MIMO) technology, which is characterized by the use of a large number of antennas, is a key enabler for the next-generation wireless communication and beyond. Despite its potential for high performance, implementing a massive MIMO system presents numerous technical challenges, including the high hardware complexity, cost, and power consumption that result from the large number of antennas and the associated front-end circuits. One solution to these challenges is the use of hybrid beamforming, which divides the transceiving process into both analog and digital domains. To perform hybrid beamforming efficiently, it is necessary to optimize the analog beamformer, referred to as the compressive measurement matrix (CMM) here, that allows the projection of high-dimensional signals into a low-dimensional manifold. Classical approaches to optimizing the CMM, however, are computationally intensive and time consuming, limiting their usefulness for real-time processing. In this paper, we propose a deep learning based approach to optimizing the CMM using long short-term memory (LSTM) networks. This approach offers high accuracy with low complexity, making it a promising solution for the real-time implementation of massive MIMO systems.

A low‐complexity transmit combining method for downlink of massive multiuser multiple input multiple output systems

AbstractMultiuser multiple input multiple output (MU‐MIMO) systems can provide high spectral efficiency (SE) that is achieved with transmit/receive combining schemes at the base station (BS) for downlink (DL) and uplink (UL). So far, various combining schemes for DL were proposed, with the regularized zero forcing (RZF) and Tomlinson–Harashima precoding (THP) being usually considered. In a case of massive MU‐MIMO systems, with a large number of antennas, the matrix inversion operation used in the RZF and THP schemes causes high computational complexity that significantly aggravates implementation and raises its costs. The authors propose a low‐complexity transmit combining scheme for the massive MU‐MIMO systems with time division duplex (TDD), devised in a similar manner as the THP scheme, based on the gram‐Schmidt orthogonalization, and show that it achieves slightly better SE than the THP scheme for the estimated channel state information (CSI), but with a much lower complexity than the THP and RZF schemes.

Deep Neural Networks for Direction of Arrival Estimation of Multiple Targets With Sparse Prior for Line-of-Sight Scenarios

Received signal Direction of Arrival (DOA) estimation represents a significant problem with multiple applications, ranging from wireless communications to radars. This problem presents significant challenges, mainly given by a large number of closely located transmitters being difficultly separable. Currently available state of the art approaches fail in providing sufficient resolution to separate and recognize the DOA of closely located transmitters, unless using a large number of antennas and hence increasing the deployment and operation costs. In this paper, we present a deep learning framework for DOA estimation under Line-of-Sight scenarios, which able to distinguish a number of closely located sources higher than the number of receivers' antennas. We first propose a formulation that maps the received signal to a higher dimensional space that allows for better identification of signal sources. Secondly, we introduce a Deep Neural Network that learns the mapping from the receiver antenna space to the extended space to avoid relying on specific receiver antenna array structures. Thanks to our approach, we reduce the hardware complexity compared to state of the art solutions and allow reconfigurability of the receiver channels. Via extensive numerical simulations, we demonstrate the superiority of our proposed method compared to state-of-the-art deep learning-based DOA estimation methods, especially in demanding scenarios with low Signal-to-Noise Ratio and limited number of snapshots.

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

AbstractMassive multiple‐input multiple‐output (MIMO) is a critical component of 5G cellular networks, which utilizes large numbers of antennas at both the transmitter and receiver to enhance throughput and radiated energy efficiency. Various linear detection techniques are employed with massive MIMO to counteract path loss and interference, and maximize throughput. The first aim of this paper is to analyse the performance of uplink massive MIMO system for different linear detection techniques including: Maximum ratio combining (MRC), zero‐forcing (ZF), regularized ZF (RZF) and minimum mean squared error (MMSE) over Rayleigh channel model. The second aim is to jointly investigate the optimal values of signal‐to‐noise ratio (SNR), the number of antennas M and the number of users K for maximizing the spectral efficiency (SE) and energy efficiency (EE) through simulation using MATLAB and 3D plotting patterns. The obtained results show that the best SE and EE are achieved by uplink massive MIMO setup while using optimal values of SNR, M and K. It is observed that MMSE achieved the best performance. However, it requires estimation of average SNR at BS. Therefore, the best choice is ZF or RZF without any need for SNR estimation.

A Performance Analysis of Massive MIMO System using Antenna Selection Algorithms

A large number of transmitting components makes Massive Multiple-Input Multiple-Output (MIMO) one of the most hopeful solution for the 5G technology. However, a large antenna system boosts the hardware intricacy and cost of the system because of RF transceivers used at the base station for every antenna element. Hence, antenna selection is one of the most effective schemes to select a good subset of antennas with the finest channel circumstances and contribute maximum to the channel capacity. This paper presents Branch and Bound (BAB) algorithm for efficient antenna selection in Massive MIMO technology. The effectiveness of the simulated BAB algorithm is evaluated based on channel capacity and compared with the traditional state of arts such as fast antenna selection algorithm, Exhaustive Search, Fast antenna selection, CBF, CBW, Random antenna selection, etc. Sunflower Optimization-based antenna selection has been shown to provide improved results in terms of channel capacity when compared to the traditional Branch and Bound algorithm. The results indicate that the Sunflower Optimization technique is a promising alternative for antenna selection in Massive MIMO systems, especially in cases where a large number of antennas are present at the transmitter and receiver ends. The proposed solution provides significant improvements over the traditional methods, making it an attractive option for optimizing MIMO performance in future wireless communication systems.