Number Of Antennas Research Articles

Channel Hardening: A Comparison Between Concentrated and Distributed Massive MIMO

In this letter, a comparative analysis of the hardening effect for concentrated and distributed massive multiple-input multiple-output channels (C-mMIMO and D-mMIMO respectively) is presented. The analysis is carried out in two buildings with similar structural characteristics, considering two frequency bands of interest for 5G deployments, 3.5 and 26 GHz; taking into account both experimental data at 3.5 GHz and simulation results obtained in the 26 GHz band using a rigorous and well-tested Ray-Tracing method (RT). Both, measurements and simulations emulated C-mMIMO and D-mMIMO systems in an indoor cell in the framework of a time division duplex (TDD) - orthogonal frequency division multiplexing (TDD-OFDM) system. To quantify the level of hardening that the specific channels under analysis offer in the frequency domain, the standard deviation of the gain of the channels is used, as well as its evolution as the number of active antennas at the base station (BS) grows. The results obtained for both mMIMO systems show that a sufficiently high level of hardening occurs in indoor environments to contribute to the reliability of communication systems or sensor networks.

New Signal Processing Techniques for Phased-Array Oceanographic Radars: Self-Calibration, Antenna Grouping, and Denoising

Abstract Original techniques are proposed for the improvement of surface current mapping with phased-array oceanographic high-frequency radars. The first idea, which works only in bistatic configuration, is to take advantage of a remote transmitter to perform an automatic correction of the receiving antennas based on the signal received in the direct path, an adjustment that is designated as “self-calibration.” The second idea, which applies to both mono- and bistatic systems, consists in applying a direction finding (DF) technique (instead of traditional beamforming), not only to the full antenna array but also to subarrays made of a smaller number of sequential antennas, a method that is referred to as “antenna grouping.” In doing this, the number of sources can also be varied, leading to an increased number of DF maps that can be averaged, an operation that is designated as “source stacking.” The combination of self-calibration, antenna grouping, and source stacking makes it possible to obtain high-resolution maps with increased coverage and is found robust to damaged antennas. The third improvement concerns the mitigation of noise in the antenna signal. These methods are illustrated with the multistatic high-frequency radar network in Toulon and their performances are assessed with drifters. The improved DF technique is found to significantly increase the accuracy of radar-based surface current when compared to the conventional beamforming technique.

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.

Multiple Access for Near-Field Communications: SDMA or LDMA?

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With the dramatically increasing number of antennas, the extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency compared with classical SDMA. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of near-field beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations with limited leakage energy at other locations. This special property could be leveraged in hybrid precoding to mitigate inter-user interferences for spectrum efficiency enhancement. Moreover, we provide the spherical-domain codebook design method for LDMA communications with the uniform planar array, which provides the sampling method in the distance domain. Additionally, performance analysis of LDMA is provided to reveal that the asymptotic optimal spectrum efficiency could be achieved with the increasing number of antennas. Finally, simulation results verify the superiority of the proposed LDMA over SDMA in different scenarios.

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.

Evaluation of massive multiple-input multiple-output communication performance under a proposed improved minimum mean squared error precoding

&lt;span lang="EN-US"&gt;The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. &lt;/span&gt;

Compact and Low Power-Consumption Solid-State Two-Dimensional Beam Scanner Integrating a Passive Optical Phased Array and Hybrid Wavelength-Tunable Laser Diode

Large-scale optical phased arrays have attracted attention as devices for solid-state beam scanning employing light detection and ranging. However, conventional optical phased arrays using electro-optic phase shifters require active phase control for each antenna. Because the power consumption of the active phase shifters is proportional to the number of antennas, the total power consumption increases in a large-scale optical phased array with a narrow beam divergence. In this study, we propose a passive beam scanner that realizes two-dimensional beam scanning through the wavelength sweep of a laser light. Additionally, most studies on optical phased arrays use external laser diodes as light sources. This increases the size of the beam-scanning system. Therefore, there is a demand for integrating an optical phased array and a laser light source in a combined chip. In this study, we integrated a 64-channel passive optical phased array and a hybrid wavelength-tunable laser diode into a combined chip. The size of the device was 5 mm × 1.5 mm, and the power consumption for beam scanning was 49.3 mW. The field of view was 44.2° × 13.7°, and the full width at half maximum of the beam divergence was 0.517° × 3.67°. Compared with the device used in a previous study, our device was nine times smaller, and the total power consumption was only tens of milliwatts. This device consumes less power and achieves a fully integrated light detection and ranging.

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.

OFDM-based Wideband Hybrid Beamformer for mmWave Massive MIMO Multiuser 5G Systems

Employing massive antennas array at the user terminals can be feasible by using millimeter wave (mmWave) transmission which significantly reduce the antennas array size. The implementation of massive multiple input multiple output (MIMO) at the user terminals facilitates accurate beamforming. In this paper, a modified orthogonal matching pursuit (OMP) algorithm is used to design a wideband hybrid combiner based on the sparse structure of mmWave channel and orthogonal frequency-division multiplexing (OFDM). Based on OFDM, the wideband channel considered as multiple narrowband channels so a modified narrowband hybrid combiner can be implemented for each subcarrier channel in a manner where the RF combiner is the same for all subcarriers, whilst the baseband combiner is obtained for each subcarrier. For the multiuser 5G system, a wideband hybrid precoder based on the block diagonalization (BD) method is used at the base station (BS) to cancel the interference at each user due to the other users. The performance of this hybrid beamformers (precoder/combiner) are tested for different scenarios of base station antennas number, numbers of users’ antennas, and number of users.

Numerical Evaluation of the MU-MIMO Beamforming Performance with User Selection Algorithm

This paper presents the numerical evaluation of the ZF beamforming algorithm using the user selection in the multiuser multiantenna (MU-MIMO) downlink system. Two user selection algorithm – semiorthogonal user selection and greedy user selection are numerically evaluated based on the open source MIMO channel model. The sum rate performance depending on number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. The selection of users is based on the orthogonality of the channels among selected users. For MIMO channel simulation the QUADRIGA channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder using user selection (G ZF) outperforms the ZF precoder with random user selection in spectral efficiency. The greedy user selection in spatially correlated channel has advantage to semi-orthogonal user selection. It isobserved that as the increasing the number of served users used for selection the greedy user selection gives better performance than semi-orthogonal algorithm.

Development of 5G NR RedCap devices

The subject of this paper is the RedCap technology. RedCap devices (known as Reduced Capability devices) are a new technology that is being studied by 3GPP in the R17 phase of 5G. RedCap can be defined as a lightweight version of 5G technology. The IoT application scenarios are extremely complex and diverse, and different scenarios have different requirements for network metrics. RedCap devices have emerged to fill the gap left by the withdrawal of 4G, i.e., to cover the needs of medium and high speeds. Specifically, it was designed to cover the needs of LTE Cat.1 and LTE Cat.4. Therefore, this article will introduce the reader to Release 17 RedCap devices by describing them from multiple perspectives, i.e. the history of RedCap devices, application scenarios, requirements, and their technical features. RedCap devices are well-balanced in their capabilities, using simpler demodulation (meaning significantly lower RF and baseband requirements), targeting bands with smaller spectrum bandwidths, and reducing the number of transceiver antennas and the number of MIMO layers. These changes have resulted in a reduction in the rate of RedCap devices, a reduction in coverage capability, and an increase in latency. Based on these changes, RedCap devices are expected to be 2-5 times less expensive than 5G. The main applications for RedCap are wearable devices (e.g. smart watches), industrial sensors and video surveillance devices.

Distributed Goppa-Coded Generalized Spatial Modulation: Optimized Design and Performance Study

The distributed Goppa-coded generalized spatial modulation (DGC-GSM) scheme with the source, relay, and destination in cooperative communications is proposed, where the source and relay adopt different Goppa codes. Goppa codes have many advantages such as the flexible codeword length, excellent code construction, and easy encoding and decoding complexity. At the relay, we select portions of the source information bits for further encoding. For each selection, the destination constructs a channel code. To obtain the best code in the destination, we propose the optimal information bit selection algorithm at the relay to choose the source information. However, Goppa codes with a large block length will impose high complexity on the optimal algorithm. Thus, the locally optimized selection algorithm is further proposed. At the destination, the joint decoding algorithm is adopted to recover the source information. Our simulated results indicate that the two selection algorithms proposed achieve better performance than the random selection method. This is because the optimized algorithms can generate the code with a larger minimum distance at the destination. Moreover, the proposed DGC-GSM scheme outperforms the non-cooperative system. Moreover, the DGC-GSM scheme can obtain the approximate performance with the distributed Goppa-coded spatial modulation (DGC-SM) scheme but has a significantly reduced transmit antenna number.

MISO System with Intelligent Reflecting Surface-Assisted Cellular Networks

This paper proposes an architecture based on Intelligent Reflecting Surfaces (IRSs) to improve the performance of future cellular networks. Specifically, we investigate the use of IRSs in combination with statistical Channel State Information (CSI) to enhance the coverage of Base Stations (BSs) in Multiple-Input Single-Output (MISO) systems. Furthermore, we exploit IRSs to reduce the complexity of the proposed architecture, and therefore the total cost, by reducing the number of required antennas at the transmitters (i.e., BSs). At first, we consider a Rayleigh fading channel between the transmitter and the receiver, and we assume the existence of a Line of Sight (LoS) between the BS and the IRS, as well as between the IRS and the destination. In the second part, we investigate the case of a Single-User Multiple-Input Single-Output (SU-MISO) system, where we study the benefits of IRSs in terms of coverage of the BS; then, we formulate a problem for a Multi-User Multiple-Input Single-Output (MU-MISO) system where the IRS is considered as a block of resources that can assist a certain number of users. The problem of managing the IRS resources is formulated as a nonlinear integer problem. We solve the optimization problem using an exhaustive search and propose two low-complexity heuristic algorithms. The performance of the system is evaluated with respect to a variable number of users, the position of the IRS, the required SNR, and the size of the cell. Simulation results corroborate the proposed approach and show that the introduction of the IRS in the network architecture enhances the overall performance of the network, extends the coverage area, enhances users’ satisfaction, and improves the SNR value, while optimizing the required number of antennas at the BS.

Full-duplex multi-user MIMO communication systems performance optimization using leakage-based precoding

The spectral efficiency (SE) can approximately double when using full-duplex (FD) multiuser MIMO communications. However, there are difficulties because of multiuser interferences, self-interference (SI), and co-channel interference (CCI). To improve the SE of the downlink (DL), this paper proposes CCI-aware enhancement to SLNR (signal-to-leakage-and-noise-ratio) signal-to-leakage-and-noise-ratio (SLNR). It considers a suppressing filter at the receiver to cancel the interferences again designing a beamformer based on CCI-plus-noise covariance matrices for every user at the transmitting side. Additionally, we propose an improvement in the SLNR method by using SI-plus-noise covariance matrices to design uplink (UL) beamformers. Unlike zero-forcing and block-diagonalization, the SLNR approach serves numerous antennas at users and BS (base station). The total SE of the communication yielded using the optimized precoder, i.e., obtained from the SLNR-based precoding. To achieve maximum energy efficiency (EE), we use a power consumption model. Simulation results confirm that full-duplex performs well compared to half-duplex (HD) when the number of antennas at every user in uplink as well downlink channels grow, for all Rician factors, for slight powers of the CCI and SI, and a limited number of antennas at the BS. With the proposed scheme for given transmit power and circuit power, we demonstrate that FD has a higher EE than HD.

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.

Suppressing grating lobes of large-aperture optical phased array with circular array design.

An optical phased array (OPA), especially a two-dimensional (2D) OPA, suffers from the trade-off among steering range, beam width, and the number of antennas. Aperiodic 2D array designs currently aimed to reduce the number of antennas and reduce grating lobes within a wide range fall short when an aperture approaches millimeter size. A circular OPA design is proposed to address this issue. The circular design substantially reduces the number of antennas while achieving the same wide steering range and narrow beam width of optimized aperiodic 2D OPA designs. Its efficient suppression of grating lobes, the key to a wide steering range with minimal number of antennas and large antenna spacing, is theoretically studied and validated by simulation. The novel, to the best of our knowledge, design allows less than 100 antennas, orders of magnitude reduction, for millimeter size aperture OPA designs. It paves the way for commercialization by significantly reducing control complexity and power consumption.

Direction of arrival estimation for BLE: Antenna array design and evaluation

The use of smart antenna arrays in wireless communication systems is becoming increasingly popular, as they improve the performance of the radio link and allow the integration of communication and sensing functions. In this paper, we present the design and performance evaluation of a uniform circular antenna array (UCAA) with 12 monopole antennas and RF switches to select the active antenna for the signal direction of arrival (DoA) estimation. The antenna configuration was optimized through simulations for a different number of antennas and antenna array radius. The frequency tuning of the antennas considered the coupling of the active antenna with adjacent antennas. The performance of the antenna array was evaluated by (1) simulations that examined the proximity of the antenna to the edge of the antenna array and the influence of adjacent antennas on the performance of the active antenna, and (2) measurements in outdoor and indoor environments. The results of the simulations show that antenna coupling can cause nulls in the radiation pattern, which affects the DoA estimation by reducing the signal-to-noise ratio. In a semi-controlled environment, we achieved a DoA estimation error of less than 1 degree by using the Bluetooth Low Energy (BLE) Angle of Arrival feature.

Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.

Performance analysis of MRT/RAS scheme in dual-hop NOMA energy harvesting relay networks

This paper investigates the outage probability (OP) performance of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) relaying networks. In the investigated network, a source simultaneously communicates multiple NOMA users with the assistance of an amplify-and-forward energy harvesting relay. We derive each user’s OP expression in closed form over the generic channel model Nakagami-m fading. Moreover, we carry out asymptotic analysis to have clear insights into the OP behavior in the high signal-to-noise ratio region. The theoretical analysis’s correctness is finally verified via Monte Carlo simulations. The results show that the OP performance improves with the increased number of antennas and enhancement in the channel conditions. Furthermore, the relay has to be near the source in order to have better OP performance. Moreover, each user has a different optimum power division ratio.

A multi-fusion integrated end-to-end deep kernel CNN based channel estimation for hybrid range UM-MIMO 6G communication systems

In today’s world, Terahertz Communication (THzCom) is envisioned as a highly promising technology to characterize 6G wireless communication within the range from 0.1 to 10 THz. This will configure emerging wireless applications due to the demand for their unique potential to support terabit-per-second transmission and effectively support ultra-high quality of service requirements. Aiming at massive dual beamforming problem in THzCom, the Ultra-Massive-Multiple Input and Multiple Output (UM-MIMO) with less array based hybrid channel modelling is adopted as the promising solutions. Further, in the case of an ultra-massive number of antennas, the beamforming matrix element is quite high-dimensional. This enlarged dimension of channel contributes to the high complexity of Channel Estimation (CE). To focus on these challenges, we presented a Hybrid Short and Long range Channel Model (HSLCM) based Multi-Fusion based Deep Kernel Convolutional Neural Network (MF-DKCNN) for THz UM-MIMO CE method. HSLCM channel modelling is used to determine the channel parameters of THz propagation paths, such as path loss, azimuth and elevation angles, distance, and the steering vector of the lens array. The MF integrated with the DKCNN model is used to estimate the noise-free received signal from accurate channel parameters and handles high-dimensional matrices with low complexity. MF employs the Channel Intersection Fusion Rule (CI-FR) to combine the received signal and channel matrix for a consistent fusion estimate of the known channel parameters from the system model. The DKCNN network structure has fifteen layers in which the MF estimates the noise-free channel matrices to be processed as training labels. Then, the kernel evaluation is used between layers to solve high dimensions and feature complexity. The proposed model is analysed in an outdoor THz channel scenario, which was simulated on the MATLAB (R2018A) platform. Hence, the evaluation results demonstrate that the proposed method outperforms high CE results with low complexity than other methods in terms of training data and Normalized Mean Square Error (NMSE).