Millimeter-wave Communication Systems Research Articles

A Novel Beam Channel Model and Capacity Analysis for UAV-Enabled Millimeter-Wave Communication Systems

A Novel Beam Channel Model and Capacity Analysis for UAV-Enabled Millimeter-Wave Communication Systems

Intra-Beam Interference Mitigation for the Downlink Transmission of the RIS-Assisted Hybrid Millimeter Wave System.

Millimeter-wave (mmWave) communication systems leverage the directional beamforming capabilities of antenna arrays equipped at the base stations (BS) to counteract the inherent high propagation path loss characteristic of mmWave channels. In downlink mmWave transmissions, i.e., from the BS to users, distinguishing users within the same beam direction poses a significant challenge. Additionally, digital baseband precoding techniques are limited in their ability to mitigate inter-user interference within identical beam directions, representing a fundamental constraint in mmWave downlink transmissions. This study introduces an innovative analog beamforming-based interference mitigation strategy for downlink transmissions in reconfigurable intelligent surface (RIS)-assisted hybrid analog-digital (HAD) mmWave systems. This is achieved through the joint design of analog beamformers and the corresponding coefficients at both the RIS and the BS. We first present derived closed-form approximation expressions for the achievable rate performance in the proposed scenario and establish a stringent upper bound on this performance in a large number of RIS elements regimes. The exclusive use of analog beamforming in the downlink phase allows our proposed transmission algorithm to function efficiently when equipped with low-resolution analog-to-digital/digital-to-analog converters (A/Ds) at the BS. The energy efficiency of the downlink transmission is evaluated through the deployment of six-bit A/Ds and six-bit pulse-amplitude modulation (PAM) signals across varying numbers of activated RIS elements. Numerical simulation results validate the effectiveness of our proposed algorithms in comparison to various benchmark schemes.

Cost-Effective Design of Polarization and Bandwidth Reconfigurable Millimeter-Wave Loop Antenna.

A singly fed reconfigurable circular loop antenna is proposed for millimeter-wave (mmWave) communication systems. This antenna's distinctive feature lies in its capacity to adjust both polarization and bandwidth characteristics, a capability made possible by the strategic integration of two PIN diodes. These diodes are engineered to function in various modes, allowing for three distinct polarization states and accommodating two distinct bandwidths. A meticulous alignment of these PIN diodes enables the utilization of a single DC bias network as a highly effective RF choke, which simplifies the design and reduces the associated losses. Additionally, a planar biasing network that consists of coplanar strip-lines (CPS) has been employed eliminating the need for lumped elements. The simple and totally planar configuration offers a choice of right-hand circularly polarized (RHCP) radiation or left-hand circularly polarized (LHCP) radiation at 28 GHz. This is accompanied by impedance matching and axial ratio (AR) bandwidths of 12.9% and 8%, respectively, over the same frequency range with a gain of 7.5 dBic. Moreover, when the PIN diodes are unbiased, the antenna offers linear polarization (LP) over two narrow bandwidths at 27 GHz and 29 GHz featuring a maximum gain of 7.2 dBic. Therefore, the proposed configuration offers three operating modes: wide-band RHCP, wide-band-LHCP, and LP over dual narrow bands. Significantly, simulated results closely align with the measured outcomes, affirming the robustness and accuracy of this design.

A novel method for estimating propagation pathloss in millimeter-wave communication systems

In millimeter wave communication systems, the construction of a path loss model that is close to empirical one plays an important role in planning coverage, system capacity, and link budgets. In this paper, we propose a novel approach applied unsupervised machine learning for propagation pathloss estimation in millimeter wave communication systems based on the iterative procedure of cooperative and iterative evaluation exchange (Co-IEE) algorithm. The propagation path loss models of the time city–an urban area in the center of Hanoi, Vietnam in both line-of-sight (LOS) and non line-of-sight (NLOS) are estimated and calculated using the proposed approach and then they are compared to the minimum mean square error (MMSE). The estimated results of the proposed model show the approximation with the optimum one returned by MMSE method. Moreover, the proposed model of estimating path loss can solve the problem of sensitivity with outliers existing in MMSE and give more choices for path loss exponents.

Joint Localization and Environment Sensing by Harnessing NLOS Components in RIS-Aided mmWave Communication Systems

This study explores the use of non-line-of-sight (NLOS) components in millimeter-wave (mmWave) communication systems for joint localization and environment sensing. The radar cross section (RCS) of a reconfigurable intelligent surface (RIS) is calculated to develop a general path gain model for RISs and traditional scatterers. The results show that RISs have a greater potential to assist in localization due to their ability to maintain high RCSs and create strong NLOS links. A one-stage linear weighted least squares estimator is proposed to simultaneously determine user equipment (UE) locations, velocities, and scatterer (or RIS) locations using line-of-sight (LOS) and NLOS paths. The estimator supports environment sensing and UE localization even using only NLOS paths. A second-stage estimator is also introduced to improve environment sensing accuracy by considering the nonlinear relationship between UE and scatterer locations. Simulation results demonstrate the effectiveness of the proposed estimators in rich scattering environments and the benefits of using NLOS paths for improving UE location accuracy and assisting in environment sensing. The effects of RIS number, size, and deployment on localization performance are also analyzed.

Location Information Assisted Beamforming Design for Reconfigurable Intelligent Surface Aided Communication Systems

The large overhead arising from conventional channel estimations in reconfigurable intelligent surface (RIS) aided millimeter-wave communication systems, may offset the performance gain brought by the RIS. To tackle this issue, we propose a location information assisted beamforming design without the requirement of the channel training process. First, we establish the geometrical relationship between the channel model and the user location, and mathematically derive an approximate channel state information (CSI) error bound based on the user location error region. Then, for combating the negative impact of the location error on the communication performance, we formulate a worst-case robust beamforming optimization problem to optimize the beamformer at the base station (BS) and the phase-shift matrix at the RIS. To solve this non-convex problem, we develop a novel relaxed alternating optimization process (RAOP) by utilizing various optimization tools, such as the Lagrange multiplier, the matrix inversion lemma, the semidefinite relaxation (SDR), as well as the branch-and-bound (BnB). Additionally, we prove sufficient conditions for the SDR to yield rank-one solutions, and modify the BnB to acquire the phase-shift solution under an arbitrary constraint of possible phase-shift values. Finally, we analyse the convergence and complexity of the proposed RAOP, and carry out simulations for performance evaluations. Compared to the conventional non-robust beamforming, our method performs better and shows strong robustness against the location-error-related CSI uncertainty. Compared to the robust beamforming based on the S-procedure and penalty convex-concave procedure (CCP), our method with BnB shows the advantages of being able to converge faster and handle arbitrary phase-shift argument sets.

Error Vector Magnitude as a Performance Standard for Antennas in the Millimeter-Wave Era: Part 1: Metric comparisons and measurement concepts

Millimeter-wave communication systems will rely on the large-scale deployment of wideband digital modulation schemes and highly integrated phased arrays to significantly expand the number of simultaneous connections and enable multigigabit per second wireless links. The quality of a digitally modulated signal transmitted and received by millimeter-wave phased arrays depends not only on radiated power and directivity, but also the wideband frequency response of phased array elements and linearity of embedded radio-frequency (RF) transceivers. Error vector magnitude (EVM) is a measure of the amplitude and phase distortion in digitally modulated signals that accounts for all of these effects, and it is increasingly used to characterize the system-level performance of over-the-air (OTA) links between antennas and phased arrays. This article examines the merits of adopting EVM as a performance standard for antennas and phased arrays by comparing it with other commonly applied OTA metrics and reviewing EVM measurement methods and concepts applied to time and frequency domain analysis. The sequel to this article [1] will provide a comprehensive survey of the various techniques applied by representative researchers to model, simulate, and measure EVM for OTA links between antennas and phased arrays, and reflect on open challenges for further research.

Low-Cost W-Band Dual-Mode SIW Bandpass Filters Using Commercially Available Printed-Circuit-Board Technology

W-band filters play a crucial role in modern millimeter-wave communication and radar systems, finding applications in 5G/6G networks, automotive radar, and passive imaging. With the growing demand for higher data rates and efficient wireless communication, W-band filter development has gained significant attention in recent years. Various design techniques and fabrication technologies have been explored for improved performance and integration possibilities. This paper presents a planar substrate-integrated waveguide (SIW) dual-mode cavity filter as a solution to the challenges faced by previous designs in terms of integration, size, and cost. The dual-mode cavity coupling principle and design parameters are analyzed to optimize the filter’s performance. A W-band 4th-order dual-mode filter prototype is designed and fabricated on a 0.127-mm-thick RO5880 substrate, and a finline waveguide-to-microstrip transition structure is employed for compatibility with the test instrument. Simulation and experimental results demonstrate that the proposed filter exhibits low insertion loss, good in-band standing wave performance, and improved out-of-band suppression. Moreover, a visible high-frequency out-of-band transmission zero is implemented to enhance the steep roll-off characteristics in the high-frequency out-of-band region.

Millimeter-Wave Wideband ±45° Dual-Polarized Phased Array Antenna Based on Compact Wideband Widebeam Dipole Element Antenna

To meet the requirements of a millimeter-wave (mmWave) communication system prototype complying with 5G (24.25-29.5GHz) and B5G/6G (30-33GHz) standards, this letter presents a compact wideband widebeam ±45° dual-polarized dipole element antenna to form a wideband phased array antenna (PAA) covering the entire 24-33GHz (∼31.6%) band. First, by placing two orthogonal coupling feeding baluns over a dual-polarized dipole to excite the dipole's two pairs of orthogonal arms and employing four parasitic patches, a ±45° dual-polarized dipole element antenna is developed, showing a compact size (0.34λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ×0.34λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> @24GHz), wide -14-dB impedance bandwidth (24-33GHz, 31.6%), wide 3-dB beamwidth (>100°), high polarization isolation (>25dB), and high cross-polarization discrimination (XPD) (>25dB) within the -14-dB impedance bandwidth. Then, based on the element antenna, a 4×4-element ±45° dual-polarized PAA is designed, fabricated using the LTCC process, and measured. The measurements show that, within the entire 24-33GHz band, the broadside scanning gain of the PAA is up to 14dBi, and the 3-dB gain variation scanning range is >±41° with a peak up to ±60º.

Tensor-Based Joint Beamforming with Ultrasonic and RIS-Assisted Dual-Hop Hybrid FSO mmWave Massive MIMO of V2X

Reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and with low power consumption. Tensor-based joint beamforming with low-cost ultrasonic and RIS-assisted Dual-Hop Hybrid free space optical (FSO) mm Wave massive Multiple Input Multiple Output (MIMO) of vehicle-to-everything (V2X) is proposed. To address the occlusion problem for high-speed mobility of the vehicle, an RIS-assisted mixed FSO-MIMO V2X system is proposed. The low-cost ultrasonic array signal model is developed to solve the accurate direction-of-arrival (DOA) estimation. The ultrasonic-assisted RIS phase shift matrix based on subspace self-organizing iterations is designed to track the beam direction between RIS and vehicle. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of subproblems, where the subarray of phase shifters and RIS elements is jointly optimized to maximize each subarray’s rate. The vehicle motion state is transformed into a two-dimensional model for prior distribution to calculate the particle weights of the RIS phase. Multi-vehicle Tucker tensor decomposition is used to describe the high-dimensional beam space. We conceive a multi-vehicle joint optimization method for designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. A cascaded channel decomposition method based on Singular Value Decomposition (SVD) is used to obtain the combined matrix beamforming of BS and vehicle. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.

Red Deer Optimisation-Based Delay Minimisation for mmWave Communication System Enabled with Mobile Edge Computing

Mobile edge computing (MEC)-oriented solutions are essential for various 5G wireless communication systems. It is considered a key technology for future communication systems because of its capability for fulfilling a broad variety of necessities of the developing wireless terminals in the form of Intelligent Vehicles, augmented reality and virtual reality devices like huge computation, low latency and high data rate. Further, the resource package has made the research on mobile data offloading. One probable novel spectrum in the subsequent generation networks is the millimetre wave (mmWave) communication systems. It attains important attention because of its high rate. This research work, this area focuses on the delay of minimisation strategy in mmWave MEC by jointly optimising the hybrid beam-forming and also resource allocation. Here, the utilisation of a well-performing Red Deer Algorithm (RDA) is the ultimate aim of the suggested model that intends to optimise the analogue beam-forming vectors at the users, the analogue and digital beam-forming matrices at the Base Station (BS), the computation task offloading ratios and resource allocation at the MEC server. Here, the minimisation of the delay or latency is attained. The comparative analysis of the proposed model over the other models demonstrates the superiority of the proposed algorithm in assisting the mmWave MEC system.

AgRIS: wind-adaptive wideband reconfigurable intelligent surfaces for resilient wireless agricultural networks at millimeter-wave spectrum

Wireless networks in agricultural environments are unique in many ways. Recent measurements reveal that the dynamics of crop growth impact wireless propagation channels with a long-term seasonal pattern. Additionally, short-term environmental factors, such as strong wind, result in variations in channel statistics. Next-generation agricultural fields, populated by autonomous tractors, drones, and high-throughput sensing systems, require high-throughput connectivity infrastructure, resulting in the future deployment of high-frequency networks, where they have not been deployed before. More specifically, when millimeter-wave (mmWave) communication systems, a viable candidate for 5G and 6G high-throughput solutions, are deployed for higher throughput, these issues become more prominent due to the relatively small wavelength at this frequency band. To improve coverage in the mmWave spectrum in agricultural settings, reconfigurable intelligent surfaces (RISs) are a promising solution with low energy consumption and high cost efficiency when compared to half-duplex active relays with multiple antennas. To ensure link resiliency under dynamic channel behavior, an adaptive RIS for broadband wireless agricultural networks (AgRIS) at mmWave band is designed in this work. AgRIS relies on output from a time-series model that forecasts the short-term wind speed based on measured wind data, which is readily available in most farms. The temporal correlation between link reliability and wind speed is demonstrated through extensive field experiments. Our simulation results demonstrate that AgRIS with a small footprint of 11 × 11 elements can help mitigate the adversarial effects of wind-induced signal level drop by up to 8 dB and provides high energy efficiency of 1 Gbits/joule.

Two-Stage Channel Estimation Using Convolutional Neural Networks for IRS-Assisted mmWave Systems

Intelligent reflecting surface (IRS) is expected to be an essential component of next-generation wireless communication networks due to its potential to provide similar or higher array gain with lower hardware cost and energy consumption compared with massive multiple-input--multiple-output (MIMO) technology. However, channel estimation in IRS-assisted communication systems is more challenging than that in conventional systems. In this article, we propose a two-stage channel estimation approach using deep learning in millimeter-wave (mmWave) communication systems with a hybrid passive/active IRS structure. In the first stage, the sparsity of the mmWave massive MIMO channel in the angular domain is exploited to estimate the amplitude of the sparse channel through a convolutional neural network. In this way, the indices of the nonzero entries of the sparse channel can be simultaneously obtained. In the second stage, the channel is reconstructed by solving a least squares problem with the acquired indices. The simulation results show that the proposed channel estimation scheme could achieve better performance with manageable complexity over the existing solutions.

Fast Multibeam Training for mmWave MIMO Systems With Subconnected Hybrid Beamforming Architecture

In this article, we present novel multibeam training strategies for millimeter-wave communication systems with the subconnected hybrid architecture. To begin with, we propose an iterative selecting search (ISS) algorithm that can reduce the training complexity of multibeam training by a large margin, and its central concept is exploiting the prior information of scanned beam combinations to iteratively optimize the beam of each subarray in order to determine the possible optimal beam combination. Then, we apply the concept of the ISS algorithm to optimize the original beam set in line with channel sparsity, with the goal of effectively characterizing the large original beam set with a small beam subset, resulting in a much smaller search space for the optimized beam subset than for the original beam set. Furthermore, we adopt two linear search algorithms on our optimized beam subset to develop efficient multibeam training methods. As shown in the simulation results, the ISS scheme shines in terms of low training complexity but performs poorly in spectral efficiency. In comparison to individual linear search methods, beam-subset-optimization-based linear search schemes not only have a greater spectral efficiency, but also have a very low training complexity.

A Double-Layer Dual-Polarized Huygens Metasurface and Its Meta-Lens Antenna Applications.

In this paper, a dual-polarized Huygens unit is proposed, which has a double-layer metallic pattern etched on both sides of one dielectric substrate. Induced magnetism enables the structure to support Huygens' resonance, thus obtaining nearly complete available transmission phase coverage. By optimizing the structural parameters, a better transmission performance can be achieved. When the Huygens metasurface was used for the design of a meta-lens, good radiation performance was exhibited, with a maximum gain of 31.15 dBi at 28 GHz, an aperture efficiency of 42.7% and a 3 dB gain bandwidth of 26.4 GHz to 30 GHz (12.86%). Due to its excellent radiation performance and very simple fabrication, this Huygens meta-lens has important applications in millimeter-wave communication systems.

Developing novel channel estimation and hybrid precoding in millimeter-wave communication system using heuristic-based deep learning

The digital or analog precoders are not carried the optimal energy efficiency in the mm-wave massive MIMO and so, every antenna is needed with one radio frequency chain in the system. Based on this view, a cost-efficient technique is developed for hybrid precoding. From this technique, the short dimensional precoding is obtained from the high dimensional beam-formers in the digital domain for steering antenna elements. Therefore, the main intention of this paper is to develop a channel estimation and hybrid pre-coding model for the mm-wave massive MIMO communication system. The channel estimation phase is performed by the Adaptive Deep Convolutional Neural Network (A-Deep CNN), which covers both channel estimation and channel reconstruction. The introduction of a hybrid meta-heuristic algorithm with Forest-Tunicate Swarm Algorithm (F-TSA) is used for enhancing A-Deep CNN that is adaptable for efficient channel estimation. Once the channel estimation is done, the deployment of Optimized Recurrent Neural Networks (O-RNN) is used for hybrid precoding. Simulation results demonstrate that the proposed A-Deep CNN-based channel estimation scheme outperforms the existing schemes in terms of the Normalized Mean-Squared Error (NMSE) and spectral efficiency, while the O-RNN-hybrid precoder design method has better spectral efficiency performance than other methods.

Energy efficient degree of freedom aided multi beam transmit antenna precoding for mmWave MIMO system

Millimeter-wave communication system provides the feasible solution for the bandwidth limitation present in the sub 6-GHz system. This millimeter-wave communication system requires large antenna array for transmission and needs better RF chain utilization methods. Often single beam transmission over multiple antennas are deployed for RF chain reduction and inter user interference reduction. Multi beam Multi RF transmission system despite capable of providing maximum capacity is affected by power leakage and hence experiences poor energy efficiency. A Multi Beam Transmit Antenna Precoding for Multi beam Multi RF mmWave MIMO system based on analog power splitter arrangement was proposed instead of existing large digital switching network to reduce complexity. In this Multi Beam Transmit Antenna Precoder setup four new precoding algorithms based on alamouti, hadamard, toeplitz and zero correlation sequence were proposed. Sum rate and energy efficiency of the proposed methods were derived theoretically, simulated and compared with existing methods. Computational complexity and degree of freedom were also analyzed for the proposed methods.

Beam Direction-Based Modulation for Millimeter-Wave Communication Systems: Design and Optimization

In this paper, we investigate a novel beam direction-based modulation transmit scheme for millimeter-wave (mmWave) communication systems equipped with distributed antennas, and propose an enhanced space-domain index modulation (ESDIM) scheme, in which the number of beamforming directions (BDs) for transmitting information bits can be one or two. The ESDIM system jointly utilizes the combination of the index of BDs and signal constellations to transmit information bits, in which one or two beam directions are considered. In addition, we propose power allocation (PA) aided schemes for ESDIM to further improve the system reliability by reducing the system symbol error rate (SER), where three constraints to the transmit power for beamforming are investigated. To be specific, a total transmit power constraint (TTPC) and two per-beamforming power constraints (PBPC) are considered. We first propose a suboptimal PA algorithm for the TTPC-PA aided ESDIM scheme using a powerful solver based on advanced algebra theories, which only considers the PA aspects for a given selected beamforming pair. Then, we extend the TTPC-PA aided scheme to the average per-beamforming power constraint (APBPC)-PA aided scheme and the strict per-beamforming power constraint (SPBPC)-PA aided scheme. Simulation results show that the proposed schemes improve the system SER performance compared to other existing counterparts.

Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems

Wireless communications systems provide channel state information (CSI), which can be used to characterize the physical propagation environment. The small physiological motion of human subjects in that environment, such as that associated with respiration, can modulate the CSI, thus allowing wireless physiological sensing through which a communications system detects and monitors cardiopulmonary motion. NextG millimeter-wave communications systems present even greater opportunities for wireless physiological sensing due to the advantages of small wavelength and high directionality. But challenges also arise due to the multipath effect and phase aliasing caused by larger-than-wavelength motion displacement. This work introduces a comprehensive mathematical CSI model to accurately characterize physiological motion captured by the amplitude and phase of the CSI of a millimeter-wave communications system. The model allows for the interpretation of intricate CSI pattern variations to avoid aliasing error and has been validated with experiments involving both parametric measures conducted with a robotic mover and respiration rate measurements for human subjects. In all cases, rate measurements could be consistently resolved within 10%, or 0.02 Hz, demonstrating the potential for incorporating physiological sensing in NextG millimeter-wave communications systems.

A Body-Blockage Analysis and Comparison Between Humans and a Full-Body Phantom: Using measurements at 28 GHz

Human blockage is an important phenomenon that should be considered in millimeter-wave (mm-wave) communication systems. Realizing that blockage behavior could be very valuable in mm-wave link simulations, several blockage models have been proposed in previous works based on knife-edge diffraction (KED), high-frequency simulations, and measurements. This article investigates blockage using a full-body phantom and proves that a body phantom is accurate enough to replace a human subject in blockage studies for both data and talk modes. A campaign of measurements for these two modes with 17 human subjects of different heights and genders is conducted. The effect of different types of clothing on body blockage is studied for a body phantom and a human subject. The impact of the relative antenna-user position on the body-blockage intensity is investigated as accurate measurements are difficult to achieve with human subjects.