Wave Communication Systems Research Articles

Tin can telephone-inspired self-powered mechanical wave communication integrated with self-charge excitation triboelectric nanogenerator

As an alternative to electromagnetic wave communication, mechanical waves (MW) retain their advantage in several aspects, including cost-effectiveness, simplicity, interference resistance, and short-distance transmission. Here, a string-vibrated self-powered mechanical wave communication system (SSMWC) is proposed. Inspired by tin can telephone, modulated voltage signals are converted into MW by a vibrator and then transmitted along a string to the receiver. The receiver integrates a string-vibrated triboelectric nanogenerator (SV-TENG) and a self-charge excitation triboelectric nanogenerator (SCE-TENG). The SV-TENG can detect MW and convert them into electrical signals without external power sources, while the SCE-TENG is integrated to improve the sensitivity of the receiver. After charges pumping of SCE-TENG, the average output of SV-TENG can increase by 88 times within the frequency range of 0-1000Hz, indicating the successful application of self-charge excitation in triboelectric sensing for the first time. Furthermore, a demonstration of information coding and real-time decoding proves the potential application of SSMWC as an alternative in specific environments. This work shows the breakthrough of the bottlenecks faced by triboelectric sensors that the essential preparatory contact electrification before packaging, which indeed affect sensitivity and durability. Therefore, this scheme can be widely applied for output enhancement, especially for triboelectric sensors with lower output.

A novel pathloss prediction and optimization approach using deep learning in millimeter wave communication systems

Millimeter wave communication systems design requires accurate pathloss prediction as well as optimization. Traditional pathloss models gives less accuracy therefore pathloss models using deep learning is alternative solution for traditional method. These methods overcome the time consuming pathloss prediction parameter prediction based on large dataset which is available for different frequencies. In this proposed work, for prediction of pathloss, Convolutional Neural Network (CNN) and for optimization of pathloss Particle Swarm Optimization (PSO) is proposed. Towards implementation of the same, outdoor scenario with two roads and one cross section is used and dataset is generated using DeepMIMO generic dataset. The simulation result shows that using optimization technique reduces pathloss and CNN give accurate prediction of 95.7 %. Furthermore, root mean square error value of 6.6 dB has been achieve which is less in comparison to alternative approaches designed for outdoor environments. The forthcoming development of multifrequency path loss models has sparked significant interest due to their forecasting ability to support higher frequency bands and address blockage scenarios within future wireless networks.

Frequency down-conversion of terahertz waves at optically induced temporal boundaries in GaAs waveguides

Abstract In this study, the frequency down-conversion of terahertz waves is analytically and experimentally demonstrated at the temporal boundaries within a GaAs waveguide. The temporal boundary is established by photoexciting the top surface of the waveguide, thereby instantaneously increasing its electrical conductivity. This photoexcited waveguide supports a transverse electromagnetic (TEM) mode with a frequency lower than those of the transverse magnetic (TM) modes present in the original waveguide. At the temporal boundary, the incident TM mode couples with the TEM mode, resulting in frequency down-conversion. Subtracting the propagation loss from the frequency-converted components indicates that the frequency conversion occurs with an efficiency consistent with the analytical predictions. The propagation loss is primarily due to ohmic loss, caused by the finite electrical conductivity of the photoexcited region. Given that the frequency of transverse electric modes is up-converted at the temporal boundary, our findings suggest that the direction of frequency conversion (upward or downward) can be controlled by manipulating the incident polarization. The polarization-dependent frequency conversion in waveguides holds significant potential for applications in devices designed for the interconversion of terahertz signals across various frequency channels. This capability is instrumental in the development of frequency-division-multiplexed terahertz wave communication systems, thereby enabling high data transfer rates.

Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems

Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there’s no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches.

Weakly restoring forces and shallow water waves with dynamical analysis of periodic singular solitons structures to the nonlinear Kadomtsev–Petviashvili-modified equal width equation

In this study, the nonlinear two-dimensional Kadomtsev–Petviashvili-modified equal width (KP-MEW) equation is under investigation, which is described as a nonlinear wave model in weakly restoring forces and shallow water waves in the way of ferromagnetic, solitary waves in two dimensions with short amplitude and long wavelength and are also helpful in the investigation of various behaviors in nonlinear sciences. We successfully found the interesting and novel solitary wave results in bright solitons, kink solitons, dark solitons, anti-kink solitons, periodic singular solitons for nonlinear two-dimensional KP-MEW equation on the base of extension of simple equation technique under symbolic computational software Mathematica. The created solitons play an important role in nonlinear engineering and physics such as nonlinear optics, nonlinear dynamics, soliton wave theory, optical fiber and communication system. We are sure that these constructed results are novel and interesting which does not exist in the previous literature works. The graphical representation of constructed results is shown by 2D, 3D and contour graphs. The investigated research work proved that utilized technique is more concise, straightaway and efficient for study of other nonlinear partial differential equations.

AFC-TH Hybrid Precoding With High Energy Efficiency for Millimeter Wave Communication Systems

AFC-TH Hybrid Precoding With High Energy Efficiency for Millimeter Wave Communication Systems

Circularly Polarized Exotic Tri-Shakti Shaped Patch Antenna for 5G Millimeter Wave Communication Systems

Circularly Polarized Exotic Tri-Shakti Shaped Patch Antenna for 5G Millimeter Wave Communication Systems

Performance analysis of IRS-aided multi-user millimeter wave communications system

Performance analysis of IRS-aided multi-user millimeter wave communications system

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.

Common structured sparsity based multi-user channel estimation for double-RIS assisted millimeter wave systems

Compared to a single reconfigurable intelligent surface (RIS) system, a double RIS system can achieve better passive beamforming gain, while requiring more accurate channel state information (CSI). However, due to the presence of double-reflection links, channel estimation with low pilot overhead is quite challenging. In this paper, we investigate channel estimation of double-RIS assisted multi-user millimeter wave (mmWave) communication systems. Firstly, the channel estimation problem for double-reflection links is formulated as a sparse signal recovery problem. Then, we analyze and exploit the common structured sparsity of the cascaded channel among multiple users, i.e., the common row and column sparsity in the angular channel matrix, and the corresponding common sparsity after being transformed into the channel vector. Inspired by this, we further propose a novel common structured sparsity based orthogonal matching pursuit (CSS-OMP) algorithm for multi-user joint channel estimation. Simulation results show that CSS-OMP algorithm can provide more accurate channel estimation with reduced pilot overhead compared to the conventional OMP algorithm.

IRS-Aided Joint Spatial Division and Multiplexing for mmWave Multiuser MISO Systems

Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) communication systems have gained considerable attention recently. However, the benefits brought by IRS require the instantaneous channel state information (I-CSI) of the cascaded base station (BS)-IRS and IRS-user channel which is difficult to obtain in practice, especially for the multiuser scenario. To address this issue, in this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.

Antenna Selections Strategies for Massive MIMO Systems With Limited-Resolution ADCs/DACs

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.

LoneSTAR: Analog Beamforming Codebooks for Full-Duplex Millimeter Wave Systems

This work develops LONESTAR, a novel enabler of full-duplex millimeter wave (mmWave) communication systems through the design of analog beamforming codebooks. LONESTAR codebooks deliver high beamforming gain and broad coverage while simultaneously reducing the self-interference coupled by transmit and receive beams at a full-duplex mmWave transceiver. Our design framework accomplishes this by tolerating some variability in transmit and receive beamforming gain to strategically shape beams that reject self-interference spatially while accounting for digitally-controlled analog beamforming networks and self-interference channel estimation error. By leveraging the coherence time of the self-interference channel, a mmWave system can use the same LONESTAR design over many time slots to serve several downlink-uplink user pairs in a full-duplex fashion without the need for additional self-interference cancellation. Compared to those using conventional codebooks, full-duplex mmWave systems employing LONESTAR codebooks can mitigate higher levels of self-interference, tolerate more cross-link interference, and demand lower SNRs in order to outperform half-duplex operation—all while supporting beam alignment. This makes LONESTAR a potential standalone solution for enabling simultaneous transmission and reception in mmWave systems, from which it derives its name.

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.

Spatio-Temporal Coherence of mmWave/THz Channel Characteristics and Their Forecasting Using Video Frame Prediction Techniques

Channel state information in millimeter wave (mmWave) and terahertz (THz) communications systems is vital for various tasks ranging from planning the optimal locations of BSs to efficient beam tracking mechanisms to handover design. Due to the use of large-scale phased antenna arrays and high sensitivity to environmental geometry and materials, precise propagation models for these bands are obtained via ray-tracing modeling. However, the propagation conditions in mmWave/THz systems may theoretically change at very small distances, that is, 1 mm–1 μm, which requires extreme computational effort for modeling. In this paper, we first will assess the effective correlation distances in mmWave/THz systems for different outdoor scenarios, user mobility patterns, and line-of-sight (LoS) and non-LoS (nLoS) conditions. As the metrics of interest, we utilize the angle of arrival/departure (AoA/AoD) and path loss of the first few strongest rays. Then, to reduce the computational efforts required for the ray-tracing procedure, we propose a methodology for the extrapolation and interpolation of these metrics based on the convolutional long short-term memory (ConvLSTM) model. The proposed methodology is based on a special representation of the channel state information in a form suitable for state-of-the-art video enhancement machine learning (ML) techniques, which allows for the use of their powerful prediction capabilities. To assess the prediction performance of the ConvLSTM model, we utilize precision and recall as the main metrics of interest. Our numerical results demonstrate that the channel state correlation in AoA/AoD parameters is preserved up until approximately 0.3–0.6 m, which is 300–600 times larger than the wavelength at 300 GHz. The use of a ConvLSTM model allows us to accurately predict AoA and AoD angles up to the 0.6 m distance with AoA being characterized by a higher mean squared error (MSE). Our results can be utilized to speed up ray-tracing simulations by selecting the grid step size, resulting in the desired trade-off between modeling accuracy and computational time. Additionally, it can also be utilized to improve beam tracking in mmWave/THz systems via a selection of the time step between beam realignment procedures.

Weighted Sum-Rate Maximization in Multi-IRS-Aided Multi-Cell mmWave Communication Systems for Suppressing ICI

Intelligent reflecting surface (IRS) has been envisioned as a disruptive technology with the capability of reconfiguring the wireless transmission environment. Hence, the IRS is used to assist the communication system, however, the impacts of inter-cell interference (ICI) on cell-edge users are severe in multi-cell communications but have been ignored in most literature about IRS-assisted communication systems. In this paper, we consider the downlink transmission of cell-edge users in multi-cell millimeter wave (mmWave) communication systems with the aid of multiple IRSs deployed at the cell boundary. To suppress the ICI, we maximize the weighted sum-rate (WSR) by jointly optimizing the IRS-user association matrix, the transmit beamforming of mmWave base stations (MBSs), and the phase shifts of IRSs, subject to the power limits of MBSs, the reflection constraints of IRSs, and the association constraints. Then, a joint IRS-user association and beamforming algorithm with alternating optimization (AO) framework is developed to tackle the mixed-integer non-convex problem. Specifically, a simplified objective function with respect to the association variables is derived and the non-convex binary constraints are handled by the relaxation operation and the first-order Taylor expansion, while the arithmetic-geometric mean inequality (AGMI)-based rank-one relaxation algorithm and the ring-based penalty convex-concave procedure (CCP) method are proposed for obtaining the transmit beamforming matrix of MBSs and the phase shifts of IRSs, respectively. Simulation results demonstrate the superiority of the proposed algorithm over the benchmark schemes. The useful insights into the optimization of IRS-user association and design of the number of elements of IRSs are also presented for future networks.

Compressed channel estimation for RIS-assisted wireless systems: An efficient sparse recovery algorithm

The reconfigurable intelligent surface (RIS) technology plays an essential role in controlling the propagation environment of wireless systems, specifically millimeter wave (mmWave) communication systems. However, a considerable number of pilot signals should be provided at the base station (BS) side for channel estimation due to the significant passive elements installed in the RIS. In this paper, we propose a novel sparse recovery algorithm by exploiting the row, common, and individual sparse elements of the channel in a new way. Furthermore, as an essential presumption of real environments, a sparse channel with unknown parameters is considered. Then a halting method is introduced to stop the proposed algorithm at the best time. The numerical results demonstrate that the proposed algorithm can alleviate the pilot overhead problem substantially while the mean square error (MSE) still approaches the performance bound.

Performance Analysis of a Millimeter Wave Communication System in Urban Micro, Urban Macro, and Rural Macro Environments

The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models.

Robust Beamforming for IRS-Aided Multi-Cell mmWave Communication Systems

Intelligent reflecting surface (IRS) has recently been envisioned to enhance the power of the desired received signal or suppress the interference signal by deploying low-cost passive reflection elements. This paper investigates an IRS-aided multi-cell millimeter wave (mmWave) communication system for suppressing inter-cell interference (ICI) to assist the downlink transmission of cell-edge users. We aim for maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) through jointly optimizing the active beamforming vectors of mmWave base stations (MBSs), the phase shifts of the IRS, and the location of the IRS. Especially since it is challenging to obtain the perfect channel state information (CSI) related to the IRS links, we also study the performance of IRS-aided mmWave communication in the case of imperfect CSI. First, in the case of perfect CSI, to tackle the challenging and non-convex minimum weighted SINR maximization problem, we develop an alternating optimization (AO)-based beamforming algorithm via updating the active beamforming vectors at MBSs, the phase shifts at the IRS, and the location of the IRS alternately. Then, in the case of imperfect CSI, we propose a low-complexity majorization-minimization (MM)-based robust beamforming algorithm to obtain the benchmark performance. Moreover, the proposed algorithms are also extended to multi-IRS-aided multi-cell mmWave scenarios. Finally, the simulation results demonstrate the advantages in terms of the SINR, effective sum rate as well as energy efficiency after introducing the IRS to mitigate the ICI.

ETRFI Based Iterative Channel Estimation Scheme and Its Optimal Number of Iterations to Enhance PER performance in WAVE Communication Systems

ETRFI Based Iterative Channel Estimation Scheme and Its Optimal Number of Iterations to Enhance PER performance in WAVE Communication Systems