Multipath Components Research Articles

An empirical study of ISAC channel characteristics with human target impact at 105 GHz

AbstractLeveraging the ultra‐wideband advantages of the terahertz band, Integrated sensing and communication (ISAC) facilitates high‐precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g. radar cross‐section and micro‐Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analysing the power angular delay profiles with and without human target, the changes in quantity and power of multipath components (MPCs) are observed. Then, a parameter called power control factor is proposed to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, the MPCs belonging to target channel are extracted within target‐oriented power delay profile to count the power proportion of each bounce MPCs of the target‐Rx link, which supports the necessity of multi‐bounce (indirect) paths modelling in target channel.

Robust Bluetooth AoA Estimation for Indoor Localization Using Particle Filter Fusion

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most cellular systems, such as Bluetooth indoor positioning systems, due to multipath and non-line-of-sight (NLOS) propagation, indoor positioning accuracy is severely affected. In this paper, a comprehensive algorithm that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates is investigated, which is obtained using particle filtering. This algorithm allows us to explore new optimized methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by approximately 25.7% on average.

Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles

We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably.

Spatial Scattering Modulation With Multipath Component Aggregation

Spatial Scattering Modulation With Multipath Component Aggregation

Multistage interference cancellation for cyclic interleaved frequency division multiplexing

AbstractCyclic interleaved frequency division multiplexing (CIFDM), a variant of IFDM, has recently been proposed. While CIFDM employs cyclic interleaving at the transmitter to make multipath components resolvable at the receiver, the current approach of matched filtering followed by multipath combining does not fully exploit the diversity available. This is primarily because the correlation residues among the codes have a significant impact on multipath resolution. As a solution, we introduce a novel multipath successive interference cancellation (SIC) technique for CIFDM, which replaces the conventional matched filtering approach. We have examined the performance of this proposed CIFDM‐SIC technique and compared it with the conventional CIFDM‐matched filter bank and IFDM schemes. Our simulation results clearly demonstrate the superiority of the proposed scheme over the existing ones.

A Graph-Based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.

Multi-Frequency Channel Measurement and Characteristic Analysis in Forested Scenario for Emergency Rescue

Wireless communication has been widely used in emergency rescue, including from command vehicles to command vehicles, command vehicles to rescue teams, command vehicles to wireless sensors, and rescue teams to intelligent platforms such as unmanned vehicles. Compared to those used in cities and suburbs, when the same communication equipment is used in forests, its communication performance, such as transmission distance, is entirely different. The main reason for this phenomenon is the extraordinary complexity of wireless signal propagation in forest scenarios. Therefore, in order to accurately and quantitatively describe the wireless channel characteristics in forest scenarios, a frequency channel measurement activity is conducted in a forest and analysis is performed to acquire the channel characteristics in forest scenarios. The measurements are carried out at 380 MHz, 640 MHz, and 1420 MHz in virgin forest. Based on the measurement data, the average power delay profile (APDP) is obtained, and multipath components (MPCs) are extracted. Root mean square (RMS) delay spread and path loss (PL) are analyzed according to MPCs. Furthermore, a new path loss model is proposed. Finally, a new path loss model and relative analysis are provided.

Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation

In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE.

Location-Aware Beam Training and Multi-Dimensional ANM-Based Channel Estimation for RIS-Aided mmWave Systems

In this paper, we propose a location-aware channel estimation based on the atomic norm minimization (ANM) for the reconfigurable intelligent surface (RIS)-aided millimeter-wave multiple-input-multiple-output (MIMO) systems. The beam training overhead at the base station (BS) is reduced by the direct beam steering towards the RIS with the location of the BS and the RIS. The RIS beamwidth adaptation is proposed to reduce the beam training overhead at the RIS, and also it enables accurate channel estimation by ensuring the user equipment receives all the multipath components from the RIS. After the beam training, the cascaded effective channel of the RIS-aided MIMO systems is estimated by ANM. Depending on whether the beam training overhead at the BS or at the RIS is reduced or not, the channel is represented as a linear combination of either 1D atoms, 2D atoms, or 3D atoms, and the ANM is applied to estimate the channel. Simulation results show that the proposed location-aware channel estimation via 2D ANM and 3D ANM achieves superior estimation accuracy to benchmarks.

Local Predictability Analysis-Based Significant Multipath Component Identification for OFDM Systems

Local Predictability Analysis-Based Significant Multipath Component Identification for OFDM Systems

Multipath Component Power Delay Profile Based Ranging

Multipath Component Power Delay Profile Based Ranging

Scedasticity descriptor of terrestrial wireless communications channels for multipath clustering datasets

Fifth-generation (5G) wireless systems increased the bandwidth, improved the speed, and shortened the latency of communications systems. Various channel models are developed to study 5G. These channel models reproduce the stochastic properties of multiple-input multiple-output (MIMO) antennas by generating wireless multipath components (MPCs). The MPCs that have similar properties in delay, angles of departure, and angles of arrival form clusters. The multipaths and multipath clusters serve as datasets to understand the properties of 5G. These datasets generated by the Cooperation in Science and Technology 2100 (COST 2100), International Mobile Telecommunications-2020 (IMT-2020), quasi deterministic radio channel generator (QuaDRiGa), and wireless world initiative new radio II (WINNER II) channel models are tested for their homoscedasticity based on Johansen's procedure. Results show that the COST 2100, QuaDRiGa, and WINNER II datasets are heteroscedastic, while the IMT-2020 dataset is homoscedastic.

Integrated Sensing and Communication With Delay Alignment Modulation: Performance Analysis and Beamforming Optimization

Delay alignment modulation (DAM) has been recently proposed to enable manipulable channel delay spread for efficient single- or multi-carrier communications. In particular, with perfect delay alignment, inter-symbol interference (ISI) can be eliminated even with single-carrier (SC) transmission, without relying on sophisticated channel equalization. The key ideas of DAM are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delay pre-compensation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path-based beamforming</i> , so that all multi-path signal components may arrive at the receiver simultaneously and be superimposed constructively, rather than causing the detrimental ISI. Compared to the classic orthogonal frequency division multiplexing (OFDM) transmission, DAM-enabled SC communication has several appealing advantages, including low peak-to-average-power ratio (PAPR) and high tolerance for Doppler frequency shift, which renders DAM also appealing for radar sensing. Therefore, in this paper, DAM is investigated for integrated sensing and communication (ISAC) systems. We first study the output signal-to-noise ratios (SNRs) for ISI-free SC communication and radar sensing, respectively, and then derive the closed-form expressions for DAM-based sensing in terms of the ambiguity function (AF) and integrated sidelobe ratio (ISR). Furthermore, we study the beamforming design problem for DAM-based ISAC to maximize the communication SNR while guaranteeing the sensing performance in terms of the sensing SNR and ISR. Finally, we provide performance comparison between DAM and OFDM for ISAC, and it is revealed that DAM signal may achieve better communication and sensing performance, thanks to its low PAPR, reduced guard interval overhead, as well as higher tolerance for Doppler frequency shift. Simulation results are provided to show the great potential of DAM for ISAC.

A Shared Multipath Components Evolution Model for Integrated Sensing and Communication Channels

A Shared Multipath Components Evolution Model for Integrated Sensing and Communication Channels

Affine Frequency Division Multiplexing for Next Generation Wireless Communications

Affine Frequency Division Multiplexing (AFDM), a new chirp-based multicarrier waveform for high mobility communications, is introduced here. AFDM is based on discrete affine Fourier transform (DAFT), a generalization of discrete Fourier transform, which is characterized by two parameters that can be adapted based on the Doppler spread of doubly dispersive channels. First, we derive the explicit input-output relation in the DAFT domain showing the effect of AFDM parameters in the input-output relation. Second, we show how the DAFT parameters underlying AFDM have to be set so that the resulting DAFT domain impulse response conveys a full delay-Doppler representation of the channel. Then, we show analytically that AFDM can achieve the optimal diversity order in doubly dispersive channels, where optimal diversity order refers to the number of multipath components separable in either the delay or the Doppler domain, due to its full delay-Doppler representation. Furthermore, we present a low complexity detection method taking advantage of zero-padding. We also propose an embedded pilot-aided channel estimation scheme for AFDM, in which both channel estimation and data detection are performed within the same AFDM frame. Finally, simulations corroborate the validity of our analytical results and show the significant performance gains of AFDM over state-of-the-art multicarrier schemes in high mobility scenarios.

Assessing the Performance of a Hybrid Geolocation Algorithm Integrating FP and TOA Techniques across Diverse Environmental Conditions

This paper presents a validation study of indoor geolocation accuracies using a hybrid approach that combines fingerprinting (FP) and time of arrival (TOA) techniques. The investigation focuses on three dense environments, examining the influence of furniture density and multipath components on geolocation accuracy, particularly in non-line-of-sight (NLOS) scenarios. The results indicate that geolocation performance improves in denser environments with higher furniture density due to increased multipath components. Additionally, optimizing the hybrid method with a polygon size of 50 cm and sampling rate of 80 GHz leads to further accuracy enhancements. These findings underscore the significance of furniture density and demonstrate the effectiveness of the hybrid method in addressing NLOS challenges. The research contributes to the advancement of indoor geolocation techniques and provides valuable insights for designing precise indoor positioning systems across various applications.

Model-based clustering of multipath propagation in powerline communication channels

Powerline communication (PLC) channels are known to exhibit multipath propagation behaviour. The authors present a model-based framework to address the challenge of clustering multipath propagation components (MPCs) in PLC channels for indoor low-voltage (LV) environments. The framework employs a range of finite-mixture models (FMMs), including the gamma mixture model, the inverse gamma mixture model, the Gaussian mixture model, the inverse Gaussian mixture model, the Nakagami mixture model, the inverse Nakagami mixture model (INMM) and the Rayleigh mixture model, to identify clusters of MPCs. A measurement campaign of an unknown indoor LV PLC channel is conducted to obtain a channel response. From the channel response, the delay and magnitude parameters of the MPCs are extracted using the space-alternating generalised expectation maximisation algorithm adopted only for these parameters. A maximum likelihood approach and the expectation–maximisation algorithm are employed to fit the FMMs to the MPC delay-magnitude dataset to cluster MPCs in the delay domain. The results of the model-fitting process are then evaluated using the corrected Akaike information criterion (AICc), which enables a fair comparison of the candidate models over the feasible and finite range of clusters. A novel algorithm is introduced for estimating the feasible and finite range of clusters using the extracted delay and magnitude MPC parameters. The AICc’s ranking results show that the INMM model provides the best fit. Davies–Bouldin (DB) and Calinski–Harabasz (CH) indexes are used to compare the model-based clustering approach to the conventional distance-based clustering methods. Validation results show that CH and DB indexes closely agree in the optimal number of MPC clusters for model-based clustering, which corresponds to the most within-cluster compactness of MPCs and to the most between-cluster separation in the delay domain.

Channel State Information Based Equalizer Design for Frequency Selective Channels

Abstract—Equalizer design is an extremely critical aspect for wireless networks. This paper presents an approach combining the decision feedback mechanism and successive signal detection to equalize frequency selective channel effects for signals traversing different run-lengths. In this approach, the errors on comparison between the transmitted signal and received signal are fed to the equalizer to adjust the tap weights. Still the irreversible nature of inter symbol interference is a huge challenge due to multi path propagation mechanisms in wireless channels. It is a common observation that signals traversing a smaller path reach the receiver earlier compared to the multi-path component traversing a longer path. Assuming similar shadowing effects, it is seen that fading effects make it difficult to accurately receive long distance MPCs, thereby degrading the BER performance. It has been shown that the proposed system attains almost similar BER performance irrespective of the shadowing effect or run length of the MPCs. Keywords— Multi Path Component (MPC), Inter Symbol Interference (ISI), Adaptive Equalizer, Channel State Information (CSI), , Bit Error Rate (BER), Probability of Error

Toward Multi-User Authentication Using WiFi Signals

User authentication nowadays has become an important support for not only security guarantees but also emerging novel applications. Although WiFi signal-based user authentication has achieved initial success, it works in single-user scenarios while multi-user authentication remains a challenging task. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> , a multi-user authentication system that can authenticate multiple users with a single pair of commodity WiFi devices. The basic idea is to profile multipath components of WiFi signals, and leverage the multipath components to characterize each user individually for multi-user authentication. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> first profiles multipath components of WiFi signals through a proposed MUltipath Time-of-Arrival estimation algorithm (MUTA). Then, after matching corresponding multipath components to each user in complex multi-user scenarios, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> constructs individual CSI based on the multipath components to characterize each user individually. An AoA-based approach is exploited to further separate individual CSI constructed by the users with same ToA. To identify users through their activities, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> extracts user behavior profiles based on the individual CSI, and leverages a dual-task neural network for robust user authentication. Extensive experiments involving 3 simultaneously present users demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> is effective in multi-user authentication with 86.2% average accuracy and 9.5% average false accept rate.

Performance Analysis of DoA Estimation for FDD Cell Free Systems Based on Compressive Sensing Technique

The concept of cell free (CF) massive MIMO systems is a prospective fifth generation communication technology that effort with base stations for the privilege of user-centric coverage. Most studies on the CF massive MIMO system in the past imply that systems that use time division duplexing (TDD), even despite the systems using frequency division duplex (FDD) predominate in today’s wireless communications. When the number of antennas increases in FDD systems, channel state information (CSI) collection and feedback overhead become major issues. In order to mitigate these issues, we make use of the condition that the so-called uplink and downlink multipath components are comparable. Base station takes use of the angle reciprocity may immediately obtain information on channel parameters from the uplink training signal. In this paper, for CF massive MIMO system based on FDD, we provide compressive sensing (CS) of directions of arrival (DoAs) estimation approach of access point cooperation based on the channel parameters. The suggested estimation approach outperforms the established subspace-based technique, according to simulation findings. Additionally, we showed that the results of our compressive sensing estimator against the conventional estimation method. The former demonstrates way far better outcome and performance accordingly than the latter.