This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and customizable data pipeline built upon the CARLA simulator and Sionna framework, and features high-resolution communication channel state information (CSI) fully synchronized with five other sensor modalities, namely LiDAR, RGB and depth camera, inertial measurement unit (IMU) and radar, all sampled at 100 Hz. It contains approximately 160,000 frames collected across four virtual towns, sixteen communication scenarios, and three weather conditions. This paper provides a comprehensive overview of the dataset, outlining its key features, overall framework, and technical implementation details. In addition, it explores potential research applications concerning communication and collaborative perception, exemplified by beam prediction using a multimodal large language model. The dataset is open in https://le-liang.github.io/mmw/.

Massive multiple-input multiple-output (MIMO) is a key enabler for the high data rates required by the sixth-generation networks, yet its performance hinges on effective beam management with low training overhead. This paper proposes an interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments. Our approach utilizes multimodal data to construct virtual base stations (VBSs), which are geometrically defined as mirror images of the base station across reflecting surfaces reconstructed from 3D LiDAR points. These VBSs provide a sparse and spatial representation of the dominant features of the wireless environment. Based on the constructed VBSs, we develop a VBS-assisted beam alignment scheme comprising coarse channel reconstruction followed by partial beam training. Numerical results demonstrate that the proposed method achieves near-optimal performance in terms of spectral efficiency.

The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is emerging as a key enabler for future communication systems. By exploiting the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries including frame structure, channel modeling, and signal models are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-MIMO prototype system are presented. Underpinned by a novel architecture, the proposed prototype system supports specifications aligned with standardization, including a bandwidth of 200 MHz, up to 1024 antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing mode, the prototype enables multiuser communication for up to 12 users, while retaining standard communication procedures. Built on hybrid software-defined radio and field programmable gate array platforms, the prototype is programmable and allows for flexible deployment of advanced algorithms. Moreover, the modular architecture ensures high scalability, making the prototype adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results demonstrate that the prototype handles real-time digital sample processing at 1453.33 Gbps and achieves a peak data throughput of 15.81 Gbps for 12 users.

Integrated sensing and communication (ISAC) is a key feature of future cellular systems, enabling applications such as intruder detection, monitoring, and tracking using the same infrastructure. However, its potential for structural health monitoring (SHM), which requires the detection of slow and subtle structural changes, remains largely unexplored due to challenges such as multipath interference and the need for ultra-high sensing precision. This study introduces a novel theoretical framework for SHM via ISAC by leveraging reconfigurable intelligent surfaces (RIS) as reference points in collaboration with base stations and users. By dynamically adjusting RIS phases to generate distinct radio signals that suppress background multipath interference, measurement accuracy at these reference points is enhanced. We theoretically analyze RIS-aided collaborative sensing in three-dimensional cellular networks using Fisher information theory, demonstrating how increasing observation time, incorporating additional receivers (even with self-positioning errors), optimizing RIS phases, and refining collaborative node selection can reduce the position error bound to meet SHM’s stringent accuracy requirements. Furthermore, we develop a Bayesian inference model to identify structural states and validate damage detection probabilities. Both theoretical and numerical analyses confirm ISAC’s capability for millimeter-level deformation detection, highlighting its potential for high-precision SHM applications.

Integrated Sensing and Communication (ISAC) has the potential to enhance both energy and spectral efficiency in modern communication systems. Although Probability Hypothesis Density (PHD)-based Simultaneous Localization and Mapping (SLAM) is a key algorithm for positioning and environmental mapping in ISAC, the advantages of multi-base-station (multi-BS) fusion remain underexplored, despite the considerable attention given to multi-sensor and multi-user data fusion in existing research. This paper leverages the distinct roles of Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) channel parameters, employing LoS for agent localization and NLoS for environment mapping. An Extended Kalman Filter (EKF) framework is proposed to fuse LoS path angle parameters for localization, for which the corresponding Cramér-Rao Lower Bound (CRLB) is derived. To facilitate landmark mapping, a virtual reference point (VRP) is introduced to model reflecting surfaces consistently across base stations (BSs), replacing the conventional approach of using multiple virtual anchors for multiple BSs. Furthermore, map fusion algorithms are developed to address the challenges of merging PHD-SLAM maps with varying observation quality and overlapping fields of view. To reduce the computational complexity of particle-based PHD-SLAM, agent location estimates derived from EKF fusion are used as priors, significantly improving particle efficiency and enabling the unified exploitation of LoS and NLoS data for comprehensive situational awareness. Simulation and experimental results confirm that the proposed EKF-based LoS fusion strategy significantly improves sensing performance while maintaining low computational overhead.

Wireless imaging has become a vital function in future integrated sensing and communication (ISAC) systems. However, traditional model-based and data-driven deep learning imaging methods face challenges related to multipath extraction, dataset acquisition, and multi-scenario adaptation. To overcome these limitations, this study innovatively combines implicit neural representation (INR) with explicit physical models to realize wireless imaging in reconfigurable intelligent surface (RIS)-aided ISAC systems. INR employs neural networks (NNs) to project physical locations to voxel values, which is indirectly supervised by measurements of channel state information with physics-informed loss functions. The continuous shape and scattering characteristics of targets are embedded into NN parameters through training, enabling arbitrary image resolutions and off-grid voxel value prediction. Additionally, three issues related to INR-based imager are further addressed. First, INR is generalized to enable efficient imaging under multipath interference by jointly learning image and multipath information. Second, the imaging speed and accuracy for dynamic targets are enhanced by embedding prior image information. Third, imaging results are employed to assist in RIS phase design for improved communication performance. Extensive simulations demonstrate that the proposed INR-based imager significantly outperforms traditional model-based methods with super-resolution abilities, and the focal length characteristics of the imaging system is revealed. Moreover, communication performance can benefit from the imaging results. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/INRImager.

The low-altitude economy is emerging as a key driver of future economic growth, necessitating effective flight activity surveillance using existing mobile cellular network sensing capabilities. However, traditional monostatic and localization-based sensing methods face challenges in fusing sensing results and matching channel parameters. To address these challenges, we model low-altitude surveillance as a compressed sensing (CS)-based imaging problem by leveraging the cooperation of multiple base stations and the inherent sparsity of aerial images. Additionally, we derive the point spread function to analyze the influences of different antenna, subcarrier, and resolution settings on the imaging performance. Given the random spatial distribution of unmanned aerial vehicles (UAVs), we propose a physics-embedded learning method to mitigate off-grid errors in traditional CS-based approaches. Furthermore, to enhance rare UAV detection in vast low-altitude airspace, we integrate an online hard example mining scheme into the loss function design, enabling the network to adaptively focus on samples with significant discrepancies from the ground truth during training. Simulation results demonstrate the effectiveness of the proposed low-altitude surveillance framework. The proposed physics-embedded learning algorithm achieves a 97.55% detection rate, significantly outperforming traditional CS-based methods under off-grid conditions. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/LAEImager.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for wireless communication systems due to its ability to manipulate electromagnetic waves. With advantages such as low hardware complexity and low power consumption, RIS shows significant potential in positioning applications. This paper presents an RIS-based wireless signal sensing method that operates under the constraint of passive reflection while leveraging the space-time coding capabilities of RIS. By applying a space-time coding matrix on the RIS, the beamspace domain and the angle of arrival (AoA) of signals incident on the RIS can be efficiently estimated, requiring only the processing of single-channel received signals at the access point. Building upon this, a positioning prototype system utilizing two 27 GHz millimeter-wave RIS panels is developed and implemented, supporting real-time user positioning. Experimental results demonstrate that the prototype system achieves centimeter-level positioning accuracy, with errors below 10 cm in 97.22% of measurement cases, thereby validating the effectiveness of the proposed sensing and positioning scheme. These findings may pave the way for further exploration of RIS-based integration of sensing and communication technologies.

Millimeter-wave (mmWave) 5G New Radio (NR) communication systems, with their high-resolution antenna arrays and extensive bandwidth, offer a transformative opportunity for high-throughput data transmission and advanced environmental sensing. Although passive sensing-based SLAM techniques can estimate user locations and environmental reflections simultaneously, their effectiveness is often constrained by assumptions of specular reflections and oversimplified map representations. To overcome these limitations, this work employs a mmWave 5G NR system for active sensing, enabling it to function similarly to a laser scanner for point cloud generation. Specifically, point clouds are extracted from the power delay profile estimated from each beam direction using a binary search approach. To ensure accuracy, hardware delays are calibrated with multiple predefined target points. Pose variations of the terminal are then estimated from point cloud data gathered along continuous trajectory viewpoints using point cloud registration algorithms. Loop closure detection and pose graph optimization are subsequently applied to refine the sensing results, achieving precise terminal localization and detailed radio map reconstruction. The system is implemented and validated through both simulations and experiments, confirming the effectiveness of the proposed approach.

The low-altitude economy has emerged as a critical focus for future economic development, emphasizing the urgent need for flight activity surveillance utilizing the existing sensing capabilities of mobile cellular networks. Traditional monostatic or localization-based sensing methods, however, encounter challenges in fusing sensing results and matching channel parameters. To address these challenges, we propose an innovative approach that directly draws the radio images of the low-altitude space, leveraging its inherent sparsity with compressed sensing (CS)based algorithms and the cooperation of multiple base stations. Furthermore, recognizing that unmanned aerial vehicles (UAVs) are randomly distributed in space, we introduce a physicsembedded learning method to overcome off-grid issues inherent in CS-based models. Additionally, an online hard example mining method is incorporated into the design of the loss function, enabling the network to adaptively concentrate on the samples bearing significant discrepancy with the ground truth, thereby enhancing its ability to detect the rare UAVs within the expansive low-altitude space. Simulation results demonstrate the effectiveness of the imaging-based low-altitude surveillance approach, with the proposed physics-embedded learning algorithm significantly outperforming traditional CS-based methods under off-grid conditions.

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optimized once and applied over fixed sensing durations, limiting their ability to adapt to different targets and reducing sensing accuracy. To overcome these limitations, this study proposes an advanced wireless communication system that multiplexes downlink signals for environmental sensing and introduces an intelligent recognizer powered by deep learning techniques. Specifically, we design a novel neural network based on the long short-term memory architecture and the physical channel model. This network iteratively captures and fuses information from previous measurements, adaptively customizing RIS phases to gather the most relevant information for the recognition task at subsequent moments. These configurations are dynamically adjusted according to scene, task, target, and quantization priors. Furthermore, the recognizer includes a decision-making module that dynamically allocates different sensing durations, determining whether to continue or terminate the sensing process based on the collected measurements. This approach maximizes resource utilization efficiency. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques while minimizing the impact on communication performance, even when sensing and communication occur simultaneously. Part of the source code for this paper can be accessed athttps://github.com/kiwi1944/CRISense.

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread, and slight Doppler effects were also observed following the onset of precipitation. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model in the sub-6GHz band that leverages multipath and temporal features. Two variants are developed: RainGaugeNet-R using ResNet1D and RainGaugeNet-T using a Transformer encoder. Using only 20 seconds of CSI data, RainGaugeNet-R achieves up to 95% an average classification accuracy in line-of-sight(LoS) scenarios and 85% in non-line-of-sight(NLoS) conditions. RainGaugeNet-T attains 90% accuracy in LoS and 99% in NLoS settings, demonstrating superior robustness. Both models significantly outperform state-of-the-art baselines while maintaining low computational complexity.

Integrated sensing and communication (ISAC) in millimeter wave is a key enabler for next-generation networks, which leverages large bandwidth and extensive antenna arrays, benefiting both communication and sensing functionalities. The associated high costs can be mitigated by adopting a hybrid beamforming structure. However, the well-studied monostatic ISAC systems face challenges related to full-duplex operation. To address this issue, this paper focuses on a three-dimensional bistatic configuration that requires only half-duplex base stations. To intuitively evaluate the error bound of bistatic sensing using orthogonal frequency division multiplexing waveforms, we propose a positioning scheme that combines angle-of-arrival and time-of-arrival estimation, deriving the closed-form expression of the position error bound (PEB). Using this PEB, we develop two hybrid beamforming algorithms for joint waveform design, aimed at maximizing achievable spectral efficiency (SE) while ensuring a predefined PEB threshold. The first algorithm leverages a Riemannian trust-region approach, achieving superior performance in terms of SE and convergence speed compared to the conventional gradient-based methods, but with higher complexity. In contrast, the second algorithm, which employs orthogonal matching pursuit, offers a more computationally efficient solution, delivering reasonable SE while maintaining the PEB constraint. Numerical results are provided to validate the effectiveness of the proposed designs.

Simultaneous localization and mapping (SLAM) plays a critical role in integrated sensing and communication (ISAC) systems for sixth-generation (6G) millimeter-wave (mmWave) networks, enabling environmental awareness and precise user equipment (UE) positioning. While cooperative multi-user SLAM has demonstrated potential in leveraging distributed sensing, its application within multi-modal ISAC systems remains limited, particularly in terms of theoretical modeling and communication-layer integration. This paper proposes a novel multi-modal SLAM framework that addresses these limitations through three key contributions. First, a Bayesian estimation framework is developed for cooperative multi-user SLAM, along with a two-stage algorithm for robust radio map construction under dynamic and heterogeneous sensing conditions. Second, a multi-modal localization strategy is introduced, fusing SLAM results with camera-based multi-object tracking and inertial measurement unit (IMU) data via an error-aware model, significantly improving UE localization in multi-user scenarios. Third, a sensing-aided beam management scheme is proposed, utilizing global radio maps and localization data to generate UE-specific prior information for beam selection, thereby reducing inter-user interference and enhancing downlink spectral efficiency. Simulation results demonstrate that the proposed system improves radio map accuracy by up to 60%, enhances localization accuracy by 37.5%, and significantly outperforms traditional methods in both indoor and outdoor environments.

The advancement of millimeter-wave communication technology heralds new sensing capabilities. By leveraging channel multipath parameter estimates, we can harness simultaneous localization and mapping (SLAM) for precise user equipment (UE) localization and radio map construction in 6 G communication systems. Particularly in multi-UE scenarios, SLAM empowers base stations to amalgamate the local radio maps of various UEs efficiently. This study introduces a novel Bayesian framework specifically designed for multi-UE SLAM, complemented by a tailored factor graph. We also unveil a two-stage multi-UE SLAM algorithm. Our simulation results reveal that this algorithm substantially enhances radio map construction accuracy by $\mathbf{4 8. 5 \%}$ and UE localization accuracy by $13.5 \%$, outperforming single-UE cases. Moreover, the algorithm demonstrates remarkable adaptability to environmental changes, showcasing its potential for long-term evolution in dynamic settings.

This paper studies the beamformer design for an integrated sensing and communication system where simultaneous communication for multiple downlink users and bistatic sensing for a point target are realized. Firstly, to avoid self-interference in monostatic settings, we establish the bistatic sensing model where the sensing performance is measured by the Cramér-Rao bound (CRB) for the target's coordinates and the communication performance by the signal-to-interference-plus-noise ratio (SINR). We aim to minimize the CRB while ensuring a predefined SINR for each user. Then, we derive a closed-form beamformer for the single-user case that proves near-optimal. For the multi-user case, we introduce a beamformer design algorithm based on semidefinite relaxation and a suboptimal design algorithm based on successive convex approximation with reduced complexity. Finally, numerical results are provided to validate the solutions.

Wi-Fi access point (AP) and IoT device localization are essential for smart home functionalities, including indoor localization and privacy protection. Yet, complex multipath channels in indoor settings often hinder precise localization. To overcome this, we introduce an Artificial Intelligence (AI) technique that amalgamates channel state information from proximate trajectory points, thus elevating the accuracy of line of sight (LoS) angle of arrival (AoA) estimation. Our methodology initiates with an AI-based anomaly detection system to eliminate questionable measurements. Thereafter, our AI-optimized LoS-AoA network proficiently identifies the primary LoS path from the several multipaths detected by the multipath estimation process and autonomously fine-tunes the LoS-AoA estimation. Using simulations in an indoor office environment with Wireless Insite, our results reveal that our approach considerably improves LoS-AoA estimations, even under challenging indoor scenarios. Notably, our technique enhanced AP positioning accuracy in 68% of instances, reducing a 2-meter error to 0.6 meters, and in 95% of instances, cutting down a 10-meter error to 2 meters when measured against top benchmarks.

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G New Radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing Simultaneous Localization and Mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM.

Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors within a home environment, yet struggles with precision due to inherent inaccuracies in these anchor positions. In response, we introduce a cutting-edge smartphone-based localization system for IoT devices, leveraging the precise positioning capabilities of smartphones equipped with motion sensors. Our system employs artificial intelligence (AI) to merge channel state information from proximal trajectory points of a single smartphone, significantly enhancing Line of Sight (LoS) Angle of Arrival (AoA) estimation accuracy, particularly under severe multipath conditions. Additionally, we have developed an AI-based anomaly detection (AD) algorithm to further increase the reliability of LoS-AoA estimation. This algorithm improves measurement reliability by analyzing the correlation between the accuracy of reversed feature reconstruction and the LoS-AoA estimation. Utilizing a straightforward least squares algorithm in conjunction with accurate LoS-AoA estimation and smartphone positional data, our system efficiently identifies IoT device locations. Validated through extensive simulations and experimental tests with a receiving antenna array comprising just two patch antenna elements in the horizontal direction, our methodology has been shown to attain decimeter-level localization accuracy in nearly 90% of cases, demonstrating robust performance even in challenging real-world scenarios. Additionally, our proposed AD algorithm trained on Wi-Fi data can be directly applied to ultrawideband, also outperforming the most advanced techniques.

Retrieving range information in three-dimensional (3D) radio imaging is particularly challenging due to the limited communication bandwidth and pilot resources. To address this issue, we consider a reconfigurable intelligent surface (RIS)-aided uplink communication scenario, generating multiple measurements through RIS phase adjustment. This study successfully realizes 3D single-frequency imaging by exploiting the near-field multi-view image correlations deduced from user mobility. We first highlight the significance of considering anisotropy in multi-view image formation by investigating radar cross-section properties and diffraction resolution limits. We then propose a novel model for joint multi-view 3D imaging that incorporates occlusion effects and anisotropic scattering. These factors lead to slow image support variation and smooth coefficient evolution, which are mathematically modeled as Markov processes. Based on this model, we employ the Expectation Maximization-Turbo-Generalized Approximate Message Passing algorithm for joint multi-view single-frequency 3D imaging with limited measurements. Simulation results reveal the superiority of joint multi-view imaging in terms of enhanced imaging ranges, accuracies, and anisotropy characterization compared to single-view imaging. Combining adjacent observations for joint multi-view imaging enables a reduction in the measurement overhead by 80%.

Radio imaging is rapidly gaining prominence in the design of future communication systems, with the potential to utilize reconfigurable intelligent surfaces (RISs) as imaging apertures. Although the sparsity of targets in three-dimensional (3D) space has led most research to adopt compressed sensing (CS)-based imaging algorithms, these often require substantial computational and memory burdens. Drawing inspiration from conventional Fourier transform (FT)-based imaging methods, our research seeks to accelerate radio imaging in RIS-aided communication systems. To begin, we introduce a two-stage wavenumber domain 3D imaging technique: first, we modify RIS phase shifts to recover the equivalent channel response from the user equipment to the RIS array, subsequently employing traditional FT-based wavenumber domain methods to produce target images. We also determine the diffraction resolution limits of the system through k-space analysis, taking into account factors including system bandwidth, transmission direction, operating frequency, and the angle subtended by the RIS. Addressing the challenge of limited pilots in communication systems, we unveil an innovative algorithm that merges the strengths of both FT- and CS-based techniques by substituting the expansive sensing matrix with FT-based operators. Our simulation outcomes confirm that our proposed FT-based methods achieve high-quality images while demanding few time, memory, and communication resources.

Channel reciprocity has been commonly assumed in time division duplex (TDD) massive multiple-input multiple-output (MIMO) communications, when acquiring downlink channel state information (CSI) at the base station through the uplink channel estimation. However, such channel reciprocity suffers from severe impairments when low-resolution analog-to-digital converters (ADCs) are introduced to reduce hardware cost and power consumption in practical systems. To compensate for such impairments, in this paper, we propose an efficient calibration state diagnosis scheme built upon compressive sensing techniques, leveraging the sparsity property of the calibration operations at the BS antennas under an additive quantization noise model. Compared with the traditional pilot-based approaches, our proposed scheme achieves comparable accuracy performance with 1-2 quantization bits and hence significantly reduces pilot overhead therein.

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G new radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28-GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160-MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve submeter-level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems.

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the significant overhead required by the mmWave beam management when implementing SLAM with angle estimates. This study proposes a joint beam management and SLAM design that utilizes the strong coupling between the radio map and channel multipath for simultaneous beam management, localization, and mapping. In this approach, we first propose a hierarchical sweeping and sensing service design. The path angles are estimated in the hierarchical sweeping, enabling angle-based SLAM with the aid of an inertial measurement unit (IMU) to realize sensing service. Then, feature-aided tracking is proposed that utilizes prior angle information generated from the radio map and IMU. Finally, a switching module is introduced to enable flexible switching between hierarchical sweeping and feature-aided tracking. Simulations show that the proposed joint design can achieve sub-meter level localization and mapping accuracy (with an error < 0.5 m). Moreover, the beam management overhead can be reduced by approximately 40% in different wireless environments.

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.

Integrated sensing and communication (ISAC) is considered as a promising technique to provide mutually enhanced performance in future millimeter-wave communication systems. However, the non-line-of-sight (NLOS) components are usually treated as interference for radio-based localization in the existing literature, although they are proved to capture certain information about the radio propagation environment. In this study, we focus on the simultaneous estimation of location and velocity for user equipment (UE) as well as locations for scatterers by harnessing NLOS path measurements. Specifically, we integrate LOS and NLOS path measurements into a onestage linear weighted least squares estimator, where NLOS paths contribute to the estimation of scatterers (environment sensing), and also assist the localization of UE. We have also proved that the estimator is capable of localization in terrible situations when all the LOS paths are blocked. Comprehensive simulation results show that the estimator can attain the Cramer-Rao lower bound under small noise levels and outperform the state-of-the-art methods.

Integrating sensing functions into future mobile equipment has become an important trend. Realizing different types of sensing and achieving mutual enhancement under the existing communication hardware architecture is a crucial challenge in realizing the deep integration of sensing and communication. In the 5G New Radio context, active sensing can be performed through uplink beam sweeping on the user equipment (UE) side to observe the surrounding environment. In addition, the UE can perform passive sensing through downlink channel estimation to measure the multipath component (MPC) information. This study is the first to develop a hybrid simultaneous localization and mapping (SLAM) mechanism that combines active and passive sensing, in whichmutual enhancementbetween the two sensing modes is realized in communication systems. Specifically, we first establish a common feature associated with the reflective surface to bridge active and passive sensing, thus enabling information fusion. Based on the common feature, we can attain physical anchor initialization through MPC with the assistance of active sensing. Then, we extend the classic probabilistic data association SLAM mechanism to achieve UE localization and continuously refine the physical anchor and target reflections through the subsequent passive sensing. Numerical results show that the proposed hybrid active and passive sensing-based SLAM mechanism can work successfully in tricky scenarios without any prior information on the floor plan, anchors, or agents. Moreover, the proposed algorithm demonstrates significant performance gains compared with active or passive sensing only mechanisms.

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers’ location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the “double count” and “data reliability” problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation (6G) mobile communication systems have been conducted. Future mobile communication systems are evidently evolving toward a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication, as well as sense, control, and optimize wireless environments. Thus, integrating communication and localization using the highly directional transmission characteristics of millimeter waves (mmWaves) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithms. We also provide an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with an extremely large antenna array and model-based (or model-driven) neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, we outline the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths and antenna arrays and increased densities of base stations enhance the delay and angular resolution. This study considers the joint location and velocity estimation of user equipment (UE) and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results by using neural networks (NNs) for localization. However, the black box NN localization method has limited robustness and accuracy and relies on a prohibitive amount of training data to increase localization accuracy. Thus, we propose a model-based learning network for localization to address these problems. In comparison with the black box NN, we combine NNs with geometric models. Specifically, we first develop an unbiased weighted least squares (WLS) estimator by utilizing hybrid delay and angular measurements, which determine the location and velocity of the UE in only one estimator, and can obtain the location and velocity of scatterers further. The proposed estimator can achieve the Cramér-Rao lower bound under small measurement noise and outperforms other state-of-the-art methods. Second, we establish a NN-assisted localization method called NN-WLS by replacing the linear approximations in the proposed WLS localization model with NNs to learn the higher-order error components, thereby enhancing the performance of the estimator, especially in a large noise environment. The solution possesses the powerful learning ability of the NN and the robustness of the proposed geometric model. Moreover, the ensemble learning is applied to improve the localization accuracy further. Comprehensive simulations show that the proposed NN-WLS is superior to the benchmark methods in terms of localization accuracy, robustness, and required time resources.

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increases the computational complexity of the system. Motivated by the inherent advantages of lens antenna arrays in reducing system complexity, we consider communication and localization problems with an extremely large lens antenna array, which we call “ExLens”. Since radiative near-field property emerges in the setting, we derive the closed-form array response of the lens antenna array with spherical wave, which includes the array response obtained on the basis of uniform plane wave as a special case. Our derivation result reveals a window effect for energy focusing property of ExLens, which indicates that ExLens has great potential in position sensing and multi-user communication. We also propose an effective method for location and channel parameters estimation, which is able to achieve the localization performance close to the Cramér-Rao lower bound. Finally, we examine the multi-user communication performance of ExLens that serves coexisting near-field and far-field users. Numerical results demonstrate the effectiveness of the proposed channel estimation method and show that ExLens with a minimum mean square error receiver achieves significant spectral efficiency gains and complexity-and-cost reductions compared with a uniform linear array.

This paper tackles the problem of channel state information acquisition in mobile frequency- division-duplex large scale antenna systems and proposes a novel low-complexity low overhead method to track time-varying channels. Given the spatial reciprocity between uplink and downlink, the frequency independent parameters are tracked from the uplink, greatly reducing the training and feedback overhead in the downlink. The uplink tracking method consists of two major modules. The first detection module works at the initial time instance to accurately estimate parameters by a comprehensive algorithm. Then, the second tracking module works at the subsequent instances to track the changes by utilizing a low-overhead algorithm as well as the parameters obtained at the previous instance. Especially, a simplified dictionary is further designed to decrease the computational complexity of the tracking module. Numerical results demonstrate that the proposed tracking method can successfully detect the newly occurred and disappeared paths, and accurately trace the changes of the time-varying channel.

5G millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angular resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging problems. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits for the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer- Rao lower bound and attain the desired sub-decimeter level accuracy.

This paper considers the beamspace channel estimation problem in three-dimensional (3D) lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a two-dimensional (2D) natural image; that is, the channel is sparse and the changes between most of adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse noninformative parameter estimator-based cosparse analysis approximate message passing for imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation-maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also introduce a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%.