Millimeter-wave (mmWave) communications are a key enabler of next-generation wireless networks, thanks to their abundant spectrum resources. Cluster-based models offer a compact and physically interpretable abstraction by grouping multipath components (MPCs) with similar properties, but their accuracy and adaptability are often limited by insufficient environmental information. To address this limitations, we propose a predictive channel modeling framework that jointly estimates MPCs clusters and associated parameters by leveraging multimodal environmental data, including light detection and ranging (LiDAR), images, and locations. The framework extracts modality-specific features and employs a hierarchical attention-based fusion architecture to integrate complementary information. Furthermore, a transfer learning strategy is introduced to support efficient model adaptation across different indoor regions. Experimental results demonstrate that multimodal fusion significantly outperforms single-modality baselines, which reduces a relative root mean squared error (RMSE) of RMS delay spread (DS) prediction by over 50% and improves cluster classification accuracy by around 6%. Meanwhile, the multi-task learning brings an additional about 5% gain in cluster prediction. Moreover, in inter-region transfer scenarios, the model achieves over 87% accuracy using only 20% data, highlighting strong data efficiency and adaptability.

In Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) near-field (NF) sensing, target motion induces an antenna-dependent bistatic Doppler variation across the array aperture. Ignoring this spatial Doppler variation leads to a model mismatch that degrades NF localization. In this paper, we propose a low-complexity recursive framework for joint radial/transverse velocity estimation and Doppler-aware localization. Initialized by a constant-Doppler coarse localization, the method alternates between closed-form Least Squares Estimator (LSE)-based velocity estimation and antenna-dependent Doppler-aware localization refinement. Simulation and measurement results demonstrate the effectiveness of the proposed framework against two benchmark methods. Compared with a low-complexity constant-Doppler baseline method, the proposed algorithm improves range, angle, and radial velocity estimation results, while also enabling transverse velocity estimation. In the measurement results, the overall localization error decreases from 0.268 m to 0.064 m. The radial and transverse velocity estimation errors are 0.032 m/s and 0.069 m/s, respectively. Compared with a high-complexity exhaustive four-dimensional (4D) Maximum Likelihood Estimator (MLE), the proposed method achieves comparable velocity estimation results while yielding a more accurate localization result when the 4D MLE has a practical finite search grid.

Abstract Integrated sensing and communication (ISAC) has emerged as a key enabling technology for future 6G networks. Existing implementations encounter fundamental trade-offs: single-band full-duplex systems require complex self-interference cancellation (SIC), time-division approaches compromise continuous sensing, and multi-waveform systems lack hardware reuse, limiting integration efficiency. Time-domain-sampled wideband and multiband prototypes, which could simultaneously improve communication throughput and real-time velocity estimation, remain largely unexplored, despite their potential for practical ISAC deployment. This work presents a wideband dual-band full-duplex ISAC prototype operating in the upper FR3 band with 360 MHz bandwidth per channel. The system operates at 24.25 GHz for bistatic communication and 25.175 GHz for monostatic sensing. Each function uses a dedicated transmit and a receive channel, and the 925 MHz frequency separation provides spectrum-domain isolation between the communication and sensing functions. Experimental validation shows that frequency-separated operation achieves BER = 0 over the tested QPSK-OFDM communication frames, matching the communication-only baseline, while same-frequency dual-function operation results in clear degradation (BER of ~0.05). These results confirm that spectrum-domain resource partitioning can suppress cross-function interference without requiring complex digital cancellation. A unified OFDM baseband architecture enables hardware sharing while allowing independent waveform optimization: Zadoff-Chu sequences for sensing improve the peak-to-sidelobe ratio (PSLR) and integrated-sidelobe-level ratio (ISLR) by 2.49 dB and 4.43 dB, respectively, compared with QPSK-based sensing. Beyond system demonstration, we provide a hardware-grounded analysis of wideband ISAC feasibility. Experimental characterization shows that at least 8192 subcarriers are required for stable ranging due to common phase error scaling, and identifies 360 MHz as the maximum sustainable bandwidth under real-time processing constraints. Real-time implementation achieves continuous velocity tracking at 10 Hz with 0.21 m/s resolution using 1000 OFDM symbols per frame. These results reveal a fundamental processing-spectrum trade-off in full-duplex ISAC design: self-interference mitigation can be shifted from an active computational task with $${\mathcal{O}}(LN)$$ O ( L N ) complexity—where N is the number of OFDM subcarriers and L is the number of taps in the self-interference channel model—to a passive spectrum-allocation decision, at the expense of increased spectrum occupancy and duplicated RF chains. The proposed architecture thus defines a practical operating point for spectrum-abundant deployments and a framework for hardware-constrained wideband ISAC system design.

In sixth-generation (6G) application scenarios like industry 5.0, augmented reality (AR), autonomous transportation, and eHealth, there is a growing demand for Human Activity Recognition (HAR). Meanwhile, with the deployment of millimeter-wave (mmWave) technologies in fifth-generation (5G) cellular communications, higher-resolution sensing becomes feasible. Utilizing mmWave for communication and HAR has garnered attention, necessitating accurate modeling of sensing channels. This paper proposes a mmWave scattering channel model for indoor HAR, which facilitates system design, optimization, and implementation. In the proposed model, we integrate primitive-based human body scattering where the human body is indicated by a set of primitives, and cluster-based environment scattering models, enabling detailed modeling of self-shadowing and double-bounce environment scattering. Additionally, we develop a simulation framework encompassing signal transmission, sensing channels, and processing, allowing adjustment of system parameters. Simulation results indicated by micro-Doppler signatures including multi-link effects show good agreements with measurements, validating the effectiveness of the proposed model. Meanwhile, the time consumption of the proposed simulation workflow for generating micro-Doppler signatures for most human activities is within 10 minutes.

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for 6G networks. Existing implementations encounter significant trade-offs: single-band full-duplex systems require complex self-interference cancellation, time-division approaches compromise continuous sensing, and multi-waveform systems lack hardware reuse, resulting in limited integration. Time-domain–sampled wideband and multiband prototypes, which could simultaneously improve communication throughput and real-time velocity estimation, have not yet been systematically investigated, even though they offer a promising path toward practical ISAC deployments. This work presents a wideband dual-band full-duplex ISAC prototype operating in the upper FR3 band with a 360 MHz bandwidth. Operating at 24.25 GHz (bistatic communication) and 25.175 GHz (monostatic sensing), each with a transmit channel and a receive channel, the system achieves complete interference elimination through 925 MHz frequency separation. Experimental validation demonstrates zero bit-error rate (BER = 0) for QPSK-OFDM communication, matching communication-only baseline performance, while same-frequency operation for dual functions suffers severe degradation (BER = 0.05), confirming that frequency-domain resource partitioning eliminates cross-function interference without complex digital cancellation. Unified OFDM baseband processing enables architectural sharing with independent waveform optimization, providing 2.49 dB peak-to-sidelobe ratio (PSLR) and 4.43 dB integrated-sidelobe-level ratio (ISLR) improvement using Zadoff-Chu sequences for sensing. These improvements, which are from indoor measurements with a human target moving within 0.5 m to 2 m away from the monostatic sensing antennas, enhance the target discrimination that is critical for resolving human motions. Comprehensive analysis of hardware and signal parameters establishes design guidelines: the number of subcarriers should be larger than 8192 to ensure stable ranging, and 360 MHz maximum bandwidth determined through systematic underflow characterization. Real-time implementation achieves continuous velocity tracking providing 10 velocity estimation results per second (10 Hz refresh rate) with 0.2 m/s resolution, using 1000 symbols per frame for velocity estimation of the target. Results establish frequency-separated dual-band architectures as practical solutions for spectrum-abundant deployments.

Wireless signals can sense subtle physiological motion, such as human respiration, but their reliability is often undermined by Fresnel-zone limitations where amplitude or phase information collapses. We show that distributed Cell-Free Massive MIMO (CF-mMIMO) architectures provide a natural remedy, yet naive fusion of heterogeneous measurements leads to a new challenge of blind fusion. Here we present a unified framework that resolves both issues. At the single-AP level, we reveal that respiration induces arc-like trajectories in the IQ plane and introduce Circle Fitting (CF) and principal component analysis (PCA) to unmask Fresnel-zone limitations. At the multi-AP level, we design adaptive fusion strategies, including weighted antenna combining (WAC) and PCA fusion, to align distributed observations efficiently. Simulations and experiments on a 64-antenna testbed show that PCA consistently outperforms conventional approaches at the single-AP level, while PCA-WAC achieves the best trade-off between accuracy and scalability at the multi-AP level. This work establishes a practical foundation for robust, unobtrusive respiration monitoring and advances the role of integrated sensing and communication (ISAC) as a core capability of sixth-generation (6G) networks.

This paper presents sub-band full-duplex (SBFD) as an alternative to in-band full-duplex (IBFD) for enabling simultaneous wireless communication and sensing in cell-free massive MIMO (CF-mMIMO) systems. Unlike IBFD integrated sensing and communication (ISAC) systems that require self-interference cancellation and the decoupling of mutual interference between uplink communication and radar signals, SBFD employs non-overlapping frequency resources for uplink and downlink or radar transmissions within a predefined SBFD timeslot. In the proposed SBFD CF-mMIMO ISAC system, we demonstrate a multi-target position tracking scheme, where range and angle-of-arrival (AoA) measurements are fused with an extended Kalman filter. We further characterize the joint impact of residual self-interference (SI), access point (AP)-to-AP cross-link interference (CLI), uplink communication interference, and AP-user equipment association on the Cramér–Rao lower bounds (CRLBs) of range and AoA estimation. Results indicate that, in the high residual SI/CLI power regime, SBFD experiences less degradation in range and AoA CRLBs compared to IBFD due to its inherent frequency isolation. Specifically, increasing residual SI/CLI power from 30 dBm to 50 dBm increases the CRLBs by 3.01 m2 and 0.09 rad2 for SBFD compared to an increase of 12.94 m2 and 0.34 rad2 for IBFD at a residual UL communication interference of −10 dBm. Further, performance analysis reveals a performance dependence on power allocation and the ratio of radar to communication sub-band allocation.

Respiration monitoring via radio signals enables contactless health sensing but suffers from interference caused by nearby motion. We propose a robust respiration sensing framework using Cell-free Massive MIMO (CF-mMIMO), which leverages spatial macro-diversity for interference resilience. Specifically, we analyze respiration sensing in single-antenna channels using Power Spectral Density (PSD) to reveal the impact of interference on the breathing channel’s movement spectrum. Based on this, we introduce a new metric, Sensing-Signal-to-Interference Ratio (SSIR), to evaluate local channel quality without requiring ground truth. Then, we design a Weighted Antenna Combining (WAC) method to prioritize reliable sensing links and suppress distortion. Experimental validation using a 64-antenna CF-mMIMO testbed with 100 Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers over an 18 MHz bandwidth confirms the framework’s robustness. In the presence of interference, the WAC method achieves a mean waveform correlation of 0.81 with ground truth, significantly outperforming single-antenna (0.52), averaging-based methods (0.53), and existing Wi-Fi approaches. Finally, we analyze the impact of time, frequency, and spatial resource allocation on both communication and sensing performance. Results show that increasing bandwidth and antenna count benefits both communication and sensing. With a sufficient number of antennas, respiration sensing remains accurate even with long coherence times (1 second) and narrow bandwidths (3 subcarriers), enabling its integration into communication systems with negligible overhead, making it practically “for free”. This makes CF-mMIMO a promising architecture for robust and scalable Integrated Sensing and Communication (ISAC) health monitoring.

Integrated sensing and communication (ISAC) is a key enabler for future radio networks. This paper presents a sub-band full-duplex (SBFD) ISAC system that assigns non-overlapping OFDM subbands to sensing and communication, enabling simultaneous operation with minimal interference. A distributed testbed with three SIMO nodes is implemented using USRP X410 devices operating at 6.8 GHz with 20 MHz bandwidth per channel. A total of 2048 OFDM subcarriers are partitioned into three subbands: two for sensing using Zadoff-Chu sequences and one for communication using QPSK. Each USRP transmits one subband while receiving signals across all three, forming a 1 x 3 SIMO node. Time synchronization is achieved through host-server coordination without external clock distribution. Indoor measurements, validated against MOCAP ground truth, confirm the feasibility of the SBFD ISAC system. The results demonstrate monostatic sensing with a velocity resolution of 0.145 m/s, and communication under NLoS conditions with a BER of 3.63e-3. Compared with a multiband benchmark requiring three times more spectrum, the SBFD configuration achieves comparable velocity estimation accuracy while conserving resources. The sensing and communication performance trade-off is determined by subcarrier allocation strategy rather than mutual interference.

This letter presents a measurement and Cramer-Rao lower bound (CRLB) characterization-based performance analysis of a distributed sub-band full-duplex (SBFD) integrated sensing and communication (ISAC) system, benchmarked against in-band full-duplex (IBFD), interleaved subcarrier allocation, and multi-band systems. Results highlight that SBFD ISAC enables efficient sensing-communication coexistence, achieving a bit error rate of 3.63 & times; 10(-3 )under non-line-of-sight conditions and a median velocity root mean squared error of 0.298 m/s for indoor human targets compared to 5.4 m/s for IBFD. CRLB analysis shows that IBFD ISAC requires 20.04% more transmit power and sufficient interference cancellation to be competitive.

Accurate channel models are the prerequisite for communication-theoretic investigations as well as system design. Channel modeling generally relies on statistical and deterministic approaches. However, there are still significant limits for the traditional modeling methods in terms of accuracy, generalization ability, and computational complexity. The fundamental reason is that establishing a quantified and accurate mapping between the physical environment and channel characteristics becomes increasingly challenging for modern communication systems. Here, in the context of COST CA20120 Action, we evaluate and discuss the feasibility and implementation of using artificial intelligence (AI) for channel modeling, and explore where the future of this field lies. Firstly, we present a framework of AI-based channel modeling to characterize complex wireless channels. Then, we highlight in detail some major challenges and present the possible solutions: estimating the uncertainty of AI-based channel predictions; integrating prior knowledge of propagation to improve generalization capabilities; and interpretable AI for channel modeling. We present and discuss numerical results to showcase the capabilities of AI-based channel modeling.

Accurate localization in Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) systems depends critically on phase coherence across subcarriers and antennas. However, practical systems suffer from frequency-dependent and (spatial) antenna-dependent phase offsets, degrading localization accuracy. This paper analytically studies the impact of phase incoherence on localization performance under a static User Equipment (UE) and Line-of-Sight (LoS) scenario. We use two complementary tools. First, we derive the Cramér-Rao Lower Bound (CRLB) to quantify the theoretical limits under phase offsets. Then, we develop a Spatial Ambiguity Function (SAF)-based model to characterize ambiguity patterns. Simulation results reveal that spatial phase offsets severely degrade localization performance, while frequency phase offsets have a minor effect in the considered system configuration. To address this, we propose a robust Channel State Information (CSI) calibration framework and validate it using real-world measurements from a practical massive MIMO testbed. The experimental results confirm that the proposed calibration framework significantly improves the localization Root Mean Squared Error (RMSE) from 5 m to 1.2 cm, aligning well with the theoretical predictions.

Integrated Sensing and Communication (ISAC) is a key enabler in 6G networks, where sensing and communication capabilities are designed to complement and enhance each other. One of the main challenges in ISAC lies in resource allocation, which becomes computationally demanding in dynamic environments requiring real-time adaptation. In this paper, we propose a Deep Reinforcement Learning (DRL)-based approach for dynamic beamforming and power allocation in ISAC systems. The DRL agent interacts with the environment and learns optimal strategies through trial and error, guided by predefined rewards. Simulation results show that the DRL-based solution converges within 2000 episodes and achieves up to 80% of the spectral efficiency of a semidefinite relaxation (SDR) benchmark. More importantly, it offers a significant improvement in runtime performance, achieving decision times of around 20 ms compared to 4500 ms for the SDR method. Furthermore, compared with a Deep Q-Network (DQN) benchmark employing discrete beamforming, the proposed approach achieves approximately 30% higher sum-rate with comparable runtime. These results highlight the potential of DRL for enabling real-time, high-performance ISAC in dynamic scenarios.

In-band Full-duplex joint communication and sensing systems require self interference cancellation as well as decoupling of the mutual interference between UL communication signals and radar echoes. We present sub-band full-duplex as an alternative duplexing scheme to achieve simultaneous uplink communication and target parameter estimation in a cell-free massive MIMO system. Sub-band full-duplex allows uplink and downlink transmissions simultaneously on non-overlapping frequency resources via explicitly defined uplink and downlink sub-bands in each timeslot. Thus, we propose a sub-band full-duplex cell-free massive MIMO system with active downlink sensing on downlink sub-bands and uplink communication on uplink sub-band. In the proposed system, the target illumination signal is transmitted on the downlink (radar) sub-band whereas uplink users transmit on the uplink (communication) sub-band. By assuming efficient suppression of inter-sub-band interference between radar and communication sub-bands, uplink communication and radar signals can be efficiently processed without mutual interference. We show that each AP can estimate sensing parameters with high accuracy in SBFD cell-free massive MIMO JCAS systems.

The abundant bandwidth in the mmWave band supports high data rates and low latency communication, making it ideal for delivering realistic and seamless virtual reality experiences. However, a key challenge lies in adapting the mmWave beams to the highly dynamic user movements, which often cause beam misalignment, resulting in signal degradation and potential outages. Additionally, maintaining uninterrupted signal reception during beam re-alignment due to head rotation requires low-overhead and timely beam transitions to prevent signal drops caused by delayed switching. This paper addresses these challenges with a joint solution at both the access point (AP) and head-mounted display (HMD) ends. Specifically, the proposed solution integrates coordinated multi-point networks with dual-beam reception at the HMD to enhance diversity, improve channel gain, and mitigate outages caused by user movement. Evaluation using real HMD movement datasets demonstrates that dual-beam reception within a coordinated multi-AP setup achieves up to a 22.8% improvement in reliability by reducing outage rates compared to single-beam reception. Experimental validation further highlights the effectiveness of combining widely distributed APs with a locally distributed subarray configuration on the HMD, improving angular coverage during head rotations. Furthermore, our predictive beam transition approach anticipates the future beam during user movements, preventing received signal degradation from delayed transitions while reducing overhead by 43.8% compared to exhaustive periodic beam searches.

Integrated sensing and communication (ISAC) has emerged as a key enabler for the next-generation radio network. ISAC systems aim to enable both wireless sensing and wireless data transmission functionality in one integrated system. While existing ISAC prototypes in the literature predominantly focus on single-band full-duplex operations with array antennas and spatially separated sensing targets and communication user equipment (UE) to mitigate signal interference, this paper presents a novel multi-band full-duplex ISAC prototype leveraging a software-defined radio (SDR) USRP X440. In this initial implementation, constrained by host PC performance limitations, the proposed system supports a monostatic sensing link at 24.8 GHz with 150 MHz bandwidth for range estimation alongside a bistatic communication link at 24.2 GHz with 150 MHz bandwidth, each employing distinct orthogonal frequency -division multiplexing (OFD M) waveforms. Experimental results demonstrate successful target detection with a range resolution of 1 m while maintaining robust wireless communication performance with minimal signal interference between sensing and communication functionalities.

The integration of sensing and communication capabilities within a single platform is a significant advantage of sixth-generation (6G) communication systems. Multi-beam technology offers an efficient front-end solution for joint communication and sensing (JCAS) at the base station (BS), enabling simultaneous communication with multiple users and sensing multiple targets through analog beamforming. This work introduces a scenario-based tiling array design methodology for a JCAS BS, employing a tiled planar array (TPA) that emphasizes cost-effectiveness, modularity, and scalability. We adopt a low-complexity channel-matching method to optimize the tiles by leveraging self- and cross-correlations of communication and sensing channels. Key performance metrics for this design include the signal-to-interference-plus-noise ratio (SINR) for both communication and sensing tasks. Numerical results indicate that the optimum design of TPAs for JCAS necessitates a proper knowledge of the scenario and environment in which the apertures will be employed. In conflicting scenarios, such as communication operates in non-line-of-sight (NLOS) conditions while sensing relies on line-of-sight (LOS), the scatterers that enable communication also appear as clutter to the sensing function. For example, if NLoS communication clusters fully obstruct the radar targets, the JCAS system can suffer up to a 10 dB drop in sensing SINR; however, these clusters can benefit communication, when the scatterers are positioned in regions of higher aperture gain.

The detailed mechanisms of Ni-catalyzed reductive arylalkylation of unactivated alkenes with aryl bromides to synthesize benzene-fused 5-exo and 6-endo cyclic compounds were systematically investigated by DFT calculations. Our finding reveals that, under the catalysis of a Ni/biOx system with Zn as a reductant, bromobenzene containing a terminal olefin unit preferentially undergoes traditional Heck cyclization and cross-coupling reactions, favoring the formation of 5-exo cyclization products. In contrast, when Zn is absent, NiIII-alkyl species play a pivotal role, facilitating a rare 1,2-aryl migration followed by H-atom abstration, which selectively yields 6-endo cyclization products.

In this paper, we consider a full-duplex (FD) Inte-grated Sensing and Communication (ISAC) system, in which the base station (BS) performs downlink and uplink communications with multiple users while simultaneously sensing multiple targets. In the scope of this work, we assume a narrowband and static scenario, aiming to focus on the beamforming and power allocation strategies. We propose a joint beamforming strategy for designing transmit and receive beamformer vectors at the BS. The optimization problem aims to maximize the communication sum-rate, which is critical for ensuring high-quality service to users, while also maintaining accurate sensing performance for detection tasks and adhering to maximum power constraints for efficient resource usage. The optimal receive beamformers are first derived using a closed-form Generalized Rayleigh Quotient (GRQ) solution, reducing the variables to be optimized. Then, the remaining problem is solved using floating-point Genetic Algorithms (GA). The numerical results show that the proposed GA-based solution demonstrates up to a 98% enhancement in sum-rate compared to a baseline half-duplex ISAC system and provides better performance than a benchmark algorithm from the literature. Additionally, it offers insights into sensing performance effects on beam patterns as well as communication-sensing trade-offs in multi-target scenarios.