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.

The rapid expansion of battery-less Internet of Things (IoT) deployments demands energy delivery strategies that minimise maintenance while ensuring reliable operation. This paper studies the placement optimisation of photovoltaic (PV)-equipped donor IoT nodes that harvest dedicated/ambient light power and redistribute surplus energy via radiofrequency (RF) transmissions, together with targeted ceiling-mounted RF transmitters.We formulate an illuminance-informed donor placement problem across heterogeneous light zones and extend it to a co-design framework that integrates dedicated RF support with donor placement. To address these problems, we develop a greedy illuminance-aware algorithm and a joint greedy–genetic algorithm for co-design, supported by a complexity analysis. Numerical results in a 5×5 m2 indoor simulation testbed demonstrate that the co-design approach reduces donor counts by up to 33% while maintaining the required energy thresholds. These results indicate that deployment-aware optimisation can effectively sustain dense IoT networks under practical illumination and RF constraints.

This work investigates the spatially wideband (SWB) antenna array factor (AF) of uniform linear arrays. First, the SWB AF approximation is derived for narrowband (NB) signals and a large aperture with element spacing satisfying the Nyquist criterion. The derivation accounts for different spatial and spectral windows. Next, an approximation of the SWB AF for a wideband (WB) signal is developed under uniform spatial and spectral weighting. The analysis shows that for fully populated arrays, increasing the bandwidth effectively suppresses the AF sidelobes. Finally, a universal SWB AF approximation is introduced, which is based on recognizing the SWB AF as a spatially variant convolution. In this formulation, the SWB AF is expressed as a convolution of the spatially narrowband (SNB) AF and the SWB kernel, providing insight into how the bandwidth and the spectral weighting affect the resulting SWB AF. The proposed approximation is shown to be accurate for a wide range of bandwidths and element spacings, including sparse arrays. In particular, for sparse arrays, the bandwidth enables suppression of grating-lobe amplitudes by spreading their energy over a wider angular range. An approximation of the grating lobe envelope as a function of the bandwidth-aperture product is provided.

Due to the continuous increase in communication bandwidth and the use of highly efficient yet nonlinear power amplifiers, Digital Predistortion (DPD) algorithms are becoming increasingly complex. In particular, neural network (NN) based DPD approaches using Phase-Normalized NN architectures often incur substantially higher computational costs than widely deployed polynomial-based methods, such as the Memory Polynomial (MP) and Generalized Memory Polynomial (GMP) models. To bridge this gap between research performance and practical implementation, we propose a low-complexity Feature Selection NN DPD architecture. The proposed method employs an offline feature-engineering pipeline based on the Least Absolute Shrinkage and Selection Operator (LASSO) and the Minimum Redundancy Maximum Relevance (MRMR) algorithm to construct a compact and informative input representation. Using measured wideband FR3 power amplifier datasets that are publicly released with this work, we demonstrate up to 30

As the proliferation of devices on the Internet of Things (IoT) continues, efficient and sustainable power solutions are becoming increasingly essential, particularly for battery-less systems operating in dense networks. This work comprehensively defines the concepts in IoT devices and categorizes them based on their power consumption levels. It introduces a practical framework for power redistribution using light energy harvesting (EH) and RF-based wireless power transfer (WPT). A novel energy donation mechanism is proposed that enables energy-abundant nodes equipped with photovoltaic (PV) panels to share excess harvested energy with nearby nodes via RF transmission. Through analytical modeling, link budget analysis, and experimental validation, the study demonstrates the feasibility and effectiveness of the proposed architecture in maintaining reliable operation across densely deployed IoT nodes. To the best of our knowledge, this work presents the first experimentally validated framework that autonomously integrates light-to-RF energy donation, combining indoor light EH, short-range RF redistribution, and node placement optimization within a single deployable system. This approach directly addresses the power sustainability challenge, paving the way toward fully autonomous and battery-less IoT ecosystems.

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.

Intelligent Transportation Systems (ITS) aim to improve traffic efficiency, management, driver comfort, and safety. It comprises various components, including vehicles, sensors, base stations, and road infrastructure. In the near future, ITS will need to support multi-modal transportation schemes, including aerial vehicles. Therefore, ITS must be integrated with Unmanned Aircraft Systems (UAS) and rely on 3-D connectivity provided by Non-Terrestrial Networks (NTNs) to achieve this support. In other words, various Unmanned Aerial Vehicles (UAVs) will become integral parts of future ITS due to their mobility, autonomous operation, and communication/processing capabilities. This article presents our view on next-generation 3-D ITS, its benefits over existing ITS, enabling technologies, and key challenges. As a case study, we developed and demonstrated the need and advantages of having separate models for pedestrians and vehicular users.

This paper compares the sensing performance of a narrowband near-field system across several practical antenna array geometries and SIMO/MISO and MIMO configurations. For identical transmit and receive apertures, MIMO processing is equivalent to squaring the near-field array factor, resulting in improved beamdepth and sidelobe level. Analytical derivations, supported by simulations, show that the MIMO processing improves the maximum near-field sensing range and resolution by approximately a factor of 1.4 compared to a single-aperture system. Using a quadratic approximation of the mainlobe of the array factor, an analytical improvement factor of √(2) is derived, validating the numerical results. Finally, MIMO is shown to improve the poor sidelobe performance observed in the near-field by a factor of two, due to squaring of the array factor.

Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.

This article investigates the range ambiguity function of near-field (NF) systems where bandwidth and NF beamfocusing jointly determine the resolution. First, the general matched filter ambiguity function is derived and the NF array factors of different antenna array geometries are introduced. Next, the NF ambiguity function is approximated as a product of the range-dependent NF array factor and the ambiguity function due to the utilized waveform and bandwidth. An approximation criterion based on the aperture-bandwidth product is formulated, and its accuracy is examined. Finally, the improvements to the ambiguity function offered by the NF beamfocusing, as compared to the far-field case, are presented. The performance gains are evaluated in terms of resolution improvement offered by beamfocusing, peak-to-sidelobe, and integrated-sidelobe-level improvement for a few popular array geometries. The gains offered by the NF regime are shown to be range-dependent and substantial only in close proximity to the array.

Cell-Free Massive MIMO is a promising solution for next-generation radio access networks. Especially the user-centric variant, where users are served by a subset of access points, a cluster, has garnered significant interest. However, managing these clusters in case of user mobility remains a significant issue. Additionally, the practical implementation of such a network remains an open problem. This work introduces a realistic temporal channel model for Cell-Free Massive MIMO. Subsequently, we propose a framework to facilitate handover procedures in a distributed topology considering limited channel knowledge for cluster formation. Next, we develop two clustering and handover strategies: 1) a fixed clustering strategy where the central processing unit computes the ideal cluster when a cluster handover threshold is exceeded and 2) an opportunistic strategy where access points make autonomous decisions based on the local channel. Finally, we establish a significant connection between our findings and O-RAN, an emerging network architecture that offers open interfaces and network-wide control capabilities. Simulation results show that even low-complexity handover mechanisms can significantly improve spectral efficiency under user mobility compared to cellular networks.

This work focuses on channel estimation in extremely large aperture array (ELAA) systems, where near-field propagation and spatial non-stationarity introduce complexities that hinder the effectiveness of traditional estimation techniques. A physics-based hybrid channel model is developed, incorporating non-binary visibility region (VR) masks to simulate diffraction-induced power variations across the antenna array. To address the estimation challenges posed by these channel conditions, a novel algorithm is proposed: Visibility-Region-HMM-Aided Polar-Domain Simultaneous Orthogonal Matching Pursuit (VR-HMM-P-SOMP). The method extends a greedy sparse recovery framework by integrating VR estimation through a hidden Markov model (HMM), using a novel emission formulation and Viterbi decoding. This allows the algorithm to adaptively mask steering vectors and account for spatial non-stationarity at the antenna level. Simulation results demonstrate that the proposed method enhances estimation accuracy compared to existing techniques, particularly in low-SNR and sparse scenarios, while maintaining a low computational complexity. The algorithm presents robustness across a range of design parameters and channel conditions, offering a practical solution for ELAA systems.

Precise channel modeling is crucial for analyzing the potential of autonomous aerial vehicles (AAVs) as aerial base stations (ABS) for communication in complex urban environments. Traditional air-to-ground (A2G) channel models, such as probability of line-of-sight (P-LoS) and path loss (PL) models, typically assume uniform free space between buildings, neglecting the distinction between roads and sidewalks. This work addresses this gap by proposing a geometry-based 3-D PL model for urban AAV communication, explicitly considering both roads and sidewalks. The proposed model extends the city layout defined by International Telecommunication Union parameters to account for the distinct geometries of these two areas, catering to different users, including fast-moving vehicles and slow-paced pedestrians. The model accurately predicts P-LoS and PL variations between roads and sidewalks for ABS operating at various altitudes, distances, inclinations, and azimuth angles. The model's accuracy is validated through a custom-made simulator.

Accurate Probability of Line-of-Sight (PLoS) modeling is important in evaluating the performance of Unmanned Aerial Vehicle (UAV)-based communication systems in urban environments, where real-time communication and low latency are often major requirements. Existing PLoS models often rely on simplified Manhattan grid layouts using International Telecommunication Union (ITU)-defined built-up parameters, which may not reflect the randomness of real cities. Therefore, this paper introduces the Urban LoS Simulator (ULS) to model PLoS for three random city layouts with varying building sizes and shapes constructed using ITU built-up parameters. Based on the ULS simulated data, we obtained the empirical PLoS for four standard urban environments across three different city layouts. Finally, we analyze how well Manhattan grid-based models replicate PLoS results from random and real-world layouts, providing insights into their applicability for time-critical communication systems in urban IoT networks.

This paper presents a novel multi-attribute decision-making approach for managing handovers in Optical Wireless Communication/Radio Frequency Heterogeneous Networks (OWC/RF HetNets). The proposed method systematically evaluates potential Access Points (APs) using multiple criteria, including fixed and variable handover costs, the ratio of the node's data rate demand to the achievable rate from APs, and the ratio of the utilized capacity of APs. Using local parameters, nodes assess these attributes over a finite future horizon and construct a decision matrix. The VIKOR method is then applied to decide whether to initiate a handover to a new AP or remain connected to the current one. Simulation results show that the proposed decentralized scheme effectively manages handovers, leading to improved load balancing and reduced handover latency in OWC/RF HetNets.

Connecting aerial and terrestrial users with a single base station (BS) is increasingly challenging due to the rising number of aerial users like unmanned aerial vehicles (UAVs). Traditional BSs, designed with down-tilted beams, focus mainly on ground users, but massive MIMO (mMIMO) systems can significantly enhance coverage in low-altitude airspace. This paper analyzes how a mMIMO BS serves both aerial and terrestrial users in a 3D spectrum-sharing scheme. Using Semi-orthogonal User Selection (SUS) and random scheduling, we assess the spectral efficiency and performance limits of these systems. Results reveal that mMIMO effectively supports more terrestrial users, influenced by channel characteristics and user scheduling strategies, providing key insights for future 3D aerial-terrestrial networks.

The extensive deployment of edge nodes for the Internet of Things (IoT) raises concerns about electronic waste, especially considering batteries for powering those edge nodes. Energy harvesting presents a promising transition from battery-powered IoT nodes to battery-less ones. This paper presents a hybrid light-RF energy harvesting system utilising a metasurface patch antenna capable of simultaneous light and RF energy harvesting without mutual performance degradation. The meta surface design improves the bandwidth and mitigates the negative effect of the solar panel on the antenna performance, achieving RF transparency. Our design operates across the entire RFID band (0.865-0.928 GHz) with a peak gain of 7 dBi at 0.868 GHz, harvesting 176 mu W of light power at 155 lx and 20 mu W of RF power over the air at 0.85 GHz, offering a sustainable pathway towards battery-less IoT nodes.

A frequency-diverse array (FDA) is an alternative array architecture in which each antenna is preceded by a mixer instead of a phase shifter. The mixers introduce a frequency offset between signals transmitted by each antenna, resulting in a time-varying beam pattern. However, time-dependent beamforming is not desirable for communication or sensing. In this paper, the FDA is combined with orthogonal frequency-division multiplexing (OFDM) modulation. The proposed beamforming method partitions the OFDM symbol transmitted by all antennas into subcarrier blocks, which carry the same data but are precoded differently. The frequency offset between the antennas is equal to the subcarrier block width. Consequently, each antenna transmits a differently precoded subcarrier block at the center frequency, resulting in overlap and coherent summation of the blocks. Proposed architecture enables fully digital beamforming over a single block while requiring only a single digital-to-analog converter. The system's performance and tradeoffs are investigated in the context of joint communication and sensing.

Existing works on Cell-Free Massive MIMO primarily focus on optimising system throughput and energy efficiency under high-traffic scenarios with only a limited focus on variable user demand as required by higher network layers. Additionally, existing works only minimise the transmitted power instead of the consumed power at the power amplifier. This work introduces a penalty-method-based approach to minimise the amplifier's power consumption while scaling much better with network size than current solutions and promoting sparsity in the power allocated to each access point. Furthermore, we demonstrate substantial reductions in power consumption (up to 24 %) by considering the non-linear power consumption.

The analysis of the trade-off between joint communication and sensing critically depends on the validity of the joint channel or environment model. In this demo, we show a set-up that can jointly characterize a monostatic sensing channel and a bistatic communication channel in the same environment. We show the CSI processing pipelines and based on that illustrate the trade-offs in resource allocation for joint communication and sensing. Concretely, we demonstrate the sensing and communication trade-off, when communication resources are allocated using communication-centric waterfilling and the remaining subcarriers can be used for monostatic sensing. This shows the fundamental trade-offs between communication and sensing in a realistic environment. The setup uses a single X410 NI USRP connected to a high-performance cloud server to handle the real-time processing.