Next-generation wireless systems aim to enable on-demand connectivity through dynamic spectrum utilization. Motivated by this vision, this paper investigates the propagation characteristics and MIMO performance of the upper mid-band, spanning approximately 7-24 GHz and unofficially referred to as FR3. Using site-specific ray-tracing (RT) simulations based on the Sionna framework, we analyze indoor and outdoor environments at representative frequencies across FR1, FR3, and FR2, including 3.5, 7, 10, 14, 20, 24, and 28 GHz, under both single-antenna and multi-antenna configurations. The results show that FR3 exhibits intermediate propagation behavior between sub-6 GHz and millimeter-wave bands while sustaining effective spatial multiplexing and favorable spectral efficiency. Furthermore, large-array analysis indicates that performance gains in FR3 are closely tied to antenna scaling, highlighting the importance of large-size or large-aperture MIMO architectures for practical deployments.

The time-modulated array is a simple array architecture in which each antenna is connected to an RF switch that serves as a modulator. The phase shift is achieved by digitally controlling the relative delay between the periodic modulating sequences of the antennas. Two factors limit the practical use of this architecture for communication and sensing. First, the switching frequency is high, as it must be a multiple of the sampling frequency. Second, the discrete modulating sequence introduces undesired harmonic replicas of the signal, which are out-of-band interference. This paper proposes the OFDM modulation with an appropriate precoder to facilitate the aliasing of the harmonic components to simultaneously reduce sideband radiation and switching frequency. The transmit signal has a repeated block structure in the frequency domain to facilitate coherent combining of the aliased signal blocks. As a result, a factor $A$ reduction in switching frequency is achieved at the cost of a factor $A$ reduction in communication capacity. Doubling $A$ reduces sideband radiation by around 2.9 dB. The feasibility of the proposed method is experimentally validated for wideband signals. Full-wave simulations are performed to validate the beamforming performance based on the experimental results.

This paper presents a comprehensive measurement-based trajectory-aware characterization of low-altitude Air-to-Ground (A2G) channels in a suburban environment. A 64-element Massive Multi-Input Multi-Output (MaMIMO) array was used to capture channels for three trajectories of an Uncrewed Aerial Vehicle (UAV), including two horizontal zig-zag flights at fixed altitudes and one vertical ascent, chosen to emulate AUE operations and to induce controlled azimuth and elevation sweeps for analyzing geometry-dependent propagation dynamics. We examine large-scale power variations and their correlation with geometric features, such as elevation, azimuth, and 3D distance, followed by an analysis of fading behavior through distribution fitting and Rician K-factor estimation. Furthermore, temporal non-stationarity is quantified using the Correlation Matrix Distance (CMD), and angular stationarity spans are utilized to demonstrate how channel characteristics change with the movement of the UAV. We also analyze Spectral Efficiency (SE) in relation to K-factor and Root Mean Square (RMS) delay spread, highlighting their combined influence on link performance. The results show that the elevation angle is the strongest predictor of the received power, with a correlation of more than 0.77 for each trajectory, while the Nakagami model best fits the small-scale fading. The K-factor increases from approximately 5 dB at low altitudes to over 15 dB at higher elevations, indicating stronger LoS dominance. Non-stationarity patterns are highly trajectory- and geometry-dependent, with azimuth most affected in horizontal flights and elevation during vertical flight. These findings offer valuable insights for modeling and improving UAV communication channels in 6G Non-Terrestrial Networks (NTNs).

Beamforming is a highly effective technique for enhancing wireless power transfer efficiency by focusing transmitted power on receiver nodes. To eliminate the need for a local oscillator in each sensor for uplink pilot transmission and per-node channel estimation, we examine three power beamsteering techniques: beamsharing, where nearby targets share a single uplink pilot; beamtracking, which uses a dedicated pilot per target; and beamfilling, which covers the entire device plane. This approach reduces the energy consumption of the nodes and minimizes the number of uplink pilots. We assess the trade-off between array gain and pilot cost in a scenario with multiple power-receiving nodes, deriving analytical expressions for the received power spot profile based on physical distance. Our findings indicate that beamsharing with a collocated array is more effective than a distributed transmitter array, as the former provides a larger power spot size, thereby serving more sensors and reducing the number of required pilots, resulting in increased energy efficiency. Our analysis further shows that beamfilling does not extend the sensor region that can be wirelessly powered by the transmitter. Additionally, we derive the optimal pilot transmission periodicity for beamtracking to a mobile target, demonstrating that less frequent and coarser beamforming yields the highest dynamic wireless power transfer efficiency. This implies that more sensors can be powered without the need for all to transmit a pilot. Finally, based on the derived power spot size and beam profile, an opportunistic strategy is proposed for sensors to decide whether to transmit pilots autonomously.

This paper addresses the problem of dual-technology scheduling in hybrid Internet-of-things (IoT) networks that integrate Optical Wireless Communication (OWC) alongside Radio Frequency (RF). We begin by presenting an optimization formulation that jointly considers throughput maximization and delivery-based Age of Information (AoI) minimization between access points and IoT nodes under energy and link availability constraints. However, given the intractability of solving such NP-hard problems at scale and the impractical assumption of full channel observability, we propose the Dual-Graph Embedding with Transformer (DGET) framework, a supervised multi-task learning architecture combining a two-stage Graph Neural Networks (GNNs) with a Transformer-based encoder. The first stage employs a transductive GNN that encodes the known graph topology and initial node and link states (e.g., energy levels, available links, and queued transmissions). The second stage introduces an inductive GNN for temporal refinement, which learns to generalize these embeddings to the evolved states of the same network, capturing changes in energy and queue dynamics over time, by aligning them with ground-truth scheduling decisions through a consistency loss. These enriched embeddings are then processed by a classifier for the communication links with a Transformer encoder that captures cross-link dependencies through multi-head self-attention via classification loss. Simulation results show that hybrid RF-OWC networks outperform standalone RF systems by handling higher traffic loads more efficiently and reducing AoI by up to 20%, all while maintaining comparable energy consumption. The proposed DGET framework, compared to traditional optimization-based methods, achieves near-optimal scheduling with over 90% classification accuracy, reduces computational complexity, and demonstrates higher robustness under partial channel observability.

Long-range (LoRa) is a widely adopted physical layer technology for low-power wide-area Internet of Things (IoT) networks, yet its performance under concurrent LoRa transmissions is not fully understood. Most existing analytical models focus on payload interference and overlook the preamble, which is essential for LoRa nodes synchronization, as a potential contributor to interference effects. In this article, we analyze the LoRa preamble as a source of interference by applying existing symbol error rate (SER) models to the specific case of preamble transmissions. Building on this analysis, we derive a preamble-aware frame error rate (FER) model that jointly accounts for preamble-based detection and payload symbol decoding. Interestingly, our analysis demonstrates that preamble interference has a greater impact than payload interference, a result validated through controlled experiments conducted using commercial LoRa transceivers. This asymmetry indicates that, in time-slotted LoRa networks, interference can be mitigated by controlling transmitter-receiver timing to avoid preamble-preamble overlap, leading to the experimentally validated concept of Pair Synchronization.

Research on low-power Internet of Things (IoT) systems has gained significant momentum within the broader context of green and sustainable IoT. In this setting, batteryless IoT has emerged as a promising solution to reduce maintenance costs and environmental impact by eliminating the need for battery replacements. As large-scale IoT deployments for monitoring and sensing applications continue to expand, important challenges arise in the design and operation of sustainable batteryless IoT networks, including long-term reliability, performance evaluation, and the analysis of energy-harvesting behavior under real-world conditions. To help address these challenges, this paper presents a comprehensive dataset capturing the behavior of batteryless IoT devices deployed in an indoor environmental sensing network. The dataset comprises 100 days of measurements collected from sensors installed throughout an office building and includes data from two types of IoT devices: (1) plugged-in sensors with continuous power supply and (2) batteryless sensors powered exclusively by energy harvested from indoor light. This dualdevice deployment enables direct comparison of sensing performance, reliability, and energy dynamics between powered and energy-harvesting systems. The final dataset contains over three million samples collected from 28 sensors (14 plugged-in and 14 batteryless) deployed across approximately 300 m2 of office space spanning nine rooms. The dataset provides a valuable resource for the systematic investigation of batteryless IoT systems, enabling rigorous analysis of deployment strategies, spatial-temporal sensing dynamics, energy-harvesting behaviors, system performance, and long-term sustainability considerations in next-generation self-powered IoT sensing networks.

The demand for high-precision indoor localization has grown significantly with the rise of smart environments, industrial automation, and location-aware applications. While massive Multiple-Input and Multiple-Output (MIMO) systems enable millimeter-level accuracy by leveraging rich Channel State Information (CSI), most existing solutions are optimized for static environments, where users or devices remain fixed during data collection and inference. Real-world applications, however, often require real-time localization in changing environments, where rapid movement, unpredictable blockages, and dynamic channel conditions pose significant challenges. To address these challenges, we introduce two data augmentation techniques designed to resemble blocked antennas, enhancing the generalizability of localization models to dynamic scenarios. Additionally, we enhance an existing Deep Learning (DL) model by incorporating attention modules, improving its ability to focus on relevant channel features and antennas. We train our model on data from a static scenario, augmented with the proposed techniques, and evaluate it on a dataset collected in changing scenarios. We investigate the performance enhancements achieved by the data augmentation techniques and the Attention modules, and observe a localization accuracy improvement from a mean error of 286 mm, when trained without Attention and without data augmentations, to 66 mm, when trained with Attention and data augmentation. This shows that high localization accuracy can be maintained in changing environments, even without training data from those scenarios.

Integrated sensing and communication (ISAC) relies on monostatic sensing (MS) and bistatic positioning (BP) to enable comprehensive environmental awareness and user localization. However, existing frameworks predominantly assume static geometries and optimize these modalities independently, neglecting user mobility and sequential information sharing. In this paper, we propose a velocity-aware sequential beamforming framework that dynamically couples MS and BP in time. We derive the Cramer-Rao bounds (CRBs) in the position domain to formulate a non-convex resource allocation problem. Instead of relying on static weighted-sum tradeoffs, we introduce a sequential Bayesian optimization strategy where MS is executed first to construct a reliable structural prior on the UE and passive targets (PTs). This covariance prior is subsequently passed to the UE to regularize the BP estimation stage. We demonstrate that optimizing a single shared beamformer globally across both phases yields superior synergistic gains compared to a two-stage greedy approach. Simulation results validate that the shared sequential design efficiently balances limited symbol resources, achieving centimeter-level positioning accuracy for both the UE and PTs, robust velocity estimation, and a significantly reduced computational runtime.

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.

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 a new transparent amplifying intelligent surface (TAIS) architecture for improving multi-user uplink performance in indoor-to-outdoor communications. Unlike passive reconfigurable intelligent surfaces (RIS) that primarily manipulate phase shifts, TAIS operates as an amplifier-based transmissive intelligent surface. It possesses the unique ability to refract and amplify signals from all users. Utilizing indium tin oxide coating and cutting-edge printing techniques, TAIS can be fabricated on windows without causing any visible alterations. This paper focuses on leveraging the TAIS to enhance the uplink spectral efficiency (SE) of multiple users in indoor-to-outdoor communications. Our analysis builds upon key assumptions, including the availability of perfect channel state information (CSI) and the use of a third-order memoryless polynomial model for tractable nonlinear PA characterization. The Bussgang decomposition is applied for nonlinear performance analysis, which remains approximately accurate for practical non-Gaussian signals. By collaboratively optimizing the refraction coefficient matrix of the TAIS and the combiner of the base station, the multi-user uplink SE maximization is achieved with consideration of the nonlinearity in the amplification process of TAIS. An efficient alternating optimization framework is developed to solve the non-convex problem approximately. Another important aspect is that we develop a zero-forcing-based successive convex approximation (ZF-SCA) algorithm to solve the problem with lower computational complexity. By employing the zero-forcing combiner at the BS, the problem is reduced to the refraction coefficient optimization at TAIS, using SCA techniques. Simulations demonstrate that the proposed TAIS system can significantly enhance SE by up to 39.1%, as compared to its alternative methods.

Cell-free massive multiple-input multiple-output (CF mMIMO) networks, in which multiple antennas simultaneously serve multiple user equipments (UEs), offer significant spectral efficiency (SE) gains. However, their energy efficiency (EE) performance still requires further investigation. Traditional approaches to radio resource allocation in CF mMIMO systems focus on solving the two-dimensional UE-antenna precoding problem. In this work, we propose a novel resource allocation framework that addresses the four-dimensional UE-antennafrequency-time precoding allocation for EE maximization. Considering the frequency-domain fast fading variations and timedomain traffic dynamics, we develop algorithms for EE maximization. Our heuristic delayed scheduling algorithm enhances EE by up to 10% compared to the algorithm designed for sum SE maximization. Furthermore, we demonstrate that EE performance is highly sensitive to system load, achieving a 7.4% higher EE at 69% system load compared to full load under the simulation settings. Finally, we analyze the impact of UE load on IP packet delay, establishing a relationship between the maximum UE load and packet delay budget.

Indoor localization using Distributed Multiple-Input Multiple-Output (D-MIMO) and machine learning (ML) achieves sub-centimeter accuracy but faces midhaul capacity bottlenecks when transmitting raw Channel State Information (CSI) in Open Radio Access Networks (O-RAN) architectures. To address this, we propose a lightweight, distributed ML framework that shifts initial processing to the network edge. By deploying localized models as dApps on Distributed Units (DUs), each requiring just 1.39 MB of memory and 1.96 MFLOPs, the system performs CSI feature extraction and reduction on the edge. The reduced low-dimensional features are transmitted to the Central Unit (CU), where another dApp is deployed for location estimation. Evaluated on a high-density dataset, this framework reduces midhaul traffic by 100x while maintaining an average error of 8.5 mm, even with half the deployed Radio Units (RUs), providing a scalable blueprint for practical D-MIMO localization.

Integrated localization and communication systems aim to reuse communication waveforms for simultaneous data transmission and localization, but delay resolution is fundamentally limited by the available bandwidth. In practice, large contiguous bandwidths are difficult to obtain due to hardware constraints and spectrum fragmentation. Aggregating non-contiguous narrow bands can increase the effective frequency span, but a non-contiguous frequency layout introduces challenges such as elevated sidelobes and ambiguity in delay estimation. This paper introduces a point-spread-function (PSF)-centric framework for dual-band OFDM delay estimation. We model the observed delay profile as the convolution of the true target response with a PSF determined by the dual-band subcarrier selection pattern, explicitly linking band configuration to resolution and ambiguity. To suppress PSF-induced artifacts, we adapt the RELAX algorithm for dual-band multi-target delay estimation. Simulations demonstrate improved robustness and accuracy in dual-band scenarios, supporting ILC under fragmented spectrum.

Future wireless systems are expanding toward multi-gigahertz (GHz) bandwidths and sub-terahertz (THz) frequencies. Conventional solutions struggle with high sampling rates, strong nonlinearities, and the diminishing efficiency gains of analog circuits in complementary metal-oxide-semiconductor (CMOS) technologies. Wideband radio-frequency (RF) architectures require a new signal representation and processing paradigm to address these challenges, enabling energy-efficient wideband access and linearization. The Walsh sequency domain offers such an opportunity: its orthogonal basis enables highly parallel and energyefficient wideband operations, reducing RF signal-processing complexity while remaining fully compatible with CMOS technologies. Operating directly in the Walsh domain allows compact implementations of RF conversion, channelization, and nonlinear compensation. These capabilities have been experimentally demonstrated through proof-of-concept integrated circuits in CMOS fully depleted silicon-on-insulator (FDSOI) technologies, including GHz-range RF conversion, digital pre-distortion (DPD), and channel-aggregation techniques. Furthermore, a Walshnative end-to-end wireless autoencoder shows improved robustness to amplifier nonlinearities while benefiting from reduced sampling requirements. Walsh-based RF processing opens a new design space for multi-GHz bandwidth, energy-efficient, and hardware–algorithm co-design in next-generation artificial intelligence (AI)-assisted communication systems.

Integrated Sensing and Communications (ISAC) is emerging as a key enabler for 6G networks, with signaling design at the core of its evolution. This paper reviews the paradigm shift of ISAC signaling designs from pilot-aided sensing to data payload-based approaches, with a particular focus on how these techniques can be realized within existing 5G New Radio (NR) structures. We commence with the reuse of pilots and reference signals that exploit existing 5G NR structures for sensing. Then, we extend to more advanced approaches that integrate the data payload through novel constellation shaping, modulation bases, and pulse shaping filters. We highlight the opportunities and tradeoffs that arise when extending sensing from sparse pilot and reference signal resources to the full communication frame, emphasizing how constellation properties and modulation choices directly determine sensing performance. To illustrate practical feasibility, a case study on sensing-assisted NR Vehicle-to-Infrastructure (V2I) networks demonstrates how exploiting both reference signals and payload echoes can reduce signaling overhead and enable proactive beam management and handover.

We present an approach for spatially-consistent semi-deterministic Air-to-Ground (A2G) channel modeling in Unmanned Aerial Vehicle-assisted networks. We use efficient 3D building shadow projections to determine Line-of-Sight (LOS) regions, enabling fast generation of LOS maps. By integrating LOS-aware deterministic path loss with stochastic shadow fading, the approach produces spatially consistent A2G radio maps suitable for environment- and mobility-aware channel evaluation and performance prediction. Simulation results in ITU-compliant Manhattan grid environments demonstrate the model's ability to reflect key urban propagation characteristics, such as LOS blockage patterns and outage behavior. The proposed approach provides an efficient alternative to ray tracing or fully stochastic models, with particular relevance for user mobility, link planning, and radio map generation in 6G non-terrestrial networks.

This paper investigates how end-to-end (E2E) channel autoencoders (AEs) can achieve energy-efficient wideband communications by leveraging Walsh-Hadamard (WH) interleaved converters. WH interleaving enables high sampling rate analog-digital conversion with reduced power consumption using an analog WH transformation. We demonstrate that E2E-trained neural coded modulation can transparently adapt to the WH-transceiver hardware without requiring algorithmic redesign. Focusing on the short block length regime, we train WH-domain AEs and benchmark them against standard neural and conventional baselines, including 5G Polar codes. We quantify the system-level energy tradeoffs among baseband compute, channel signal-to-noise ratio (SNR), and analog converter power. Our analysis shows that the proposed WH-AE system can approach conventional Polar code SNR performance within 0.14dB while consuming comparable or lower system power. Compared to the best neural baseline, WH-AE achieves, on average, 29% higher energy efficiency (in bit/J) for the same reliability. These findings establish WH-domain learning as a viable path to energy-efficient, high-throughput wideband communications by explicitly balancing compute complexity, SNR, and analog power consumption.