Low-Complexity Optimization of Antenna Switching Schemes for Dynamic Channel Sounding

Understanding wireless channels is crucial for the design of wireless systems. For mobile communication, sounders and antenna arrays with short measurement times are required to simultaneously capture the dynamic and spatial channel characteristics. Switched antenna arrays are an attractive option that can overcome the high cost of real arrays and the long measurement times of virtual arrays. Optimization of the switching sequences is then essential to avoid aliasing and increase the accuracy of channel parameter estimates. This paper provides a novel and comprehensive analysis of the design of switching sequences. We first review the conventional spatio-temporal ambiguity function, extend it to dual-polarized antenna arrays, and analyze its prohibitive complexity when designing for ultra-massive antenna arrays. We thus propose a new method that uses the Fisher information matrix to tackle the estimation accuracy. We also propose to minimize the ambiguity by choosing a switching sequence that minimizes side lobes in its Fourier spectrum. In this sense, we divide the sequence design problem into Fourier-based ambiguity reduction and Fisher-based accuracy improvement, and coin the resulting design approach as Fourier-Fisher. Simulations and measurements show that the Fourier-Fisher approach achieves identical performance and significantly lower computational complexity than that of the conventional ambiguity-based approach.

Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization

Radio-based localization in dynamic environments, such as urban and vehicular settings, requires systems that can efficiently adapt to varying signal conditions and environmental changes. Factors such as multipath interference and obstructions introduce different levels of complexity that affect the accuracy of the localization. Although generalized models offer broad applicability, they often struggle to capture the nuances of specific environments, leading to suboptimal performance in real-world deployments. In contrast, specialized models can be tailored to particular conditions, enabling more precise localization by effectively handling domain-specific variations and noise patterns. However, deploying multiple specialized models requires an efficient mechanism to select the most appropriate one for a given scenario. In this work, we develop an adaptive localization framework that combines shallow attention-based models with a router/switching mechanism based on a single-layer perceptron (SLP). This enables seamless transitions between specialized localization models optimized for different conditions, balancing accuracy, computational efficiency, and robustness to environmental variations. We design three low-complex localization models tailored for distinct scenarios, optimized for reduced computational complexity, test time, and model size. The router dynamically selects the most suitable model based on real-time input characteristics. The proposed framework is validated using real-world vehicle localization data collected from a massive MIMO base station (BS), demonstrating its ability to seamlessly adapt to diverse deployment conditions while maintaining high localization accuracy.

Physically Large Apertures for Wireless Power Transfer: Performance and Regulatory Aspects

Wireless power transfer (WPT) is a promising service for the Internet of Things, providing a cost-effective and sustainable solution to deploy so-called energy-neutral devices on a massive scale. The power received at the device side decays rapidly with the distance from a conventional transmit antenna with a physically small aperture. New opportunities arise from the transition from conventional far-field beamforming to near-field beam focusing. We argue that a "physically large" aperture, i.e., large w.r.t. the distance to the receiver, enables a power budget that remains practically independent of distance. Distance-dependent array gain patterns allow focusing the power density maximum precisely at the device location, while reducing the power density near the infrastructure. The physical aperture size is a key resource in enabling efficient yet regulatory-compliant WPT. We use real-world measurements to demonstrate that a regulatory-compliant system operating at sub-10GHz frequencies can increase the power received at the device into the milliwatt range. Our empirical demonstration shows that power-optimal near-field beam focusing inherently exploits multipath propagation, yielding both increased WPT efficiency and improved human exposure safety in real-world scenarios.

Joint Near-Field Sensing and Visibility Region Detection with Extremely Large Aperture Arrays

In this paper, we consider near-field localization and sensing with an extremely large aperture array under partial blockage of array antennas, where spherical wavefront and spatial non-stationarity are accounted for. We propose an Ising model to characterize the clustered sparsity feature of the blockage pattern, develop an algorithm based on alternating optimization for joint channel parameter estimation and visibility region detection, and further estimate the locations of the user and environmental scatterers. The simulation results confirm the effectiveness of the proposed algorithm compared to conventional methods.

A Measurement-Based Spatially Consistent Channel Model for Distributed MIMO in Industrial Environments

Future wireless communication systems are envisioned to support ultra-reliable and low-latency communication (URLLC), which will enable new applications such as compute offloading, wireless real-time control, and reliable monitoring. Distributed multiple-input multiple-output (D-MIMO) is one of the most promising technologies for delivering URLLC. This paper classifies obstruction and derives a channel model from a D-MIMO measurement campaign carried out at a carrier frequency of 3.75 GHz with a bandwidth of 35 MHz using twelve distributed fully coherent dipole antennas in an industrial environment. Channel characteristics are investigated, including statistical measures such as small-scale fading, large-scale fading, delay spread, and transition rates between line-of-sight and obstructed line-of-sight conditions for the different antenna elements, laying the foundations for an accurate channel model for D-MIMO systems in industrial environments. Furthermore, correlations of large-scale fading between antennas, spatial correlation, and tail distributions are included to enable proper evaluations of reliability and rare events. Based on the results, a channel model for D-MIMO in industrial environments is presented together with a recipe for its implementation.

Spatial separation of closely-spaced users in measured distributed massive MIMO channels

Aiming for the sixth generation (6G) wireless communications, distributed massive multiple-input multiple-output (MIMO) systems hold significant potential for spatial multiplexing. In order to evaluate the ability of a distributed massive MIMO system to spatially separate closely spaced users, this paper presents an indoor channel measurement campaign. The measurements are carried out at a carrier frequency of 5.6 GHz with a bandwidth of 400 MHz, employing distributed antenna arrays with a total of 128 elements. Multiple scalar metrics are selected to evaluate spatial separability in line-of-sight, non line-of-sight, and mixed conditions. Firstly, through studying the singular value spread, it is shown that in line-of-sight conditions, better user orthogonality is achieved with a distributed MIMO setup compared to a co-located MIMO array. Furthermore, the dirty-paper coding (DPC) capacity and zero forcing (ZF) precoding sum-rate capacities are investigated across varying numbers of antennas and their topologies. The results show that in all three conditions, the less complex ZF precoder can be applied in distributed massive MIMO systems while still achieving a large fraction of the DPC capacity. Additionally, in line-of-sight conditions, both sum-rate capacities and user fairness benefit from more antennas and a more distributed antenna topology. However, in the given NLoS condition, the improvement in spatial separability through distributed antenna topologies is limited.

Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems

The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.

Achieving Distributed MIMO Performance with Repeater-Assisted Cellular Massive MIMO

5G systems are being deployed all over the world and one key enabler of these systems is massive multiple-input multiple-output (MIMO). This technology has brought large performance gains in terms of serving many users. Despite the possibility to further exploit the spatial domain, there are situations where it is not possible to offer more than a single, or a few, data streams per user and where cell-edge coverage is an issue due to the lack of enough efficient channel scatterers. Looking ahead, distributed MIMO systems, where the antennas are spread over a larger area, are investigated for next generation systems. However, distributed MIMO comes with many practical deployment issues, making it a big challenge to adopt. As another way forward, we envision repeater-assisted cellular massive MIMO, where repeaters are deployed to act as channel scatterers to increase the rank of the channel and provide macro diversity for improved coverage and reliability. After elaborating on the requirements and hardware aspects of repeaters that enable this vision, we demonstrate through simulations the potential of repeater-assisted cellular massive MIMO to achieve distributed MIMO performance. Following this, we discuss open questions and future research directions.

Attention-aided Outdoor Localization in Commercial 5G NR Systems

The integration of high-precision cellular localization and machine learning (ML) is considered a cornerstone technique in future cellular navigation systems, offering unparalleled accuracy and functionality. This study focuses on localization based on uplink channel measurements in a fifth-generation (5G) new radio (NR) system. An attention-aided ML-based single-snapshot localization pipeline is presented, which consists of several cascaded blocks, namely a signal processing block, an attention-aided block, and an uncertainty estimation block. Specifically, the signal processing block generates an impulse response beam matrix for all beams. The attention-aided block trains on the channel impulse responses using an attention-aided network, which captures the correlation between impulse responses for different beams. The uncertainty estimation block predicts the probability density function of the UE position, thereby also indicating the confidence level of the localization result. Two representative uncertainty estimation techniques, the negative log-likelihood and the regression-by-classification techniques, are applied and compared. Furthermore, for dynamic measurements with multiple snapshots available, we combine the proposed pipeline with a Kalman filter to enhance localization accuracy. To evaluate our approach, we extract channel impulse responses for different beams from a commercial base station. The outdoor measurement campaign covers Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and a mix of LoS and NLoS scenarios. The results show that sub-meter localization accuracy can be achieved.

Channel Performance Metrics and Evaluation for XR Head-Mounted Displays with mmWave Arrays

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent HMD rotation, can lead to poor performance. We introduce six channel performance metrics to evaluate the performance of an HMD equipped with mmWave arrays. We analyze the metrics using analytical models, discuss their impact for the application, and apply them to 28 GHz channel sounding data, collected in a conference room using eight HMD patch-antenna arrays, offset by 45 degrees from each other in azimuth. Our findings confirm that a single array performs poorly due to the narrow FoV, and featuring multiple arrays along the HMD's azimuth is required. Namely, the broader FoV stabilizes channel gain during HMD rotation, lessens the attenuation caused by line of sight (LoS) obstruction, and increases the channel's spatial multiplexing capability. In light of our findings, we conclude that it is imperative to either equip the HMD with multiple arrays or, as an alternative approach, incorporate macroscopic diversity by leveraging distributed access point (AP) infrastructure.