Spectrum Anomaly Detection in OFDMA Systems: Simulation Framework and Benchmark Dataset

Wireless connectivity underpins modern society and industry, enabling critical applications such as 5G ultra-reliable low-latency communication (URLLC) for industrial automation. However, the openness of the wireless medium exposes it to spectrum anomalies, including unintentional interference and malicious jamming, which threaten communication and sensing functionalities in 5G and emerging 6G networks. Despite its importance, spectrum anomaly detection research is hindered by a lack of publicly available datasets reflecting real-world scenarios. To address this, we present a benchmark dataset for spectrum anomaly detection in orthogonal frequency-division multiplexing access (OFDMA) systems, a core technology for 5G and beyond. The dataset includes spectrograms generated across a distributed network of sensing units, covering five distinct jammer types, from simple noise to advanced pilot-aware attacks. These anomalies are simulated in an industrial factory environment using a versatile open-source framework developed and published as part of this work, enabling extensibility to new scenarios and interference types. We provide baseline evaluations for supervised and unsupervised learning methods, demonstrating the challenges posed by different jammers and highlighting areas for further research. The dataset and framework support reproducible studies and serve as a foundation for advancing spectrum anomaly detection, with applications extending to network digital twins. By bridging the gap in open dataset availability, this work empowers the research community to validate and compare advanced detection methods for resilient next-generation wireless systems.

Impact of Phase Errors on Distributed NTN Beam Focusing

This paper investigates distributed beam focusing for coordinated satellite constellations with phased arrays, motivated by future non-terrestrial network (NTN) systems. A geometric and channel model is developed by incorporating satellite positions, array orientations, antenna directivity, and polarization effects. Under ideal synchronization, the achievable coherent combining gain is analyzed for different constellation geometries, showing that maximum ratio transmission (MRT) enables quadratic scaling of the received power with the number of satellites. The impact of phase errors caused by residual synchronization, timing, mobility, and localization mismatches is then investigated. Closed-form expressions for the average coherent gain are derived for uniformly distributed timing offsets, demonstrating the transition from coherent to non-coherent combining. The results show that synchronization and timing mismatches reduce the coherent combining gain, while geometry dependent effects govern the resulting spatial focusing behavior. Numerical results further show that linear and circular constellations provide different focusing characteristics and spatial separation capabilities. However, MRT-based focusing results in strong sidelobes and limited spatial division capability, motivating the need for joint analog beamforming and digital precoding optimization to improve spatial selectivity and robustness against mobility and localization errors.

Experimental Comparison of Local and Over-the-Air Phase Calibration for MIMO Arrays

Communication performance and channel estimation accuracy in MIMO systems are known to be limited by hardware impairments. Specifically, the presence of phase impairments, such as phase noise, makes real-time coherent transmission a challenging task. While phase impairment compensation is typically performed at the receiver, practical methods for enabling coherent transmission at the transmitter side remain underexplored. Established methods for OTA calibration of MIMO systems face several limitations such as assumptions of phase stationarity and accurate channel knowledge. In this work, a real-time local phase calibration method is experimentally compared with OTA calibration on a fully digital array of USRP X310 software-defined radios. Using RMS cycle-to-cycle jitter as a metric, it is shown that for low and high synchronization signal bandwidths, both approaches effectively eliminate phase drift and whiten the phase noise. Local calibration achieves higher phase stability and is channel-independent, whereas OTA calibration requires no additional hardware but is sensitive to multipath effects and channel-induced impairments. Practical deployment trade-offs are discussed based on the measurement results.

Energy-Efficient Mobile Communications using an Adaptive Gearbox-PHY under Hardware Constraints

Future mobile networks must achieve substantial improvements in energy efficiency to offset the anticipated traffic growth. Despite this requirement, many discussions regarding physical layer design remain primarily focused on peak data rates and spectral efficiency, even though typical network operation is dominated by low-data-rate regimes. To address this mismatch, the Gearbox-PHY was proposed as an energy-efficient physical layer architecture that dynamically switches between modulation schemes and their associated analog front ends in order to adapt to varying operating requirements. This paper quantifies the achievable energy savings by jointly modeling front end power consumption and hardware-aware spectral efficiency to formulate an energy-per-bit minimization problem. To move beyond idealized assumptions, non-ideal hardware effects, including oscillator phase noise and limited quantizer resolution, are incorporated. These impairments simultaneously affect power consumption and achievable spectral efficiency, thereby introducing trade-offs between front end complexity, hardware non-linearities, spectral efficiency, and energy efficiency. Numerical results demonstrate that the Gearbox-PHY enables significant energy savings, particularly at low data rates. Evaluations with spatially distributed users confirm that gains of up to two orders of magnitude persist in a cellular deployment scenario.

Estimating PLL Phase Noise Parameters from Measurements for System-Level Modeling

In current MIMO mobile communication systems, phase noise can significantly impair performance. To allow for compensation of these impairments, accurate phase noise modeling is necessary. Numerical modeling of the phase noise process at a phase-locked loop (PLL) output is established in the literature and commonly represented by an Ornstein-Uhlenbeck (OU) process. The corresponding spectrum can be represented by a multi-pole/zero model. This work presents a least squares (LS) method for estimating the PLL parameters such as oscillator constants or PLL bandwidth from a measured phase noise spectrum. The method is applied on the MAX2870 and MAX2871 PLL chips and parameter estimates such as oscillator constants and PLL bandwidths are provided. The resulting parameter set enables both time- and frequency-domain numerical simulations.

Proof of Concept: Local TX Real-Time Phase Calibration in MIMO Systems

Channel measurements in MIMO systems hinge on precise synchronization. While methods for time and frequency synchronization are well established, maintaining real-time phase coherence remains an open requirement for many MIMO systems. Phase coherence in MIMO systems is crucial for beamforming in digital arrays and enables precise parameter estimates such as Angle-of-Arrival/Departure. This work presents and validates a simple local real-time phase calibration method for a digital array. We compare two different approaches, instantaneous and smoothed calibration, to determine the optimal interval between synchronization procedures. To quantitatively assess calibration performance, we use two metrics: the average beamforming power loss and the RMS cycle-to-cycle jitter. Our results indicate that both approaches for phase calibration are effective and yield RMS of jitter in the 2.1 ps to 124 fs range for different SDR models. This level of precision enables coherent transmission on commonly available SDR platforms, allowing investigation on advanced MIMO techniques and transmit beamforming in practical testbeds.

Cramer-Rao Bound for Angle of Arrival Estimates in True-Time-Delay Systems

In the context of joint communication and sensing JC&S, the challenge of obtaining accurate parameter estimates is of interest. Parameter estimates, such as the AoA can be utilized for solving the initial access problem, interference mitigation, localization of users or monitoring of the environment and synchronization of MIMO systems. Recently, TTD systems have gained attention for fast beam training during initial access and mitigation of beam squinting. This work derives the CRB for angle estimates in typical TTD systems. Properties of the CRB and the Fisher information are investigated and numerically evaluated. Finally, methods for angle estimation such as ML and established estimators are utilized to solve the angle estimation problem using a uniform linear array.

A Practical Analysis: Understanding Phase Noise Modelling in Time and Frequency Domain for Phase-Locked Loops

In MIMO systems, the presence of phase noise is a significant factor that can degrade performance. For MIMO testbeds build from SDR devices, phase noise cannot be ignored, particular in applications that require phase synchronization. This is especially relevant in MIMO systems that employ digital beamforming, where precise phase alignment is crucial. Accordingly, accurate phase noise modelling of SDR devices is essential. However, the information provided in data sheets for different SDR models varies widely and is often insufficient for comprehensive characterization of their phase noise performance. While numerical simulations of PLL phase noise behavior are documented in the literature, there is a lack of extensive measurements supported by appropriate system modelling. In this work, we present a practical phase noise modeling methodology applied to an SDR from the USRP X310 series. Based on measurement data, we derive estimates of key PLL performance indicators such as cycle-to-cycle jitter, oscillator constants, and PLL bandwidth. Furthermore, we propose a parametric model for the phase noise PSD of the PLL circuit and provide corresponding parameter estimates. This model can be used for further investigation into the impact of phase noise on MIMO system performance implemented by similar SDR devices.

Wireless Channel Prediction via Gaussian Mixture Models

In this work, we utilize a Gaussian mixture model (GMM) to capture the underlying probability density function (PDF) of the channel trajectories of moving mobile terminals (MTs) within the coverage area of a base station (BS) in an offline phase. We propose to leverage the same GMM for channel prediction in the online phase. Our proposed approach does not require signal-to-noise ratio (SNR)-specific training and allows for parallelization. Numerical simulations for both synthetic and measured channel data demonstrate the effectiveness of our proposed GMM-based channel predictor compared to state-ofthe-art channel prediction methods.

A Low-Complexity Machine Learning Design for mmWave Beam Prediction

The 3rd Generation Partnership Project (3GPP) is currently studying machine learning (ML) for the fifth generation (5G)-Advanced New Radio (NR) air interface, where spatial and temporal-domain beam prediction are important use cases. With this background, this letter presents a low-complexity ML design that expedites the spatial-domain beam prediction to reduce the power consumption and the reference signaling overhead, which are currently imperative for frequent beam measurements. Complexity analysis and evaluation results showcase that the proposed model achieves state-of-the-art accuracy with lower computational complexity, resulting in reduced power consumption and faster beam prediction. Furthermore, important observations on the generalization of the proposed model are presented in this letter.