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.

Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless Networks

The increasing relevance of resilience in wireless connectivity for Industry 4.0 stems from the growing complexity and interconnectivity of industrial systems, where a single point of failure can disrupt the entire network, leading to significant downtime and productivity losses. It is thus essential to constantly monitor the network and identify any anomaly such as a jammer. Hereby, technologies envisioned to be integrated in 6G, in particular joint communications and sensing (JCAS) and accurate indoor positioning of transmitters, open up the possibility to build a digital twin (DT) of the radio environment. This paper proposes a new approach for anomaly detection in wireless networks enabled by such a DT which allows to integrate contextual information on the network in the anomaly detection procedure. The basic approach is thereby to compare expected received signal strengths (RSSs) from the DT with measurements done by distributed sensing units (SUs). Employing simulations, different algorithms are compared regarding their ability to infer from the comparison on the presence or absence of an anomaly, particular a jammer. Overall, the feasibility of anomaly detection using the proposed approach is demonstrated which integrates in the ongoing research on employing DTs for comprehensive monitoring of wireless networks.

Waveforms for sub-THz 6G: Design Guidelines

The projected sub-THz (100 - 300 GHz) part of the upcoming 6G standard will require a careful design of the waveform and choice of slot structure. Not only that the design of the physical layer for 6G will be driven by ambitious system performance requirements, but also hardware limitations, specific to sub-THz frequencies, pose a fundamental design constraint for the waveform. In this contribution, general guidelines for the waveform design are given, together with a non-exhaustive list of exemplary waveforms that can be used to meet the design requirements.

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

As cellular networks evolve towards the 6th Generation (6G), Machine Learning (ML) is seen as a key enabling technology to improve the capabilities of the network. ML provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific Quality of Service (QoS) requirement. With predictive Quality of Service (pQoS), a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or HD mapping applications. We discuss the entire ML workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable ML models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on ML technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple ML models within diverse scenarios. We also use Explainable AI (XAI) to show that ML can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.