Over-the-air Research Articles

The Curious Case of Effective Isotropic Sensitivity: Polarization mismatch in over-the-air testing of wireless user equipment

This article provides the theory behind the two-step over-the-air (OTA) test method widely used in 3rd Generation Partnership Project (3GPP) test specifications and, in particular, the testing and characterization of 5G millimeter-wave user equipment (UE) in compact antenna test ranges (CATRs). Special attention is paid to receiver (Rx) characteristics and the polarization mismatch problem, which is encountered in spherical coverage tests. The received power of a UE with a dual-polarized antenna system is derived, and the output signal-to-noise ratio (SNR) is calculated using the maximal ratio combining (MRC) principle. And finally, flaws in the dominant interpretation of this test method in the development of 5G millimeter-wave standards and their consequences are addressed.

Smart Gateway for Healthcare Networks Based on Beam Steering Technology

To ensure high-reliability communication in healthcare networks, this paper presents a smart gateway system that includes an angle-of-arrival (AOA) estimation and a beam steering function for a small circular antenna array. To form a beam toward healthcare sensors, the proposed antenna estimates the direction of the sensors utilizing the radio-frequency-based interferometric monopulse technique. The fabricated antenna was assessed based on the measurements of complex directivity and the over-the-air (OTA) testing in Rice propagation environments using a two-dimensional fading emulator. The measurement results reveal that the accuracy of the AOA estimation agrees well with that of the analytical data obtained through the Monte Carlo simulation. This antenna is embedded with a beam steering function employing phased array technology, which can form a beam spaced at 45° intervals. The ability of full-azimuth beam steering with regard to the proposed antenna was evaluated by beam propagation experiments using a human phantom in an indoor environment. The received signal of the proposed antenna with beam steering increases more than that of a conventional dipole antenna, confirming that the developed antenna has great potential of achieving high-reliability communication in a healthcare network.

Stable-Sample-Level-Based Digital Predistortion for 2-D Millimeter-Wave Phased Array Steering

This letter proposes a stable-sample-level (SL)-based digital predistortion (DPD) technique for 2-D phased array with fixed-position over-the-air (OTA) feedback (FB) to overcome significant SL fluctuation when the beam is steered. It is achieved by optimizing the phase of the beamformer based on the DPD requirement, which can control the SL at a given value without affecting the main lobe of radiation pattern, relaxing the requirement of FB dynamic range. The radiation pattern and DPD performances are measured on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> phased array at 26 GHz. The measured results show that the proposed method can realize the stable SL with good DPD performance.

Over-the-Air Noise Figure Characterization of mm-Wave Active Integrated Antennas Using a Reverberation Chamber

Active integrated antennas (AIAs) are used in low-noise receivers for their good noise performance compared with their nonintegrated counterparts. However, it is challenging to accurately determine one of these antennas’ most important characteristics: the noise figure (NF). To overcome the dependencies of the existing methods, we show a new method that can estimate the NF of a wireless system over-the-air (OTA), using a reverberation chamber (RC). Using the RC, we could create a consistent and repeatable measurement environment. Since this method characterizes the NF of the total system, it also includes factors such as noise matching. An extensive uncertainty analysis shows that the NF can be determined within only 0.7 dB expanded uncertainty in the frequency range from 21 to 29 GHz.

A D-Band Joint Radar-Communication CMOS Transceiver

A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> -band joint radar-communication complementary metal–oxide–semiconductor (CMOS) transceiver featuring a dual-function mode multiplexer, a power-combining PA with high output power, a current choking high-gain mixer, a two-point modulation (TPM) frequency-modulated continuous-wave (FMCW) digital phase-locked loop (PLL) with a dual-core DCO, and a wideband I/Q local oscillator (LO) generator is implemented in 28-nm CMOS technology. In the radar mode, the RF front end demonstrates 46-GHz bandwidth (BW), and the on-chip PLL/LO generated FMCW chirp achieves a BW of 30 GHz and a slope of 30 GHz/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$50~\mu \text{s}$ </tex-math></inline-formula> . In the communication mode, the transceiver including the analog baseband realizes 20-GHz BW, and the image rejection ratio (IRR) is better than 40 dB. The measured transmitter (TX) saturated output power is 13 dBm, and the output 1-dB compression point (OP1dB) is 8.3 dBm. The measured typical PLL phase noise is −111.3 dBc/Hz at a 1-MHz offset from an 11.69-GHz carrier frequency. The TX-to-RX over-the-air (OTA) modulation–demodulation measurement with QPSK and 16-QAM signals shows the error vector magnitude (EVM) of −16.5 and −19.7 dB, respectively.

Learning Joint Detection, Equalization and Decoding for Short-Packet Communications

We propose and practically demonstrate a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all “at once”, detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not only in a higher spectral efficiency, but also translates into the possibility of shorter messages compared to using a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline and our simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> =96 information bits. Finally, we practically evaluate and confirm the improved performance of the proposed system over-the-air (OTA) using a software-defined radio (SDR)-based measurement testbed.

Measurement and Estimation Methodology for EMC and OTA Testing in the VIRC

In recent years, promising results have been reported with the vibrating intrinsic reverberation chamber (VIRC) combining performance and cost-efficiency for electromagnetic compatibility (EMC) measurements. This also makes it a potentially attractive solution for over-the-air (OTA) testing, which is yet to be investigated. Therefore, this article proposes the first systematic and thorough methodology to characterize the VIRC for use in EMC and OTA testing of wireless baseband algorithms of narrow-band single-input single-output channels. This methodology has been developed to measure and estimate the channel first- and second-order temporal and spectral characteristics taking into consideration the effect of different carrier frequencies, rotational speeds of VIRC motors, and loading conditions. It is then applied to a channel setup inside a VIRC for a preliminary investigation before the VIRC itself is fully characterized. It is shown that mounting an absorber in a specific location on the hatch significantly improves the rejection rate of the chi-squared goodness-of-fit test for Rician distribution without increasing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -factor above <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula> 10 dB over the frequency range 755–2740 MHz in the VIRC under investigation. However, the proposed methodology has been devised to be universal to any reverberation chamber, and the obtained results can be used to improve EMC testing due to the better understanding of the unique VIRC environment.

Joint Client Selection and Receive Beamforming for Over-the-Air Federated Learning With Energy Harvesting

Federated learning (FL) is a well-regarded distributed machine learning technology that leverages local computing resources while protecting privacy. The over-the-air (OTA) computation has been adopted for FL to prevent excessive consumption of communication resources by employing the superposition nature of wireless waveform. Meanwhile, energy harvesting technology can relieve the energy constraint of clients and enable durable computation for FL. However, few of the existing works on OTA FL have considered jointly performing client selection and receive beamforming optimization with energy harvesting clients. The objective of this work is to address this issue to improve the learning performance of OTA FL. Specifically, we first derive the expression of the optimality gap regarding client selection and receive beamforming design. Then, to minimize the optimality gap, a mixed-integer nonlinear programming (MINLP) problem is formulated and decomposed into two sub-problems. Next, the semidefinite relaxation method and the channel-energy-data (CED)-based method are developed to optimize the receive beamforming sub-problem and client selection sub-problem iteratively. One alternative optimization method is proposed to deal with the decoupled sub-problems for obtaining the solutions to the original MINLP problem. Our simulation results demonstrate that the proposed solution is superior to the other comparison schemes in various parameter settings.

Nonlinear and Load-Pulling Effects in an Octave-Bandwidth Transmit Array

This article presents the design and nonlinear analysis of an octave-bandwidth transmit in-phase power combiner feed network. The goal is to evaluate the effect of nonlinearities on the in-phase spatial power combiner figures of merit (FoMs), namely effective isotropically radiated power (EIRP), overall efficiency, and linearity across a wide bandwidth and as a function of active antenna impedance and power amplifier (PA) supply voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\textrm {DD}}$ </tex-math></inline-formula> ). The analysis is showcased on a 6–12 GHz four-element array implemented with electrically small flared horns fed with microstrip-to ridge waveguide transitions, 2-W commercial gallium nitride (GaN) monolithic microwave integrated circuit (MMIC) amplifiers, and an in-phase Wilkinson power splitter. Over-the-air (OTA) measurements show an EIRP of 50 dBm over the octave bandwidth. Adjacent channel power ratio (ACPR) variation over frequency, power, and supply voltage is also measured through OTA and shows a variation of 10 dB over the considered variables.

Riemannian Low-Rank Model Compression for Federated Learning With Over-the-Air Aggregation

Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing the low-rank constraint of the model weights using a regularized nuclear norm penalty, which requires an appropriate hyperparameter that can be difficult to determine in practice. Furthermore, existing compression techniques are not directly applicable to efficient over-the-air (OTA) aggregation in federated learning (FL) systems for distributed Internet-of-Things (IoT) scenarios. In this paper, we propose a novel manifold optimization formulation for low-rank model compression in FL that does not relax the low-rank constraint. Our optimization is conducted directly over the low-rank manifold, guaranteeing that the model is exactly low-rank. We also introduce a consensus penalty in the optimization formulation to support OTA aggregation. Based on our optimization formulation, we propose an alternating Riemannian optimization algorithm with a precoder that enables efficient OTA aggregation of low-rank local models without sacrificing training performance. Additionally, we provide convergence analysis in terms of key system parameters and conduct extensive experiments with real-world datasets to demonstrate the effectiveness of our proposed Riemannian low-rank model compression scheme compared to various state-of-the-art baselines.

Online Reinforcement Learning for Beam Tracking and Rate Adaptation in Millimeter-wave Systems

In this paper, we propose MAMBA, a restless multi-armed bandit framework for beam tracking in directional millimeter-wave (mmW) cellular systems. Instead of relying on explicit control messages, MAMBA utilizes the ACK/NACK packets transmitted by user equipments (UEs) to the base station (BS) as a part of the hybrid automatic repeat request (HARQ) procedure. These packets are used to measure the quality of the currently operating downlink beam, and select a new downlink beam along with an appropriate modulation and coding scheme (MCS) for future transmissions. At its core, MAMBA implements an online reinforcement learning technique called adaptive Thompson sampling (ATS), which determines a good beam and associated MCS to be used for the upcoming transmissions. To evaluate MAMBA's performance, we conduct extensive simulations and over-the-air (OTA) experiments over the 28 GHz band using phased-array antennas. We study fixed- as well as adaptive-rate variants of MAMBA, and contrast it with four other beam tracking strategies: a beam selection scheme similar to the one used in 5G NR (called ‘static oracle’), a theoretically optimal but practically infeasible beam tracking scheme (called ‘dynamic oracle’), an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy algorithm [1], and the Unimodal Beam Alignment (UBA) algorithm [2]. Our results show that MAMBA achieves 182% throughput gain over the ‘static oracle’ and is reasonably close to the throughput of the ‘dynamic oracle’. Compared to UBA, MAMBA achieves 25-35% gain in throughput, depending on UE mobility. Finally, when operated at a fixed MCS, MAMBA/ATS achieves 21% gain over the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy algorithm at the lowest applied MCS index, and 255% gain at the highest MCS index.

Multifunctional Frequency Selective Absorber Enabling FR1 and FR2 5G OTA Tests in a Hybrid Reverberation Chamber

Due to the special advantages of the reverberation chamber (RC), it has been recognized as a standardized over-the-air (OTA) testing methodology by the cellular telecommunications industry association (CTIA). Currently, OTA tests of the fifth-generation (5G) wireless terminals are usually performed in different testing environments, e.g., in a reverberation chamber for fast isotropic tests in the sub-6 GHz frequency band, and in an anechoic chamber for directional tests in the millimeter-wave (mm-wave) frequency band, which are inconvenient and costly. In this work, two multi-functional frequency selective absorbers (FSAs) are designed to realize absorption in the 5G mm-wave band and reflection/transmission in the sub-6 GHz band. By loading the FSAs to the RC, an integrated testing environment for 5G terminals can be established. Namely, a quasi-anechoic environment in the mm-wave band and a reverberation environment in the sub-6 GHz band can be simultaneously achieved in the RC loaded with the proposed FSAs. Experimental and simulated results are presented to demonstrate the effectiveness of the proposed scheme.

Bandwidth-Scalable Digital Predistortion of Active Phased Array Using Transfer Learning Neural Network

This paper proposes a transfer learning neural network (TLNN) approach for digital predistortion (DPD) of mm-Wave active phased arrays (APA) operated under variable signal bandwidth regimes. Compared with the conventional artificial neural network (ANN) method, the proposed approach can achieve similar linearization performance with much lower computational complexity by transferring part of a trained model from one bandwidth to another bandwidth. In the recently introduced 5G, the increased signal bandwidth triggers considerable memory effects in the APA. Moreover, dealing with different signal bandwidths typically requires a time-consuming recalculation of the predistorter parameters. In this paper, the authors propose to have those challenges solved by using a DPD model based on the transfer learning method. The proposed approach was validated with over-the-air (OTA) measurements on an APA excited with signals of varying bandwidth, namely from 20 MHz to 100 MHz. Experimental results show a significant reduction in the training time while ensuring good linearization performance. With the applied TLNN DPD, an 8.5 dB improvement of adjacent channel leakage ratio (ACLR) and 8.6 % points improvement of error vector magnitude (EVM) is achieved. Under the variable bandwidth regime, the complexity of the DPD model in terms of the number of multiplications is reduced from 199168 to 160. The proposed TLNN DPD proved to be robust concerning variation in the bandwidth of the APA excitation signal.

Platform Independent Embedded Linux OTA Method

Embedded devices are often used at remote locations. Accordingly, since it is difficult and costly to physically access the devices for firmware updates, the updates are made with Over-the-Air (OTA). OTA is a firmware update method that allows embedded devices to be updated wirelessly without requiring physical access. OTA applications for Embedded Linux devices are platform-dependent. Therefore, the aim of this study is to develop an easy integrated, steady, and robust OTA method. This method runs in the background without any user action on a platform-independent Embedded Linux device and it has a small footprint. The firmware is compressed in this method. The compressed firmware is uploaded to a storage area such as a remote drive, cloud, and repository. The storage area is used efficiently thanks to compressed firmware. The OTA application checks versions of firmware in the storage area and compares them with firmware running on the device. It decides which version is more up-to-date. If the version of the firmware in the storage area is more up-to-date, the OTA application runs in the background and downloads the updated firmware without interfering with the working functions on the device. After the download is completed, compressed firmware is extracted and flashed into the specified memory location. When the device is rebooted after the procedure is completed, the device is going to boot with the new version of the firmware. Consequently, the OTA application can be integrated easily on most Embedded Linux platforms. Besides the OTA application can be enhanced with security and power-safe features.

Radio Frequency Fingerprinting Exploiting Non-Linear Memory Effect

Radio frequency fingerprint (RFF) identification distinguishes wireless transmitters by exploiting their hardware imperfection that is inherent in typical radio frequency (RF) front ends. This can reduce the risks for the identities of legitimate devices being copied, or forged, which can also occur in conventional software-based identification systems. This paper analyzes the feasibility of device identification exploiting the unique non-linear memory effect of the transmitter RF chains consisting of matched pulse shaping filters and non-linear power amplifiers (PAs). This unique feature can be extracted from the received distorted constellation diagrams (CDs) with the help of image recognition-based classification algorithms. In order to validate the performance of the proposed RFF approach, experiments are carried out in cabled and over the air (OTA) scenarios. In the cabled experiment, the average classification accuracy among systems of 8 PAs (4 PAs of the same model and the other 4 of different models) is around 92% at signal to noise ratio (SNR) of 10 dB. For the OTA line-of-sight (LOS) scenario, the average classification accuracy is 90% at SNR of 10 dB; for the non-line-of-sight (NLOS) scenario, the average classification accuracy is 79% at SNR of 12 dB.

Probe-AUT Multiple Reflection Suppression in the Over the Air Measurement of 5G Antennas Using Matrix Pencil Method

The effect of probe proximity to the antenna under test (AUT) in the fixed-probe spherical near field (SNF) measurement system is investigated. The matrix pencil method (MPM) is utilized to suppress the undesired multiple reflections between AUT and probe, and the optimal angular region for correction is determined in this scenario. The simulation results are compared with a reference set of data in terms of transmission coefficients and radiation patterns to evaluate the performance of the MPM in the probe-generated echo filtering procedure. To verify the functionality of the MPM in practice, two antennas are exploited as different probes to implement a spherical measurement system in a mini-anechoic chamber, which is developed for conducting the over the air (OTA) measurements of the fifth-generation (5G) antennas. By comparing the radiation pattern results, it is observed that applying the MPM to the sampled data leads to more accurate results in a compact near field (NF) measurement setup.

1λ Spaced Dual Radiating Modes Based Phased Array Antenna Using Silicon RFICs With Grating Lobe Cancellation

A dual radiating modes (TM11 and TM21)-based phased array antenna with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\lambda $ </tex-math></inline-formula> interelement spacing at Ku-band is demonstrated using silicon beamforming RFICs, which offers grating lobe cancellation. The radiating element consists of concentric microstrip circular patch antennas supporting TM11 and TM21 modes. Excitation of both modes (TM11 and TM21), with the proper amplitude and phase values, allows for the suppression of grating lobe present in the array radiation pattern. The array utilizes Anokiwave RFICs (AWMF-0117), which provides flat panel integration of the beamforming network with the antenna aperture. A suitable beamforming network consisting of wideband Wilkinson power dividers was used to feed the RFICs from a single input. We demonstrate a grating lobe suppression ability of up to 30.8 dB with a first sidelobe level (SLL) of 15.2 dB. We also confirm the grating lobe mitigation technique through the demonstration of an over-the-air (OTA) link using digitally modulated signals and observing the error vector magnitude (EVM) of the received signal.

Over-the-Air Software-Defined Vehicle Updates Using Federated Fog Environment

With recent advancements in software-defined vehicles, over-the-air (OTA) software updates are crucial to roll out new software and patches for connected vehicles. Traditionally, outdated vehicles are recalled by the manufacturers, however, owners are notoriously difficult to reach with recall notices. Also, organizational and procedural challenges result in many outdated vehicles with insecure and unstable software. In this paper, we propose a dissemination framework for OTA updates using a federated fog environment. Pushing the update to all vehicles via the fog nodes may congest the network, leading up to a single-point failure for update dissemination, as well as, disruption to other installed services at the fog nodes. We propose a software-defined mechanism to select a pivot, which gets the update from the fog node and streams it to other vehicles. Moreover, a timer-barrier mechanism is proposed to identify any malicious vehicles that are barred from participating in pivot selection. The experimental evaluation demonstrates that the proposed scheme improves network convergence time by 40% with 95% node trustworthiness.

DL-Based Anomaly Detection at the Physical-Layer of Cognitive Radio by Deep Support Vector Data Description

Aiming at achieving anomaly detection at the physical-layer of cognitive radio, a Deep Support Vector Data Description (Deep SVDD) based anomaly detection scheme is proposed in this paper. Specifically, two novel techniques with different sensitivities related to Signal-to-Noise Ratio (SNR)-walls are proposed for two typical application scenarios. On one hand, for studying the optimal boundary of the desired signal in the feature space, an Improved Deep SVDD (IDSVDD) is conceived for extracting low-dimensional features of the samples represented in the time-frequency domain. On the other hand, a joint algorithm of Deep SVDD and Modulation Classification (JDSMC) is developed, which is capable of extracting low-dimensional features utilizing deep Convolutional Neural Networks (CNN). Extensive simulation and experimental results demonstrate that: 1) the detection performance and the real-time performance of the proposed techniques outperform conventional control algorithms. 2) The proposed techniques can effectively detect abnormal signals in complex electromagnetic environments and effectively improve the self-aware ability of Cognitive Radio Network (CRN). 3) Over-the-Air (OTA) measurements using Software Defined Radio (SDR) in the lab validate the proposed techniques’ anomaly detection performance.

Adaptive Beam Sweeping With Supervised Learning

Utilizing millimeter-wave (mmWave) frequencies for wireless communication in mobile systems is challenging since continuous tracking of the beam direction is needed. For the purpose, beam sweeping is performed periodically. Such approach can be sufficient in the initial deployment of the network when the number of users is small. However, a more efficient solution is needed when lots of users are connected to the network due to higher overhead consumption. We explore a supervised learning approach to adaptively perform beam sweeping, which has low implementation complexity and can improve cell capacity by reducing beam sweeping overhead. By formulating the beam tracking problem as a binary classification problem, we applied supervised learning methods to solve the formulated problem. The methods were tested on two scenarios: ray-tracing outdoor scenario and over-the-air (OTA) testing dataset from Ericsson. Both experimental results show that the proposed methods significantly increase cell throughput comparing with existing exhaustive sweeping and periodical sweeping strategies.