Interference Mitigation Research Articles

Mitigation of Biotin Interference in Manual and Automated Immunoassays by Preconjugating Biotinylated Antibodies to the Streptavidin Surface as an Alternative to Biotin Depletion.

Streptavidin-to-biotin binding is one of the strongest noncovalent interactions in nature and incorporated into many immunoassays. Biotin-streptavidin coupling assays are susceptible to interference from free biotin in patient specimens, which may falsely decrease or increase results. To prevent biotin interference, we evaluated a method to preconjugate biotinylated antibodies to the assay's streptavidin solid surface before adding patient specimen and compared this technique to a biotin depletion protocol. Biotin interference in 3 manual ELISAs and 2 automated immunoassays was established. Mitigation of biotin interference by preincubation was evaluated in each assay by adding biotinylated antibody to the streptavidin-coated surface before adding biotin- or PBS-spiked serum. Lastly, the preincubation method was compared to a biotin-depletion protocol to compare the effectiveness of mitigating biotin interference. In the presence of 400 µg/L biotin, analyte detection was reduced to 10% to 15% of total in the ELISA assays and to 15.2% in the automated sandwich (thyroglobulin) immunoassay. In the automated competitive (free thyroxine) immunoassay, biotin caused an increased detection of 551.6%. Preconjugation of the biotinylated capture antibody to the streptavidin surface in the ELISA assays resulted in 84% to 99% activity recovery, compared to 84% to 97% by a biotin depletion protocol. Similarly, automated sandwich and competitive immunoassays obtained 97.1% and 116.5% recovery by preconjugation, compared to 95.6% and 100.3% by the depletion method, respectively. This study demonstrates how assay redesign to include preconjugation of biotinylated capture antibody to streptavidin is an effective alternative to biotin-depletion methods to mitigate biotin interference.

Insights and prospects for ion mobility-mass spectrometry in clinical chemistry

Introduction : Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements. Areas covered : Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds. Expert Opinion : Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.

Interference Mitigation Based on Low Complexity Subspace Tracking Method for Vehicle Positioning

Advanced driver assistance systems and automatic driving have drawn lots of attentions due to user’s multiapplication needs. Among different technologies, the integration of Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) is a key technique with the decreasing costs of Inertial Measurement Unit (IMU) and the increasing deployment of GNSS for navigation applications. Although the integration may have immune ability to interference in a certain extent, interference suppression strategy applied on the integration system can greatly improve the performance. A complete framework including interference monitor, detection, and mitigation is provided jointly using subspace tracking method and Kalman filter measurements innovation of INS and GNSS. The proposed monitor is simple, efficient, and can decide when to start the interference mitigation procedure, which can reduce the computation complexity significantly; Subspace‐based method is introduced to track the interference basis matrix that can be feed to RootMUSIC to detect the frequency of interference, followed by the interference mitigation with a notch filter. The proposed framework has superior performance than the well‐known time‐frequency algorithm, such as short‐time Fourier transform (STFT) and smoothed pseudo Wigner‐Ville distribution (SPWVD), and can improve the performance of INS/GNSS system in the presence of interference with simulation verification.

Prior-Guided Deep Interference Mitigation for FMCW Radars

In this paper, the interference mitigation problem is tackled as a regression problem. A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. Considering the complex-valued nature of radar signals, complex-valued convolutional neural network, which is different from the conventional real-valued counterparts, is utilized as an architecture for implementation. Meanwhile, as the desired beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared with the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. The average SINR of interfered signals has been improved from -9.13 dB to 10.46 dB. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.

Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.

A Design of Fixed-Complexity Sphere Decoder Combined With Interference Mitigation Algorithm for Downlink MU-MIMO Systems

In this paper, a design and implementation of fixed-complexity sphere decoder (FSD) combined with interference mitigation algorithm for downlink (DL) multiuser (MU) multiple-input multiple-output (MIMO) system is presented. To overcome performance degradation from inter-user interference in DL MU-MIMO systems, a hardware friendly optimized interference mitigation algorithm is proposed. The proposed algorithm achieves 0.5 dB near-optimal performance, which is 4 dB better than existing interference whitening filter for the soft-output decision. In this paper, both hard and soft-output FSD detectors are implemented with the proposed interference mitigation algorithm to function 4 × 4 antenna MIMO detection and interference mitigation for 4 additional streams. A fully pipelined architecture is employed to support 3.6 Gbps at 150 MHz for signals modulated by 64-QAM. From the synthesis result, the designed symbol detector has a gate count of 887 K for the FSD without pre-processor and a gate count of 1220 K for interference mitigation. By comparing previously implemented MIMO detectors, the proposed design represents a promising architecture for DL MU-MIMO systems in terms of significant performance improvement relative to inter-user interference and the compatible hardware implementation maintaining high-throughput within acceptable gate count.

Time-Varying Wideband Interference Mitigation for SAR via Time-Frequency-Pulse Joint Decomposition Algorithm

Wide-band interference (WBI) may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Since it highly overlaps with useful signals in the 1-dimensional (1-D) time or frequency domain, the existing WBI mitigation methods usually transform 1-D echoes into a 2-D transform domain. However, they usually suffer from a model mismatch, which results in the loss of the useful signal. To tackle this problem, a novel algorithm combining time-frequency-pulse (TFP) joint characteristics and robust principal component analysis (RPCA) is proposed for WBI mitigation. The TFP joint feature of SAR echo is introduced for interference mitigation for the first time. We first transform the SAR echoes into the time-frequency domain, and construct a new TFP matrix by reshaping the STFT matrices between adjacent pulses. In terms of the WBI-occupied SAR echoes, the short-time Fourier transformation (STFT) in adjacent pulses can be modeled as a combination of a low-rank part (i.e. useful SAR echoes) and a sparse counterpart (i.e. WBIs), which well fits the assumption of RPCA. Then, the TFP matrix is decomposed into the useful signal TFP matrix and the WBI TFP part by taking full advantage of the low-rank and sparse properties. Finally, the WBIs can be reconstructed and subtracted from the echoes to realize interference mitigation. Experimental results on both simulated and measured datasets show that the proposed algorithm not only suppresses WBIs effectively but also preserves useful information as much as possible.

Двухэтапная концепция подавления помех в автомобильных радарах

The present paper is focused on the interference mitigation problem for automotive radars in complex signal-interference-noise environments. Current situations are characterized by the fact that cars can be both sources of reflected signals and at the same time interference. Significant number of cars is equipped with radars of the same type (for example, radars with linear frequency modulated signals and with similar parameters). The concept of a two-stage procedure for interference mitigation is proposed. Adaptive beamforming algorithm that constructs a "nulls" in the antenna pattern in the direction of interference sources is developed for the first stage. The second stage is based on the proposed projection method of interference "subtraction" in the frequency domain to eliminate the beat frequencies of interference signals from the spectrum on the "velocity-range" plane. As an example, it is considered the scenario of five targets and one interference source (one of the targets) with the same parameters (linear frequency modulated radars with the same time duration and bandwidth). Numerical simulation results show the high efficiency of the proposed concept.

Inter-Radar Interference Analysis Based on L and S Band Propagation Models and Radar Beam-Scanning Scenarios

This paper presents an overview and novel method on inter-radar interference analysis based on propagation models and radar beam-scanning scenarios. Many studies on radar interference have provided proper methodologies and recommendations for system safety, coexistence, and higher spectrum utilization. However, the propagation models and radar beam-scanning scenarios have not given detail consideration because they usually focus on the proposed interference mitigation techniques. This paper provides a statistical interference analysis method to understand selection of propagation model and various beam-scanning scenarios on the radar interference. The time, duration, probability of the worst-case interference and expected performance degradation is presented statistically. From the simulation result, the maximum gain alignment under 1 degree between two radar occurs with 1.82 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> probability in an example scenario. The proposed simulation and analysis method can help to derive radar operational parameters for interference mitigation and efficient spectrum utilization.

Rate Splitting With Wireless Edge Caching: A System-Level-Based Co-Design

Rate splitting (RS) and wireless edge caching are essential means for meeting the quality of service requirements of future wireless networks. In this work, we focus on the cross-layer co-design of wireless edge caching schemes with sophisticated physical layer techniques, which facilitate non-orthogonal multiple access and interference mitigation. A flexible caching-aided RS (CRS) technique is proposed that operates in various modes that specify the cache placement at the receivers. We consider two caching policies: the intelligent coded caching (CC), as well as the well-known most popular content (MPC) policy. Both caching policies are integrated within the design parameters of RS in order to serve multiple cache-enabled receivers. The proposed technique is investigated from a system level perspective by taking into account spatial randomness. We consider a single cell network consisting of center and edge receivers and provide a comprehensive analytical framework for the evaluation of the proposed technique in terms of achieved rates. Specifically, we derive the rate achieved at each receiver under minimum rate constraints while incorporating the cache placement characteristics. Numerical results are presented which highlight the flexibility of the proposed technique and show how caching can be exploited in order to further boost the performance of RS.

Performance Analysis of Power Control in Urban UAV Networks With 3D Blockage Effects

In urban UAV networks, UAVs deployment locations and air-to-ground (AtG) communication links will possibly conflict with densely located buildings, which exacerbate network irregularity and make interference management more complicated. This paper proposes interference coordination via power control under 3D blockage effects in urban environments, where buildings are distributed according to Boolean model with heights (<inline-formula><tex-math notation="LaTeX">$H_k$</tex-math></inline-formula>) followed Rayleigh distribution. Specifically, a dynamic UAV group (UAVG) is organized to serve each user for interference coordination, which consists several nearest <i>visible</i> UAVs with line-of-sight (LOS) connections considering buildings blockage effects, and the modified distribution from user to its nearest unblocked UAV is derived. Power control is executed in UAVG where adjacent interfering UAVs within the group will mute their transmission for interference mitigation, and optimal UAVG radius coefficient <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> is obtained, which reveals the trade-off between interference mitigation and resource utilization. Leveraging stochastic geometry, theoretical expressions of network metrics are derived with Nakagami-<inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading assumption, including network coverage probability and network connectivity. Analytical results show that in urban city scenario, comparing with traditional terrestrial networks, coverage performance can achieve 4.1&#x00D7; gain by deploying UAVs with optimal height, and achieve additional 26<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> gain with respect to scenarios where power control is disabled.

Dynamic Algorithm for Interference Mitigation Between Cells in Networks Operating in the 250 MHz Band

The growing demand for Internet of Things (IoT) applications in agribusiness increases the necessity of reliable and secure connectivity in rural areas. Thus, in the particular case of Brazil, some initiatives aim to take advantage of frequency bands dedicated to limited private services. For instance, cellular networks based on orthogonal frequency-division multiple access (OFDMA) in 250 MHz bands require specialized adaptations because the interference between cells increases when these systems operate in the Very High Frequency (VHF) band. This work presents an analysis based on a reliable simulation of interference mitigation in OFDMA systems at 250 MHz using a network simulator. The simulator is calibrated with data obtained in the field by an extensive and rigorous drive test. Therefore, the analysis is based on a comparison of traditional frequency reuse schemes with a machine learning approach based on deep reinforcement learning (DRL) to reduce inter-cell interference. The numerical results indicate that the DRL approach outperforms the traditional frequency reuse (FR) schemes in four different typical agribusiness scenarios.

Wireless Federated Learning (WFL) for 6G Networks—Part II: The Compute-Then-Transmit NOMA Paradigm

As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation (6G) of wireless networks. To this end, in this second part we introduce and optimize a novel communication protocol for WFL networks, that is based on non-orthogonal multiple access (NOMA). More specifically, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Compute-then-Transmit NOMA (CT-NOMA)</i> protocol is introduced, where users terminate concurrently the local model training and then simultaneously transmit the trained parameters to the central server. Moreover, two different detection schemes for the mitigation of inter-user interference in NOMA are considered and evaluated, which correspond to fixed and variable decoding order during the successive interference cancellation process. Furthermore, the computation and communication resources are jointly optimized for both considered schemes, with the aim to minimize the total delay during a WFL communication round. Finally, the simulation results verify the effectiveness of CT-NOMA in terms of delay reduction, compared to the considered benchmark that is based on time-division multiple access.

Greenland Ice Sheet Subsurface Temperature Estimation Using Ultrawideband Microwave Radiometry

Ice sheet subsurface temperature is important for understanding glacier dynamics, yet existing methods to obtain the temperature of the ice sheet column are limited to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> sources at present. The ultrawideband software-defined microwave radiometer (UWBRAD) has been developed to investigate the remote sensing of ice sheet internal temperatures. UWBRAD measures brightness temperature spectra from 0.5 to 2 GHz using 12 subchannels and employs a sophisticated algorithm for detection and mitigation of radio frequency interference (RFI). The instrument was deployed during a flight over northwestern Greenland in September 2017 and acquired the first wideband low-frequency brightness temperature spectra over the ice sheet and coastal regions. The results reveal strong spatial and spectral variations that correlate well with internal ice sheet temperature information. In this article, the section of the flight path ranging from the Camp Century to NEEM to NGRIP boreholes is used for subsurface temperature estimation. A “partially coherent” forward model is applied along with a Robin model for the temperature profile and a two-scale model of ice sheet density variations to describe measured brightness temperatures. Using this model, vertical temperature profiles are retrieved along the flight path using a sequential Bayesian estimator; borehole measurements at the three campsites are used to obtain Bayesian priors. The retrieved temperature profiles show reasonable behaviors and demonstrate the potential of ultrawideband microwave radiometry for remotely sensing internal ice sheet temperatures.

Radio Frequency Interference Characterization and Mitigation for Polarimetric Weather Radar: A Study Case

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

Over-the-Air Calibration of Phase Shifter Network for Hybrid MIMO Systems

Phase shifter networks (PSN) are now widely used in multi-input multi-output (MIMO) systems for various purposes, from hybrid beamforming to strong interference mitigation. These applications require the phases of the PSN elements to be precisely calibrated, but the phase deviations always exist in practice. This paper proposes a novel over-the-air (OTA) approach to estimate the deviations of the phase shifters at each gear. Having some signal sources transmit pilot signals from unknown directions, we make the phases of the PSN shift across different gears and take the samples of the PSN output. Then the maximum likelihood (ML) estimation of the PSN errors which constitutes a three-way tensor, can be solved effectively by an iterative algorithm. The simulation results verify the effectiveness of the proposed algorithm and show that the root mean square errors (RMSEs) of the phase estimates can closely approach the Cramer Rao Bounds (CRBs).

Interference Mitigation for Non-Overlapping Sub-Band Full Duplex for 5G-Advanced Wireless Networks

With new services emerging in fifth generation (5G)-advanced, the evolution in duplex modes plays an important role to meet more stringent requirements for both downlink (DL) and uplink (UL) transmissions. In this paper, sub-band full duplex (SBFD) at base station (BS) is studied as an attainable evolution of the traditional time division duplex (TDD). Both user equipment (UE) transparent SBFD and UE perceptive SBFD are proposed and studied to serve different types of UEs. To tackle the interference introduced by SBFD, a model including both BS self-interference and cross-link interference (CLI) is presented as a first step, and new interference management schemes are proposed. Three approaches to mitigate BS self-interference, namely the passive suppression, analog interference cancellation and digital interference cancellation are analyzed. A new framework for CLI management is illustrated along with enhancements for interference identification, spatial domain interference coordination and power domain adjustment. To validate the feasibility and performance of the proposed SBFD methods under indoor and dense urban scenarios, system-level simulations (SLSs) are carried out and a proof-of-concept (PoC) is developed for the purpose of obtaining experimental results.

On Properties of Periodic Binary Sequences in the Presence of System Non-Linearities

Phase-modulated continuous-wave (PMCW) radar systems possess inherent interference mitigation capabilities and can be useful for many civilian applications including autonomous driving. For PMCW radar systems, binary sequences with good autocorrelation properties are used as probing sequences due to their low cost to generate in practical hardware systems. However, the non-linearity (NL) suffered by practical PMCW radar systems can produce undesirable cross-products and distort the receiver matched filter outputs. The purpose of this paper is to investigate the properties of the well-known periodic binary almost perfect autocorrelation sequences (APAS) under NL conditions, including even-order and odd-order NL conditions. We also consider designing long binary periodic sequences with low autocorrelation sidelobes within a low correlation zone (LCZ) using a computational algorithm. The computationally optimized sequences (COS) do not suffer from sequence length restrictions and offer much higher diversity than their APAS counterparts. We compare COS with APAS under NL conditions. We show that both types of sequences can be used to avoid ghost peaks in the range profiles under NL conditions.

Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder

Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation. They are rendered difficult due to a growing number of emitter types and varied effects of real-world channels upon the radio signal. Existing spectrum monitoring techniques are capable of acquiring massive amounts of radio and real-time spectrum data using compact sensors deployed in a variety of settings. However, state-of-the-art methods that use such data to classify emitter types and detect communication schemes struggle to achieve required levels of accuracy at a computational efficiency that would allow their implementation on low-cost computational platforms. In this paper, we present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals, and infer modulation or technology type using the learned features. The algorithm utilizes a compact neural network architecture readily implemented on a low-cost computational platform while exceeding state-of-the-art accuracy. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals, often demonstrating superior performance compared to state-of-the-art methods. Source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WuLoli/LSTMDAE</uri> .

Low Complexity Interference Mitigation Technique in IRCI-Free SAR Imaging Algorithms

Inter range cell interference (IRCI)-free synthetic aperture radar (SAR) imaging algorithms experience antenna-based interference which severely degrades the SAR image. In this letter, we propose a low complexity interference mitigation technique based on array processing for IRCI-free SAR imaging algorithms. A low complexity array processing is proposed, by designing a linear array with a limited number of fully controllable elements. The complex weight of array elements is optimized for the desired array pattern with constraints on close-in sidelobe levels (SLL). The proposed methodology reduces the desired two-way SLL to 40.5 dB by using only 18 fully controllable elements out of 100 array elements. The state-of-the-art improved-gray wolf optimization (I-GWO) is used for optimization and is shown to be computationally more efficient than some well-known optimization algorithms. Finally, simulated SAR images validate the effectiveness of the proposed technique.