Interference Mitigation Research Articles

Autonomous radar interference detection and mitigation using neural network and signal decomposition

<p>Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference-noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A neural network (NN) with four inputs, one hidden layer, and one output is trained with various signal-to-noise (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed neural network can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2dB higher than the NN-CFAR-Z algorithm.</p>

Interference Mitigation for Network-Level ISAC: An Optimization Perspective

Interference Mitigation for Network-Level ISAC: An Optimization Perspective

Security, privacy and performance concerns in ultra dense networks

Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

An energy-efficient JT-CoMP enabled framework with adaptive OMA/NOMA in HetNets

The increasing demand for higher data rates and improved energy efficiency (EE) in next-generation wireless networks necessitates the optimized selection of multiple-access and coordination techniques. A hybrid joint transmission (JT)-coordinated multi-point (CoMP) enabled orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) technique, combining the spectral efficiency (SE) and capacity benefits of CoMP NOMA with the interference mitigation of CoMP OMA, offers a highly adaptable solution for future wireless networks. This paper studies the joint optimization of CoMP/non-CoMP selection, OMA/NOMA selection, power allocation, and user pairing, with the objective of maximizing the EE in the network. A Dynamic CoMP user selection with energy-efficient adaptive multiple access (DCEAMA) algorithm to solve the formulated problem is proposed. Our Monte Carlo simulations show that the DCEAMA surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity.

Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

Frame-rate adaptive fractionally spaced equalization enabled high-throughput optical camera communication.

Optical camera communication (OCC) has garnered worldwide research attention, due to its immunity to electromagnetic interference (EMI) and efficient utilization of spectrum resources. However, the limited bandwidth of the OCC system and the timing offset of the camera result in low system throughput. To enhance the OCC throughput, we propose and experimentally demonstrate a frame-rate adaptive fractionally spaced equalization algorithm (FA-FSE) for the joint mitigation of severe inter-symbol interference (ISI) and timing offset arising in OCC. Experimental results validate its correct and power-efficient function, leading to a record aggregated throughput of 250.96 kbit/s, when the 8-level pulse amplitude modulation (PAM-8) signals are independently modulated to eight chip-on-board light emitting diode (COB-LED) light strips, while simultaneously received by a smartphone 10 cm away.

A visual biosensor chip based on the enhanced electrochemiluminescence with low triggered potential for ochratoxin A detection

The introduction of a coreaction accelerator can effectively enhance the electrochemiluminescence (ECL) response. However, the development of coreaction accelerators for ruthenium (Ru) complex, one of the most widely used ECL emitters, is still in its early stages. In this study, the application of BiOI as a multifunctional coreaction accelerator for the Ru/N-Butyldiethanolamine (DBAE) system for the first time. The BiOI microspheres not only exhibit a fivefold enhancement in ECL signal but also reduce the trigger potential, potentially mitigating interference factors associated with high applied voltage. The nanoflower structure of BiOI microspheres also facilitates the immobilization of Ru(bpy)32+. Building upon this, a portable visual biosensor chip was developed for the detection of ochratoxin A (OTA). The biosensor chip comprises a sensing cell and a reporting cell. When OTA toxins are detected in the microenvironment, the aptamers attached to the surface of the sensing cell are released, resulting in a decrease in impedance on the electrode surface. Consequently, the ECL emission from Ru(bpy)32+/BiOI microspheres modified onto the reporting cell is enhanced. The concentration of OTA can be determined by analyzing the intensity change of ECL imaging, eliminating the need for specific instruments. This visual ECL biosensor chip offers the advantages of portability, ease of operation, low cost, and demonstrates excellent performance in detecting beer samples.

Cross-link Interference Mitigation Method for Heterogeneous Frame Structures of 5G-Advanced Networ

Cross-link Interference Mitigation Method for Heterogeneous Frame Structures of 5G-Advanced Networ

Towards an optimal 3-D design and deployment of 6G UAVs for interference mitigation under terrestrial networks

Unmanned aerial vehicles (UAVs) have opened new communication possibilities by being able to access remote areas. Their ability to serve a large number of users based on demand and adaptability is a key strength. In Sixth Generation (6G) networks, UAVs are highly valued for their cost-efficiency and versatile deployment. However, the mobility of UAVs introduces different types of interference issues, resulting in a decrease in network performance and quality of service (QoS) for edge users. To address these challenges, this paper introduces a clustering-based solution involving three main steps. Firstly, UAVs are deployed in three-dimension (3D) space based on user requests using mini-batch K-mean clustering Subsequently, re-clustering is explored to tackle load balancing within clusters. Finally, outliers and boundary users are classified to enhance QoS for edge users. This model effectively reduces interference and boosts UAV reliability in terrestrial networks. Also, a case study is presented to show how UAVs can mitigate interference in maritime communication within terrestrial networks. Numerical results demonstrated that the proposed scheme increases throughput by 33.06% and reduces energy consumption and time delay by 73.15% and 9.15%, respectively, as compared to the existing baseline schemes.

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

Interference Mitigation for Automotive FMCW Radar With Tensor Decomposition

Interference Mitigation for Automotive FMCW Radar With Tensor Decomposition

Mitigation of Suppressive Interference in AMPC SAR Based on Digital Beamforming

Multichannel Synthetic Aperture Radar (MC-SAR) systems, such as Azimuth Multi-Phase Centre (AMPC) SAR, provide an effective solution for achieving high-resolution wide-swath (HRWS) imaging by reducing the pulse repetition frequency (PRF) to increase the swath width. However, in an Electronic Countermeasures (ECM) environment, the image quality of multichannel SAR systems can be significantly degraded by electromagnetic interference. Previous research into interference and counter-interference techniques has predominantly focused on single-channel SAR systems, with relatively few studies addressing the specific challenges faced by MC-SAR systems. This paper uses the classical spatial filtering technique of adaptive digital beamforming (DBF). Considering the Doppler ambiguity present in the echoes, two schemes—Interference Reconstruction And Cancellation (IRC) and Channel Grouping Nulling (CGN)—are designed to effectively eliminate suppressive interference. The IRC method eliminates the effects of interference without losing spatial degrees of freedom, ensuring effective suppression of Doppler ambiguity in subsequent processing. This method shows significant advantages under conditions of strong Doppler ambiguity and low jammer-to-signal ratio. Conversely, the CGN method mitigates the effect of interference on multichannel imaging at the expense of degrees of freedom redundant to Doppler ambiguity suppression. It shows remarkable interference suppression performance under weak-Doppler-ambiguity conditions, allowing for better image recovery. Simulations performed on point and distributed targets have validated that the proposed methods can effectively remove interfering signals and achieve high-resolution wide-swath (HRWS) SAR images.

Numerical model of the QiTai radio Telescope PAF receiver signal and simulation of interference mitigation

Xinjiang Qitai is constructing a 110-m fully steerable radio telescope (QiTai radio Telescope [QTT]) equipped with a phased array feeds (PAF) receiver, which will install the focal plane operating from 0.7 to 1.8 GHz. In this article, we introduce a PAF receiver model for beamforming, and the numerical model of the internal and external noise for this PAF receiver is provided using electromagnetic field simulation software. The linear constraint minimum variance (LCMV) algorithm is used to simulate the interference mitigation. The interference mitigation rate is from 0.581 to 0.921, and the signal restoration rate is from 0.998 to 1.512 within the error range of an interference arrival angle of 10°. This conclusion can be used in the signal correction of the PAF receiver for interference mitigation.

Optimal interference mitigation with deep learningbased channel access in wireless body area networks

SummaryWireless body area networks (WBANs) are essential for medical applications, especially in remote health monitoring, as they transmit crucial and time‐sensitive data collected by nodes positioned around or within the body. However, the coexistence of WBANs with wireless channels can degrade performance due to interference. This study introduces OIM‐DLCAM, an optimal interference mitigation scheme for WBANs, which utilizes a deep learning‐based channel access method. OIM‐DLCAM addresses interference through the multiobjective Hungarian optimization (MOHO) algorithm, considering design constraints such as node transmission power, packet delivery ratio, and interference range. Additionally, it employs a deep probabilistic neural network‐based channel access method (DPNN‐CAM) to effectively mitigate interference by making decisions regarding contention window size, frame length, and buffer size. The proposed OIM‐DLCAM scheme ensures fairness between users while enhancing system performance. Simulation results from both static and dynamic sensor node scenarios demonstrate its effectiveness under various conditions, showcasing its potential to improve WBAN performance in medical applications. The simulations reveal that OIM‐DLCAM outperforms existing state‐of‐the‐art schemes across various scenarios, with efficiency gains of up to 86.187%, 72.452%, and 47.954% for WBAN node density, mobility, and packet arrival rate, respectively. Moreover, it significantly reduces the average end‐to‐end delay and packet drop rate while improving throughput and packet delivery ratio compared with existing schemes. Additionally, comparisons with industry standards, such as the IEEE 802.15.4e norm, validate the suitability of OIM‐DLCAM for cofounded WBANs.

Mitigation of Humidity Interference by Graphene Derivatives for Efficient Temperature Sensors without Encapsulation

AbstractTemperature monitoring and regulation are essential in various environments, including modern industry and living and storage spaces. The growing demand for temperature sensors calls for affordable, efficient, interference‐resistant, and eco‐friendly solutions. The challenge of humidity interference in constructing temperature sensors often leads to compromising on the dynamic sensor properties in particular due to the need for encapsulation. To this end, this study introduces a temperature sensor leveraging a carefully designed graphene derivative to mitigate the humidity interference. The material, synthesize through scalable fluorographene chemistry with benzylamine, is optimized in order to enhance its properties, which led to achieving peak efficiency with a minimal humidity impact. The sensor demonstrated full functionality across a temperature range from 10 to 90 °C, with a temperature coefficient of resistivity 8.63 × 10−3 K−1, which is more than twice as high as that of conventional platinum thermometers. Remarkably, the sensor exhibited only a 2% change in resistance when exposed to relative humidity in the range of 20 to 70%. Notably, the sensor continues to give a consistent performance even after six months, which proved its stability. The presented device holds promise for evolving into a fully printed, cost‐effective and reliable next‐generation temperature sensors.

Mitigation of electronic crosstalk interference in graphene transistor biosensors

The need for higher sensitivity and parallel detection of multiple biological targets drives the research on electronic biosensing platforms based on two-dimensional (2D) materials. However, signal fidelity from individual sensors may be compromised due to the existing nonidealities of 2D electronic devices. Here, we use graphene field effect transistors (gFETs) as a model system to investigate a multisensor device architecture. We identify two potential sources of crosstalk interference between sensors in such a platform: resistive interference through shared gFET channels and capacitive coupling through the shared device substrate. Having illustrated these sources of interference, we offer solutions to mitigate them for ensuring the fidelity of the biosensor signal. This study provides important practical insights for researchers working on 2D biosensor platforms.

Research on RFI Mitigation Methods for Fast Radio Burst Observational Data

To quickly search for rare fast radio bursts (FRBs) from massive astronomical observational data, Radio Frequency interference (RFI) mitigation is one of the key and challenging task. RFI will cause search algorithm to output a large number of false positive candidates, and even submerges real astronomical events. Due to the complexity of the sources and types of RFI. there is currently no universal method to solve this problem. In order to reduce the impact of RFI on FRB search, the RFI in L band observational data of the Nanshan 26m radio telescope (NSRT-26m) was analyzed and studied. A three-layer RFI mitigation procedure was established for the main narrowband RFI and broadband RFI, which effectively alleviated the RFI pollution of observational data. Embedding this procedure into the FRB DDSS (Dispersion Dynamic Spectra Search) pipeline, experimental results show that the detection rate and accuracy of the search algorithm have been further improved. This method provides valuable reference for RFI mitigation of FRB observational data.

Hybrid RIS-assisted interference mitigation for heterogeneous networks

Reconfigurable intelligent surfaces (RIS) have evolved as a low-cost and energy- efficient option to increase wireless communication capacity. In this research, we suggest using hybrid RIS (H-RIS) to reduce interference in heterogeneous networks (HetNet). In contrast to traditional passive RIS, a hybrid RIS is suggested, which is fitted with a few active elements to not only reflect but also amplify incident signals for a significant performance increase. By jointly optimising the passive and active coefficients of the H-RIS, we aim to maximise the rate of the small cell user (SUE). We presented an effective alternating optimisation (AO)-based phase shift matrix coefficients (AO-PMC) technique to tackle this problem by iteratively optimising these variables because the optimisation problem is not convex. The simulation results demonstrate that, in comparison to the passive RIS-assisted HetNet scheme and the scheme without RIS, the suggested scheme, with just 8% of active elements, can enable HetNet to gain superior spectral efficiency (SE) and energy efficiency (EE). The outcomes also demonstrate that, in the majority of the cases taken into account, H-RIS can outperform the active RIS-assisted HetNet scheme.

Reinforcement Learning-based Joint Interference Mitigation and Resource Allocation in Dense Beyond 5G Heterogeneous Networks

Reinforcement Learning-based Joint Interference Mitigation and Resource Allocation in Dense Beyond 5G Heterogeneous Networks