Interference-to-noise Ratio Research Articles

Digital self-interference cancellation for full-duplex systems based on deep learning

Full-duplex (FD) systems can double the spectrum efficiency by allowing users to transmit and receive signals at the same time and in the same frequency band. However, in FD systems the receiver suffers severe self-interference (SI) caused by the transmitter and eliminating the SI is challenging. In this paper, we propose a novel digital self-interference cancellation (SIC) scheme based on deep learning, which combines the power of sliding window, gated recurrent unit (GRU) network and attention mechanism (AM). The proposed scheme can accurately reconstruct the SI through two steps. It first processes the sample signals utilizing the sliding window and then extracts the dependence in preprocessed signals by applying the GRU with AM. We adopt the orthogonal frequency division multiplexing (OFDM) signal as the baseband transmitted signal to verify the proposed scheme. Extensive experiments demonstrate that the SIC capability of the developed scheme is at least 14.46 dB higher than the existing polynomial and neural network (NN) schemes. Furtherly, the effectiveness of the scheme is verified in different interference-to-noise ratio (INR), multipath channel, training sample and OFDM symbol situations.

Wireless interference classification with low complexity multi-branch networks

In non-cooperative communication systems, wireless interference classification (WIC) is one of the most essential technologies. Recently, deep learning (DL) based WIC methods have been proposed. However, conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy, especially when the interference-to-noise ratio (INR) is low. To this end, we propose three effective approaches. Firstly, we introduce multi-branch convolutional neural networks (CNNs) for interference recognition. The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology, and it notably improves the recognition ability for WIC. Our design avoids the carefully crafted selection of each transformation. Unfortunately, multi-branch CNNs are computationally expensive and memory-inefficient. To this end, we further propose Low complexity multi-branch networks (LCMN), which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference. Thirdly, we present novel loss function, which encourages networks to have consistent prediction probabilities for samples with high visual similarities, resulting in increasing recognition accuracy of LCMN. Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.

Digital Self-Interference Cancellation for Full-Duplex UAV Communication System over Time-Varying Channels

Full-duplex unmanned aerial vehicle (UAV) communication systems are characterized by mobility, so the self-interference (SI) channel characteristics change over time constantly. In full-duplex UAV communication systems, the difficulty is to eliminate SI in time-varying channels. In this paper, we propose a pilot-aid digital self-interference cancellation (SIC) method. First, the pilot is inserted into the data sequence uniformly, and the time-varying SI is modeled as a linear non-causal function. Then, the time-varying SI channel is estimated by the discrete prolate spheroidal basis expansion model (BEM). The error of block edge channel estimation is reduced by cross-block interpolation. The result of channel estimation is convolved with the transmitted data to obtain the reconstructed SI, which is subtracted from the received signal to achieve SIC. The simulation results show that the SIC performance of the proposed method outperforms the dichotomous coordinate descent recursive least square (DCD-RLS) and normalized least mean square (NLMS) algorithms. When the interference to noise ratio (INR) is 25 dB, the performance index normalized least mean square (NMSE) is reduced by 5.5 dB and 4 dB compared with DCD-RLS and NLMS algorithms, which can eliminate SI to the noise floor, and the advantage becomes more obvious as the INR increases.

On Outage Analysis in Overlay CCRN with RF Energy Harvesting and Co-channel Interference

This paper studies the effect of co-channel interference (CCI) on energy harvesting (EH) from radio frequency (RF) signal and the performance of reliable communication. An overlay mode of cooperative cognitive radio network (CCRN) model is considered that may support one-way communication between primary users (PUs) and two-way communications between secondary users (SUs). Closed form PU and SU outage expressions are derived in presence of CCI at two SU nodes and multiple antennas at PU transmitter. The absolute impact of CCI on the performance of the proposed system is analysed with respect to various parameters like, transmission power, interference-to-noise-ratio (INR), power splitting factor, number of CCI sources and number of antennas. About 20% and 15% improvements are achieved for PU and SU outage using two antennas compared to single antenna system model.

FDA-MIMO Radar Target Recognition Based on SVM Classification with Multi-channel Feature Extraction

FDA-MIMO radar has good anti-active interference performance and has received extensive attention due to its distance dependence. However, there is still a lack of target and interference recognition methods for FDA-MIMO radar in complex environments. Therefore, in this paper, we propose a multi-channel feature extraction classification method based on the support vector machine (SVM), which realizes the classification of four active interferences and targets. In short, we classify the fine signal characteristics of different interference signals by extracting effective features and selecting efficient classifiers. Simulation results show that when the interference to noise ratio (INR) is greater than -15 dB, the recognition accuracy is greater than 0.95. It also shows that the proposed method can distinguish target and interference well in a variety of complex environments.

Localizing GNSS Spoofing Attacks Using Direct Position Determination

Global Navigation Satellite System (GNSS) receivers are vulnerable to all types of interferences due to the inherently low power of GNSS signals. Whereas GNSS jammers aim at denial-of-service attacks, GNSS spoofers bring even more risks, since they can fabricate a fake position and/or time without recognizing it. This paper presents a direct position determination method to improve the localization accuracy of GNSS spoofing attacks, which is called despreading direct position determination (DS-DPD). The localization algorithm utilizes a prior knowledge of the satellites’ code sequences, which in turn provides significant gains in localization accuracy. Firstly, the received signal model is established, which takes the time delay, Doppler shift, direction-of-arrival (DOA), and modulation codes into account. Then, the maximum likelihood (ML) criterion is utilized to construct the objective function of DS-DPD. Finally, the location parameters of the spoofers can be obtained through a two-dimensional spectral search of the objective function. Numerical simulations verify the localization performance improvement of the proposed DS-DPD algorithm. Especially when the interference-to-noise ratio (INR) approaches −30dB, the localization accuracy could be improved by more than ten times due to its full utilization of the delay, Doppler shift, DOA, and code sequences information.

Outage Performance of Cognitive Radio Networks With a Coverage-Limited RIS for Interference Elimination

In this letter, considering the reconfigurable intelligent surface’ (RIS’) service coverage limitation, we investigate the outage probability (OP) of an RIS-aided cognitive radio network where the RIS is used only to eliminate the interference from the secondary transmitter to the primary receiver. Specifically, a closed-form expression for the OP is derived and the asymptotic OP is obtained at high signal-to-noise ratio (SNR). We also present the asymptotic OP when the number of reflecting elements is large. Simulation results validate our analysis, and show that at low tolerable interference-to-noise ratio (INR), the considered scheme can achieve significant performance gains over the coverage-neglected scheme and the signal enhancement scheme.

Multi-Domain Networks for Wireless Interference Recognition

In the field of military communications, electromagnetic interference has posed a serious threat to wireless communication systems, and wireless interference recognition (WIR) is considered as one of the most indispensable steps to defend against adversarial attacks for anti-interference communication. Following the success in advancing many disciplines, deep learning (DL) brings revolutionary changes to WIR. However, most conventional DL-based methods only use single domain information of interference signals, such as time domain or frequency domain, resulting in low recognition performance under low interference-to-noise ratio (INR). To this end, we propose to exploit multi-domain information of interference signals to take full advantage of the complementarity between domains for WIR. We propose multi-domain networks (MDN) that consist of convolutional layers and transformers to simultaneously strengthen locality and establish long-range dependencies for extracting the features of each domain information. Additionally, to fuse the extracted features from multiple transformation domains together, we propose novel fusion mechanisms, which can be seamlessly incorporated into the MDN. The experimental results demonstrate that the proposed methods notably boost recognition performance as compared to conventional methods for WIR.

One-Bit ADCs/DACs Based MIMO Radar: Performance Analysis and Joint Design

Extremely low-resolution (e.g. one-bit) analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) can substantially reduce hardware cost and power consumption for MIMO radar especially with large scale antennas. In this paper, we focus on the detection performance analysis and joint design for the MIMO radar with one-bit ADCs and DACs. Specifically, under the assumption of low signal-to-noise ratio (SNR) and interference-to-noise ratio (INR), we derive the expressions of probability of detection ($\mathcal{P}_d$) and probability of false alarm ($\mathcal{P}_f$) for one-bit MIMO radar and also the theoretical performance gap to infinite-bit MIMO radars for the noise-only case. We further find that for a fixed $\mathcal{P}_f$, $\mathcal{P}_d$ depends on the defined quantized signal-to-interference-plus-noise ratio (QSINR), which is a function of the transmit waveform and receive filter. Thus, an optimization problem arises naturally to maximize the QSINR by joint designing the waveform and filter. For the formulated problem, we propose an alternatin\emph{g} wavefo\emph{r}m and filt\emph{e}r d\emph{e}sign for QSINR maximiza\emph{t}ion (GREET). At each iteration of GREET, the receive filter is upadted via the minimum variance distortionless response (MVDR) method, and the one-bit waveform is optimized based on the alternating direction method of multipliers (ADMM) algorithm where the closed-form solutions are obtained for both the primary and slack variables. Numerical simulations are consistent to the theoretical performance analysis and demonstrate the effectiveness of the proposed design algorithm.

Narrowband Interference Cancellation using Set-membership Adaptive Predictor for GPS Receiver

The global positioning system (GPS) provides accurate positioning and timing information that is useful in various civil and military applications. The adaptive filtering predictor for GPS jamming suppression applications is proposed. This research uses the gLab-G software to substitute for the hardware receiver to record the GPS signal waveform. The normalized least-mean-square (NLMS) and set-membership NLMS (SM-NLMS) filtering methods are employed for continuous wave interference suppression. Simulation results reveal that our proposed methods can provide the better performances when the interference-to-noise ratios (INR) are varied from 20 to 50 dB. The anti-jamming performances are evaluated via extensive simulation by computing mean squared prediction error (MSPE) and signal-to-noise ratio (SNR) improvements.

Improving Log-Likelihood Ratio Estimation with Bi-Gaussian Approximation under Multiuser Interference Scenarios

Accurate estimation of channel log-likelihood ratio (LLR) is crucial to the decoding of modern channel codes like turbo, low-density parity-check (LDPC), and polar codes. Under an additive white Gaussian noise (AWGN) channel, the calculation of LLR is relatively straightforward since the closed-form expression for the channel likelihood function can be perfectly known to the receiver. However, it would be much more complicated for heterogeneous networks where the global noise (i.e., noise plus interference) may be dominated by non-Gaussian interference with an unknown distribution. Although the LLR can still be calculated by approximating the distribution of global noise as Gaussian, it will cause performance loss due to the non-Gaussian nature of global noise. To address this problem, we propose to use bi-Gaussian (BG) distribution to approximate the unknown distribution of global noise, for which the two parameters of BG distribution can easily be estimated from the second and fourth moments of the overall received signals without any knowledge of interfering channel state information (CSI) or signaling format information. Simulation results indicate that the proposed BG approximation can effectively improve the word error rate (WER) performance. The gain of BG approximation over Gaussian approximation depends heavily on the interference structure. For the scenario of a single BSPK interferer with a 5 dB interference-to-noise ratio (INR), we observed a gain of about 0.6 dB. The improved LLR estimation can also accelerate the convergence of iterative decoding, thus involving a lower overall decoding complexity. In general, the overall decoding complexity can be reduced by 25 to 50%.

Robust adaptive beamforming based on covariance matrix reconstruction via correlation coefficient of steering vector

Traditional adaptive beamformers are sensitive to model mismatch and they have poor performances when the input signal-to-noise ratio (SNR) is close to or more than interference-to-noise ratio (INR). In this letter, a novel robust adaptive beamforming (RAB) algorithm based on twice interference-plus-noise covariance (INC) matrix reconstruction is proposed. Firstly, the desired signal (DS) steering vector is estimated by the alternating projection algorithm based on the antenna array geometry and angular sector prior information. Then, the proposed method obtains the eigenvalue corresponding to DS by correlation coefficient and takes the place of it by noise power to obtain the first INC matrix. Furthermore, the second precise covariance matrix is reconstructed based on the theoretical INC matrix expression. The interferences powers are estimated by the theoretical formulation of the interference covariance matrix (ICM), which is derived from the first INC matrix. The simulation results demonstrate that the proposed algorithm achieves good performance especially when the input SNR is high, and its performance is close to the optimal beamformer in the case of look direction error.

Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems.

In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, , has an inverse relationship with the detection threshold and frame length. Particularly, to maintain for a frame length of 300 ns, it is required that the threshold should be greater than . It is also shown that for a fix , the probability of detection increases with an increase in interference-to-noise ratio (INR). Furthermore, approaches 1 for INR dB even for a very low i.e., . It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed and . Finally, the derived performance expressions are corroborated through simulation.

Multiuser Detection for MIMO-OFDM system in Underwater Communication Using a Hybrid Bionic Binary Spotted Hyena Optimizer

Multi Access Interference (MAI) is the main source limiting the capacity and quality of the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic (UWA) communication. Multi User Detection (MUD) is needed to overcome the performance degradation caused by MAI. In this research, both local and global optimal solutions are obtained in Bionic Binary Spotted Hyena Optimizer (BBSHO) algorithm using the Position Coordinate Vectors (PCVs) of the social behavior of spotted hyenas to achieve MUD. Further, Extremal Optimization (EO) is introduced in BBSHO algorithm to improve the local search ability within the search space. Hence, a hybrid BBSHO algorithm is proposed for achieving MUD at the receiver of the MIMO-OFDM system whose transceiver model in underwater is implemented using BELLHOP simulation system. By MATLAB simulation, it is shown that the Bit Error Rate (BER) performance of the proposed hybrid algorithm outperforms with best optimal solution within the search space towards MUD for Interference to Noise Ratio (INR) at 10 dB, 20 dB, and 40 dB over conventional detectors and metaheuristic approaches such as Binary Spotted Hyena Optimizer (BSHO), Binary Particle Swarm Optimization (BPSO) in the UWA network.

Interference‐to‐noise ratio estimation in long‐term evolution passive radar based on cyclic auto‐correlation

This letter considers the interference-to-noise ratio (INR) estimation problem in long-term evolution (LTE)-based passive bistatic radar. The INR is of great importance in the evaluation of interference level, the prediction of the target detection capability and the target parameter estimation. Traditional passive radar uses the cross-correlation function between the reference and surveillance signals to estimate the INR. This is not applicable to LTE-based passive radar due to the uncorrelation between the interference and reference signals in LTE-based passive radar. In this letter, we propose a cyclic auto-correlation method to estimate the INR. Simulation results show the effectiveness of the proposed method.

An impedance matching algorithm for common-mode interference removal in vagus nerve recordings

BackgroundThe peripheral nervous system is involved in a multitude of physiological functions. Recording neural signals provides information that can be used by diagnostic bioelectronic medicine devices, closed-loop neuromodulation therapies and other neuroprosthetic applications. The ability to accurately record these signals is challenging, due to the presence of various biological and instrument-related interference sources. New methodWe developed a common-mode interference rejection algorithm based on an impedance matching approach for bipolar cuff electrodes. Two unipolar channels were recorded from the two electrode contacts of a bipolar cuff. The impedance mismatch was estimated and used to correct one of the two channels. ResultsWhen applied to electrocardiographic (ECG) artifacts collected from three mice using CorTec electrodes, the algorithm reduced the interference to noise ratio (INR) over simple subtraction by 12 dB on average. The algorithm also reduced the INR of stimulation artifacts in recordings from three rats collected using flexible electrodes by an additional 2.4 dB. In the same experiments evoked electromyographic (EMG) interference was suppressed by 1.3 dB. Comparison with existing methodsSimple subtraction is the common approach for reducing common-mode interference in bipolar recordings, however impedance mismatches that exist or emerge compromise its efficiency. ConclusionsThe algorithm significantly reduced the common-mode interference from ECG artifacts, stimulation artifacts, and evoked EMG interference, while retaining neural signals, in two animal models and two recording setups. This approach can be used in a variety of different neurophysiological setups to remove common-mode interference from a variety of sources.

On the Capacity of Symmetric M-User Gaussian Interference Channels With Feedback

A general time-varying feedback coding scheme is proposed for M-user fully connected symmetric Gaussian interference channels. Based on the analysis of the general coding scheme, we prove a theorem which gives a criterion for designing good time-varying feedback codes for Gaussian interference channels. The proposed scheme improves the Suh-Tse and Kramer inner bounds of the channel capacity for the cases of weak and not very strong interference when M = 2. This capacity improvement is more significant when the signal-to-noise ratio (SNR) is not very high. In addition, our coding scheme can be proved mathematically and numerically to outperform the Kramer code for M ≥ 2 when the SNR is equal to the interference-to-noise ratio (INR). Besides, the generalized degrees-of-freedom (GDoF) of our proposed coding scheme can be proved to be optimal in the all network situations (very weak, weak, strong, very strong) for any M. The numerical results show that our coding scheme can attain better performance than the Suh-Tse coding scheme for M = 2 or the MohajerTandon-Poor lattice coding scheme for M > 2. Furthermore, the simplicity of the encoding/decoding algorithms is another strong point of our proposed coding scheme compared with the Suh-Tse coding scheme when M = 2 and the Mohajer-TandonPoor lattice coding scheme when M > 2. More importantly, our results show that an optimal coding scheme for the symmetric Gaussian interference channels with feedback can be achieved by only using marginal posterior distributions under a better cooperation strategy between transmitters.

Interference in Automotive Radar Systems: Characteristics, Mitigation Techniques, and Current and Future Research

This article examines the problem of interference in automotive radar. Different types of automotive radar as well as mechanisms and characteristics of interference and the effects of interference on radar system performance are described. The interference-to-noise ratio (INR) at the output of a detector is a measure of the susceptibility of a radar to interference. The INR is derived from different types of interfering and victim radars and depends on the location of both as well as parameters such as transmit power, antenna gain, and bandwidth. In addition, for victim radar with beamscanning, INR depends on the location of the target the victim radar is attempting to detect. Analysis is presented to show the effects of various interference scenarios on the INR. A review of the current state of the art in interference mitigation techniques previously deployed as well as areas of research currently being addressed is then provided. Finally, important future research directions are suggested.

Detection of Radio Frequency Interference in Microwave Radiometers Operating in Shared Spectrum

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at −5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate.

On the Generalized Degrees of Freedom of the MIMO Interference Channel With Delayed CSIT

The generalized degrees of freedom (GDoF) of the two-user multiple-input multiple-output interference channel is studied under the assumption of delayed channel state information at the transmitters. In particular, with $M$ antennas at each transmitter and $N$ antennas at each receiver, and in the non-trivial case when $M>N$ (with the case of $M\leq N$ not needing any CSIT), new lower and upper bounds on the symmetric GDoF are obtained that are parameterized by $\alpha $ , which links the interference-to-noise ratio (INR) and the signal-to-noise ratio (SNR) at each receiver via $\mathrm {INR}=\mathrm {SNR}^{{\alpha }}$ . A new upper bound for the symmetric GDoF is obtained by maximizing a bound on the weighted sum rate, which in turn is obtained from a combination of genie-aided side-information and an extremal inequality. The maximum weighted sum rate in the high SNR regime is shown to occur when the transmit covariance matrix at each transmitter is full rank. An achievability scheme is developed that is based on block-Markov encoding and backward decoding, and which incorporates channel statistics through interference quantization and digital multicasting. This symmetric GDoF lower bound is maximized separately for different ranges of ${\alpha }$ , by optimizing the transmit power levels in the achievability scheme separately in the very weak $[0\leq {\alpha }\leq ({1}/{2})]$ , weak $[({1}/{2}) , and strong $({\alpha }>1)$ interference regimes. The lower and upper bounds coincide when ${\alpha }\geq [({{r}+1})/({{r}+2})]$ , where ${r}=\min (2,{M}/{N})$ , thus characterizing the symmetric GDoF completely for strong interference and a range of values of weak interference. It is also shown that treating interference as noise is strictly sub-optimal from a GDoF perspective even when the interference is very weak.