217 research outputs found
Adaptive Nonlinear RF Cancellation for Improved Isolation in Simultaneous Transmit-Receive Systems
This paper proposes an active radio frequency (RF) cancellation solution to
suppress the transmitter (TX) passband leakage signal in radio transceivers
supporting simultaneous transmission and reception. The proposed technique is
based on creating an opposite-phase baseband equivalent replica of the TX
leakage signal in the transceiver digital front-end through adaptive nonlinear
filtering of the known transmit data, to facilitate highly accurate
cancellation under a nonlinear TX power amplifier (PA). The active RF
cancellation is then accomplished by employing an auxiliary transmitter chain,
to generate the actual RF cancellation signal, and combining it with the
received signal at the receiver (RX) low noise amplifier (LNA) input. A
closed-loop parameter learning approach, based on the decorrelation principle,
is also developed to efficiently estimate the coefficients of the nonlinear
cancellation filter in the presence of a nonlinear TX PA with memory, finite
passive isolation, and a nonlinear RX LNA. The performance of the proposed
cancellation technique is evaluated through comprehensive RF measurements
adopting commercial LTE-Advanced transceiver hardware components. The results
show that the proposed technique can provide an additional suppression of up to
54 dB for the TX passband leakage signal at the RX LNA input, even at
considerably high transmit power levels and with wide transmission bandwidths.
Such novel cancellation solution can therefore substantially improve the TX-RX
isolation, hence reducing the requirements on passive isolation and RF
component linearity, as well as increasing the efficiency and flexibility of
the RF spectrum use in the emerging 5G radio networks.Comment: accepted to IEE
Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks
Full-duplex systems require very strong self-interference cancellation in
order to operate correctly and a significant part of the self-interference
signal is due to non-linear effects created by various transceiver impairments.
As such, linear cancellation alone is usually not sufficient and sophisticated
non-linear cancellation algorithms have been proposed in the literature. In
this work, we investigate the use of a neural network as an alternative to the
traditional non-linear cancellation method that is based on polynomial basis
functions. Measurement results from a full-duplex testbed demonstrate that a
small and simple feed-forward neural network canceler works exceptionally well,
as it can match the performance of the polynomial non-linear canceler with
significantly lower computational complexity.Comment: Presented at the IEEE International Workshop on Signal Processing
Advances in Wireless Communications (SPAWC) 201
Noise Cancellation in Cognitive Radio Systems: A Performance Comparison of Evolutionary Algorithms
Noise cancellation is one of the important signal processing functions of any
communication system, as noise affects data integrity. In existing systems,
traditional filters are used to cancel the noise from the received signals.
These filters use fixed hardware which is capable of filtering specific
frequency or a range of frequencies. However, next generation communication
technologies, such as cognitive radio, will require the use of adaptive filters
that can dynamically reconfigure their filtering parameters for any frequency.
To this end, a few noise cancellation techniques have been proposed, including
least mean squares (LMS) and its variants. However, these algorithms are
susceptible to non-linear noise and fail to locate the global optimum solution
for de-noising. In this paper, we investigate the efficiency of two global
search optimization based algorithms, genetic algorithm and particle swarm
optimization in performing noise cancellation in cognitive radio systems. These
algorithms are implemented and their performances are compared to that of LMS
using bit error rate and mean square error as performance evaluation metrics.
Simulations are performed with additive white Gaussian noise and random
nonlinear noise. Results indicate that GA and PSO perform better than LMS for
the case of AWGN corrupted signal but for non-linear random noise PSO
outperforms the other two algorithms
Identification of Non-Linear RF Systems Using Backpropagation
In this work, we use deep unfolding to view cascaded non-linear RF systems as
model-based neural networks. This view enables the direct use of a wide range
of neural network tools and optimizers to efficiently identify such cascaded
models. We demonstrate the effectiveness of this approach through the example
of digital self-interference cancellation in full-duplex communications where
an IQ imbalance model and a non-linear PA model are cascaded in series. For a
self-interference cancellation performance of approximately 44.5 dB, the number
of model parameters can be reduced by 74% and the number of operations per
sample can be reduced by 79% compared to an expanded linear-in-parameters
polynomial model.Comment: To be presented at the 2020 IEEE International Conference on
Communications (Workshop on Full-Duplex Communications for Future Wireless
Networks
Ultra-Wideband Spectrum Hole Identification Using Principal Components and Eigen Value Decomposition
Ultra-Wideband Spectrum Hole identification using Principal Components and Eigen Value Decomposition evolve a method of detecting spectrum hole from complex and corrupted wide band spectrum signal, due to the effect of noise spectrum hole detection is usually a challenge in wideband signal, as the presence of noise give rise to error alert, that is, noise can be misconstrued for signal. Dimensionality reduction was first used as the first level of denoising technique, Principal component Analysis (PCA) was used in dimensioning Wide Band Spectrum Data; this was able to reduce the noise level in the signal which made it convenient for Fast Fourier Transform (FFT) to act on it. FFT was used to decompose the signal to 64 sub band channels and on further reduction using principal Component Analysis (PCA), a 32 Level sub-band decomposition was carried out. Eigen Value generated shows that the magnitude of the signal to Noise ratio between Eigen Value 1 to 19 was high enough to show the that there exist a signal, while between 20 to 32 shows no signal by implication it indicates that these areas have high possibility of unoccupied spectrum holes
A Channel Ranking And Selection Scheme Based On Channel Occupancy And SNR For Cognitive Radio Systems
Wireless networks and information traffic have grown exponentially over the last decade. Consequently, an increase in demand for radio spectrum frequency bandwidth has resulted. Recent studies have shown that with the current fixed spectrum allocation (FSA), radio frequency band utilization ranges from 15% to 85%. Therefore, there are spectrum holes that are not utilized all the time by the licensed users, and, thus the radio spectrum is inefficiently exploited. To solve the problem of scarcity and inefficient utilization of the spectrum resources, dynamic spectrum access has been proposed as a solution to enable sharing and using available frequency channels. With dynamic spectrum allocation (DSA), unlicensed users can access and use licensed, available channels when primary users are not transmitting. Cognitive Radio technology is one of the next generation technologies that will allow efficient utilization of spectrum resources by enabling DSA. However, dynamic spectrum allocation by a cognitive radio system comes with the challenges of accurately detecting and selecting the best channel based on the channelâs availability and quality of service. Therefore, the spectrum sensing and analysis processes of a cognitive radio system are essential to make accurate decisions. Different spectrum sensing techniques and channel selection schemes have been proposed. However, these techniques only consider the spectrum occupancy rate for selecting the best channel, which can lead to erroneous decisions. Other communication parameters, such as the Signal-to-Noise Ratio (SNR) should also be taken into account. Therefore, the spectrum decision-making process of a cognitive radio system must use
techniques that consider spectrum occupancy and channel quality metrics to rank channels and select the best option. This thesis aims to develop a utility function based on spectrum occupancy and SNR measurements to model and rank the sensed channels.
An evolutionary algorithm-based SNR estimation technique was developed, which enables adaptively varying key parameters of the existing Eigenvalue-based blind SNR estimation technique. The performance of the improved technique is compared to the existing technique. Results show the evolutionary algorithm-based estimation performing better than the existing technique. The utility-based channel ranking technique was developed by first defining channel utility function that takes into account SNR and spectrum occupancy. Different mathematical functions were investigated to appropriately model the utility of SNR and spectrum occupancy rate. A ranking table is provided with the utility values of the sensed channels and compared with the usual occupancy rate based channel ranking. According to the results, utility-based channel ranking provides a better scope of making an informed decision by considering both channel occupancy rate and SNR. In addition, the efficiency of several noise cancellation techniques was investigated. These techniques can be employed to get rid of the impact of noise on the received or sensed signals during spectrum sensing process of a cognitive radio system. Performance evaluation of these techniques was done using simulations and the results show that the evolutionary algorithm-based noise cancellation techniques, particle swarm optimization and genetic algorithm perform better than the regular gradient descent based technique, which is the least-mean-square algorithm
Self-Interference Cancellation for Full-Duplex Underwater Acoustic Systems
This work develops and investigates self-interference (SI) cancellation (SIC) techniques for full-duplex (FD) underwater acoustic (UWA) systems. To enable the FD operation in UWA systems, a high level of SIC is required. The main approach used in this work is the digital cancellation based on adaptive filtering. A general structure of the digital canceller is proposed which addresses key factors affecting the SIC performance, including the power amplifier and pre-amplifier nonlinearities, up- and down-sampling effects. With the proposed structure, the SI can be effectively cancelled in time-invariant channels by classical recursive least-square (RLS) adaptive filters, e.g., the sliding-window RLS (SRLS), but the SIC performance degrades in time-varying channels. A new SRLS adaptive filter based on parabolic interpolation of the channel time variations is proposed, which improves the SIC performance at the expense of the high complexity. To reduce the complexity, while providing the high SIC, a new family of interpolating adaptive filters which combine the SRLS adaptive algorithm with Legendre polynomials (SRLS-L) is proposed. A sparse adaptive filter is further proposed to exploit the sparsity in the expansion coefficients of the Legendre polynomials. For interpolating adaptive filtering algorithms, the mean squared error is unsuitable for measuring the SIC performance due to the overfitting. Therefore, a new evaluation metric, SIC factor, is proposed. The SIC performance of the proposed adaptive filters is investigated and compared with that of the classical SRLS algorithm by simulation, water tank and lake experiments. Results indicate that the proposed adaptive filters significantly improve the SIC performance in time-varying scenarios, especially with high-order sparse SRLS-L adaptive filter. Furthermore, SIC schemes with multiple antennas are investigated to explore the possibility of achieving extra amount of SIC in acoustic domain and cancelling the fast-varying surface reflections by adaptive beamforming
Suppression of narrowband interference generated by the power supply of the railway systems in public defibrillators devices
A specific problem using the public access defibrillators arises at the railway stations.
Some countries as Germany, Austria, Switzerland, Norway, Sweden and Slovenia are
using AC railroad net power-supply system with rated frequency of 16.6(6) Hz,
frequency modulated from 15.69 Hz to 17.36 Hz. The power supply frequency
contaminates the electrocardiogram (ECG). It is difficult to be suppressed or eliminated
due to the fact that it considerably overlaps the frequency spectra of the ECG. The
interference impedes the automated decision of the public access defibrillators
whether a patient should be (or should not be) shocked.
The aim of study of this thesis is the suppression of the 16.6(6) Hz interference
generated by the power supply of the railway systems in few central european
countries. For this purpose, an adaptive filter and a band-stop filter are used and the
results obtained are compared in order to get the most suitable solution.Ingeniería Técnica de Telecomunicación, especialidad Sonido e ImagenTelekomunikazio Ingeniaritza Teknikoa. Soinua eta Irudia Berezitasun
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