A Wire-Line Secure Communication System Based on CCFD Self-Interference Cancellation

This paper presents a design scheme of wire-line telephone system using self-interference (SI) cancellation technology in co-frequency co-time full-duplex (CCFD) system to realize absolute secure communication at the physical layer. This scheme can hide the target signal by skillfully releasing the high-power artificial noise to the whole link at the receiving node, and then make use of the receiver’s knowledge of the SI signal to achieve high dB SI cancellation with the help of analog domain SI cancellation technology in CCFD domain, so that the signal-to-noise ratio (SNR) received by the eavesdropper at any position of the link is far lower than that of the legitimate receiver, so as to realize the absolutely secure communication in the sense of Wyner principle. This paper not only puts forward the specific design scheme of absolutely secure communication telephone, but also analyzes the calculation of security capacity under different eavesdropping positions, different SI cancellation capability and different system parameters according to Shannon theory.

Wideband RFI Cancellation for High-sensitivity Phased Array Receivers with True Time Delays and Truncated Hadamard Projection

<div>Radio frequency interference (RFI) is a significant challenge for high-sensitivity phased array instruments. RFI can be suppressed using digital signal processing, but to improve dynamic range for wideband RFI, it can be desirable to remove interference in the analog domain before sampling. In previous work, it has been shown that analog true time delay (TTD) stages with a truncated Hadamard transform can place a wide-band spatial null on RFI from a given direction of arrival. We show that TTD and Hadamard projection is mathematically equivalent to the classical, narrow-band subspace projection beamformer, but with a structure that allows efficient implementation in analog circuitry. Simulation results show that TTD and Hadamard projection can place deep nulls on wideband RFI signals and still achieve SNR performance comparable to the optimal digital maximum signal to interference and noise ratio beamformer.</div>

Wideband RFI Cancellation for High-sensitivity Phased Array Receivers with True Time Delays and Truncated Hadamard Projection

<div>Radio frequency interference (RFI) is a significant challenge for high-sensitivity phased array instruments. RFI can be suppressed using digital signal processing, but to improve dynamic range for wideband RFI, it can be desirable to remove interference in the analog domain before sampling. In previous work, it has been shown that analog true time delay (TTD) stages with a truncated Hadamard transform can place a wide-band spatial null on RFI from a given direction of arrival. We show that TTD and Hadamard projection is mathematically equivalent to the classical, narrow-band subspace projection beamformer, but with a structure that allows efficient implementation in analog circuitry. Simulation results show that TTD and Hadamard projection can place deep nulls on wideband RFI signals and still achieve SNR performance comparable to the optimal digital maximum signal to interference and noise ratio beamformer.</div>

On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers

One of the major impediments in the design and operation of a full-duplex radio transceiver is the presence of self-interference (SI), that is, the transceiver’s transmitted signal, 60–100 dB stronger than the desired signal of interest. To reduce the SI signal below the receiver’s sensitivity before coupling it to the receiver, radio frequency (RF)/analog domain cancellation is carried out. Even after SI cancellation to the required level in the analog domain, the residual SI signal still exits and lowers the transceiver’s performance. For residual SI cancellation, a digital domain cancellation is carried out. RF impairments are the major obstacle in the residual SI cancellation path in the digital domain. Linearization of RF impairments such as IQ mixer imbalance in the transmitter and receiver chain, non-linear PA with memory, and non-linear LNA are also carried out. Performance evaluation of the proposed techniques is carried out based on SINR, the power of different SI signal components, PSD, output to input relationship, SNR vs. BER, spectrum analyzer, constellation diagram, and link budget analysis. The proposed techniques provide attractive RF/analog SI cancellation of up to 80–90 dB, digital residual SI cancellation of up to 35 to 40 dB, total SI cancellation of up to 110 to 130 dB, and an SINR improvement of up to 50 dB.

A Cluster of FPAAs to Recognize Images using Neural Networks

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.<br>

A Cluster of FPAAs to Recognize Images Using Neural Networks

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.<br>

A Cluster of FPAAs to Recognize Images Using Neural Networks

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.<br>