arXiv subjects

Daniel Chew

Publications and source records attributed to Daniel Chew.

7 recordsLinked to original sources

arXiv2026

Two-Level Decorrelated Coded Modulation on the $D_4$ Lattice

We propose \textit{two-level decorrelated coding} (TLDC), a novel coded modulation scheme for the $D_4$ lattice that combines Voronoi shaping with a two-stage decoding process to achieve lattice shaping and coding gains at low complexity. In TLDC, the decoded values of the first level allow the several random variables in the second level to become approximately uncorrelated. The resulting independence of the variables in level two permits decoding in parallel or consolidation into a larger codeword, enhancing performance. TLDC supports flexible choice of FEC within each level. Using bit-interleaved or multi-level polar codes at each level, the resulting coded modulation scheme exhibits a gain of up to 0.5 dB over analogous state-of-the-art coded modulation schemes on a 16-QAM under AWGN at block sizes of 64 and 1024 bits.

cs.IT↗

arXiv2026

Unitary Spreading for Robust LPI/AJ TRANSEC in CPM Systems

Adversarial feature extraction and blocking jamming threaten tactical CPM links. This paper presents a unitary spreading-based Transmission Security (TRANSEC) enhancement to obscure physical-layer signatures and improve anti-jamming (AJ) resilience. The enhancement can be used to augment existing techniques. The enhancement preserves the constant-envelope (0 dB PAPR) nature of CPM, ensuring compatibility with high-efficiency tactical amplifiers. Analysis of symbol distributions, spectra, and cyclostationary features demonstrates that the technique masks inherent signatures, preventing modulation classification. We leverage convex optimization to recover symbols under blocking jamming, reducing uncoded BER from 6.25% to 0.04%. Finally, we characterize the engineering trade-offs between security, bandwidth, and BER.

eess.SP↗

arXiv2026

Multilevel Coset Codes on Lattices

This work introduces coset Bombe codes, a novel class of multilevel coset codes that generalize polar codes to dense lattice structures. By leveraging multilevel coding with non-binary codes designed for the lattice modulations and making use of Voronoi shaping, Bombe codes integrate the geometric strengths of dense lattices such as $D_4$ with the capacity-approaching properties of polar codes. Experimental results in additive white Gaussian noise (AWGN) channels demonstrate that coset Bombe codes significantly outperform both BICM and MLC state-of-the-art schemes on 16-QAM. The proposed scheme simulated on AWGN achieves up to 0.8 dB of gain and reduces block size latency by half while maintaining superior bit and block error rate (BER/BLER) performance on codewords of 256 and 1024 bits.

cs.IT↗

arXiv2023

Improved OFDM Signal Cancellation through Window Estimation

The ability to cancel an OFDM signal is important to many wireless communication systems including Power-Domain Non-orthogonal Multiple Access (PD-NOMA), Rate-Splitting Multiple Access (RSMA), and spectrum underlay for dynamic spectrum access. In this paper, we show that estimating the windowing applied at the transmitter is important to that cancellation. Windowing at the transmitter is a popular means to control the bandwidth of an Orthogonal Frequency Division Multiplexed (OFDM) symbol and is overlooked in most literature on OFDM signal cancellation. We show the limitation to the amount of cancellation that can be achieved without knowledge of OFDM windowing. We show that the window can be estimated from received samples alone, and that window estimate can be used to improve the signal cancellation. The window is estimated in the presence of noise and imperfect estimates of the center frequency offset (CFO) and the channel. We conclude with results using synthetic and over-the-air data where we demonstrate a 5.3 dB improvement to OFDM signal cancellation over existing methods in an over-the-air experiment.

eess.SP↗

arXiv2022

Persistent Weak Interferer Detection in WiFi Networks: A Deep Learning Based Approach

In this paper, we explore the use of multiple deep learning techniques to detect weak interference in WiFi networks. Given the low interference signal levels involved, this scenario tends to be difficult to detect. However, even signal-to-interference ratios exceeding 20 dB can cause significant throughput degradation and latency. Furthermore, the resultant packet error rate may not be enough to force the WiFi network to fallback to a more robust physical layer configuration. Deep learning applied directly to sampled radio frequency data has the potential to perform detection much cheaper than successive interference cancellation, which is important for real-time persistent network monitoring. The techniques explored in this work include maximum softmax probability, distance metric learning, variational autoencoder, and autoreggressive log-likelihood. We also introduce the notion of generalized outlier exposure for these techniques, and show its importance in detecting weak interference. Our results indicate that with outlier exposure, maximum softmax probability, distance metric learning, and autoreggresive log-likelihood are capable of reliably detecting interference more than 20 dB below the 802.11 specified minimum sensitivity levels. We believe this presents a unique software solution to real-time, persistent network monitoring.

eess.SP↗

arXiv2022

Covert Communications through Imperfect Cancellation

We propose a method for covert communications using an IEEE 802.11 OFDM/QAM packet as a carrier. We show how to hide the covert message so that the transmitted signal does not violate the spectral mask specified by the standard, and we determine its impact on the OFDM packet error rate. We show conditions under which the hidden signal is not usable and those under which it can be retrieved with a usable bit error rate (BER). The hidden signal is extracted by cancellation of the OFDM signal in the covert receiver. We explore the effects of the hidden signal on OFDM parameter estimation and the covert signal BER. We conclude with an experiment using Over-The-Air recordings of 802.11 packets, inject the hidden

eess.SP↗

arXiv2019

Model-based geometrical optimisation and in vivo validation of a spatially selective multielectrode cuff array for vagus nerve neuromodulation

Background. Neuromodulation by electrical stimulation of the human cervical vagus nerve may be limited by adverse side effects due to stimulation of off-target organs. It may be possible to overcome this by spatially selective stimulation of peripheral nerves. Preliminary studies have shown this is possible using a cylindrical multielectrode human-sized nerve cuff in vagus nerve selective neuromodulation. New method. The model-based optimisation method for multi-electrode geometric design is presented. The method was applied for vagus nerve cuff array and suggested two rings of 14 electrodes, 3 mm apart, with 0.4 mm electrode width and separation and length 0.5-3 mm, with stimulation through a pair in the same radial position on the two rings. The electrodes were fabricated using PDMS-embedded stainless steel foil and PEDOT: pTS coating. Results. In the cervical vagus nerve in anaesthetised sheep, it was possible to selectively reduce the respiratory breath rate (RBR) by 85 +/- 5% without affecting heart rate, or selectively reduce heart rate (HR) by 20 +/- 7% without affecting respiratory rate. The cardiac- and pulmonary-specific sites on the nerve cross-sectional perimeter were localised with a radial separation of 105 +/- 5 degrees (P < 0.01, N = 24 in 12 sheep). Conclusions. Results suggest organotopic or function-specific organisation of neural fibres in the cervical vagus nerve. The optimised electrode array demonstrated selective electrical neuromodulation without adverse side effects. It may be possible to translate this to improved treatment by electrical autonomic neuromodulation for currently intractable conditions.

physics.med-ph↗