Sensing and understanding all signals in an RF-secure military or civilian setup is important, this includes unin-tended RF emissions called emanations. Prior work in detecting emanations involves profiling, which is hardware (HW) specific and hence not a scalable approach. Our technique looks for a generic signature of harmonics in the frequency domain without knowledge of HW. It detects emanation and characterizes it by estimating the pitch frequency of harmonic. A signal model for emanations, HW, and channel effects is provided. A mathematical treatment showcases the removal of artifacts and extracts the harmonic structure. The performance is showcased on In-phase and Quadrature-phase (IQ) data collected using a Signal Hound software-defined radio (SDR) in a shielded room. Data is collected from 0.1-1.1 GHz and 100 ms duration. Emanations are detected for a laptop, and a desktop connected to a monitor. The unique contribution of this work is the detection of emanation without HW knowledge, with mathematical justification and demonstrated performance on wideband real data.
Signal/image reconstruction problems are typically posed as linear inverse problems with the objective of minimizing a least-squares (LS) loss function and a regularization term derived from a suitable prior. Several decades of work have gone into the design and efficient application of various penalty functions, whilst the design of the data-fidelity loss function has remained relatively unexplored. This paper proposes a distance-minimization approach to the design of the data-fidelity loss for solving linear inverse problems and discusses proximal algorithms for solving them. We discuss the implications of using the proposed formulation as opposed to the standard LS in terms of its effect on noise and convergence. We consider solving sparse signal recovery and image restoration tasks, in particular, image deblurring, and show that distance minimization techniques out-perform standard LS for the various models under consideration.
Generalized frequency division multiplexing (GFDM) offers more flexibility in choosing subsymbols, subcarriers, and pulse-shaping filters for efficient data transmission. Due to inherent non-orthogonality, GFDM produces intrinsic self-interference, namely inter-subsymbol interference (ISI) and inter-subcarrier interference (ICI). An Eigendecomposition-based GFDM (ED-GFDM) utilizes a matched filter (MF) receiver, this architecture can mitigate self-interference without any noise enhancement. Nevertheless, in wireless channels, the propagation of ED-GFDM is affected by channel characteristics like non-linearity and non-homogeneity. Usually, a generalized alpha - mu fading channel is modeled to capture the channel characteristics in terms of fading parameters alpha and mu. Specific combinations of alpha and mu result in various fading environments like Rayleigh, Exponential, Nakagami-m, etc. This paper aims to derive accurate closed-form expressions for Symbol Error Rate (SER) and ergodic channel capacity for ED-GFDM using M-PSK modulation with a MF receiver. Obtained simulation results are analyzed and validated with the expected theoretical results.
This work examines a cognitive millimeter-wave communication network with two operators, a primary and a secondary, each with different channel access priorities. The primary operator can transfer its licenced spectrum to a secondary opera-tor, while the secondary operator follows an optimal interference-threshold criteria which was pre-specified by primary operator in its secondary licenses. The study uses medium-access-probability and activity factor to quantify channel access opportunities and number of active secondary transmitter-receiver pairs. For better network performance, primary operators should promote adaptive directional sensing, allowing secondary users to switch on the narrower antenna beamwidth regimes based on their proximity to other primary devices.
Extensive use of voice assistants by children in their day-to-day life activities demand for better performance of Automatic Speech Recognition (ASR) for children's speech. The recent advancements in ASR perform better for adult speech. However, due to acoustic mismatch (in particular, higher $F_{0}$ and thus, poor spectral resolution, and less availability of children data), it remains a challenge to improve the performance of children's ASR. In this context, a shared task on ASR for children's speech 2021 was organized during INTERSPEECH 2021. In this paper, the voice conversion-based data augmentation technique using a cycle-consistent generative adversarial network (CycleGAN) is investigated for closed track in the challenge. Here, CycleGAN training is exploited for children-to-children voice conversion. Training of CycleGAN and ASR experiments are performed on the speech data provided during the challenge. In our experiments, CycleGAN-based data augmentation showed a relative improvement of 9% and 3% in WER compared to the baseline system on the Dev and Eval sets, respectively. This work may find its significance in ASR for low resource languages, where Librispeech or any other external dataset is not an option.
