Gaussian multiple access channels with one-bit quantizer at the receiver

The capacity region of a two-transmitter Gaussian multiple access channel (MAC) under average input power constraints is studied, when the receiver employs a zero-threshold one-bit analogue-to-digital converter (ADC). It is proven that the input distributions of the two transmitters that achieve the boundary points of the capacity region are discrete. Based on the position of a boundary point, upper bounds on the number of the mass points of the corresponding distributions are derived. Furthermore, a lower bound on the sum capacity is proposed that can be achieved by time division with power control. Finally, inspired by the numerical results, the proposed lower bound is conjectured to be tight

Active privacy-utility trade-off against inference in time-series data sharing

Internet of things devices have become highly popular thanks to the services they offer. However, they also raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. We model the users personal information as the secret variable, to be kept private from an honest-but-curious service provider, and the useful variable, to be disclosed for utility. We consider an active learning framework, where one out of a finite set of measurement mechanisms is chosen at each time step, each revealing some information about the underlying secret and useful variables, albeit with different statistics. The measurements are taken such that the correct value of useful variable can be detected quickly, while the confidence on the secret variable remains below a predefined level. For privacy measure, we consider both the probability of correctly detecting the secret variable value and the mutual information between the secret and released data. We formulate both problems as partially observable Markov decision processes, and numerically solve by advantage actor-critic deep reinforcement learning. We evaluate the privacy-utility trade-off of the proposed policies on both the synthetic and real-world time-series datasets

Thermal fluctuations on the freeze-out surface of heavy-ion collisions and their impact on particle correlations

Particle momentum distributions originating from a quark-gluon plasma asproduced in high-energy nuclear collisions can be influenced by thermalfluctuations in fluid dynamic fields. We study this effect by generalizing thecommonly used kinetic freeze-out prescription by allowing for smallfluctuations around an average in fluid velocity, chemical potentials andtemperature. This leads to the appearance of specific two-body momentumcorrelations. Combining a blast-wave parametrization of the kinetic freeze-outsurface with the thermal correlation functions of an ideal resonance gas, weperform an exploratory study of angular net-charge correlations induced bythermal fluctuations around vanishing chemical potential. We note a diffusionof the near-side peak around $\Delta y=\Delta\phi=0$ induced by variances ofdifferent chemical potentials, which could be investigated experimentally.<br

Semi-federated learning: convergence analysis and optimization of a hybrid learning framework

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resource at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks

Deep Joint Source-Channel Coding for Wireless Image Transmission

We propose a novel joint source and channel coding (JSCC) scheme for wireless image transmission that departs from the conventional use of explicit source and channel codes for compression and error correction, and directly maps the image pixel values to the complex-valued channel input signal. Our encoder-decoder pair form an autoencoder with a nontrainable layer in the middle, which represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms separation-based digital transmission at low signal-to-noise ratio (SNR) and low channel bandwidth regimes in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the “cliff effect” as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC can learn to communicate without explicit pilot signals or channel estimation, and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values

Nano-bioceramic synthesis from tropical sea snail shells (Tiger Cowrie - Cypraea Tigris) with simple chemical treatment

In this study several bioceramic materials (i.e. hydroxyapatite, whitlockite) were prepared by using chemical synthesis method from sea snail shells (Tiger Cowrie - Cypraea Tigris), originated from Pacific Ocean. Marine shells usually present aragonite-calcite structures and generally, complicated and pressurized equipment is necessary to convert these structures into bioceramics. Instead of using complicated systems, a basic ultrasonic equipment and simple chemical synthesis method was used in the process. DTA analysis was performed to calculate the required amount of H3PO4 solution in order to set the appropriate stoichiometric ratio of Ca/P equal to 1.667 for HA bioceramic or to 1.5 for β-TCP bioceramic in the titration. The prepared batches were sintered at 800°C and 400 °C for hydroxyapatite (HA) and β-tri calcium phosphate (β-TCP) forms respectively. X-ray diffraction analysis, scanning electron microscopy (SEM) and infrared observations (FTIR) were implemented for both TCP and HA bioceramics. By applying the chemical synthesis with basic ultrasonic equipment, this study proposes a simple way of production for nano-HA/TCP powders from a natural marine sources