This work presents a novel mathematical framework of a machine learning algorithm for linear regression under non-Gaussian estimation noise. The Laplacian noise is selected as a contender for the non-Gaussian noise, as it is found in many real-world scenarios, like the differential privacy of users and as impulsive noise in wireless communication channels. In particular, we show the use-case of our analytical work in the regression-based application of wireless communication systems, specifically channel estimation. We exhibit the fundamental technique of linear regression to evaluate the wireless channel coefficients over additive Laplacian noise. In order to establish the need for the proposed framework, we illustrate a comparison between the behaviour of Gaussian and non-Gaussian noises. Furthermore, we investigate the maximum-likelihood estimator using gradient descent, the maximum a posteriori estimator, and the mean square error performances for the considered system scenario; to observe that the estimators derived under the actual non-Gaussian noise assumption yield better results as compared to those found under mathematically tractable simplified Gaussian assumption.

This work is concerned with the physical layer security of the free space optical (FSO) communication network comprising of a transmitter, a receiver, an eavesdropper (Eve), and a friendly reactive jammer that aims to thwart the activity of the Eve. As the key feature of this work, we calculate the discrete-input-continuous-output for memory-less channel (DCMC) capacity of the authentic and eavesdropping channel in the presence of the atmospheric turbulence characterised by$\alpha -\mu $probability density function (PDF), zero and non-zero boresight pointing error, and partial band or broadband jamming noise. For this purpose, novel PDFs for all-encompassing conditions of FSO channel and the received signals at different nodes of the considered system are formulated. Thereafter, we devise schemes to wane the effect of a vicious Eve by formulating novel closed-form expressions of average secrecy capacity for both strategies. Our derived analytical expressions can be used as tools to examine the DCMC capacity and secrecy capacity for different turbulence regimes of FSO link, severity of boresight errors, and activity of a friendly jammer. The mathematical results are affirmed by Monte-Carlo simulation. We exhibit that deploying a friendly jammer to assist enhancing the security of an FSO system is very conducive as it makes the Eve to hike its signal-to-jamming-noise ratio by almost 10 dB when the jammer’s activity increases from 10% to 100% of the time frame of data transmission.

This work studies the impact of jamming on an unmanned aerial vehicle (UAV) transmitting to a ground station through free-space optical communication. The UAV transmission link gets deteriorated from Gaussian noise and jamming signal simultaneously. The optical channel is modeled as Gamma-Gamma for atmospheric turbulence with pointing error. The error performance of the system is evaluated and closed-form expression of the bit error rate (BER) is derived considering different degrees of jamming activity. The derived analytical results are confirmed by simulations. It is observed that as the probability of the jammer's activity raises from 0.1, the error performance of the system gets saturated. Furthermore, it is noted that the jammer has more adverse impact on system's BER than the Gaussian noise, irrespective of the turbulence regime. Additionally, the jammer aggravates the effect of turbulence on the system.