An empirical polarization domain channel availability model for cognitive radio

In dynamic spectrum access networks, cognitive radio terminals monitor their spectral environment in order to detect and opportunistically access unoccupied frequency channels. The overall performance of such networks depends on the spectrum occupancy or availability patterns. Accurate knowledge on the channel availability enables optimum performance of such networks in terms of spectrum and energy efficiency. This work proposes a novel probabilistic channel availability model that can describe the channel availability in different polarizations for mobile cognitive radio terminals that are likely to change their orientation during their operation. A Gaussian approximation is used to model the empirical occupancy data that was obtained through a measurement campaign in the cellular frequency bands within a realistic operational scenario.

A statistical framework for channel availability modelling in the polarisation domain

Cognitive radio has been proposed as a means of improving the spectrum utilisation and increasing spectrum efficiency of wireless systems. This can be achieved by allowing cognitive radio terminals to monitor their spectral environment and opportunistically access the unoccupied frequency channels. Due to the opportunistic nature of cognitive radio, the overall performance of such networks depends on the spectrum occupancy or availability patterns. Appropriate knowledge on channel availability can optimise the sensing performance in terms of spectrum and energy efficiency. This work proposes a statistical framework for the channel availability in the polarization domain. A Gaussian Normal approximation is used to model real-world occupancy data obtained through a measurement campaign in the cellular frequency bands within a realistic scenario.

Power Spectral Density Side-Channel Attack Overlapping Window Method

Cryptographic algorithms have been designed to be computationally secure, however it has been shown that when they are implemented in hardware, that these devices leak side channel information that can be used to mount an attack that recovers the secret encryption key. In this paper an overlapping window power spectral density (PSD) side channel attack, targeting an FPGA device running the Advanced Encryption Standard is proposed. This improves upon previous research into PSD attacks by reducing the amount of pre-processing (effort) required. It is shown that the proposed overlapping window method requires less processing effort than that of using a sliding window approach, whilst overcoming the issues of sampling boundaries. The method is shown to be effective for both aligned and misaligned data sets and is therefore recommended as an improved approach in comparison with existing time domain based correlation attacks.