Dual-Branch MRC Receivers under Spatial Interference Correlation and Nakagami Fading

Despite being ubiquitous in practice, the performance of maximal-ratio combining (MRC) in the presence of interference is not well understood. Because the interference received at each antenna originates from the same set of interferers, but partially de-correlates over the fading channel, it possesses a complex correlation structure. This work develops a realistic analytic model that accurately accounts for the interference correlation using stochastic geometry. Modeling interference by a Poisson shot noise process with independent Nakagami fading, we derive the link success probability for dual-branch interference-aware MRC. Using this result, we show that the common assumption that all receive antennas experience equal interference power underestimates the true performance, although this gap rapidly decays with increasing the Nakagami parameter $m_{\text{I}}$ of the interfering links. In contrast, ignoring interference correlation leads to a highly optimistic performance estimate for MRC, especially for large $m_{\text{I}}$. In the low outage probability regime, our success probability expression can be considerably simplified. Observations following from the analysis include: (i) for small path loss exponents, MRC and minimum mean square error combining exhibit similar performance, and (ii) the gains of MRC over selection combining are smaller in the interference-limited case than in the well-studied noise-limited case.Comment: to appear in IEEE Transactions on Communication

A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research