Biblio

The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.

This paper investigates the secrecy outage performance of a dual-hop decode-and-forward (DF) mixed radio-frequency/underwater optical wireless communication (RF/UOWC) system. We consider a one-antenna source node ( S), communicating with one legitimate destination node (D) via a multi-antenna DF relay (R) node. In this context, the relay node receives the incoming signal from S via an RF link, which is subject to Rayleigh fading, then performes selection-combining (SC) followed by decoding and then re-encoding for transmission to the destination over a UOWC link, subject to mixture Exponential-Gamma fading. Under the assumption of eavesdroppers attempting to intercept the S-R (RF side), a closed-form expression for the secrecy outage probability is derived. Our analytical results are corroborated through computer simulations, which verifies their validity.