Spectral Vacancies Prediction Method for Cognitive Radio Applications

Abstract

Abstract-Cognitive radio technology is in fast development and is considered a possible solution to improve the efficiency of radio spectrum use. Many studies have been recently carried out in order to improve spectrum-sharing techniques between primary and secondary users. This paper investigates one of the basic decision problems faced by a cognitive radio: given a time window of a specific size, a secondary user (SU) should decide if it will use it or not, minimizing the chances of collision with a primary user (PU). For this purpose, an algorithm is proposed that does not require previous information about PU occupancy behavior. The proposed algorithm shows very good performance when compared to three other methods recently proposed to tackle with this problem.

Multi-Step-Ahead Spectrum Prediction for Cognitive Radio in Fading Scenarios

“…Other types of ML algorithms described in the literature that can be used for spectrum prediction include the Hidden Markov Model (HMM) [22], nearest neighbor [22], classification trees [23], maximum likelihood [24] and Bayesian estimators. In [25], Chen et al developed the Minimum Bayesian Risk based Robust Spectrum Prediction algorithm which outperforms Neural-Networks prediction.…”

“…The index is described above and ∈ 1, …, 2 ( ×3) . The product inside the brackets in (24) is associated to the quantity of future states analyzed, thus we have a sequence of multiplications of the eight rows in Table III of [13] and as a result ∈ 1, …, 8.…”