OFDM Signal Type Recognition and Adaptability Effects in Cognitive Radio Networks

Abstract

Abstract-The ability of adapting to the environment in the most efficient way is a crucial issue in Cognitive Radio (CR) networks. For this purpose, an accurate estimation of the characteristics and activity of the Primary Users (PUs) is required. A system that takes into account heterogeneous PUs with several features is developed. A new scheme is integrated in the system to exploit these motleys and to improve the adaptability in CR networks. Through the proposed PU signal type recognition, the PU signal is detected and classified. The features of each PU type: the allowed interference levels, the bandwidth and the idle time, are extracted and exploited for CR adaptability effects. For this, a new CR throughput/interference adapter is proposed. The CR throughput is efficiently increased depending on the specific characteristics of PU types. Simulation results show that the proposed PU type recognition detects, distinguishes and classifies PU signals in Additive White Gaussian Noise (AWGN). It is shown that CR throughput varies with PU features for the improvement of CR adaptability.

System architecture in cognitive radio networks using a radio environment map

“…Specifically, the measurements obtained by CR users are collected in the REM SA and processed by the REM manager to detect and classify the existing PU types in the considered geographical area. The detection and classification process [5] used is not detailed in this paper because it is out of the scope of this work.…”

“…The maximum of the autocorrelation function and the interval time in which this maximum is detected [5] are the type of information stored for the considered scenario.…”

“…In particular, the CR users utilize a feature detector [5] able to detect and classify heterogeneous PUs. This information about different detected PUs is sent to the CR base station through the air interface (Itf A).…”

Cognitive Radio Resource Management Exploiting Heterogeneous Primary Users

“…For this purpose, a Cyclostationary Autocorrelation Function (CAF) is utilized, which detects and classifies OFDM PU signals by exploiting the periodicities of the OFDM signals [4]. Moreover, a new PU activity index φ j (i) [2] is considered to capture the PU activity fluctuation, successfully overcoming the drawbacks of the usual Poisson modeling.…”

“…Specifically, we use the value of subcarrier spacing to classify heterogeneous PUs [4]. After the classification process, the PU features are used for the calculation of the available capacity in a given cluster and for the Cognitive RRM design.…”