Spectrum Sensing in Cognitive Radios Based on Multiple Cyclic Frequencies

Abstract

Cognitive radios sense the radio spectrum in order coding scheme, training or pilot signals, guard periods, and to find unused frequency bands and use them in an agile manner. the power level or correlation properties of the signal, just Transmission by the primary user must be detected reliably even to mention a few. These properties may be used to design a in the low signal-to-noise ratio (SNR) regime and in the face of shadowing and fading. Communication signals are typically deteto at worseinavy low SNRer an d hasl cyclostationary, and have many periodic statistical properties complexity and consequently low power consumpton. These related to the symbol rate, the coding and modulation schemes are very desirable properties especially for cognitive radios as well as the guard periods, for example. These properties in mobile applications. In the absence of any knowledge of can be exploited in designing a detector, and for distinguishing the signal, one may have to resort to classical techniques between the primary and secondary users' signals. In this paper, such as energy detection [1]. An energy detector may need to a generalized likelihood ratio test (GLRT) for detecting the suct a over lon [1]. ofene o detect the to presence of cyclostationarity using multiple cyclic frequencies is collect data over a long period of time to detect the primary proposed. Distributed decision making is employed by combining users reliably. Moreover, controlling the false alarm rates in the quantized local test statistics from many secondary users. mobile applications is difficult because the statistics of the User cooperation allows for mitigating the effects of shadowing signals, noise and interference may be time-varying. Another and provides a larger footprint for the cognitive radio system. . . 'Simulation examples demonstrate the resulting performance snficant drawback iS that energy detecton has no capability gains in the low SNR regime and the benefits of cooperative to distinguish among different types of transmissions or to detection. dichotomize between primary and secondary users of the

Two-stage spectrum sensing for cognitive radios

“…Furthermore, a Kaiser window of length 61 is applied in the simulations. Denoting the OFDM symbol length by Ts, the described OFDM signal exhibits cyclostationarity with cyclic frequencies of α = 2πm Ts , m = ±1, ±2, ... [6]. Here, we only use α = 2π Ts as the cyclic frequency which has to be detected by the cyclostationary detector.…”

“…If the constituent detection metric is greater than a threshold γ, the channel is declared occupied, else it is declared to be empty. In the following we shall discuss the two stages of energy detection [2], [9] and cyclostationary detection [3], [6] …”

“…Two sensing techniques that have been commonly considered in cognitive radios are energy detection [1], [2], [9] and cyclostationary detection [6], [8]. While energy detection is a simple detection technique, its performance is not robust to noise and is known to be poor at low SNRs.…”

A Spectrum Sensing Framework for UWB-Cognitive Network

“…where γ is the decision threshold, Almost all modulated signals have the cyclostationarity in their carrier frequency, bit rate, cyclic prefix and so on [9]. We also exploit the built in cyclostationarity of the modulated signals to sense the signals.…”

Cognitive Radio: Spectrum Sensing Problems in Signal Processing

“…This is called cyclostationary detection. Cyclostationary detection is achieved from cyclic autocorrelation function of the received signal or, equivalently, its two-dimensional spectrum correlation function [11]. Due to its noise rejection property, cyclostationary detection works better even under very low SNR region [3], where the traditional signal detection method, such as the Energy Detection, fails.…”