To realize dynamic spectrum access (DSA), spectrum sensing is performed to detect the presence or absence of primary users (PUs). This paper proposes a sensing architecture. This architecture enables use cases such as DSA with PU detection using a single spectrum sensor and DSA with distributed sensing, such as cooperative sensing, collaborative sensing, and selective sensing. In this paper we focus on distributed sensing. These sensing schemes employ distributed spectrum sensors (DSSs) where each sensor uses energy detection (ED) in Rayleigh fading environment. To theoretically analyze the performance of the three sensing schemes, a closed-form expression for the probability of detection by ED with selective combining (SC) in Rayleigh fading environment is derived. Applying this expression to the PU detection problem, we obtain analytical models of the three sensing schemes. Analysis shows that at 5-dB signal-to-noise ratio (SNR) and with a false alarm rate of 0.004, the probability of detection is increased from 0.02 to 0.3 and 0.4, respectively, by cooperative sensing and collaborative sensing schemes using using three DSSs. Results also show that the selected sensing scheme matches the performance of the collaborative sensing scheme. Moreover, it provides a low false alarm rate.

We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.

In distributed spectrum sensing, spatially distributed sensors perform radio frequency (RF) sensing and forward the result to a fusion center (FC). Cognitive radio (CR) obtains spectral information from the FC. Distributed spectrum sensing facilitates reliable discovery of spectrum opportunity while providing enhanced protection to legacy systems. The overall performance of distributed spectrum sensing depends both on the quality of sensing at the individual sensors and the forwarding scheme from the individual sensors. In this aspect the choice of media access control (MAC) plays a significant role. We can improve the system performance by optimizing the MAC and the spectrum sensing parameters jointly. In this paper we propose an enhanced MAC scheme based on existing scheduled MAC protocols to yield a high performance distributed spectrum sensing. To demonstrate our idea, we provide computer simulation by considering energy detection based distributed spectrum sensors and IEEE 802.15.4 PHY and MAC parameters.