# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 1 Introduction
By considering this algorithm, it was possible to obtain, on average, 98% certainty of detecting the beginning and end of a \(PU\) transmission, for an SNR greater than 0 dB. These results were obtained in a simulated environment on the MATLAB platform. Thus, the previously mentioned technique is now implemented in some easy-access SDR (software-defined radio) devices deployed in a real wireless communication environment. This paper allows appreciating the performance of theoretical contributions in a commercial electronic device based on SDR. According to the literature, there were some recent and high-impact works in the field of interest related to MBSS and its implementation in a simulated or experimental environment with SDR and USRP (universal software radio peripheral) devices. The authors in [13] proposed a cooperative detection algorithm with a sub-Nyquist sampling approach to design a system with significantly reduced cost and energy consumption. In [14], the critical problem of the hidden terminal was considered (in this case, the hidden \(PU\)), and a specific spatial distribution of the \(SU_{8}\) was proposed to counteract this problem. In [15], a compression sampling technique was proposed that can effectively reduce the cost of signal acquisition, whose principal objective is to accurately acquire the signal for detection. In [16], individual and cooperative broadband spectrum detection schemes were proposed to reduce power consumption in signal acquisition, processing, and transmission. In [17], an algorithm based on a low-speed multichannel architecture was proposed to detect frequency edges using wavelets in a multiband context for CR networks. In [18], the authors analyzed the detection capacity and developed a self-configured system with dynamic intelligence networks without causing any interference to the \(PU\). In this work, two spectrum detection techniques were also mentioned during quantitative analysis: energy detection and band-limited white-noise detection. Some important contributions related to improving spectrum efficiency over TV with spaces (TVWS) by considering low-cost devices and reduced computational complexity have been made. A good approach to this topic appears in [19]; here, the authors used an affordable prototype to sense TVWS. The results showed that the portable prototype was capable of detecting unoccupied frequency bands from 500 MHz to 698 MHz in urban areas. Moreover, in [20], a prototype able to detect up to 10 TV channels was presented. The sensing device was examined in a real environment, and a spectrum occupancy of 30.25% was calculated. In [21], an energy detection model was examined, combined with noise and primary user activity. This proposal was tested in a low-cost open-source sensing station. The proposed model detected TVWS with 9.6% more accuracy than current models. In [22], SDR and USRP devices were used to implement a communication system to detect interferences performing significantly better than the ED in the detection of weak interference, offering the capability of detecting 8 to 10 dB lower values of interference. In [23], experimental results of the USRP hardware implementation for the detection of '_gaps_' in the spectrum were presented with the advantage of requiring fewer samples than the ED and obtaining the same performance. In [24], a new SDR platform architecture was proposed that implements different modulation techniques, and this architecture showed favorable gains with an advantage over traditional techniques, such as spatial multiplexing multiple-input multiple-output (MIMO) systems. In [25], a distributed CR network architecture was presented, in which each CR knows local network state information and performs spectrum sensing, channel estimation for joint routing, and channel access subject to realistic network topology and physical channel effects.