# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 1 Introduction
Amongst these, two major concerns can be distinguished: (i) SUs need to know the training information about the PUs' signals, and (ii) the synchronization of the SU with the PU system is required. Regarding the former, however, it is possible for the SUs to obtain information about the PUs' signals if both users operate under a cooperation agreement in the CR context. For example, recent studies have considered PUs that opt to share part of their spectral resources with the SUs to get relaying services [13] or to improve the overall spectral efficiency, amongst other sorts of rewards [14]. Therefore, in such a scenario, it is also possible to share the PU's signal information, such as the modulation type or pilot sequences. Regarding the latter, the training information of the PUs can be used by the SUs to synchronize their receivers with the PU if a certain amount of time is assigned for this purpose. In this paper, a new approach that allows SUs to exploit the training sequence using a synchronized received sequence is proposed to improve the energy detector. After the sample collection of the spectrum sensing phase, the new method uses a first sample processing period to perform a synchronization procedure at the time the energy detection is carried out since it does not require the SU to be synchronized with the PU's transmissions. Then, in a second sample processing period, the new combined approach utilizes the synchronized sequence to implement an enhanced training-based detection algorithm, as long as the energy detector decides the absence of the PU's signal. Hence, the focus is on the spectrum sensing of superimposed training (ST)-based PU's transmissions, given the fact that ST signals might benefit both PUs and SUs. On the one hand, PUs' receivers can improve their own channel estimation task without reducing the data transmission bandwidth, which is of great importance for high data rate transmissions [15; 16]. On the other hand, SUs can consider the known ST sequence in the design of spectrum sensing algorithms to improve their detection performance, which reduces the unwanted interference to PUs' transmissions and increases the SUs' throughput. Additionally, ST allows the implementation of robust synchronization algorithms at the SUs' receivers, like those introduced in [17; 18]. Therefore, a detector for ST PUs' signals (called ST-Det) is designed and used in the second sample processing period of the proposed superimposed training combined approach for a reduced phase (SCAR-Phase) of spectrum sensing to implement an enhanced detection, considering the synchronized training and the data sequences. Furthermore, in order to reduce the computational complexity, a simplified version of the SCAR-Phase of spectrum sensing is presented. The proposed schemes are useful in the very low SNR region, where synchronization with the PU can be achieved to improve the spectrum sensing algorithms by exploiting the ST sequence. In order to show how the energy detector can be improved by means of the ST sequence in the very low SNR region, the proposed approaches are compared with this spectrum sensing algorithm. The results show that both, SCAR-Phase and simplified SCAR-Phase, requires a significantly reduced number of samples to achieve a target probability of detection in the low SNR, in contrast to the energy detector. Moreover, the SU's throughput gain is quantified for the proposed methods. The rest of the paper is organized as follows: In Section 2 the spectrum sensing scenario under consideration is described for ST PU's transmissions and the detection problem is formulated. In Section 3 the proposed SCAR-Phase of spectrum sensing is detailed along with the design of a detector for ST-based PUs' signals under a flat fading channel. The performance metrics of the SCAR-Phase of spectrum sensing are presented in Section 4. In Section 5, a simplified version of the SCAR-Phase spectrum sensing is described. Moreover, the results are shown in Section 6. Finally, Section 7 concludes the work.