## 6 Conclusions and Future Work  
Received signal strength is affected by both the interruption of the signal in the time domain and the variation of carriers in the frequency domain. Due to the diverse modulation schemes and medium access control mechanisms, RSSI from many real-life technologies exhibit highly distinctive features. Experimental studies show that these features remain visible when RSSI is computed from samples acquired at a sub-Nyquist sampling rate, making them suitable for narrow-band devices to recognize wideband technologies.  
This work investigates two types of features from RSSI, (i) features extracted from the time domain RSSI series (i.e., the distribution of packet duration) and (ii) features acquired from the distribution of RSSI itself. The former can be used for distinction among non-streaming technologies, as illustrated by the case study of Wi-Fi and Bluetooth and discussed in several existing studies; while the latter is a novel contribution of this work, which is verified for application such as detecting the presence of non-streaming technologies and recognizing streaming technologies beyond the ISM bands.  
The usage of the RSSI distribution-based feature space is illustrated with a sample algorithm to identify three representative technologies (i.e., Wi-Fi, LTE and DVB-T), which are highly active in the context of dynamic spectrum access. Experimental results show that more than 92% recognition accuracy can be reached under the condition of a 1-MHz ADC sample rate, 5-kHz RSSI collection rate and 50-ms observation time. Literature studies indicate that these conditions are supported by several small-scale and narrow-band devices. Furthermore, considering the operation of LTE-U in ISM bands, the basic feature space is extended for mixed Wi-Fi and LTE signals as a positive first step towards the operation in a heterogeneous wireless environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual**Predicted** & **Wi-Fi** & **LTE** & **DVB-T** & **Mixed LTE Wi-Fi** \\ \hline Wi-Fi & 94.4\% & 0 \% & 0\% & 5.60\% \\ LTE & 0\% & 100\% & 0\% & 0\% \\ DVB-T & 0\% & 1.85\% & 98.15\% & 0\% \\ Mixed LTE Wi-Fi & 0\% & 1.85\% & 0\% & 98.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of the extended validation for \(N=20\), \(T=1\) s; noise and unknown signal classes are omitted because of all being 0% results.  
In the context of RSSI-based technology recognition, this work completes existing solutions in the sense that it covers a different set of technologies and uses a different feature space. It should be noted that the focus is not to design a perfect classification algorithm, nor to recognize all technologies exclusively using the RSSI distribution. With this work, we do want to prove that features from the probability distribution of RSSI are complementary to time domain features and remain valid under the sub-Nyquist condition, hence highly valuable to identify wideband technologies on constrained devices, for achieving better dynamic spectrum sharing in the next generation wireless networks.  
This research has received funding from the Flanders' Agency for Innovation by Science and Technology under Grant agreement No. IWT140048 SAMURAI Project, and from the European Horizon 2020 Programme under Grant agreement No. 688116 (eWINE Project) and No. 732174 (ORCA Project).  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, the data is analyzed together with [PERSON]; [PERSON], [PERSON] and [PERSON] provided essential input for state-of-art analysis, complexity and scalability analysis; [PERSON] made initial draft of the paper and the co-authors improved the text.  
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.