## 5 Automatic Signal Recognition
### Extended Validation for Mixed Signals
#### 5.3.2 Algorithm and Feature Space Extension  
Ideally, one should be capable of making a distinction between Wi-Fi and LTE-U within the mixed traces. However, this is only meaningful when the observation period is much shorter than the on period of LTE-U, otherwise the RSSI collected in one observation duration will easily span several appearances of LTE-U and Wi-Fi, making the classification meaningless. Currently, observation  
Figure 13: An illustration of the fabricated RSSI trace, consisting of arbitrarily interleaved RSSI from Wi-Fi (indicated by ’W’) and LTE (indicated by ’L’). Note that the duration of continuous LTE RSSI is chosen within \((2,3,8,10)\) ms to be compliant with the recent LTE License Assisted Access (LAA) specification. periods are in the range of 1 ms to 1 s (see Section 5.2.3), which are comparable or much longer than the LTE-U on period. Though it is possible to further reduce the observation time, this action would sacrifice the recognition accuracy by reducing in RSSI sample size. In addition, we believe it is more beneficial for the constrained devices to know the present technology types at a larger time scale, rather than the sub-millisecond precision, since the devices have limited reaction speed. Hence, instead of considering adding LTE-U as an independent signal class, the mixed signal is considered as a new class 'MIX_LTE_Wi-Fi'. In this way, we gain more insights into the interference type while avoiding requirements in short observation and reaction time, making it more practical to apply on constrained devices. The operation of LTE-U is adapted to non-streaming mode for fair spectrum access. This means that the mixed signal will have a noise peak just like Wi-Fi; thus, the basic algorithm can already tell it apart from LTE and DVB-T. The only addition needed is the ability to distinguish the mixed signal from the pure Wi-Fi signal. To this end, two additional features are included, as shown in Figure 8 and displayed below:  
* \(\sigma_{\overrightarrow{\pi}}\): the standard deviation of the locations (indicated by grey flashes in Figure 8 and represented by vector \(\overrightarrow{\pi^{2}}\)), where the histogram elements located to the right side of \(\theta_{n}\) are equal to zero. It indicates the amount of discontinuities in the RSSI histogram. * \(p_{max}\): the amplitude of the highest peak in the histogram apart from the noise peak. According to [39], LTE-U only considers downlink; hence, only one sender is present, causing the RSSI distribution to have many densely-located peaks. While Wi-Fi almost always has multiple distributed senders, the RSSI distribution often contains widespread and independent peaks. This difference is well represented by \(\sigma_{\overrightarrow{\pi^{2}}}\). Furthermore, the mixed signals tend to occupy the medium more than Wi-Fi alone, leading to smaller \(p(\overline{\pi})\) and bigger \(p_{max}\). A simple voting algorithm is formed based on these conditions to distinguish Wi-Fi and MIX_LTE_Wi-Fi, as shown in Algorithm 2. This code replaces the part in between the'star' separation lines in Algorithm 1. ```
Variables: \(vote\leftarrow\) // A variable to count the vote for the mixed signals Function: \(vote=0\); if\(\sigma_{\overrightarrow{\pi}}\leq max(MIX\_LTE\_Wi-Fi.\sigma_{ \overrightarrow{\pi^{2}}})\)then \(vote\gets vote+1\) else \(vote\gets vote-1\) endif if\(p_{max}\geq min(MIX\_LTE\_Wi-Fi.p_{max})\)then \(vote\gets vote+1\) else \(vote\gets vote-1\) endif if\(p(\overrightarrow{n})\leq max(MIX\_LTE\_Wi-Fi.p(\overrightarrow{n}))\)then \(vote\gets vote+1\) else \(vote\gets vote-1\) endif if\(vote>0\)then \(sig\leftarrow\) MIX_LTE_Wi-Fi else if\(\sigma_{\overrightarrow{\pi^{2}}}\geq min(Wi\_Fi.p_{max})\)then \(sig\leftarrow\) Wi-Fi else \(sig\leftarrow\) noise endif endif
```  
**Algorithm 2** Extension to distinguish Wi-Fi from MIX_LTE_Wi-Fi. The new algorithm requires the supervised learning process of certain thresholds to be readjusted.