## 10 Conclusions and Roadmap Ahead
The effect of the wireless channels upon the RFF algorithms can be further investigated via simulation- or measurement-based approaches and it remains a topic of future investigation;
* Understanding the impact of the receiver HW features upon the RFF methods: while the same receiver is capturing either genuine GNSS signals or a mixture of genuine signals and spoofer(s), and thus the same receiver effects are present in both situations (spoofer present or spoofer absent), the receiver also has local oscillators, ADC and filter blocks, etc., and each of them can introduce additional phase noises, non-linearities and I/Q imbalances. Intuitively, such effects will have a negative impact upon the classification accuracy compared to an ideal receiver (without any HW imperfections), but such effects need to be further analysed based on measurements or simulated data. * Dealing with the negative impact of high noise levels on RFF performance, especially when dealing with low-power signals such as those in the pre-correlation domain: GNSS signals in urban scenarios, such as GNSS receivers on-board of drones flying through tall buildings, can be received at relatively low CNRs, and these low CNRs are likely to act as smoothers of the transmitter features, to the point of fading them out. It remains an open research question what the CNR threshold is above which the RFF methods with pre-correlation GNSS samples are likely to work;
* Validating through real-field measurements the promising RFF performance for authenticating GNSS signals. One of the main contributions of our paper was presenting a step-by-step methodological approach proposed to be adopted for a designer wishing to build an RFF algorithm in a GNSS receiver. The identified transmitter HW features are likely to be reflected not only in the pre-correlation data (illustrated in our examples through the paper), but also in the post-correlation and navigation domains, thus our four-step methodology also paves the road towards more advanced RFF GNSS processing in all three domains (pre-correlation, post-correlation, and navigation), with a future aim to offer robust and hybrid anti-spoofing solutions. An additional contribution of this paper has been to present an ample survey of existing RFF methods in the literature used with both GNSS and non-GNSS signals and already showing promising results. As described in this last section, several challenges are still to be overcome towards the success of RFF methods, especially when relying on the low-power GNSS I/Q raw data. It is our belief that this survey bridges the missing gap between the RFF studies in the non-GNSS context and the anti-spoofing methods studied to date only at the post-correlation and navigation levels in the GNSS context. It is our intent that this paper sheds new light on how to approach an RF fingerprinting process to identify hidden transmitter features, by first decomposing the problem into the relevant transmitter features and then by selecting the most suitable pair of feature-extraction transform and classifier algorithm in order to classify the transmitters according to their features or HW impairments. While many challenges still remain in the RFF GNSS research field, it is also the authors' belief, based on our understanding of the research problem, that by combining various authentication methods, at different levels (pre-correlation, post-correlation, and navigation levels), one is more likely to obtain good results than by using a single authentication method. The simulation-based results presented here are only for some selected illustrative parameters and are useful in the context of down selecting the most important HW features of a GNSS transmitter. We saw that, even under ideal conditions such as 100 dBHz carrier-to-noise ratio, the phase noise and the DAC nonlinearities are not differentiating features, while P non-linearities, I/Q imbalances, and band-pass filters carry the potential of being good RF 'fingerprints'.