## 1 Introduction  
Due to the quick growth of wireless communications, radio spectrum bands are either already claimed by licensed users or heavily loaded by applications in the unlicensed bands. The shortage of spectrum resource has become a key limitation of wireless communications. One way to resolve the issue is to extend the spectrum towards a higher frequency band via new technologies, such as millimeter wave communication [1] assisted by massive MIMO [2]; another way is to improve the efficiency of the already allocated frequency bands by allowing dynamic spectrum access (DSA) [3]. DSA essentially means sharing spectrum among heterogeneous technologies, which can be achieved either \"horizontally\" [4] or \"vertically\" [5]. Vertical spectrum sharing refers to the opportunistic access of licensed bands without compromising the incumbents' communication quality, while horizontal spectrum sharing refers to the access of unlicensed bands by multiple technologies with equal privileges. Several technologies (e.g., Wi-Fi, ZigBee, Bluetooth) are already sharing the IndustrialScientific and Medical (ISM) bands in the horizontal style. Attracted by the free spectrum, licensed technologies are also considering to operate in the unlicensed bands. The LTE-U --LTE unlicensed, a recent enhancement of LTE to boost cell coverage via the operation in the ISM band--is the best example here [6]. In the near future, communication above 60 GHz is foreseen to meet the throughput demand of the next generation wireless networks (NGWN). Hyper dense deployment of small cells will be necessary to compensate for higher path loss in these frequency ranges. In this scenario, exclusive frequency assignment per technology is beyond feasibility, thus the need for autonomous DSA mechanisms. Detecting signals of other technologies is the prerequisite of DSA. The most straightforward approach is to sense the presence of energy in the medium, commonly referred to as energy detection [7; 8; 9; 10]. Sometimes, merely detecting the signal's existence is not sufficient; it is also crucial to identify the technology type. In the context of vertical spectrum sharing, if the detected signal is primary, the secondary user should back off, otherwise the delay is unnecessary. For horizontal spectrum sharing, recognizing the technology provides extra insights to counteract the interference: e.g., for a Wi-Fi network, if the concurrent technology is LTE-U [11], it would be beneficial to maximize the usage of the periodical off-time (LTE-U switches off traffic periodically so that Wi-Fi can share the medium). From a broader perspective, real-time technology recognition is essential for the interoperability among Multiple Radio Access Technologies (Multi-RAT) in the NGWN [12; 13]. Mainstream technology identifiers often share the following limitations: (i) the requirement for a priori knowledge of the target technology (i.e., the known pattern inside the signal of the target technology, such as the preamble or training sequence during the data transfer), making it difficult to distinguish multiple technologies in a single implementation; (ii) the requirement for the RF signal to be sampled above Nyquist frequency; else a certain part of the signal will be \"cut off\", making it impractical to be identified by techniques such as matched filtering or preamble detection; (iii) the involvement of long-term observation and intensive computation is not suitable for real-time applications on embedded devices; finally, (iv) most solutions can identify technologies that operate either purely in streaming mode (likely in the licensed spectrum) or in non-streaming mode (likely in ISM bands); hence, they are insufficient to achieve DSA across the spectrum as a whole. To overcome these constraints, this work presents technology recognition in both licensed and the unlicensed spectrum using novel features from RSSI distribution and time domain statistics acquired at a sub-Nyquist sampling rate.