Name
Abstract | ||
|---|---|---|
Finite spectrum resources and increasing application bandwidth requirements have made dynamic spectrum access (DSA) central to future wireless networks. Modulation recognition (modrec) is an essential component of DSA, and thus, has received significant attention in the literature. The majority of modrec work focuses on single antenna (SISO) communication, however, multi -antenna transmitters have recently become ubiquitous driving the need for recognition of MIMO modulated signals. Existing MIMO modrec assumes multiple antenna sensors, imposing a prohibitive monetary, storage, and computational cost for spectrum sensing. In this work we propose a machine learning framework for under-determined IMMO modrec which enables robust recognition even when the MIMO signal is scanned with a single -antenna sensor. Our goal is to reduce the hardware costs of modulation recognition without compromising its accuracy. Our key insight is that MIMO modulation constellations exhibit a fractal (self-similar) structure which we exploit to derive discriminative and efficient-to -extract features based on the fractal dimension of observed IQ samples. Our evaluation results demonstrate a superior discriminative power of our fractal features compared to the widely -adopted high -order cumulant features. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/INFOCOMWKSHPS50562.2020.9163011 | IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) |
DocType | ISSN | Citations |
Conference | 2159-4228 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Xiong | 1 | 45 | 21.33 |
| Lin Zhang | 2 | 7 | 3.69 |
| Maxwell McNeil | 3 | 0 | 0.68 |
| Petko Bogdanov | 4 | 160 | 16.51 |
| Mariya Zheleva | 5 | 41 | 12.17 |