Name
Title | ||
|---|---|---|
A Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures |
Abstract | ||
|---|---|---|
In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WCNC49053.2021.9417499 | 2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) |
Keywords | DocType | ISSN |
Physical-layer security, symbol-level precoding, machine learning, channel coding, and multi-user interference | Conference | 1525-3511 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abderrahmane Mayouche | 1 | 2 | 1.39 |
| Wallace A. Martins | 2 | 1 | 4.07 |
| Christos G. Tsinos | 3 | 116 | 18.30 |
| Symeon Chatzinotas | 4 | 1849 | 192.76 |
| Björn Ottersten | 5 | 56 | 8.40 |