Abstract
In this study, with the aim of reducing power consumption for terahertz band wireless communication, we present a deep learning-based solution for transceiver design with 1-bit quantization and oversampling at the receiver, and Faster- than-Nyquist transmission. Our simulation results illustrate that the studied system with 1-bit quantization achieves bit-error- rate performance comparable to that of an end-to-end channel autoencoder without the constraint of 1-bit quantization subject to the same spectral efficiency. It is also demonstrated that reliable communication can be achieved at rates exceeding 5.3 bits/sec/Hz which corresponds to 80% of the achievable capacity at adequate SNR.
Original language | English |
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Pages (from-to) | 515-516 |
Number of pages | 2 |
Journal | Proceedings - IEEE Consumer Communications and Networking Conference, CCNC |
DOIs | |
Publication status | Published - 2022 |
Event | 19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States Duration: 2022 Jan 8 → 2022 Jan 11 |
Keywords
- autoencoder
- deep learning
- one-bit-quantization
- oversampling
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering