Unsupervised Learning for Pilot-free Transmission in 3GPP MIMO Systems
- URL: http://arxiv.org/abs/2302.02191v1
- Date: Sat, 4 Feb 2023 16:32:40 GMT
- Title: Unsupervised Learning for Pilot-free Transmission in 3GPP MIMO Systems
- Authors: Omar M. Sleem, Mohamed Salah Ibrahim, Akshay Malhotra, Mihaela Beluri,
Philip Pietraski
- Abstract summary: This paper introduces a new downlink data structure that is free from demodulation reference signals (DM-RS)
Exploiting the repetition structure at the user side, it is shown that reliable recovery is possible via canonical correlation analysis.
This paper also proposes two effective mechanisms for boosting the CCA performance in OFDM systems.
- Score: 6.352264764099531
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reference signals overhead reduction has recently evolved as an effective
solution for improving the system spectral efficiency. This paper introduces a
new downlink data structure that is free from demodulation reference signals
(DM-RS), and hence does not require any channel estimation at the receiver. The
new proposed data transmission structure involves a simple repetition step of
part of the user data across the different sub-bands. Exploiting the repetition
structure at the user side, it is shown that reliable recovery is possible via
canonical correlation analysis. This paper also proposes two effective
mechanisms for boosting the CCA performance in OFDM systems; one for repetition
pattern selection and another to deal with the severe frequency selectivity
issues. The proposed approach exhibits favorable complexity-performance
tradeoff, rendering it appealing for practical implementation. Numerical
results, using a 3GPP link-level testbench, demonstrate the superiority of the
proposed approach relative to the state-of-the-art methods.
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