Receiver nonlinearity gives rise to intermodulation products that are caused by two strong adjacent channel signals called blockers. The nonlinear distortion effects are significantly higher for multiple antenna wideband systems in dispersive environments because third order intermodulation products decreases the signal-to-noise ratio (SNR) at the output of the equalization process. This complicates the demodulation process and increases the bit error rate. This paper considers such nonlinear distortion in the context of space-time shift keying (STSK)-enabled wideband single-carrier systems and proposes an iterative space-time block equalization (ISTBE) framework for frequency domain equalization. We present our design of a practical ISTBE receiver based on the turbo principle and numerically demonstrate that it effectively removes the residual inter-symbol interference while suppressing high-power blockers and the in-band intermodulation distortion that they cause. The proposed system is thus suitable for simple wideband radio frequency front ends operating in the weak nonlinear region and enables adjacent channel spectrum coexistence with heterogeneous transmitters and receivers of different qualities.
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AI-Driven Demodulators for Nonlinear Receivers in Shared Spectrum with High-Power Blockers
Research has shown that communications systems and receivers suffer from high power adjacent channel signals, called blockers, that drive the radio frequency (RF) front end into nonlinear operation. Since simple systems, such as the Internet of Things (IoT), will coexist with sophisticated communications transceivers, radars and other spectrum consumers, these need to be protected employing a simple, yet adaptive solution to RF nonlinearity. This paper therefore proposes a flexible data driven approach that uses a simple artificial neural network (ANN) to aid in the removal of the third order intermodulation distortion (IMD) as part of the demodulation process. We introduce and numerically evaluate two artificial intelligence (AI)-enhanced receivers—ANN as the IMD canceler and ANN as the demodulator. Our results show that a simple ANN structure can significantly improve the bit error rate (BER) performance of nonlinear receivers with strong blockers and that the ANN architecture and configuration depends mainly on the RF front end characteristics, such as the third order intercept point (IP3). We therefore recommend that receivers have hardware tags and ways to monitor those over time so that the AI and software radio processing stack can be effectively customized and automatically updated to deal with changing operating conditions.
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- NSF-PAR ID:
- 10331756
- Date Published:
- Journal Name:
- 2022 IEEE Wireless Communications and Networking Conference (WCNC)
- Page Range / eLocation ID:
- 644 to 649
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WCNC51071.2022.9771613
