Particle Filter Based Nonlinear Data Detection in Presence of CFO for Frequency Selective mmWave MIMO-OFDM Systems

Priya, Preety

doi:10.1109/tvt.2021.3078103

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…Further singlestream refinements are provided, e.g., in [7], [8], while [9] develops machine learning (ML) based physical-layer receiver for single-stream OFDM transmission showing robustness against PA distortion. Additionally, different multiantenna or MIMO variants have been considered in [10]- [17]. In [10], [12], transmission of multiple parallel streams is assumed, however, channel-aware transmit precoding is not considered and the TX nonlinearity is assumed known.…”

Section: Introductionmentioning

confidence: 99%

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Digital Post-Distortion for Multi-Layer MIMO-OFDM

Babaroglu,

Anttila,

et al. 2024

IEEE Wireless Commun. Lett.

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In this letter, we propose a new digital post-distortion (DPoD) technique for precoded multi-layer orthogonal frequency division multiplexing (OFDM) systems, in order to enhance the received signal error vector magnitude (EVM) under heavily nonlinear power amplifiers (PAs) in the transmitter. The proposed multi-layer digital post-inverse (ML-DPoI) builds on layer-or stream-level nonlinear distortion modeling and corresponding signal processing at receiver, combined with true reference signal based model parameter estimation allowing to operate without any prior knowledge of the transmit precoder or PA behavior. Numerical evaluations with dual-layer rank-2 transmission in 5G New Radio (NR) uplink context with standard-compatible physicallayer transmit waveform show that despite heavily nonlinear PA units, the received signal EVM can be reliably enhanced -even in the case of very high modulation order such as 256-QAM per transmit stream. These results pave the way towards improved power-efficiency and sustainability in future networks, especially at millimeter-wave bands, where transmitter power-efficiency is known to be a major implementation challenge.

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Section: Introductionmentioning

confidence: 99%

“…In [16], an iterative receiver scheme is devised for multi-layer transmission, assuming a known TX nonlinearity. The work in [17] considers hybrid beamforming based MIMO system and devises an iterative particle-filter based receiver solution building again on the assumption of a known TX nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Digital Post-Distortion for Multi-Layer MIMO-OFDM

Babaroglu,

Anttila,

et al. 2024

IEEE Wireless Commun. Lett.

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“…By using a low dimensional equivalent channel matrix, the authors in [12] develop a new approach to estimate the CFO with power amplifier impairment in the time domain in the hybrid mmWave MIMO OFDM systems. Then, by utilizing the sparse nature of mmWave channels in the angle-delay domain and compressibility of the phase error vector, a method for joint CFO and wideband channel estimation algorithm is designed in one-bit receivers in [13].…”

Section: Introductionmentioning

confidence: 99%

Joint Frequency Offset and Channel Estimation for NB-IoT Systems

Zhang¹,

He²,

Dong³

et al. 2022

J-NaNA

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The nonlinear distortions resulting from the radio frequency (RF) impairments such as frequency offset, I/Q-imbalance, phase noise, and quantization errors. The hardware impairments severely degrade signal reception performance in NB-IoT systems and hence estimation for hardware impairments is necessary and mitigated by compensation algorithms. To this end, this paper focuses on the problem of estimating channel gains and frequency offsets in an NB-IoT system. Speciﬁcally, we ﬁrst separate channel gains and frequency offsets and derive the analytical expressions of interest, based on the maximum likelihood estimation; We derive the expectation-maximization algorithm and apply it to the NB-IoT system for solving joint estimation of frequency offsets and channel gains; We carry out extensive simulations to illustrate that how systems parameters (e.g., channel phase difference, frequency offset difference, SNR, the number of sampling) can affect estimation performance.

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