Orthogonal frequency-division multiplexing
PulseAugur coverage of Orthogonal frequency-division multiplexing — every cluster mentioning Orthogonal frequency-division multiplexing across labs, papers, and developer communities, ranked by signal.
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New CRFCAN network tackles sub-THz communication noise
Researchers have developed CRFCAN, a novel complex-valued residual FFT convolutional attention network designed for joint channel and phase noise estimation in sub-terahertz (sub-THz) communications. This end-to-end net…
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AI enhances RF interference suppression with new transformer model
Researchers have developed an AI-enhanced method for suppressing radio frequency (RF) interference, building upon previous autoregressive transformer models. This new approach incorporates a Finite Scalar Quantization (…
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AI-based receiver boosts spectral efficiency in MIMO-OFDM systems
Researchers have developed a new framework for optimizing power allocation and designing AI-based receivers for superimposed data and reference symbol transmissions in MIMO-OFDM systems. The proposed AI-ICED receiver, u…
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OpenDPDv2 framework unifies NN-DPD learning and optimization for RF power amplifiers
Researchers have developed OpenDPDv2, an open-source framework designed to enhance digital predistortion (DPD) for radio frequency power amplifiers using neural networks. This framework integrates PA modeling, NN-DPD le…
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New Mamba-Attention Architecture Improves OFDM Channel Estimation
Researchers have developed a novel hybrid Mamba-Attention neural architecture designed to enhance channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly in scenarios with a larg…
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Attention Transformer enhances OFDM channel estimation with reduced complexity
Researchers have developed an Attention-aided MMSE (A-MMSE) framework that utilizes an Attention Transformer to learn linear MMSE filters for orthogonal frequency-division multiplexing (OFDM) channel estimation. This no…
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AI framework autonomously designs wireless communication algorithms · 2 sources tracked
Researchers have developed The AI Telco Engineer (AITE), a framework that uses large language model-driven evolutionary search to autonomously design wireless communication algorithms. AITE was applied to two physical-l…
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Deep learning framework tackles interference in OFDM systems
Researchers have developed a novel deep learning framework to address narrowband interference (NBI) in orthogonal frequency-division multiplexing (OFDM) systems. The framework integrates NBI cancellation and soft demodu…
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Transformer model enhances cooperative multi-AP OFDM uplink reception
Researchers have developed a novel cross-attention Transformer model designed for cooperative multi-access point (AP) OFDM uplink reception. This model efficiently fuses signals from multiple receivers, adapting to vary…
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6G OFDM-RIS Optimization Survey: Foundation Models and Deep Learning Emerge
A new survey paper explores optimization algorithms for joint Orthogonal Frequency-Division Multiplexing (OFDM) and Reconfigurable Intelligent Surface (RIS) configuration in 6G networks. It categorizes existing research…
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Neuromorphic computing uses RF neurons for energy-efficient wireless split processing
Researchers have developed a novel neuromorphic wireless split computing architecture utilizing resonate-and-fire (RF) neurons. This system processes time-domain signals directly, bypassing the need for energy-intensive…
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Deep learning framework slashes pilot overhead in mmWave MIMO systems
Researchers have developed a novel deep learning framework called Multi-Block Attention (MBA) to improve channel estimation in millimeter-wave MIMO systems assisted by Intelligent Reflecting Surfaces (IRSs). This framew…
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New framework uses adaptive learning for AoA-based outdoor localization
Researchers have developed an adaptive framework for angle-of-arrival (AoA) based outdoor localization, crucial for applications like intelligent transportation and smart cities. The framework offers two learning strate…
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AI-enhanced RF interference rejection uses transformers for faster, clearer transmissions
Researchers have developed an AI-enhanced method for rejecting radio frequency interference, outperforming traditional techniques by training on both the desired signal and interference mixtures. The new approach utiliz…