Related papers: Adaptive Neural Network-based OFDM Receivers
A limited feedback based dynamic resource allocation algorithm is proposed for a relay cooperative network with Orthogonal Frequency Division Multiplexing (OFDM) modulation. A communication model where one source node communicates with one…
Deep neural networks (DNNs), trained with gradient-based optimization and backpropagation, are currently the primary tool in modern artificial intelligence, machine learning, and data science. In many applications, DNNs are trained offline,…
Machine learning (ML) tools such as encoder-decoder deep convolutional neural networks (CNN) are able to extract relationships between inputs and outputs of large complex systems directly from raw data. For time-varying systems the…
The orthogonal frequency division multiplexing is a very efficient modulation technique that can achieve very high throughput by transmitting many carriers simultaneously and it is spectrally efficient because of the proximity of the…
High-speed off-road autonomous driving presents unique challenges due to complex, evolving terrain characteristics and the difficulty of accurately modeling terrain-vehicle interactions. While dynamics models used in model-based control can…
We consider a joint radar estimation and communication system using orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) modulations. The scenario is motivated by vehicular applications where a…
Orthogonal frequency division multiplexing - integrated sensing and communication (OFDM-ISAC) has emerged as a key enabler for future wireless networks, leveraging the widely adopted OFDM waveform to seamlessly integrate wireless…
Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented. A novel…
In this paper, we present a multi-user resource allocation framework using fragmented-spectrum synchronous OFDM-CDMA modulation over a frequency-selective fading channel. In particular, given pre-existing communications in the spectrum…
Over the past few decades, extensive research has been devoted to the design of artificial reverberation algorithms aimed at emulating the room acoustics of physical environments. Despite significant advancements, automatic parameter tuning…
The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement of wireless communication systems. However, acquiring accurate CSI, which can be done through channel estimation…
In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading…
Acquiring the channel state information from limited and noisy observations at pilot positions is critical for wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. In this paper, we view…
In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal…
Transformers have become the dominant architecture for sequence modeling tasks such as natural language processing or audio processing, and they are now even considered for tasks that are not naturally sequential such as image…
Ensuring reliable and predictable communications is one of the main goals in modern industrial systems that rely on Wi-Fi networks, especially in scenarios where continuity of operation and low latency are required. In these contexts, the…
We introduce a neural network (NN)-based multiuser multiple-input multiple-output (MU-MIMO) receiver with 5G New Radio (5G NR) physical uplink shared channel (PUSCH) compatibility. The NN architecture is based on convolution layers to…
Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scenarios, DNN-based receivers should be periodically retrained…
In this paper, the minimum channel gain flow with uncertainty in the demand vector is examined. The approach is based on a transformation of uncertainty in the demand vector to uncertainty in the gain vector. OFDM systems are known to…
Addressing the challenges of irregularity and concept drift in streaming time series is crucial for real-world predictive modelling. Previous studies in time series continual learning often propose models that require buffering long…