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Found 14 papers, 2 papers with code
Channel-Adaptive Pilot Design for FDD-MIMO Systems Utilizing Gaussian Mixture Models
Once shared with the mobile terminal (MT), the GMM is utilized to determine a feedback index at the MT in the online phase.
An Efficient Rate Splitting Precoding Approach in Multi-User MISO FDD Systems
In this work, we develop an efficient precoding strategy for a multi-user multiple-input-single output (MU MISO) system operating in frequency-division-duplex (FDD) mode, where rate splitting multiple access (RSMA) is implemented.
Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation
This work proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models.
On the Asymptotic Mean Square Error Optimality of Diffusion Probabilistic Models
Diffusion probabilistic models (DPMs) have recently shown great potential for denoising tasks.
Wireless Channel Prediction via Gaussian Mixture Models
In this work, we utilize a Gaussian mixture model (GMM) to capture the underlying probability density function (PDF) of the channel trajectories of moving mobile terminals (MTs) within the coverage area of a base station (BS) in an offline phase.
Highly Accelerated Weighted MMSE Algorithms for Designing Precoders in FDD Systems with Incomplete CSI
In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode.
Design of a Single-User RIS-Aided MISO System Based on Statistical Channel Knowledge
Reconfigurable intelligent surface (RIS) is considered a prospective technology for beyond fifth-generation (5G) networks to improve the spectral and energy efficiency at a low cost.
Physically Consistent Modelling of Wireless Links with Reconfigurable Intelligent Surfaces Using Multiport Network Analysis
The vast majority of research publications on RIS are focussing on system-level optimization and are based on very simplistic models ignoring basic physical laws.
Reverse Ordering Techniques for Attention-Based Channel Prediction
This work aims to predict channels in wireless communication systems based on noisy observations, utilizing sequence-to-sequence models with attention (Seq2Seq-attn) and transformer models.
Asymptotic Behavior of Zero-Forcing Precoding based on Imperfect Channel Knowledge for Massive MISO FDD Systems
Although the LMMSE channel estimate exhibits a better quality in terms of the MSE due to the exploitation of the channel statistics, we show that in the case of contaminated channel observations, zero-forcing based on the LMMSE is unable to eliminate the inter-user interference in the asymptotic limit of high DL transmit powers.
Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation
In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which is learned on imperfect training data, i. e., the training data are solely comprised of noisy and sparsely allocated pilot observations.
Channel Estimation with Reduced Phase Allocations in RIS-Aided Systems
In order to illuminate the additional cascaded channel as compared to systems without a RIS, commonly an unaffordable amount of pilot sequences has to be transmitted over different phase allocations at the RIS.
Learning Representations for CSI Adaptive Quantization and Feedback
In this work, we propose an efficient method for channel state information (CSI) adaptive quantization and feedback in frequency division duplexing (FDD) systems.
Rate Splitting in FDD Massive MIMO Systems Based on the Second Order Statistics of Transmission Channels
In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output (MaMIMO) system operating in frequency-division-duplex (FDD) mode.
