Related papers: Autoencoder-Based CSI Compression for Beyond Wi-Fi…
Driven by the ultra-high throughput requirements of 6G, wireless communications are migrating to centimeter wave (cmWave) bands to overcome the limitations of current spectral resources. Massive multiple-input multiple-output (MIMO) and…
In traditional cache-enabled small-cell networks (SCNs), a user can suffer strong interference due to contentcentric base station association. This may degenerate the advantage of collaborative content caching among multiple small base…
A major challenge to implement the compressed sensing method for channel state information (CSI) acquisition lies in the design of a well-performed measurement matrix to reduce the dimension of sparse channel vectors. The widely adopted…
Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However,…
Reciprocity-based beamforming-most commonly employed in time-division duplexing-uses noisy, estimated (i.e., measured) channel state information (CSI) acquired on the uplink. While computationally efficient, reciprocity-based beamforming…
To reap the active and passive beamforming gain in an intelligent reflecting surface (IRS)-aided wireless network, a typical way is to first acquire the channel state information (CSI) relying on the pilot signal, and then perform the joint…
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently…
Distributed optimization concerns the optimization of a common function in a distributed network, which finds a wide range of applications ranging from machine learning to vehicle platooning. Its key operation is to aggregate all local…
The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, and downlink channel state information (CSI) compression.…
We show that compact fully connected (FC) deep learning networks trained to classify wireless protocols using a hierarchy of multiple denoising autoencoders (AEs) outperform reference FC networks trained in a typical way, i.e., with a…
In a frequency division duplex (FDD) massive multiple input multiple output (MIMO) system, the channel state information (CSI) feedback causes a significant bandwidth resource occupation. In order to save the uplink bandwidth resources, a…
Recently, deep learning-enabled joint-source channel coding (JSCC) has received increasing attention due to its great success in image transmission. However, most existing JSCC studies only focus on single-input single-output (SISO)…
Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless…
An orthogonal affine-precoded superimposed pilot-based architecture is developed for the cyclic prefix (CP)-aided SISO and MIMO orthogonal time frequency space systems relying on arbitrary transmitter-receiver pulse shaping. The data and…
Collaborative beamforming enables nodes in a wireless network to transmit a common message over long distances in an energy efficient fashion. However, the process of making available the same message to all collaborating nodes introduces…
This paper proposes a novel approach for designing channel estimation, beamforming and scheduling jointly for wideband massive multiple input multiple output (MIMO) systems. With the proposed approach, we first quantify the maximum number…
In intelligent reflecting surface (IRS) aided wireless communication systems, channel state information (CSI) is crucial to achieve its promising passive beamforming gains. However, CSI errors are inevitable in practice and generally…
This paper aims to improve the explainability of Autoencoder's (AE) predictions by proposing two explanation methods based on the mean and epistemic uncertainty of log-likelihood estimate, which naturally arise from the probabilistic…
Inter-user interference (IUI) mitigation has been an essential issue for multi-user multiple-input multiple-output (MU-MIMO) communications. The commonly used linear processing schemes include the maximum-ratio combining (MRC), zero-forcing…
Building on recent advances in representation learning for wireless channels, this work investigates the cost-benefit trade-offs of high-dimensional channel embeddings in practical systems. We benchmark multiple wireless representations:…