Related papers: Learning-Based Compress-and-Forward Schemes for th…
The compute-and-forward (CoF) is a relaying protocol, which uses algebraic structured codes to harness the interference and remove the noise in wireless networks. We propose the use of phase precoders at the transmitters of a network, where…
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.…
A new coding scheme for general N-node relay networks is presented for unicast, multicast, and broadcast. The proposed distributed decode-forward scheme combines and generalizes Marton coding for single-hop broadcast channels and the…
A non-regenerative dual-hop wireless system based on a distributed space-time coding strategy is considered. It is assumed that each relay retransmits an appropriately scaled space-time coded version of its received signal. The main goal of…
We consider the basic bidirectional relaying problem, in which two users in a wireless network wish to exchange messages through an intermediate relay node. In the compute-and-forward strategy, the relay computes a function of the two…
Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data. However, training is resource-intensive for edge devices, and limited network…
We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion center is assumed to contain a pretrained linear regression…
This paper considers a multiple input multiple output (MIMO) two-way relay channel, where two nodes want to exchange data with each other using multiple relays. An iterative algorithm is proposed to achieve the optimal achievable rate…
Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However, factors such as network topology and device computing power can affect its training or…
In this paper, a buffered decode and forward (DF) relay based three-node underlay cooperative cognitive relay network (CRN) is considered with a direct path to the destination. The source and the relay use multiple rates, and joint rate and…
In this letter, we propose a semantic communication scheme for wireless relay channels based on Autoencoder, named AESC, which encodes and decodes sentences from the semantic dimension. The Autoencoder module provides anti-noise performance…
We consider a backhaul-constrained coordinated cellular network. That is, a single-frequency network with $N+1$ multi-antenna base stations (BSs) that cooperate in order to decode the users' data, and that are linked by means of a common…
The trend of future communication systems is to aim for the steering and control of cyber physical systems. These systems can quickly become congested in environments like those presented in Industry 4.0. In these scenarios, a plethora of…
Training wide and deep neural networks (DNNs) require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward…
We explore a scheme that enables the training of a deep neural network in a Federated Learning configuration over an additive white Gaussian noise channel. The goal is to create a low complexity, linear compression strategy, called…
This paper studies a hierarchical over-the-air computation (AirComp) network over a large area, in which multiple relays are exploited to facilitate data aggregation from massive WDs. We present a two-phase amplify-and-forward (AF) relaying…
Recent work for single-relay channels shows that quantize-forward (QF) with long-message encoding achieves the same reliable rates as compress-forward (CF) with short-message encoding. It is shown that short-message QF with backward or…
Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients. During the training process of FL, the central server and distributed clients need to exchange a vast…
The multicast capacity of the Gaussian two-hop relay network with one source, $N$ relays, and $L$ destinations is studied. It is shown that a careful modification of the partial decode--forward coding scheme, whereby the relays cooperate…
"Amplify and Forward" and "Decode and Forward" are the two main relaying functions that have been proposed since the advent of cooperative communication. "\textit{Soft} Decode and Forward" is a recently introduced relaying principle that is…