Related papers: Broadcast Cooperation Strategies for Two Colocated…

In this paper, link selection for half-duplex buffer-aided relay systems is revisited. A new fixed scheduling policy referred as alternate distributed beamforming (ADB) is proposed, in which the relays are divided into two groups, with one…

Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare, marketing and finance. However the application of FL in…

This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The…

This work analyzes the gains of cooperative relaying in interference-limited networks, in which outages can be due to interference and fading. A stochastic model based on point process theory is used to capture the spatial randomness…

Design of energy efficient protocols for modern wireless systems has become an important area of research. In this paper, we propose a distributed optimization algorithm for the channel assignment problem for multiple interfering…

Cooperation between nodes sharing a wireless channel is becoming increasingly necessary to achieve performance goals in a wireless network. The problem of determining the feasibility and stability of cooperation between rational nodes in a…

A multi-antenna broadcast channel scenario is considered where a base station delivers contents to cache-enabled user terminals. A joint design of coded caching (CC) and multigroup multicast beamforming is proposed to benefit from spatial…

Interference is a major issue limiting the performance in wireless networks. Cooperation among receivers can help mitigate interference by forming distributed MIMO systems. The rate at which receivers cooperate, however, is limited in most…

Federated learning (FL) is a recently developed area of machine learning, in which the private data of a large number of distributed clients is used to develop a global model under the coordination of a central server without explicitly…

This paper demonstrates the significant gains that multi-access users can achieve from sharing a single amplify-forward relay in slow fading environments. The proposed protocol, namely the multi-access relay amplify-forward, allows for a…

The constantly increasing demand for interactive broadband satellite communications is driving current research to explore novel system architectures that reuse frequency in a more aggressive manner. To this end, the topic of dual satellite…

Federated Learning (FL) is an emerging learning framework that enables edge devices to collaboratively train ML models without sharing their local data. FL faces, however, a significant challenge due to the high amount of information that…

The high attenuation which increases as frequency increases in power line communication systems (PLC) makes achieving and sustaining high data rates over such channels a challenging task. In most communication systems, relays are normally…

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in…

In this work, we investigate a multi-source multi-cast network with the aid of an arbitrary number of relays, where it is assumed that no direct link is available at each S-D pair. The aim is to find the fundamental limit on the maximal…

This paper considers a system where one transmitter broadcasts a single common message to two receivers linked by a bidirectional cooperation channel, which is assumed to be orthogonal to the downlink channel. Assuming a simplified setup…

Cooperative relaying has been proposed as a promising transmission technique that effectively creates spatial diversity through the cooperation among spatially distributed nodes. However, to achieve efficient communications while gaining…

Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. The standard optimization method in FL is Federated Averaging (FedAvg), which performs…

In this paper, we propose a new cooperation model for discrete memoryless multiple access channels. Unlike in prior cooperation models (e.g., conferencing encoders), where the transmitters cooperate directly, in this model the transmitters…

Future Internet usage will be dominated by the consumption of a rich variety of online multimedia services accessed from an exponentially growing number of multimedia capable mobile devices. As such, future Internet designs will be…