Related papers: When Cellular Meets WiFi in Wireless Small Cell Ne…
This work centers on the communication aspects of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD). Considering the actual communication cost or delay caused by…
In future sixth-generation (6G) mobile networks, the Internet-of-Everything (IoE) is expected to provide extremely massive connectivity for small battery-powered devices. Indeed, massive devices with limited energy storage capacity impose…
Future wireless networks are envisioned to facilitate the seamless coexistence of communication and sensing functionalities, thereby enabling the much-touted integrated sensing and communication (ISAC) paradigm. A key challenge in ISAC is…
A cellular multiple-input multiple-output (MIMO) downlink system is studied in which each base station (BS) transmits to some of the users, so that each user receives its intended signal from a subset of the BSs. This scenario is referred…
To meet the growing spectrum demands, future cellular systems are expected to share the spectrum of other services such as radar. In this paper, we consider a network multiple-input multiple-output (MIMO) with partial cooperation model…
New challenges have emerged from the integration of renewable energy sources within the conventional electrical grid which powers base stations (BS). Energy-aware traffic offloading brings a promising solution to maintain the user…
The plethora of wirelessly connected devices, whose deployment density is expected to largely increase in the upcoming sixth Generation (6G) of wireless networks, will naturally necessitate substantial advances in multiple access schemes.…
The evolving fifth generation (5G) cellular wireless networks are envisioned to overcome the fundamental challenges of existing cellular networks, e.g., higher data rates, excellent end-to-end performance and user-coverage in hot-spots and…
Split learning (SL) offloads main computing tasks from multiple resource-constrained user equippments (UEs) to the base station (BS), while preserving local data privacy. However, its computation and communication processes remain…
Next generation cellular networks will be heterogeneous with dense deployment of small cells in order to deliver high data rate per unit area. Traffic variations are more pronounced in a small cell, which in turn lead to more dynamic…
This paper considers a mobile edge computing-enabled cell-free massive MIMO wireless network. An optimization problem for the joint allocation of uplink powers and remote computational resources is formulated, aimed at minimizing the total…
We consider the uplink of massive multicell multiple-input multiple-output systems, where the base stations (BSs), equipped with massive arrays, serve simultaneously several terminals in the same frequency band. We assume that the BS…
The evolution from wired system to the wireless environment opens a set of challenge for the improvement of the wireless system performances because of many of their weakness compared to wired networks. To achieve this goal, cross layer…
Cell switching is a promising approach for improving energy efficiency in wireless networks; however, existing studies largely rely on simplified models and energy-centric formulations that overlook key performance-limiting factors. This…
Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables…
Todays heterogeneous networks comprised of mostly macrocells and indoor small cells will not be able to meet the upcoming traffic demands. Indeed, it is forecasted that at least a 100x network capacity increase will be required to meet the…
Split Federated Learning (SFL) offers a promising approach for distributed model training in wireless networks, combining the layer-partitioning advantages of split learning with the federated aggregation that ensures global convergence.…
Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer.…
Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices without sharing raw data. While FL supports low-latency…
The LTE standards account for the use of relays to enhance coverage near the cell edge. In a traditional topology, a mobile can either establish a direct link to the base station (BS) or a link to the relay, but not both. In this paper, we…