Related papers: eAFH: Informed Exploration for Adaptive Frequency …

Heterogeneous Networks (HetNets) are known to enhance the bandwidth efficiency and throughput of wireless networks by more effectively utilizing the network resources. However, the higher density of users and access points in HetNets…

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept…

Individual links in a wireless network may experience unequal fading coherence times due to differences in mobility or scattering environment, a practical scenario where the fundamental limits of communication have been mostly unknown. This…

In indoor areas such as homes and offices, high throughput communication for multiple devices is quickly becoming a necessity. Even though an access point (AP) mounted with an omni-directional antenna can cover a whole room, it cannot…

A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device computation costs. Split Federated Learning (SFL) systems mitigate this by offloading a block of…

Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to…

In this work, we consider a multi-antenna cognitive ad hoc network (CAHNet) with heterogeneous delay requirements. To fulfill the interference and delay constraints simultaneously, we propose to perform adaptive zero-forcing beamforming…

The deployment of federated learning in a wireless network, called federated edge learning (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study…

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a…

Integrated access and backhaul (IAB) is one of the promising techniques for 5G networks and beyond (6G), in which the same node/hardware is used to provide both backhaul and cellular services in a multi-hop fashion. Due to the sensitivity…

With the increasing development of Internet of Things (IoT), the upcoming sixth-generation (6G) wireless network is required to support grant-free random access of a massive number of sporadic traffic devices. In particular, at the…

Distributed machine learning (ML) over wireless networks hinges on accurate channel state information (CSI) and efficient exchange of high-dimensional model updates. These demands are governed by channel coherence time and bandwidth, which…

Bluetooth Low Energy (BLE) has become an intrinsic wireless technology for the Internet of Things (IoT). With the proliferation of BLE-embedded IoT devices, it is important to study the security and privacy implications of BLE. The…

Spectrum access is an important function of cognitive radios for detecting and utilizing spectrum holes without interfering with the legacy systems. In this paper we propose novel cooperative communication models and show how deploying such…

This paper develops a 3GPP-inspired design for the in-band-full-duplex (IBFD) integrated access and backhaul (IAB) networks in the frequency range 2 (FR2) band, which can enhance the spectral efficiency (SE) and coverage while reducing the…

Deploying fiber optics as a last-mile solution in rural areas is not economically viable due to low population density. Nevertheless, providing high-speed internet access in these regions is essential to promote digital inclusion. 5G Fixed…

Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology,…

Federated edge learning (FEEL) is a framework for training models in a distributed fashion using edge devices and a server that coordinates the learning process. In FEEL, edge devices periodically transmit model parameters to the server,…

Interference alignment is a signaling technique that provides high multiplexing gain in the interference channel. It can be extended to multi-hop interference channels, where relays aid transmission between sources and destinations. In…

One significant challenge in research is to collect a large amount of data and learn the underlying relationship between the input and the output variables. This paper outlines the process of collecting and validating a dataset designed to…