Related papers: Nephalai: Towards LPWAN C-RAN with Physical Layer …

Compressing neural network architectures is important to allow the deployment of models to embedded or mobile devices, and pruning and quantization are the major approaches to compress neural networks nowadays. Both methods benefit when…

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS)…

This dissertation paper presents the main contributions to the design and the implementation of a Cloud-RAN solution. We concretely address the two main challenges of Cloud-RAN systems: real-time processing of radio signals and reduced…

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency. However, it incorporates…

Laparoscopic surgery offers minimally invasive procedures with better patient outcomes, but smoke presence challenges visibility and safety. Existing learning-based methods demand large datasets and high computational resources. We propose…

Split computing distributes deep neural network inference between resource-constrained edge devices and cloud servers but faces significant communication bottlenecks when transmitting intermediate features. To this end, in this paper, we…

Cloud RAN (C-RAN) is a promising enabler for distributed massive MIMO systems, yet is vulnerable to its fronthaul congestion. To cope with the limited fronthaul capacity, this paper proposes a hybrid analog-digital precoding design that…

In this paper, we study the stochastic optimization of cloud radio access networks (C-RANs) by joint remote radio head (RRH) activation and beamforming in the downlink. Unlike most previous works that only consider a static optimization…

This paper considers networked sensing in cellular network, where multiple base stations (BSs) first compress their received echo signals from multiple targets and then forward the quantized signals to the central unit (CU) via…

To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station…

Low-Power Wide-Area Networks (LPWANs) are being successfully used for the monitoring of large-scale systems that are delay-tolerant and which have low-bandwidth requirements. The next step would be instrumenting these for the control of…

In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device adopts quantized neural networks (QNNs) to train a local FL…

The heterogeneous cloud radio access network (Cloud-RAN) provides a revolutionary way to densify radio access networks. It enables centralized coordination and signal processing for efficient interference management and flexible network…

Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and fluid dynamics, generating massive datasets that often…

Radio access network (RAN) virtualization is gaining more and more ground and expected to re-architect the next-generation cellular networks. Existing RAN virtualization studies and solutions have mostly focused on sharing communication…

Physical layer key generation based on reciprocal and random wireless channels has been an attractive solution for securing resource-constrained low-power wide-area networks (LPWANs). When quantizing channel measurements, namely received…

We study the problem of uplink compression for cell-free multi-input multi-output networks with limited fronthaul capacity. In compress-forward mode, remote radio heads (RRHs) compress the received signal and forward it to a central unit…

The Internet of Things (IoT) revolution demands scalable, energy-efficient communication protocols supporting widespread device deployments. The LoRa technology, coupled with the LoRaWAN protocol, has emerged as a leading Low Power Wide…

This paper presents the first comprehensive real-world measurement campaign comparing LR-FHSS and LoRa modulations within LoRaWAN networks in urban environments. Conducted in Halifax, Canada, the campaign used a LoRaWAN platform capable of…

The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation.…