Related papers: Load Estimation in a Two-Priority mMTC Random Acce…

This paper investigates a new model to improve the scalability of low-power long-range (LoRa) networks by allowing multiple end devices (EDs) to simultaneously communicate with multiple multi-antenna gateways on the same frequency band and…

Accurately and efficiently estimating system performance under uncertainty is paramount in power system planning and operation. Monte Carlo simulation is often used for this purpose, but convergence may be slow, especially when detailed…

The Internet of Things (IoT) is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the high volumes of traffic that must be accommodated. Cellular networks are indeed a natural candidate…

The Internet of Things (IoT) has been increasingly used in our everyday lives as well as in numerous industrial applications. However, due to limitations in computing and power capabilities, IoT devices need to send their respective tasks…

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among…

The cellular Internet-of-Things has resulted in the deployment of millions of machine-type communication (MTC) devices. These massive number of devices must communicate with a single gNodeB (gNB) via the random access channel (RACH)…

This study aims to introduce and address the problem of traffic load estimation in the cell switching concept within the evolving landscape of vertical heterogeneous networks (vHetNets). The problem is that the practice of cell switching…

Massive access is one of the main use cases of beyond 5G (B5G) wireless networks and massive MIMO is a key technology for supporting it. Prior works studied massive access in the co-located massive MIMO framework. In this paper, we…

We characterize a two tier heterogeneous network, consisting of classical sub-6GHz macro cells, and multi Radio Access Technology (RAT) small cells able to operate in sub-6GHz and millimeter-wave (mm-wave) bands. For optimizing coverage and…

According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estimation choice, however, it is not always a feasible one. In network diffusion models with unobserved signal propagation, MLE requires…

For wireless systems in which randomly arriving devices attempt to transmit a fixed payload to a central receiver, we develop a framework to characterize the system throughput as a function of arrival rate and per-user data rate. The…

Mobile edge computing (MEC) networks are one of the key technologies for ultra-reliability and low-latency communications. The computing resource allocation solution needs to be carefully designed to guarantee the computing resource…

Machine-to-Machine (M2M) communication is crucial in developing Internet of Things (IoT). As it is well known that cellular networks have been considered as the primary infrastructure for M2M communications, there are several key issues to…

The high risk of random access collisions leads to huge challenge for the deployment massive Machine-Type Communications (mMTC), which cannot be sufficiently overcome by current solutions in LTE/LTE-A networks such as the extended access…

We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale…

Massive Machine-Type Communications (MTC) over cellular networks is expected to be an integral part of wireless "Smart City" applications. The Long Term Evolution (LTE)/LTE-Advanced (LTE-A) technology is a major candidate for provisioning…

Distribution Load Estimation (DLE) is a key function of Distribution Management System (DMS). In this paper a novel method for presenting historical load data in the form of Representative Load Curves (RLC) is presented. Adaptive…

Traffic pattern prediction has emerged as a promising approach for efficiently managing and mitigating the impacts of event-driven bursty traffic in massive machine-type communication (mMTC) networks. However, achieving accurate predictions…

The realization of data-driven AI-native architecture envisioned for 6G and beyond networks can eventually lead to multiple machine learning (ML) workloads distributed at the network edges driving downstream tasks like secondary carrier…

In this paper, we consider the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving aggregate throughput of the secondary users. We first present the…