Analysis of a contention-based approach over 5G NR for Federated Learning in an Industrial Internet of Things scenario

The growing interest in new applications involving co-located heterogeneous requirements, such as the Industrial Internet of Things (IIoT) paradigm, poses unprecedented challenges to the uplink wireless transmissions. Dedicated scheduling has been the fundamental approach used by mobile radio systems for uplink transmissions, where the network assigns contention-free resources to users based on buffer-related information. The usage of contention-based transmissions was discussed by the 3rd Generation Partnership Project (3GPP) as an alternative approach for reducing the uplink latency characterizing dedicated scheduling. Nevertheless, the contention-based approach was not considered for standardization in LTE due to limited performance gains. However, 5G NR introduced a different radio frame which could change the performance achievable with a contention-based framework, although this has not yet been evaluated. This paper aims to fill this gap. We present a contention-based design introduced for uplink transmissions in a 5G NR IIoT scenario. We provide an up-to-date analysis via near-product 3GPP-compliant network simulations of the achievable application-level performance with simultaneous Ultra-Reliable Low Latency Communications (URLLC) and Federated Learning (FL) traffic, where the contention-based scheme is applied to the FL traffic. The investigation also involves two separate mechanisms for handling retransmissions of lost or collided transmissions. Numerical results show that, under some conditions, the proposed contention-based design provides benefits over dedicated scheduling when considering FL upload/download times, and does not significantly degrade the performance of URLLC

Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach

This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks. As far as the Radio Access Network (RAN) is concerned, centralized pre-configured resource allocation requires scheduling grants to be disseminated to the User Equipments (UEs) before uplink transmissions, which is not efficient for URLLC, especially in case of flexible/unpredictable traffic. To alleviate this burden, we study a distributed, user-centric scheme based on machine learning in which UEs autonomously select their uplink radio resources without the need to wait for scheduling grants or preconfiguration of connections. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB) approach represents a desirable solution to allocate resources with URLLC in mind in an IIoT environment, in case of both periodic and aperiodic traffic, even considering highly populated networks and aggressive traffic.Comment: 2022 IEEE Globecom Workshops (GC Wkshps): Future of Wireless Access and Sensing for Industrial IoT (FutureIIoT

A Wireless Protocol for Smart Manufacturing using LoRa at 2.4 GHz

This thesis proposes a new medium access control protocol which lies on top of LoRa at 2,4 GHz. Its name is LoRaIN and, in fact, it is intended for a monitoring Industrial Internet of Things application. First, this document provides a general description of the network architecture and relative functionalities. In particular, the centralized nature of the protocol is described in detail, being a discriminating factor with the others IIoT layer 2 protocols. A fundamental feature of LoRaIN is that the devices are not battery driven and, conversely, they are charged via Wireless Power Transfer technology. In fact, an important emphasis is given to the time division between nodes charges and LoRa communications. Finally, some numerical computations have been performed to show the network performance; the latter have been taken as reference for the hands-on work

Wireless solutions for the industrial internet of things

The fourth industrial revolution is paving the way for Industrial Internet of Things applications where industrial assets (e.g., robotic arms, valves, pistons) are equipped with a large number of wireless devices (i.e., microcontroller boards that embed sensors and actuators) to enable a plethora of new applications, such as analytics, diagnostics, monitoring, as well as supervisory, and safety control use-cases. Nevertheless, current wireless technologies, such as Wi-Fi, Bluetooth, and even private 5G networks, cannot fulfill all the requirements set up by the Industry 4.0 paradigm, thus opening up new 6G-oriented research trends, such as the use of THz frequencies. In light of the above, this thesis provides (i) a broad overview of the main use-cases, requirements, and key enabling wireless technologies foreseen by the fourth industrial revolution, and (ii) proposes innovative contributions, both theoretical and empirical, to enhance the performance of current and future wireless technologies at different levels of the protocol stack. In particular, at the physical layer, signal processing techniques are being exploited to analyze two multiplexing schemes, namely Affine Frequency Division Multiplexing and Orthogonal Chirp Division Multiplexing, which seem promising for high-frequency wireless communications. At the medium access layer, three protocols for intra-machine communications are proposed, where one is based on LoRa at 2.4 GHz and the others work in the THz band. Different scheduling algorithms for private industrial 5G networks are compared, and two main proposals are described, i.e., a decentralized scheme that leverages machine learning techniques to better address aperiodic traffic patterns, and a centralized contention-based design that serves a federated learning industrial application. Results are provided in terms of numerical evaluations, simulation results, and real-world experiments. Several improvements over the state-of-the-art were obtained, and the description of up-and-running testbeds demonstrates the feasibility of some of the theoretical concepts when considering a real industry plant

Characterization of Orthogonal Chirp Division Multiplexing and Performance Evaluation at THz Frequencies in the Presence of Phase Noise

Due to its superior performance, Orthogonal Chirp Division Multiplexing (OCDM) has recently gained attention as a potential replacement for Orthogonal Frequency Division Multiplexing (OFDM) in beyond-5G systems. In this paper, we provide an analytical characterization of OCDM signals, elucidating the theoretical principles that enable their numerical generation through the Inverse Discrete Fresnel Transform (IDFnT), despite the presence of severe frequency-domain aliasing that substantially distorts the signal at the transmitter output. Furthermore, in light of the proposed utilization of the THz band in beyond-5G systems, we investigate the performance of OCDM in this frequency range in the presence of thermal, molecular, and phase noise. To model the latter, which is expected to be a significant challenge at THz frequencies, we take as a reference an actual Phase Locked Loop (PLL) oscillator operating at 237.7 GHz. The numerical results reveal the achievable performance of OCDM as a function of several key factors, including the modulation order, the bandwidth, the number of chirps constituting the signal, the oscillator parameters, the channel model, and the use of techniques aimed at mitigating the impact of phase noise. The findings are compared with those of OFDM, which is regarded as a benchmark due to its adoption in 4G and 5G systems, and demonstrate the superior performance of OCDM also in the presence of significant phase noise

Enabling URLLC in 5G NR IIoT Networks: A Full-Stack End-to-End Analysis

This paper addresses the problem of enabling inter-machine ultra-reliable low-latency communication (URLLC) in 5th generation (5G) NR Industrial Internet of Things (IIoT) networks. In particular, we consider a common Standalone Non-Public Network (SNPN) architecture proposed by the 5G Alliance for Connected Industries and Automation (5G-ACIA), and formalize a full-stack end-to-end (E2E) latency analysis where semi-persistent uplink scheduling is considered in detail and compared with a baseline grant-based approach. Through simulations, we demonstrate that semi-persistent scheduling outperforms the baseline scheme and provides an E2E latency below 1 ms, thereby representing a desirable solution to allocate resources for URLLC. Notably, we provide numerical guidelines for dimensioning 3GPP-compliant IIoT networks for both periodic and aperiodic traffic applications, and as a function of the number of machines in the factory and of the offered traffic