Growing congestion in current mobile networks necessitates innovative solutions. This paper contributes a novel network planning approach for mmWave 5G networks in urban settings, focusing on Integrated Access and Backhaul (IAB) and the Smart Radio Environment (SRE). The mmWave traffic will be mainly made of short bursts to transfer large volumes of data and long idle periods where data are processed, necessitating changes in how mobile radio networks are designed. Our proposed optimization models integrate IAB with SRE technologies while leveraging the maximization of achievable peak throughput rather than conventional average throughput metrics. Results highlight the advantages of this approach during the network planning phase, providing insights into better accommodating the demands of mobile traffic without sacrificing the overall network capacity.

The increasing demand for mobile bandwidth is driving 5G networks toward the use of high-frequency spectrum, particularly the upper-6 GHz and mmWave bands. While these bands offer vast bandwidth potential, their propagation characteristics raise critical deployment challenges. This paper presents the first direct, on-field comparative evaluation of 5G standalone (SA) macro-cell deployments operating in these two bands, conducted in Milan, Italy. We show that the upper-6 GHz band can deliver wide-area urban coverage (up to 600 meters) with stable gigabit-level downlink throughput, even in (NLoS) scenarios. mmWave, traditionally deemed unsuitable for NLoS, exhibits strong performance via urban reflections, achieving up to 1.3 Gbps in downlink and 250 Mbps in uplink. Furthermore, outdoor-to-indoor connectivity at mmWave frequencies proves viable through glass facades, challenging pessimistic assumptions about penetration losses. These findings, derived from synchronized deployments and extensive measurements, provide new insights into the complementary roles of these bands and the practical feasibility of their integration into future 5G networks.

Millimeter-Wave (mm-Wave) Radio Access Networks (RANs) are a promising solution to tackle the overcrowding of the sub-6 GHz spectrum, offering wider and underutilized bands. However, they are characterized by inherent technical challenges, such as a limited propagation range and blockage losses caused by obstacles. Integrated Access and Backhaul (IAB) and Reconfigurable Intelligent Surfaces (RIS) are two technologies devised to face these challenges. This work analyzes the optimal network layout of RANs equipped with IAB and RIS in real urban scenarios using MILP formulations to derive practical design guidelines. In particular, it shows how optimizing the peak user throughput of such networks improves the achievable peak throughput, compared to the traditional mean-throughput maximization approaches, without actually sacrificing mean throughputs. In addition, it indicates star-like topologies as the best network layout to achieve the highest peak throughputs.

The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks but comes with several challenges. Network densification has been proposed as a viable solution to increase RAN resilience, and the newly introduced Integrated-Access-and-Backhaul (IAB) is considered a key enabling technology with compelling cost-reducing opportunities for such dense deployments. Reconfigurable Intelligent Surfaces (RIS) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation and behave as inexpensive passive relays. However, it is not yet clear what role this technology can play in a large RAN deployment. With the scope of filling this gap, we study the blockage resilience of realistic mm-Wave RAN deployments that use IAB and RIS. The RAN layouts have been optimised by means of a novel mm-Wave planning tool based on MILP formulation. Numerical results show how adding RISs to IAB deployments can provide high blockage resistance levels while significantly reducing the overall network planning cost.

Mm-Wave 5G NR promises to provide unprecedented access throughput to mobile radio networks but comes with several challenges. Network densification is the only viable solution to increase robustness in front of link outages due to random obstacles. While Integrated-Access-and-Backhauling (IA B) architecture is commonly considered the enabling technology to reduce the cost of such dense deployments, Reconfigurable Intelligent Surfaces (RISs) and Smart Repeaters (SRs) are very recently emerging as promising tools to provide further capabilities to 6G networks in Smart Radio Environments. However, the impact of these devices on next-generation networks is yet to be fully understood. In this paper, we provide a first answer to the questions arising in the deployment of mmWave RAN equipped with SRs and RISs. By means of mathematical programming models for full network planning optimization, we produce optimal network layouts that we leverage to assess in which scenario, in which position, and with which configuration SRs and RISs can better improve the reliability of such networks.

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but its scaling properties for this purpose have not been widely studied. Machine learning algorithms are typically evaluated to learn SISO communications and low modulation orders, whereas current wireless standards use MIMO and high-order modulation schemes to increase capacity. The memory requirements of current Machine learning algorithms for wireless communications increase exponentially with the number of antennas and thus they cannot be used for advanced physical layers and massive MIMO. In this paper, we study the requirements of end-to-end Machine learning models for large-scale MIMO systems, determine the bottlenecks of the architecture, and design different solutions that vastly reduce overhead and allow training higher MIMO and modulation orders. We show that by training the autoencoder in a bit-wise manner, the memory requirements are reduced by several orders of magnitude, which is a critical step for Machine learning-based physical layer design in practical scenarios. Additionally, our design also improves performance over the classical autoencoder for MIMO.

With the capability to support gigabit data rates, millimetre-wave (mm-Wave) communication is unanimously considered a key technology of future cellular networks. However, the harsh propagation at such high frequencies makes these networks quite susceptible to failures due to obstacle blockages. Recently introduced Reconfigurable Intelligent Surfaces (RISs) can enhance the coverage of mm-Wave communications by improving the received signal power and offering an alternative radio path when the direct link is interrupted. While several works have addressed this possibility from a communication standpoint, none of these has yet investigated the impact of RISs on large-scale mm-Wave networks. Aiming to fill this literature gap, we propose a new mathematical formulation of the coverage planning problem that includes RISs. Using well-established planning methods, we have developed a new optimization model where RISs can be installed alongside base stations to assist the communications, creating what we have defined as Smart Radio Connections. Our simulation campaigns show that RISs effectively increase both throughput and coverage of access networks, while further numerical results highlight additional benefits that the simplified scenarios analyzed by previous works could not reveal.

Millimeter wave (mm-wave) communication is a topic of intensive recent study, as it allows to significantly boost data rates of future 5G networks. In this paper, we focus on a mm-wave system consisting of a single Access Point (AP) and two User Equipments (UEs), where one UE requires high throughput, while the other is characterized by a low latency demand. Given that setup, we aim at optimally allocating the available AP hardware resources for the beam training phase and data communication, in order to efficiently serve both UEs via hybrid analog-digital beamforming. We evaluate an optimization framework with the objective to maximize the expected rate of one UE, for a given latency constraint set by the other UE. The optimal data rates are illustrated for different latency constraints and for different strategies of exploiting the full RF chain set at the AP side. We observe that our proposed access schemes outperform the basic TDMA approach by up to 22 %.

In order to cope with the severe path loss, millimeter-wave (mm-wave) systems exploit highly directional communication. As a consequence, even a slight beam mis-alignment between two communicating devices (for example, due to mobility) can generate a significant signal drop. This leads to frequent invocations of time-consuming mechanisms for beam re-alignment, which deteriorate system performance. In this paper, we propose smart beam training and tracking strategies for fast mm-wave link establishment and maintenance under node mobility. We leverage the ability of hybrid analog-digital transceivers to collect channel information from multiple spatial directions simultaneously and formulate a probabilistic optimization problem to model the temporal evolution of the mm-wave channel under mobility. In addition, we present for the first time a beam tracking algorithm that extracts information needed to update the steering directions directly from data packets, without the need for spatial scanning during the ongoing data transmission. Simulation results, obtained by a custom simulator based on ray tracing, demonstrate the ability of our beam training/tracking strategies to keep the communication rate only 10% below the optimal bound. Compared to the state of the art, our approach provides a 40% to 150% rate increase, yet requires lower complexity hardware.

Millimeter-wave (mm-wave) communication systems can provide much higher data rates than systems operating at lower frequencies, but achieving such rates over sufficiently large distances requires highly directional beamforming at both the transmitter and receiver. These antenna beams have to be aligned very precisely in order to obtain sufficient link margin. In this paper, we first propose a parallel-adaptive beam training protocol, which significantly accelerates the link establishment between mm-wave devices by exploiting the ability of hybrid analog-digital beamforming antennas to scan multiple spatial sectors simultaneously. Second, we deal with practical constraints of the mm-wave transceivers and design a novel greedy geometric algorithm to synthesize sector beam patterns featuring configurable beamwidth and multi-beam radiation as required by the proposed beam training protocol. These multi-beam patterns are then also used for concurrent data communication over multiple paths, in case several suitable directions are found during the beam training. Simulation results show that our algorithm is able to shape antenna patterns very close to those attained by a fully digital beamforming architecture, yet requires lower complexity hardware compared with the state-of-the-art solutions. Exploiting such multi-beam antenna patterns, our parallel beam training protocol can provide up to 82% effective rate increase and 70% search time decrease compared with existing sequential protocols. The acceleration of the beam training phase shifts the optimum balance between the search overhead and the achieved directivity gain so that the best performance is reached with a training load 30% to 60% lower than that of the sequential beam training.

OpenVLC (www.openvlc.org) is an open-source project for research in Visible Light Communication (VLC) systems. It has the potential to help create a new type of infrastructure, an Internet of Lights (IoL), where LED- based devices (e.g., car lights, city lights, billboards, toy, etc) and photodetectors become inter- connected. OpenVLC is built upon an embedded platform and adopts off-the-shelf optical devices and essential electronic components. It has been proved to be a starter kit for VLC research, thanks to its deployments by tens of top universities/research centers in the world (www.openvlc.org/list-of-users.html). This demo introduces its latest version: OpenVLC1.1. The new features include: (1) higher resilience to ambient light noise, e.g., indoor interfering lighting; (2) interface for sensor application. A new board (OpenVLC1.1 cape) is designed and the printed circuit is developed. The cape is plugged directly into the main embedded board and external sensors can be easily connected to the cape. This demo demonstrates OpenVLC1.1's networking performance through standard networking diagnostic tools and shows an application wherein temperature and humidity sensed data are transmitted through VLC links.

Millimeter wave (mmWave) wireless technologies are expected to become key enablers of multi-gigabit wireless access in next-generation cellular and local area networks. Due to unfavorable radio propagation, mmWave systems will exploit large-scale MIMO and adaptive antenna arrays at both the transmitter and receiver to realize sufficient link margin. Unfortunately, power and cost requirements in mmWave radio frontends make the use of fully-digital beamforming very challenging. In this paper, we focus on hybrid analog-digital beamforming and address two relevant aspects of the initial access procedure at mmWave frequencies. First, we propose a beam training protocol which effectively accelerates the link establishment by exploiting the ability of mobile users to simultaneously receive from multiple directions. Second, we deal with practical constraints of mmWave transceivers and propose a novel, geometric approach to synthesize multi-beamwidth beam patterns that can be leveraged for simultaneous multi-direction scanning. Simulation results show that the proposed hybrid codebooks are able to shape beam patterns very close to those attained by a fully-digital beamforming architecture, yet require lower complexity hardware compared with the state of the art. Furthermore, the reduced duration of the beam training phase, in turn enabled by the multi-beam characteristics of our hybrid codebooks, provides a 25% to 70% increase in spectral efficiency compared to existing sequential scanning strategies.

Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients’ physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

Current Radio-Frequency Identification (RFID) technology involves two types of physical devices: tags and reader. The reader combines in a single physical device transmission (to the tags) and reception (from the tags) functions. In this paper we discuss an alternative approach, where receive functions are performed by a separate device called listener. This allows distributed tag-sensing schemes where one transmitter coexists with multiple listeners. We discuss pros and cons of both approaches and present our implementation of a passive RFID listener on GNU Radio. Our implementation is a basis for experimenting with future distributed listener-based systems, but it can be also used as a cheap and flexible protocol analyzer for currently available commercial RFID readers.