arXiv subjects

Maurizio Magarini

Publications and source records attributed to Maurizio Magarini.

At least 37 records · Page 2Linked to original sources

arXiv2024

Advancing O-RAN to Facilitate Intelligence in V2X

Vehicular communications integrated with the Radio Access Network (RAN) are envisioned as a breakthrough application for the 6th generation (6G) cellular systems. However, traditional RANs lack the flexibility to enable sophisticated control mechanisms that are demanded by the strict performance requirements of the vehicle-to-everything (V2X) environment. In contrast, the features of Open RAN (O-RAN) can be exploited to support advanced use cases, as its core paradigms represent an ideal framework for orchestrating vehicular communication. Although the high potential stemming from their integration can be easily seen and recognized, the effective combination of the two ecosystems is an open issue. Conceptual and architectural advances are required for O-RAN to be capable of facilitating network intelligence in V2X. This article pioneers the integration of the two strategies for seamlessly incorporating V2X control within O-RAN ecosystem. First, an enabling architecture that tightly integrates V2X and O-RAN is proposed and discussed. Then, a set of key V2X challenges is identified, and O-RAN-based solutions are proposed, paired with extensive numerical analysis to support their effectiveness. Results showcase the superior performance of such an approach in terms of raw throughput, network resilience, and control overhead. Finally, these results validate the proposed enabling architecture and confirm the potential of O-RAN in support of V2X communications.

cs.NI↗

arXiv2024

Open RAN-empowered V2X Architecture: Challenges, Opportunities, and Research Directions

Advances in the automotive industry and the ever-increasing demand for Connected and Autonomous Vehicles (CAVs) are pushing for a new epoch of networked wireless systems. Vehicular communications, or Vehicle-to-Everything (V2X), are expected to be among the main actors of the future beyond 5G and 6G networks. However, the challenging application requirements, the fast variability of the vehicular environment, and the harsh propagation conditions of high frequencies call for sophisticated control mechanisms to ensure the success of such a disruptive technology. While traditional Radio Access Networks (RAN) lack the flexibility to support the required control primitives, the emergent concept of Open RAN (O-RAN) appears as an ideal enabler of V2X communication orchestration. However, effectively integrating the two ecosystems is still an open issue. This paper discusses possible integration strategies, highlighting the challenges and opportunities of leveraging O-RAN to enable real-time V2X control. Additionally, we enrich our discussion with potential research directions stemming from the current state-of-the-art, and we provide preliminary simulation results that validate the effectiveness of the proposed integration.

cs.NI↗

arXiv2024

Semantic Information in MC: Chemotaxis Beyond Shannon

The recently emerged molecular communication (MC) paradigm intends to leverage communication engineering tools for the design of synthetic chemical communication systems. These systems are envisioned to operate at nanoscale and in biological environments, such as the human body, and catalyze the emergence of revolutionary applications in the context of early disease monitoring and drug targeting. Despite the abundance of theoretical (and recently also experimental) MC system designs proposed over the past years, some fundamental questions remain unresolved, hindering the breakthrough of MC in real-world applications. One of these questions is: What can be a useful measure of information in the context of MC applications? While most existing works on MC build upon the concept of syntactic information as introduced by Shannon, in this paper, we explore the framework of semantic information as introduced by Kolchinsky and Wolpert for the information-theoretic analysis of a natural MC system, namely bacterial chemotaxis. Exploiting computational agent-based modeling (ABM), we are able to quantify, for the first time, the amount of information that the considered chemotactic bacterium (CB) utilizes to adapt to and survive in a dynamic environment. In other words, we show how the flow of information between the environment and the CB is related to the effectiveness of communication. Effectiveness here refers to the adaptation of the CB to the dynamic environment in order to ensure survival. Our analysis reveals that it highly depends on the environmental conditions how much information the CB can effectively utilize for improving their survival chances. Encouraged by our results, we envision that the proposed semantic information framework can open new avenues for the development of theoretical and experimental MC system designs for future nanoscale applications.

cs.IT↗

arXiv2024

Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces

Neuropathies are gaining higher relevance in clinical settings, as they risk permanently jeopardizing a person's life. To support the recovery of patients, the use of fully implanted devices is emerging as one of the most promising solutions. However, these devices, even if becoming an integral part of a fully complex neural nanonetwork system, pose numerous challenges. In this article, we address one of them, which consists of the classification of motor/sensory stimuli. The task is performed by exploring four different types of artificial neural networks (ANNs) to extract various sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification through a comparison of their performance in terms of accuracy, F1-score, and prediction time. The design of the ANNs takes advantage of the modelling of the ENG signal as a multiple-input multiple-output (MIMO) system to describe the measures taken by state-of-the-art implanted nerve interfaces. These are based on the use of multi-contact cuff electrodes to achieve nanoscale spatial discrimination of the nerve activity. The MIMO ENG signal model is another contribution of this paper. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over $90\%$ for signal windows of $100$ and $200\,$ms with a low enough processing time to be effective for pathology recovery.

cs.AI↗

arXiv2024

Exploring ISAC Technology for UAV SAR Imaging

This paper illustrates the potential of an Integrated Sensing and Communication (ISAC) system, operating in the sub-6 GHz frequency range, for Synthetic Aperture Radar (SAR) imaging via an Unmanned Aerial Vehicle (UAV) employed as an aerial base station. The primary aim is to validate the system's ability to generate SAR imagery within the confines of modern communication standards, including considerations like power limits, carrier frequency, bandwidth, and other relevant parameters. The paper presents two methods for processing the signal reflected by the scene. Additionally, we analyze two key performance indicators for their respective fields, the Noise Equivalent Sigma Zero (NESZ) and the Bit Error Rate (BER), using the QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa), demonstrating the system's capability to image buried targets in challenging scenarios. The paper shows simulated Impulse Response Functions (IRF) as possible pulse compression techniques under different assumptions. An experimental campaign is conducted to validate the proposed setup by producing a SAR image of the environment captured using a UAV flying with a Software-Defined Radio (SDR) as a payload.

eess.SP↗

arXiv2024

A Thorough Analysis of Radio Resource Assignment for UAV-Enhanced Vehicular Sidelink Communications

The rapid expansion of connected and autonomous vehicles (CAVs) and the shift towards millimiter-wave (mmWave) frequencies offer unprecedented opportunities to enhance road safety and traffic efficiency. Sidelink communication, enabling direct Vehicle-to-Vehicle (V2V) communications, play a pivotal role in this transformation. As communication technologies transit to higher frequencies, the associated increase in bandwidth comes at the cost of a severe path and penetration loss. In response to these challenges, we investigate a network configuration that deploys beamforming-capable Unmanned Aerial Vehicles (UAVs) as relay nodes. In this work, we present a comprehensive analytical framework with a groundbreaking performance metric, i.e. average access probability, that quantifies user satisfaction, considering factors across different protocol stack layers. Additionally, we introduce two Radio Resources Assignment (RRA) methods tailored for UAVs. These methods consider parameters such as resource availability, vehicle distribution, and latency requirements. Through our analytical approach, we optimize the average access probability by controlling UAV altitude based on traffic density. Our numerical findings validate the proposed model and strategy, which ensures that Quality of Service (QoS) standards are met in the domain of Vehicle-to-Anything (V2X) sidelink communications.

cs.NI↗

arXiv2023

Parametric Channel Estimation with Short Pilots in RIS-Assisted Near- and Far-Field Communications

Considering the dimensionality of a typical reconfigurable intelligent surface (RIS), channel state information acquisition in RIS-assisted systems requires lengthy pilot transmissions. Moreover, the large aperture of the RIS may cause transmitters/receivers to fall in its near-field region, where both distance and angles affect the channel structure. This paper proposes a parametric maximum likelihood estimation (MLE) framework for jointly estimating the direct channel between the user and the base station (BS) and the line-of-sight channel between the user and the RIS, in both far-field and near-field scenarios. The MLE framework is first developed for the case of single-antenna BS and later extended to the scenario where the BS is equipped with multiple antennas. A novel adaptive RIS configuration strategy is proposed to select the RIS configuration for the next pilot to actively refine the estimate. We design a minimal-sized codebook of orthogonal RIS configurations to choose from during pilot transmission with a dimension much smaller than the number of RIS elements. To further reduce the required number of pilots, we propose an initialization strategy with two wide beams. We demonstrate numerically that the proposed MLE method needs only a few pilots for achieving accurate channel estimates and further show that the presented framework performs well under Rician fading. We also showcase efficient user channel tracking in near-field and far-field scenarios.

eess.SP↗

arXiv2023

On the Achievable Rate of MIMO Narrowband PLC with Spatio-Temporal Correlated Noise

Narrowband power line communication (NB-PLC) systems are an attractive solution for supporting current and future smart grids. A technology proposed to enhance data rate in NB-PLC is multiple-input multiple-output (MIMO) transmission over multiple power line phases. To achieve reliable communication over MIMO NB-PLC, a key challenge is to take into account and mitigate the effects of temporally and spatially correlated cyclostationary noise. Noise samples in a cycle can be divided into three classes with different distributions, i.e. Gaussian, moderate impulsive, and strong impulsive. However, in this paper we first show that the impulsive classes in their turn can be divided into sub-classes with normal distributions and, after deriving the theoretical capacity, two noise sample sets with such characteristics are used to evaluate achievable information rates: one sample set is the measured noise in laboratory and the other is produced through MIMO frequency-shift (FRESH) filtering. The achievable information rates are attained by means of a spatio-temporal whitening of the portions of the cyclostationary correlated noise samples that belong to the Gaussian sub-classes. The proposed approach can be useful to design the optimal receiver in terms of bit allocation using waterfilling algorithm and to adapt modulation order.

cs.IT↗

arXiv2023

Achievable Information Rate Analysis in Diffusive Channels with Memory and Markov Source

This paper explores the Achievable Information Rate (AIR) of a diffusive Molecular Communication (MC) channel featuring a fully absorbing receiver that counts the absorbed particles during symbol time intervals (STIs) and resets the counter at the start of each interval. The MC channel, influenced by memory effect, experiences inter-symbol interference (ISI) arising from the molecules' delayed arrival. The channel's memory is quantified as an integer multiple of the STI and a single-sample memoryless detector is employed to mitigate complexity in computing the mutual information (MI). To maximize MI, the detector threshold is optimized under Gaussian approximation of its input. The channel's MI is calculated, considering the influence of ISI, in the context of binary concentration shift keying modulation. Two distinct scenarios were considered; independent and correlated source-generated symbols, the latter modeled as a first-order Markov process. For each communication scenario, two degrees of knowledge: ISI-Aware and ISI-Unaware were considered. Remarkably, it is demonstrated that employing a correlated source enables the attainment of higher capacity. The results indicate that the capacity-achieving input distribution is not necessarily uniform. Notably, when the STI is small, corresponding to the case of strong ISI, the maximum AIR is not achieved through equiprobable symbol transmission.

cs.IT↗

arXiv2023

Achievable Rate Analysis in Molecular Channels with Reset-Counting Fully Absorbing Receivers

In this paper, we investigate the achievable rate of a diffusive Molecular Communication (MC) channel with fully absorbing receiver, which counts particles absorbed along each symbol interval and resets the counter at every interval (reset-counting). The MC channel is affected by a memory effect and thus inter-symbol interference (ISI), due to the delayed arrival of molecules. To reduce complexity, our analysis is based on measuring the channel memory as an integer number of symbol intervals and on a single-sample memoryless detector. Thus, in our model the effect of released particles remains effective for a limited number of symbol intervals. We optimize the detector threshold for maximizing capacity, approximate as Gaussian the received signal distribution, and calculate the channel mutual information affected by ISI, in the case of binary concentration shift keying modulation. To the best of our knowledge, in literature there are no previous investigations on the achievable rate in this type of system. Our results demonstrate that, in general, the optimal input probability distribution achieving the maximum achievable rate may be not uniform. In particular, when the symbol interval is small (strong ISI), the maximum achievable rate does not occur with equiprobable transmission of bits.

cs.IT↗

arXiv2023

Heuristic Barycenter Modeling of Fully Absorbing Receivers in Diffusive Molecular Communication Channels

In a recent paper it has been shown that to model a diffusive molecular communication (MC) channel with multiple fully absorbing (FA) receivers, these can be interpreted as sources of negative particles from the other receivers' perspective. The barycenter point is introduced as the best position where to place the negative sources. The barycenter is obtained from the spatial mean of the molecules impinging on the surface of each FA receiver. This paper derives an expression that captures the position of the barycenter in a diffusive MC channel with multiple FA receivers. In this work, an analytical model inspired by Newton's law of gravitation is found to describe the barycenter, and the result is compared with particle-based simulation (PBS) data. Since the barycenter depends on the distance between the transmitter and receiver and the observation time, the condition that the barycenter can be assumed to be at the center of the receiver is discussed. This assumption simplifies further modeling of any diffusive MC system containing multiple FA receivers. The resulting position of the barycenter is used in channel models to calculate the cumulative number of absorbed molecules and it has been verified with PBS data in a variety of scenarios.

cs.IT↗

arXiv2023

A New Channel Subspace Characterization for Channel Estimation in RIS-Aided Communications

A reconfigurable intelligent surface (RIS) is a holographic MIMO surface composed of a large number of passive elements that can induce adjustable phase shifts to the impinging waves. By creating virtual line-of-sight (LOS) paths between the transmitter and the receiver, RIS can be a game changer for millimeter-wave (mmWave) communication systems that typically suffer from severe signal attenuation. Reaping the benefits of RIS, however, relies on the accuracy of the channel estimation, which is a challenging task due to the large number of RIS elements. Specifically, conventional channel estimators require a pilot overhead equal to the number of RIS elements, which is impractical. Herein, we propose a novel way to approximately represent the RIS channels in a lower-dimensional subspace and derive the basis vectors for the identified subspace. We use this channel structure to only send pilots in this subspace, thereby vastly saving on the pilot overhead. Numerical results demonstrate that when the RIS has an element spacing of a quarter of the wavelength, our method reduces the pilot overhead by 80% with retained or even improved performance.

eess.SP↗

arXiv2023

Intra-Body Communications for Nervous System Applications: Current Technologies and Future Directions

The Internet of Medical Things (IoMT) paradigm will enable next generation healthcare by enhancing human abilities, supporting continuous body monitoring and restoring lost physiological functions due to serious impairments. This paper presents intra-body communication solutions that interconnect implantable devices for application to the nervous system, challenging the specific features of the complex intra-body scenario. The presented approaches include both speculative and implementative methods, ranging from neural signal transmission to testbeds, to be applied to specific neural diseases therapies. Also future directions in this research area are considered to overcome the existing technical challenges mainly associated with miniaturization, power supply, and multi-scale communications.

eess.SP↗

arXiv2022

Integrated Sensing and Communication System via Dual-Domain Waveform Superposition

Integrated Sensing and Communication (ISAC) systems are recognised as one of the key ingredients of the sixth generation (6G) network. A challenging topic in ISAC is the design of a single waveform combining both communication and sensing functionalities on the same time-frequency-space resources, allowing to tune the performance of both with partial or full hardware sharing. This paper proposes a dual-domain waveform design approach that superposes onto the frequency-time (FT) domain both the legacy orthogonal frequency division multiplexing (OFDM) signal and a sensing one, purposely designed in the delay-Doppler domain. With a proper power downscaling of the sensing signal w.r.t. OFDM, it is possible to exceed regulatory bandwidth limitations proper of legacy multicarrier systems to increase the sensing performance while leaving communication substantially unaffected. Numerical and experimental results prove the effectiveness of the dual-domain waveform, notwithstanding a power abatement of at least 30 dB of the signal used for sensing compared to the one used for communication. The dual-domain ISAC waveform outperforms both OFDM and orthogonal time-frequency-space (OTFS) in terms of Cramér-Rao bound on delay estimation (up to 20 dB), thanks to its superior resolution, with a negligible penalty on the achievable rate.

cs.IT↗

arXiv2022

The Capacity of Fading Vector Gaussian Channels Under Amplitude Constraints on Antenna Subsets

Upper bounds on the capacity of vector Gaussian channels affected by fading are derived under peak amplitude constraints at the input. The focus is on constraint regions that can be decomposed in a Cartesian product of sub-regions. This constraint models a transmitter configuration employing a number of power amplifiers less than or equal to the total number of transmitting antennas. In general, the power amplifiers feed distinct subsets of the transmitting antennas and partition the input in independent subspaces. Two upper bounds are derived: The first one is suitable for high signal-to-noise ratio (SNR) values and, as we prove, it is tight in this regime; The second upper bound is accurate at low SNR. Furthermore, the derived upper bounds are applied to the relevant case of amplitude constraints induced by employing a distinct power amplifier for each transmitting antenna.

cs.IT↗

arXiv2022

IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G

In this article, we present our vision of preamble detection in a physical random access channel for next-generation (Next-G) networks using machine learning techniques. Preamble detection is performed to maintain communication and synchronization between devices of the Internet of Everything (IoE) and next-generation nodes. Considering the scalability and traffic density, Next-G networks have to deal with preambles corrupted by noise due to channel characteristics or environmental constraints. We show that when injecting 15% random noise, the detection performance degrades to 48%. We propose an informative instance-based fusion network (IIFNet) to cope with random noise and to improve detection performance, simultaneously. A novel sampling strategy for selecting informative instances from feature spaces has also been explored to improve detection performance. The proposed IIFNet is tested on a real dataset for preamble detection that was collected with the help of a reputable commercial company.

cs.NI↗

arXiv2022

OTFS-superimposed PRACH-aided Localization for UAV Safety Applications

The adoption of Unmanned Aerial Vehicles (UAVs) for public safety applications has skyrocketed in the last years. Leveraging on Physical Random Access Channel (PRACH) preambles, in this paper we pioneer a novel localization technique for UAVs equipped with cellular base stations used in emergency scenarios. We exploit the new concept of Orthogonal Time Frequency Space (OTFS) modulation (tolerant to channel Doppler spread caused by UAVs motion) to build a fully standards-compliant OTFS-modulated PRACH transmission and reception scheme able to perform time-of-arrival (ToA) measurements. First, we analyze such novel ToA ranging technique, both analytically and numerically, to accurately and iteratively derive the distance between localized users and the points traversed by the UAV along its trajectory. Then, we determine the optimal UAV speed as a trade-off between the accuracy of the ranging technique and the power needed by the UAV to reach and keep its speed during emergency operations. Finally, we demonstrate that our solution outperforms standard PRACH-based localization techniques in terms of Root Mean Square Error (RMSE) by about 20% in quasi-static conditions and up to 80% in high-mobility conditions.

cs.NI↗

arXiv2022

LoS-Map Construction for Proactive Relay of Opportunity Selection in 6G V2X Systems

Recent advances in Vehicle-to-Everything (V2X) technology and the upcoming sixth-generation (6G) network will dawn a new era for vehicular services with enhanced communication capabilities. Connected and Autonomous Vehicles (CAVs) are expected to deliver a new transportation experience, increasing the safety and efficiency of road networks. The use of millimeter-wave (mmW) frequencies guarantees a huge amount of bandwidth (> 1GHz) and a high data rate (> 10Gbit/s), which are required for CAVs applications. However, high frequency is impaired by severe path loss, and line of sight (LoS) propagation can be easily blocked by static and dynamic obstacles. Several solutions are being investigated, and the most promising one exploits relays. However, traditional relay schemes react to link failure and leverage instantaneous information, which impedes efficient relay selection in highly mobile and complex networks, such as vehicular scenarios. In this context, we propose a novel proactive relaying strategy that exploits the cooperation between CAVs and environment information to predict the dynamic LoS-map, which describes the links' evolution in time. The proactive relaying schemes exploit the dynamic LoS-map to maximize the network connectivity. A novel framework integrating realistic mobility patterns and geometric channel propagation models is proposed to analyze the performance in different scenarios. Numerical simulations suggest that the proactive relaying schemes mitigate beam blockage and maximize the average probability of connecting CAVs with reliable links.

eess.SP↗