Uncrewed Aerial Vehicles (UAVs) are increasingly used in civilian and industrial applications, making secure low-altitude operations crucial. In dense mmWave environments, accurately classifying low-altitude UAVs as either inside authorized or restricted airspaces remains challenging, requiring models that handle complex propagation and signal variability. This paper proposes a deep learning model, referred to as CoBA, which stands for integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention which leverages Fifth Generation (5G) millimeter-wave (mmWave) radio measurements to classify UAV operations in authorized and restricted airspaces at low altitude. The proposed CoBA model integrates convolutional, bidirectional recurrent, and attention layers to capture both spatial and temporal patterns in UAV radio measurements. To validate the model, a dedicated dataset is collected using the 5G mmWave network at TalTech, with controlled low altitude UAV flights in authorized and restricted scenarios. The model is evaluated against conventional ML models and a fingerprinting-based benchmark. Experimental results show that CoBA achieves superior accuracy, significantly outperforming all baseline models and demonstrating its potential for reliable and regulated UAV airspace monitoring.

The design of accurate Digital Twins (DTs) of electromagnetic environments strictly depends on the fidelity of the underlying environmental modeling. Evaluating the differences among diverse levels of modeling accuracy is key to determine the relevance of the model features towards both efficient and accurate DT simulations. In this paper, we propose two metrics, the Hausdorff ray tracing (HRT) and chamfer ray tracing (CRT) distances, to consistently compare the temporal, angular and power features between two ray tracing simulations performed on 3D scenarios featured by environmental changes. To evaluate the introduced metrics, we considered a high-fidelity digital twin model of an area of Milan, Italy and we enriched it with two different types of environmental changes: (i) the inclusion of parked vehicles meshes, and (ii) the segmentation of the buildings facade faces to separate the windows mesh components from the rest of the building. We performed grid-based and vehicular ray tracing simulations at 28 GHz carrier frequency on the obtained scenarios integrating the NVIDIA Sionna RT ray tracing simulator with the SUMO vehicular traffic simulator. Both the HRT and CRT metrics highlighted the areas of the scenarios where the simulated radio propagation features differ owing to the introduced mesh integrations, while the vehicular ray tracing simulations allowed to uncover the distance patterns arising along realistic vehicular trajectories.

This paper addresses the challenge of achieving simultaneous synchronization and localization of all the active terminals within a cellular network from only one Base Station (BS). We propose a novel approach leveraging Digital Network Twins (DNT), which integrates these two critical tasks within a unified framework.We begin by analyzing User Equipment (UE)-to-network time synchronization, both theoretically and through experimental validation using a 5th generation (5G) testbed, identifying it as the primary obstacle to accurate localization. Then, to address this challenge, we introduce a DNT-based framework that leverages high-fidelity ray-tracing simulations on a 3D digital replica of the environment. This enables precise UE-to-network alignment, dynamic environmental mapping, and accurate real-time localization starting from one Next Generation Node Base (gNB). The proposed method integrates Angle Delay Channel Power Matrix (ADCPM) characterization and Time of Flight (ToF) data with the DNT prior knowledge of the environment, eliminating the need for network cooperation or prior on-field channel measurements for precise localization. We first validate the proposed approach through an outdoor measurement campaign and then demonstrate its effectiveness via numerical simulations, compared to existing localization techniques in scenarios where only a single gNB is available. The method achieves on average a positioning accuracy of less than 6m in the static case and 8m in the dynamic scenario, using a ray-tracing granularity that is not excessively fine (4 × 4 m), even under worst-case synchronization and Non-Line of Sight (NLoS) conditions.

Technologies developed at universities are direct means to conceive a teaching plan for promoting science, technology, engineering and mathematics (STEM) at the K-12 level of education. This work-in-progress develops this notion: We elaborate a teaching plan for K-12 students based on technologies for recording plant bioelectrical activity. The teaching plan sketches hands-on activities, where pupils can self-assemble the electronic components such as the electro-potential sensor, analog-to-digital (ADC) converter, and Arduino board for processing, as designed by research students at Politecnico di Milano. Using our project as a blueprint, we aim to support other educators at universities to promote further STEM and expose pupils to technical developments at universities.

Peer evaluation is essential in education, offering students valuable feedback to improve their work, develop critical thinking, and collaborate effectively. For instructors, it provides a scalable way to manage assessments, particularly in large classes or MOOCs where individual feedback is challenging. However, traditional peer evaluation systems often introduce biases from personal relationships, expectations, or presentation styles, impacting evaluation accuracy and failing to reflect true performance. To overcome these limitations, we have developed a peer evaluation system that employs Bayesian statistics, particularly variational inference, to reduce bias and provide a more accurate estimation of each student's true performance. In our system, peer evaluations are treated as probabilistic entities, with the biases and variances of each reviewer modeled as latent variables that can be estimated and corrected over time. Variational inference is used to iteratively refine these parameters based on the observed peer reviews. This allows the system to adjust for the subjective tendencies of individual reviewers, producing an unbiased true score for each student. After demonstrating the effectiveness of variational inference, we propose an ensembling approach that combines it with other state-of-the-art methods. This strategy harnesses the complementary strengths of each method to enhance the accuracy of final evaluations in real-world applications. We tested this peer review system with data obtained from a group of students, including both Ph.D. and M.Sc. scholars, during a two-week series of presentations. The objective of the students was to present their work by interacting with companies and universities and by taking part in a conference. The final adjusted scores were then returned to the students as feedback, providing them with a clearer and more objective evaluation of their work. This peer evaluation system offers a data-driven and scalable solution to address bias in student assessments, particularly in large educational settings. By leveraging Bayesian statistics and variational inference, the system enhances the fairness and objectivity of peer evaluations. This ultimately supports a more transparent learning environment that encourages student development and growth.

Neural Decoding and Stimulation (ND&S) systems offer a promising alternative to conventional treatments for peripheral nerve injuries by decoding neural signals and delivering targeted stimulation via implantable devices. A primary challenge in ND&S development is the accurate classification of electroneurography (ENG) signals under strict constraints on computational resources, processing time and power constraints. To address this, we propose a Parallel Spiking Neural Network (PSNN) architecture based on a novel spiking neuron model called Parallel Spiking Neurons (PSNs), optimized for ENG classification. The model combines event-driven processing with low computational complexity, making it well-suited for implantable applications. Compared to the state-of-the-art ESCAPE-Net, the PSNN achieves higher test accuracy (87.21%±4.9%) and macro F1-score (84.98%±9.39%), while reducing the number of the required model parameters by 99.97%. These results underscore the effectiveness of PSNNs in achieving high classification accuracy with minimal computational overhead, aligning with the stringent requirements of ND&S systems.

This paper introduces a new waveform design approach called COSMIC (Connectivity-Oriented Sensing Method for Imaging and Communication). The method enables the creation of radio images of the environment by applying an extended orthogonality condition to the waveforms. Unlike conventional systems that use time, frequency, or space multiplexing, COSMIC achieves orthogonality through algebraic precoding of the signals from all antennas. Additionally, COSMIC takes advantage of the fact that the imaging field of view is much smaller than the length of the transmitted signals, allowing the waveforms to carry communication data without disrupting the sensing function. Simulations show that COSMIC waveforms enable precise environmental imaging while maintaining good communication performance in terms of error rates.

The development of the new generation of wireless technologies (6G) has led to an increased interest in semantic communication. Thanks also to recent developments in artificial intelligence and communication technologies, researchers in this field have defined new communication paradigms that go beyond those of syntactic communication to post-Shannon and semantic communication. However, there is still need to define a clear and practical framework for semantic communication, as well as an effective structure of semantic elements that can be used in it. The aim of this work is to bridge the gap between two post-Shannon communication paradigms, and to define a robust and effective semantic communication strategy that focuses on a dedicated semantic element that can be easily derived from any type of message. Our work will take form as an innovative communication method called identification via semantic features, which aims at exploiting the ambiguities present in semantic messages, allowing for their identification instead of reproducing them bit by bit. Our approach has been tested through numerical simulations using a combination of machine learning and data analysis. The proposed communication method showed promising results, demonstrating a clear and significant gain over traditional syntactic communication paradigms.

Digital Twin (DT) has emerged as a promising solution for channel estimation. By leveraging high-resolution 3D models of the scenario and ray-tracing simulations, DT could provide valuable site-specific prior knowledge on the channel’s space-time (ST) invariant features of the multipath environment, such as angles of arrival, angles of departure, and propagation delays. However, the real-time characterization of these features imposes computational constraints on ray-tracing simulations, hence limiting the prior knowledge of the multipath environment and corresponding ST features, and degrading estimation accuracy. In this paper, we propose and investigate, for the first time, three distinct DT-empowered low-rank methods for channel estimation, under different degrees of prior knowledge corresponding to limited number of paths provided by DT. Specifically, these methods perform modal projection onto a joint space-time, a spatial, and a temporal subspace. We compare our proposed methods with state-of-the-art techniques, and evaluate their performance in a synthetic scenario. Numerical results show that robustness, when prior knowledge is limited to few paths, is achieved when exploiting only temporal features, while estimation accuracy is attained when joint space-time features are considered.

This study examines the impact of User Equipment (UE)-to-Next Generation Node Base (gNB) synchronization on Time of Flight (ToF)-based localization in 5th generation (5 G) networks. First, a method for measuring the UE-to-gNB overall ToF delay is introduced, leveraging the Timing Advance (TA) command and Sounding Reference Signals (SRSs). The impact of key synchronization factors, such as clock drift and reception/transmission (RX/TX) timing delays, on ToF measurements and positioning accuracy is thoroughly analyzed. To quantify the effect of these localization impairments on 3GPP-compliant devices, an experimental campaign was conducted using the OpenAirInterface (OAI) platform. Field data were collected to assess errors in UE-to-gNB distance estimation. The results show that after estimating and compensating for RX/TX timing delays, the distance error remains within 3.8 m in 95% of cases in a line-of-sight (LoS) urban scenario.

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.

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.

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.

As the deployment of fifth generation (5G) mobile wireless networks continues to gain momentum, researchers are already focusing on the challenges and opportunities of the next sixth generation (6G). To meet the ever-increasing demand for higher data rates and support the development of new services, 6G is expected to exploit millimeter wave (mmWave) frequencies. However, the complex propagation characteristics at mmWave require beamforming technology, which introduces significant complexity in the communication system. Herein, we propose a real-time testbed platform to evaluate beamforming and other communication solutions designed for multiple-input multiple-output (MIMO) mmWave-based 6G networks. This platform serves as an enabler for 6G technologies evaluation under realistic propagation conditions, accelerating the development and deployment of robust and efficient 6G networks. To demonstrate the capabilities of our platform, we have implemented a smart relay with real-time beam control and tracking. The platform is able to perform an exhaustive search of 64 reception beams in less than 256 μs. Additionally, the platform can maintain the optimal beam even in mobility scenarios using a gradient-based tracking system that achieves a low overhead of less than 5%, with an update rate of 100 Hz.

Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment that can enable multi-layer decisions at the physical communication equipment. At high-frequency bands, DTs can help to overcome the challenges emerging in high mobility conditions featuring vehicular environments. In this paper, we propose a novel data-driven workflow for the creation of the DT of a Vehicle-to-Everything (V2X) communication scenario and a multi-modal simulation framework for the generation of realistic sensor data and accurate mmWave/sub-THz wireless channels. The proposed method leverages an automotive simulation and testing framework and an accurate ray-tracing channel simulator. Simulations over an urban scenario show the achievable realistic sensor and channel modelling both at the infrastructure and at ego-vehicles. We showcase the proposed framework on the DT-aided blockage handover task for V2X link restoration, leveraging the framework’s dynamic channel generation capabilities for realistic vehicular blockage simulation.

This study aims to present a comparative analysis of existing waveforms for integrated sensing and communication (ISAC) in vehicular environments. A novel multicarrier framework called generalized adaptive spreading modulation (GASM) is proposed for ISAC-enabled vehicular environments. The GASM waveform offers symbol spreading in both time and frequency domains with tunable spreading parameters, which allows the proposed GASM-based waveform to adapt according to the rapid time-frequency variations of the fading channel. This helps to combat the most common system impairments, such as carrier frequency offset (CFO) and symbol timing offset (STO). The GASM scheme is the generalization of various existing waveforms, such as orthogonal frequency-division multiplexing (OFDM), fractional Fourier transform-based OFDM (FrFT-based OFDM), and orthogonal chirp division multiplexing (OCDM). The proposed GASM-based ISAC system is evaluated in terms of average bit error rate (ABER) for the communication and ambiguity function (AF) for sensing capabilities. The performance of the GASM-based ISAC system is found superior as compared to the existing waveforms, i.e., OFDM, FrFT-based OFDM, OCDM, generalized frequency division multiplexing (GFDM), and orthogonal time frequency space (OTFS) modulation.

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.

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, a heuristic 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.

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.

Integrated sensing and communication (ISAC) systems are recognized 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 tuning 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.

The fifth generation (5G) networks are designed to support a large range of diverse services with strict performance requirements. Studies suggest that, 5G uses machine learning technologies for variety of tasks ranging from network management, and resource optimization to automated services. The successful integration of 5G with machine learning has also led to the basis for 6G networks. However, the use of machine learning makes the 5G networks susceptible to adversarial attacks. A few works study the effect of differential privacy and adversarial attacks in the 5G systems let alone to provide the proposal of effective defense mechanism. This study proposes Denoising and Adversarial attack-based STacked AutoeNcoder (DASTAN) convolutional neural networks (CNN) to provide defense against a specific differential privacy attack, i.e. membership inference, optimized to detect the device or data distribution potentially used in the training process. DASTAN initiates an intentional attack to camouflage the characteristics of an authorized user from an adversary and uses a de noising stacked autoencoder to recover the information at service provider's end for RF fingerprinting. The aim of RF fingerprinting is to validate the authenticity and identity of the device to preserve the privacy of wireless network. Experimental results demonstrate the efficacy of DASTAN-CNN, which reduces the attack success rate by up to 52.69% in comparison to the case where no defense strategy is employed. The DASTAN-CNN also achieves 75.29% authorized user recognition rate for RF fingerprinting while reducing the attack success rate to 39.23%, which shows the effectiveness in terms of trade-off efficiency.

Recently, Unmanned Aerial Vehicles (UAVs) have been deployed in various logistics and surveillance applications. Sixth-Generation (6G) cellular networks can further enhance communications to provide ubiquitous coverage, low-latency control, and seamless connectivity among the UAVs. However, achieving constant and end-to-end 3D coverage for user devices is demanding. UAV s have limited battery capacity; thus, energy consumption should be efficiently managed. Optimizing the UAV trajectories improves network performance by diminishing Base Station (BS) load or covering areas with limited radio access. Hence, we propose a Swarm Clustering and Double-Deep-Q-Network (SC-DDQN) framework for efficient communication in aerial networks. The framework constitutes a novel SC- Particle Swarm Optimization (SC-PSO) to improve intra-UAV communication and an Intelligent Trajectory Optimization (ITO) sub-component to optimize Air-to-Ground (A2G) trajectories. The results show that the proposed SC-DDQN framework achieves 40 % faster clustering and a 1.2 % failure probability of reaching a destination compared to the conventional systems, thus providing optimal clustering and trajectory for UAV communications.

Integrated sensing and communication (ISAC) is one of the key technologies of future 6G communication networks. Waveform design for 6G ISAC systems shall guarantee a flexible communication and sensing performance trade-off with full time-frequency-space resource sharing and minimal added hardware/complexity. Legacy ISAC schemes based on orthogonal frequency division multiplexing (OFDM) or orthogonal time-frequency-space (OTFS) are currently subject to regulatory bandwidth constraints, limiting the delay/range resolution and requiring advanced processing schemes. This paper proposes to exploit a low-power, wide-bandwidth out-of-band (OOB) sensing signal superposed to the legacy OFDM one to enhance the delay/range resolution compared to standalone OFDM and OTFS ISAC systems. The proper power control of the sensing signal allows for complying with adjacent channel leakage ratio requirements. The analytical findings demonstrate the advantages of the proposed ISAC scheme over existing solutions.

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.

The use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput.

An upper bound on the capacity of multiple-input multiple-output (MIMO) Gaussian fading channels is derived under peak amplitude constraints. The upper bound is obtained borrowing concepts from convex geometry and it extends to MIMO channels notable results from the geometric analysis on the capacity of scalar Gaussian channels. Relying on a sphere packing argument and on the renowned Steiner’s formula, the proposed upper bound depends on the intrinsic volumes of the constraint region, i.e., functionals defining a measure of the geometric features of a convex body. The tightness of the bound is investigated at high signal-to-noise ratio (SNR) for any arbitrary convex amplitude constraint region, for any channel matrix realization, and any dimension of the MIMO system. In addition, two variants of the upper bound are proposed: one is useful to ensure the feasibility in the evaluation of the bound and the other to improve the bound’s performance in the low SNR regime. Finally, the upper bound is specialized for two practical transmitter configurations, either employing a single power amplifier for all transmitting antennas or a power amplifier for each antenna.

As the unlicensed band is a shared spectrum, Listen Before Talk (LBT) mechanism was introduced in Long Term Evolution (LTE) - Licensed Assisted Access (LAA) to achieve a harmonious coexistence with other incumbent operators such as Wi-Fi. Similarly, fifth generation (5G) cellular systems support operation in unlicensed band adhering to LBT as a channel access mechanism. Additionally, standalone operation of 5G in the unlicensed band has been identified as a possible deployment scenario by 3GPP. In this paper, we initially demonstrate the coexistence performance between 5G and Wi-Fi operators following the conventional LBT algorithm suggested by 3GPP. Then, we present the bandwidth part (BWP), which is the new feature introduced in New Radio (NR), and propose a new algorithm that exploits it to provide more opportunities for 5G operators to transmit in the unlicensed band. Finally, we present the coexistence performance generated by a system level simulation in terms of latency and throughput. Our numerical results demonstrate that our proposed algorithm provides a better performance in comparison with the conventional LBT.

Recently, communication in cellular networks assisted by Unmanned Aerial Vehicles (UAVs) has attracted considerable attention, as it provides wireless connectivity to devices in areas with poor coverage. With a single UAV deployed, it is difficult to achieve Line-of-Sight (LoS) probability and network availability targets for critical Ultra-Reliable Low-Latency Communication (URLLC) applications while meeting cost and system complexity requirements. To harness the advantages of UAVs in these situations, an alternative solution is to deploy a multi-UAV system, exploiting inter-connectivity to maintain uninterrupted communication with a ground transmitter. The idea is to deploy a fixed UAV on the side of a building rooftop, which acts as a relay between the ground transmitter and the flying UAV base station, thus increasing the LoS probability. Notably, a two-hop amplify-and-forward relay can provide significant improvements in the channel capacity, channel gain, and thus overall quality of service. In our study, simulations were carried out in four general environments as specified by ITU-R, namely Suburban, Urban, Dense Urban, and High Rise Urban, based on data collected in Los Angeles, USA. Numerical results demonstrate that two-hop communication via a relay UAV increases LoS probability in all environments, thus improving system reliability and feasibility.

Internet of Things (IoT) is a system of interrelated sensors and computers to transfer data over a network. However, the sensors, Unmanned Aerial Vehicles (UAVs), and other IoT equipment used are susceptible to different security attacks. Dumb sensors are used to collect data in hostile environments. Dumb sensors are low powered sensors that lack computational power to perform cryptological operations. These sensors are preferred over high powered sensors due to their low electrical signature, but they have negligible computing power. To overcome the loss of authentication data due to node capture and lack of sensor location verification, we propose the Nexus of 6G and Blockchain for Authentication (NBA) system. The NBA system utilizes a permissioned blockchain-based network of UAVs and smart sensors to prevent code tampering. The system enables two-way trusted data transfer between UAVs and dumb sensors through a novel Hybrid Physical Unclonable Function Hashing (HPUFH) model. The system also utilizes a novel Pattern-based Signal Strength Correlation (PbSSC) algorithm to detect any unexpected location changes in the dumb sensor field. The extensive security and performance evaluation demonstrates that the proposed system is highly efficient and secure with a linear computational cost proportional to the number of challenge-response pairs.

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.

Unmanned Aerial Vehicles (UAVs) can be used as low altitude platforms in several applications. In this paper, we propose their use to localize a ground Radio Frequency (RF) emitter by collecting measures of the Received Signal Strength Indicator (RSSI) at different positions. The main contribution of the work consists in the definition of an experimental setup for the simultaneous measures of RSSI and receiver position. The RSSI is measured by an actual transceiver, the Adalm Pluto Software Defined Radio (SDR) development board, programmed with the open-source software GNU Radio. The position is provided by GPS and Inertial Measuring Unit (IMU) sensors on the drone. The measures are acquired in the 865MHz Short Range Device (SRD) and 2.4 GHz Industrial Scientific Medical (ISM) unlicensed frequency bands. Since the ISM measures can be affected by interference generated by different sources (e.g. Wi-Fi access points and UAV controller), the SRD band is exploited for collecting the RSSI measures with less interference. A maximum likelihood (ML) algorithm is applied to the collected data for estimating the transmitter location. For the considered setup we show that the mean absolute localization error is around 4m without interference and 5m with interference. A threshold-based technique is proposed to improve the accuracy in presence of interference.

Vehicle-to-vehicle (V2V) resource allocation (RA) schemes have been introduced in the cellular V2V (C-V2V) standard for sidelink (SL) communications to allow for an efficient sharing of the time-frequency resources in sub-6 GHz bands. However, the recent progress in connected and automated vehicles and the introduction of new bandwidth-eager mobility services are driving towards the use of millimeter-wave (mmW) frequencies (24.25-52.6 GHz). A characteristic of propagation at mmW frequencies is the severe path loss attenuation that can be compensated through beamforming. Therefore, its introduction adds a spatial dimension that must be considered in the design of RA schemes. The current fifth-generation (5G) RA standard for SL communication, which is inherited from the previous C-V2V standard, is not designed for directional communication and does not take into account the interference impact. Hence, this paper proposes a novel RA scheme to manage spatial-interference by adding the spatial dimension, i.e. the spatial beam directivity, and cooperation between vehicles for resource selection. The simulation results confirm that the three-dimensional cooperative RA (3D-CRA) has an average improvement of 10% in packet delivery ratio, 50% in collision probability, and 60% in channel busy ratio compared to the standard RA.

In this paper an analytical model is introduced to describe the impulse response of the diffusive channel between a pointwise transmitter and a given fully-absorbing (FA) receiver in a molecular communication (MC) system. The presence of neighbouring FA nanomachines in the environment is taken into account by describing them as sources of negative molecules. The channel impulse responses of all the receivers are linked in a system of integral equations. The solution of the system with two receivers is obtained analytically. For a higher number of receivers the system of integral equations is solved numerically. It is also shown that the channel impulse response shape is distorted by the presence of the neighbouring FA interferers. For instance, there is a time shift of the peak in the number of absorbed molecules compared to the case without interference, as predicted by the proposed model. The analytical derivations are validated by means of particle based simulations.

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.

The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical for-mulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems.

The capacity of multiple-input multiple-output additive white Gaussian noise channels is investigated under peak amplitude constraints on the norm of the input vector. New insights on the capacity-achieving input distribution are presented. Furthermore, it is provided an iterative algorithm to numerically evaluate both the information capacity and the optimal input distribution of such channel.

The Gabor transform interpretation of the Generalized Frequency-Division Multiplexing (GFDM) leads to a modeling of the effect of the multipath channel as a Multiple-Input Multiple-Output (MIMO) system on each sub-carrier. In this paper, such a modeling is used to propose new pre-coding and decoding design approaches. Two different power allocation strategies based on a joint transmitter and receiver linear design are introduced. By exploiting the circularity of the resulting MIMO channel on each sub-carrier, an eigendecomposition can be implemented, once and for all, by computing the Discrete Fourier transform. The first proposed power allocation approach guarantees fairness among sub-carriers, while the second minimizes the error rate at the price of unfairness. The benefits achieved by the two approaches are demonstrated by numerical simulations and by comparison with other GFDM equalization and pre/de-coding schemes that, in contrast to the proposed one, work on a sub-symbol basis.

The phenomenon of retroactivity describes the impact that a downstream system has on an upstream one when they are connected. From a molecular communication point of view, the effect of the signal that is back propagated between the two systems leads to a reduction of the correct amount of information that can be exchanged between the input and the output of the upstream system. In this work we propose a solution to mitigate such a negative effect. Specifically, a retroactivity suppressor is introduced, which role is that of binding to the downstream system in place of the output of the primary upstream system.

The study of channel capacity is a well-known problem in Digital Communication (DC) systems. Most of the channel models used to evaluate capacity consider the additive white Gaussian noise as the sole impairment. Analytical formulas for lower and upper bounds have been obtained considering such a statistical characterization and different constraints for the transmitted signal. The field of Molecular Communication (MC) shows several analogies with DC systems. However, to the best of our knowledge, it is not possible to determine a statistical model characterizing an MC channel that considers the nonlinear effects present in the system. This paper aims to develop a data-driven methodology that, starting from in-silico or in-vitro experiments, allows estimating bounds on the constrained channel capacity of any biological system and the corresponding distribution of the source message, e.g., finite concentration levels of a protein. As experiments are time consuming, the method includes a machine learning-based data augmentation step. Our proposal is illustrated for a biological circuit composed of two prokaryotic cells. Results highlight fast and stable convergence of the algorithm to tight capacity bounds.

We evaluate capacity bounds for multiple-input multiple-output (MIMO) additive white Gaussian noise (AWGN) fading channels subject to input amplitude constraints. We focus on two practical cases, in which the transmitter: (i) employs a single antenna amplifier, which induces a constraint on the norm of the input vector, and (ii) it employs multiple amplifiers, one per antenna, which leads to independent constraints on the amplitude of each input vector entry. For both cases, we evaluate the asymptotic capacity gap between upper and lower bounds at high signal-to-noise ratio.

An upper bound on the capacity of multiple-input multiple-output (MIMO) additive white Gaussian noise fading channels is derived under peak amplitude constraints. The tightness of the bound is investigated at high signal-to-noise ratio (SNR), for any arbitrary convex amplitude constraint region. Moreover, a numerical simulation of the bound for fading MIMO channels is analyzed, at any SNR level, for a practical transmitter configuration employing a single power amplifier for all transmitting antennas.

Generalized Frequency-Division Multiplexing (GFDM) is a candidate modulation for future wireless cellular networks. The main reason stands in the flexibility of its structure that could fulfill ideas and challenges of forthcoming network scenarios. A known issue of GFDM is its worse performance compared to orthogonal frequency-division multiplexing, which is due to the interference among transmitted symbols. A mathematical model of such an interference has been proposed in a recent paper by exploiting the parallelism that exists between GFDM and discrete Gabor transform. The model allows for the design of different types of linear and non-linear equalizers. With the goal of increasing the transmission reliability, in this paper the introduction of channel coding is considered together with an appropriate interleaving. The computation of the Log-Likelihood Ratio (LLR) is described, which allows for soft decoding in the case of maximum likelihood and linear minimum mean squared error detection. The gain in performance achieved with channel coding and time-frequency interleaving is demonstrated by means of Monte Carlo simulations for the standard 64-state rate-1/2 convolutional code. A comparison with an approach for soft decoding proposed in the literature for GFDM is also reported.

The high mobility of Unmanned Aerial Vehicles (UAVs) and their capability to rapidly deploy Aerial Base Stations (ABS) in areas where the terrestrial network becomes unavailable is a key enabler for Public Safety Networks. In our work we introduce a model in order to identify Line of Sight (LoS) and Non-Line of Sight (NLoS) conditions for User Equipments (UEs) that attempt a connection to an ABS through the Physical Random Access Channel (PRACH) based on Convolutional Neural Networks (CNNs). Our method limits the number of antennas employed with respect to other methods that were developed for traditional approaches, while achieving higher than 80% accuracy for SNR of -20 dB. Finally, we study the impact of UAV’s height on the accuracy of our method and we compare it with typical computationally efficient methods based on the delay spread with and without the aid of beamforming.

Wireless communication between User Terminals (UTs) inside a building and an outdoor base station mounted onboard an unmanned aerial vehicle (UAV) is receiving a higher interest in emergency management scenarios and where users require on demand high throughput services. In such applications, a fundamental aspect is the thorough characterization of the propagation environment through parameters such as the UTUAV distance and the number of walls and floors crossed. In this paper, we characterize the indoor-to-outdoor wireless channel by using a commercial ray-tracing software. The reference scenario is a four-floor building. The UTs are uniformly distributed within each floor and two UAV positions are considered nearby the building. As a main contribution, we present numerical results in terms of path loss against the UT-UAV distance. The dependence of the path loss on the number of floors between the UT and the UAV is highlighted as well. Finally, the ray-tracing results are compared with those predicted by a few available propagation models.

An effective approach to enhance the data rate in narrowband power line communication (NBPLC) system is multicarrier modulation based on orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) transmission over multiple power line phases. A key challenge for achieving reliable communication over MIMO-OFDM NBPLC is to mitigate the effects of the correlated non-stationary additive noise. In fact, substantial components of the noise in NBPLC systems exhibit a cyclostationary behavior with a period of half the AC cycle. Moreover, when MIMO transmission is adopted, an important issue that must be considered is the cross-correlation between the different phases. In this work, we propose to classify the cyclostationary noise into three classes, based on the evaluation of second order statistics. In addition, we derive estimates of the probability density functions for each of the three classes and show that while two of them exhibit a Gaussian behavior, the third one has an impulsive behaviour similar to the Middleton class-A noise. Simulation results show that the bit error rate (BER) of MIMO-OFDM NBPLC significantly changes between different classes of noise. Hence, we develop an algorithm for switching data delivery between MIMO-OFDM NBPLC and MIMO-OFDM wireless transmission in unlicensed frequency band that takes into account knowledge of the periodicity of the three classes of noises. The result is a hybrid MIMO-OFDM wireless/NBPLC system, which we refer to as, hybrid MIMO-OFDM WiNPLC. Our simulation results demonstrate BER improvement of the proposed hybrid system over individual MIMO-OFDM NBPLC or MIMO-OFDM wireless systems.

Low altitude aerial base stations onboard unmanned aerial vehicles (UAVs) have recently gained a lot of attention to provide cellular coverage to mobile ground users. In this paper we evaluate the effect of carrier frequency offset (CFO) on the symbol error rate in the downlink of such systems, where a long-term evolution (LTE) compatible time-frequency grid based on generalized frequency division multiplexing (GFDM) is considered. The choice of GFDM as multi-carrier modulation scheme is motivated by its widely proposed application in fifth generation cellular systems and its backward compatibility with LTE. Simulation results are shown for "Better than the Nyquist (BTN)" pulse shaping filters in three different environments: Suburban, Urban and Urban High Rise. The use of BTN pulse shaping filters allows for higher robustness to CFO as compared to base line pulse shaping filer know as root-raised cosine filters for different environments conditions. Simulation results are presented considering realistic air-to-ground propagation conditions generated from the commercial Wireless InSite ray-tracing radio propagation software.

The 5thgeneration of cellular networks (5G) will provide high speed and high-availability wireless links for communication between mobile users. The usage of aerial platforms as base stations has been recently proposed to meet the above requirements, especially in densely-packed urban areas. To make an accurate prediction of the performance in such a communication system the availability of suitable channel models is a fundamental requirement. Here, we concentrate on a simple path loss and shadow fading channel model that is commonly used to describe the propagation between an aerial base station and a user on the ground. A commercial 3D ray-tracing simulator is used to extract the main parameters used in the model and the Line of Sight/Non Line of Sight probabilities as a function of the transmitter height and elevation angle. We consider three reference scenarios: Suburban, Urban and Urban High Rise generated according to ITU-R specifications. As a novel contribution, we also show simulation results for the spatial correlation of the received signal in the three considered scenarios.

Biological cells naturally exchange information for adapting to the environment, or even influencing other cells. One of the latest frontiers of synthetic biology stands in engineering cells to harness these natural communication processes for tissue engineering and cancer treatment, amongst others. Although experimental success has been achieved in this direction, approaches to characterize these systems in terms of communication performance and their dependence on design parameters are currently limited. In contrast to more classical communication systems, information in biological cells is propagated through molecules and biochemical reactions, which in general result in nonlinear input-output behaviors with system-evolution-dependent stochastic effects that are not amenable to analytical closed-form characterization. In this paper, a computational approach is proposed to characterize the information exchange in these systems, based on stochastic simulation of biochemical reactions and the estimation of information-theoretic parameters from sample distributions. In particular, this approach focuses on engineered cell-to-cell communications with a single transmitter and receiver, and it is applied to characterize the performance of a realistic system. Numerical results confirm the feasibility of this approach to be at the basis of future forward engineering practices for these communication systems.

One of the key challenges in next generation 5G networks is to deliver Ultra-Reliable Low-Latency Communications (URLLC). Recent advances in information theory about principles that govern short packet transmissions pointed out that, for the URLLC typical short packet dimension, achieving higher reliabilities comes at the price of a lower maximum achievable rate, thus reducing spectral efficiency. Hence, retransmissions are used in LTE and planned for 5G, in order to achieve reliability with a better resource consumption, at the price of increased packet latency. Keeping in mind the stringent requirements for URLLC, in this paper we analyze the tradeoffs and limitations of retransmission strategies considered in the literature, either too demanding in terms of wireless resources and aggressive URLLC performance or the contrary. Then we propose a novel scheme, whose purpose is to match the URLLC requirements, minimizing the resource consumption. We evaluate the schemes through simulations and highlight the advantages of the proposed scheme, providing also insights on the performance HARQ techniques in URLLC scenarios.

To guarantee the success of massive multiple-input multiple-output (MIMO), one of the main challenges to solve is the efficient management of pilot contamination. Allocation of fully orthogonal pilot sequences across the network would provide a solution to the problem, but the associated overhead would make this approach infeasible in practical systems. Ongoing fifth-generation (5G) standardisation activities are debating the amount of resources to be dedicated to the transmission of pilot sequences, focussing on uplink sounding reference signals (UL SRSs) design. In this paper, we evaluate the performance of various UL SRS allocation strategies in practical deployments, shedding light on their strengths and weaknesses. Furthermore, we introduce a novel UL SRS fractional reuse (FR) scheme, denoted neighbour-aware (FR-NA). The proposed FR-NA generalizes the fixed reuse paradigm, and entails a trade-off between i) aggressively sharing some UL SRS resources, and ii) protecting other UL SRS resources with the aim of relieving neighbouring BSs from pilot contamination. Said features result in a cell throughput improvement over both fixed reuse and state-of-the-art FR based on a cell-centric perspective.

Synthetic biology, through genetic circuit engineering in biological cells, is paving the way toward the realization of programmable man-made living devices, able to naturally operate within normally less accessible domains, i.e., the biological and the nanoscale. The control of the information processing and exchange between these engineered-cell devices, based on molecules and biochemical reactions, i.e., molecular communication (MC), will be enabling technologies for the emerging paradigm of the Internet of Bio-Nano Things, with applications ranging from tissue engineering to bioremediation. In this paper, the design of genetic circuits to enable MC links between engineered cells is proposed by stemming from techniques for information coding and inspired by recent studies favoring the efficiency of analog computation over digital in biological cells. In particular, the design of a joint encoder-modulator for the transmission of binary-modulated molecule concentration is coupled with a decoder that computes the a-posteriori log-likelihood ratio of the information bits from the propagated concentration. These functionalities are implemented entirely in the biochemical domain through activation and repression of genes, and biochemical reactions, rather than classical electrical circuits. Biochemical simulations are used to evaluate the proposed design against a theoretical encoder/decoder implementation taking into account impairments introduced by diffusion noise.

Regarded as one of the future enabling technologies of the Internet of Things at the biological and nanoscale domains, Molecular Communication (MC) promises to enable applications in healthcare, environmental protection, and bioremediation, amongst others. Since MC is directly inspired by communication processes in biological cells, the engineering of biological circuits through cells' genetic code manipulation, which enables access to the cells' information processing abilities, is a candidate technology for the future realization of MC components. In this paper, inspired by previous research on channel coding schemes for MC and biological circuits for cell communications, a joint encoder and modulator design is proposed for the transmission of cellular information through signaling molecules. In particular, the information encoding and modulation are based on a binary parity check scheme, and they are implemented by interconnecting biological circuit components based on gene expression and mass action reactions. Each component is mathematically modeled and tuned according to the desired output. The implementation of the biological circuit in a simulation environment is then presented along with the corresponding numerical results, which validate the proposed design by showing agreement with an ideal encoding and modulator scheme.

Molecular Communication (MC) is an enabling paradigm for the interconnection of future devices and networks in the biological environment, with applications ranging from bio-medicine to environmental monitoring and control. The engineering of biological circuits, which allows to manipulate the molecular information processing abilities of biological cells, is a candidate technology for the realization of MC-enabled devices. In this paper, inspired by recent studies favoring the efficiency of analog computation over digital in biological cells, an analog decoder design is proposed based on biological circuit components. In particular, this decoder computes the a-posteriori log-likelihood ratio of parity-check-encoded bits from a binary-modulated concentration of molecules. The proposed design implements the required L-value and the box-plus operations entirely in the biochemical domain by using activation and repression of gene expression, and reactions of molecular species. Each component of the circuit is designed and tuned in this paper by comparing the resulting functionality with that of the corresponding analytical expression. Despite evident differences with classical electronics, biochemical simulation data of the resulting biological circuit demonstrate very close performance in terms of Mean Squared Error (MSE) and Bit Error Rate (BER), and validate the proposed approach for the future realization of MC components.

Wireless Sensor Network (WSN) technologies provide advantages that allow them to replace traditional wired systems in an ever growing number of applications. This paper describes the design of a WSN for mission critical applications such as the case of avionics, in which data collected from the sensors can be delivered to a cloud application through multiple independent gateways, thereby increasing data availability in presence of failures. Since the same data might be distributed along multiple paths, system-wide synchronization must be provided in order to guarantee data consistency. A heartbeat protocol is introduced along each path in order to guarantee timely detection of any single failure. We present a solution that can be implemented using open source software and commercial off-the-shelf hardware, which makes this approach viable for networks with a large number of heterogeneous sensors. Results reported in this paper show some sample measurements as well as the performance evaluation for our heartbeat algorithm in terms of latency between a failure and a full recovery of the system.

In mission critical wireless sensor networks (WSNs) accurate timestamping of the occurrence of events measured by the sensor nodes is often required together with a high degree of reliability. While precise timestamping requires synchronization of the sensor nodes, reliability is obtained by adding redundancy in all potential single point of failure nodes. In this paper, we focus on a ZigBee-based WSN using two personal area network (PAN) coordinators with different PAN identifiers (IDs) and, for this configuration, we propose a solution where if the primary PAN coordinator goes down, connections are transferred to the other by changing the PAN ID of the nodes. Our proposed solution provides significant gains in terms of recovery speed and timing synchronization accuracy in comparison to a solution that is proposed in the literature.

Our scientific community faces a sort of paradox. A large bulk of work has been done on data-oriented techniques devised to improve peer reputation and knowledge extraction from data, so as to improve trustworthiness of digital services involving coordination and cooperation among heterogeneous peers. But, perhaps surprisingly, to the best of our knowledge, such techniques have rarely been applied to the (for our own community, crucial) process of reducing noise in the process of peer reviewing our own papers. Goal of this work is to provide initial insights on the applicability of methodologies and tools from inferential statistical to the field of peer review quality control. Our contribution is threefold. First, we propose a statistical model where each technical program committee member (reviewer) is characterized as random noise added to the “actual” value of the paper. Second, we provide an iterative data-oriented approach based on Expectation-Maximization devised to estimate mean value and variance of the noise added by each reviewer; our approach uses only the ratings provided by the reviewers themselves and does not rely on any additional source of a-priori knowledge. Third, we make use of the estimated mean values and variances to improve the accuracy of paper's evaluation and ranking.

In this paper spatial loading in a reciprocal time-varying multiple-input multiple-output (MIMO) channel is considered. We take inspiration from a blind iterative algorithm proposed in the literature to estimate the singular vectors associated to the dominant singular value to perform beamforming. Starting from that, we introduce an iterative algorithm that can estimate all the singular vectors and the associated singular values of the channel matrix. Then the estimated singular vectors are used to transmit over the parallel sub-channels and the associated singular values are considered to implement the rate and power loading algorithm described in this paper. A procedure based on the joint use of the two considered algorithms can be adopted to adaptively maximize the total rate for a given error rate performance and a given constraint on the average transmitted power. Simulation results are used to demonstrate the effectiveness of our approach compared to blind iterative beamforming transmission.

Starting from the definition of mutual information, one promptly realizes that the probabilities inferred by Bayesian tracking can be used to compute the Shannon information between the state and the measurement of a dynamic system. In the Gaussian and linear case, the information rate can be evaluated from the probabilities computed by the Kalman filter. When the probability distributions inferred by Bayesian tracking are nontractable, one is forced to resort to approximated inference, which gives only an approximation to the wanted probabilities. We propose upper and lower bounds to the information rate between the hidden state and the measurement based on approximated inference. Application of these bounds to multiplicative communication channels is discussed, and experimental results for the discrete-time phase noise channel and for the Gauss-Markov fading channel are presented.

Numerical upper and lower bounds to the information rate transferred through the additive white Gaussian noise channel affected by discrete-time multiplicative autoregressive moving-average (ARMA) phase noise are proposed in the paper. The state space of the ARMA model being multidimensional, the problem cannot be approached by the conventional trellis-based methods that assume a first-order model for phase noise and quantization of the phase space, because the number of state of the trellis would be enormous. The proposed lower and upper bounds are based on particle filtering and Kalman filtering. Simulation results show that the upper and lower bounds are so close to each other that we can claim of having numerically computed the actual information rate of the multiplicative ARMA phase noise channel, at least in the cases studied in the paper. Moreover, the lower bound, which is virtually capacity-achieving, is obtained by demodulation of the incoming signal based on a Kalman filter aided by past data. Thus we can claim of having found the virtually optimal demodulator for the multiplicative phase noise channel, at least for the cases considered in the paper.

In this paper we focus on a single carrier pilot-assisted transmission scheme where one pilot symbol is periodically inserted in the transmitted sequence on a time-division multiplexing basis. A new equalization scheme, where the knowledge of pilot symbols is exploited by the equalizer to generate an estimate of the noise affecting the symbol to be detected, is introduced and analyzed. The criterion used to compute the equalizer coefficients is the minimization of the mean-square error (MSE). The main new result of our analysis is that the optimal pilot aided equalizer (PAE) can be decomposed as the cascade of an unconstrained minimum MSE (MMSE) linear equalizer (LE) and a data- aided noise estimation filter. This result completes and extends the noise-predictive view of decision feedback equalization to general data-aided equalization. The PAE is compared here to the MMSE- LE and to the MSE decision feedback equalizer on two frequency selective wireless channels.

The hybrid decision feedback equalizer (DFE) is a combined time-frequency domain implementation of the conventional time-domain DFE that is able to provide a good trade-off between performance and computational complexity in single carrier transmission over severely frequency-selective channels. In the hybrid DFE the implementation of the feedforward filter is done in the frequency domain, while the feedback filter (FBF) is implemented in the time-domain. The computation of the coefficients for the two filters is usually done in the same domain where they are implemented. A method for frequency-domain computation of the FBF is proposed in the paper. As is known, the key operation in the computation of the FBF is the spectral factorization. In the paper it is proposed to adopt the (cepstral) method for spectral factorization due to Kolmogoroff, which can be efficiently implemented by using the fast Fourier transform (FFT). The application of the method is considered for highly dispersive channels. By using simulations we show that for this type of channels the performance of the proposed method is virtually the same as that obtained by using time-domain approaches. The advantage of the proposed approach is that the efficient FFT gives a substantial reduction of complexity compared to time-domain methods.

Turbo equalization is an iterative equalization decoding scheme for detecting encoded data transmitted over channels that introduce intersymbol interference. In such a scheme, the complexity of the optimal soft-input soft-output (SISO) equalizer, which implements the BCJR a posteriori probability computation algorithm, is one of the major concerns. We extend the reduced state sequence detection (RSSD) algorithm to a BCJR-SISO equalizer matched to a non binary modulation. The resulting reduced-state SISO (RS-SISO) algorithms can take full advantage from the flexibility offered by RSSD in reducing the number of trellis states. However, extension of RSSD algorithm for obtaining the RS-SISO equalizers requires attention in the recombination procedure of the forward and backward metrics.

Decision feedback equalization is a popular method for signal detection that allows a good trade off between complexity and performance. The complexity of the decision feedback equalizer (DFE) is mainly concentrated in the feedforward filter, which is often realized in FIR form. When the number of taps of the FIR filter needed to obtain nearly optimal performance becomes large, one may take advantage of the efficiency of the FFT/IFFT algorithm realizing the feedforward filter in the discrete frequency domain. In this paper we consider the minimum mean square error (MMSE) DFE with feedforward filter in the discrete frequency domain, assuming perfect knowledge of a static channel. We move from the observation that the optimal MMSE-DFE feedforward filter is IIR, and point out that the classical scheme of unconstrained frequency domain filtering with overlap/save digital signal processing may induce substantial increase in the error rate when the approximation of the linear convolution between the received signal and the mentioned IIR, to a circular convolution is poor. Our main finding is that the error rate can be significantly improved by deliberately augmenting the power of the noise in the computation of the transfer function of the frequency domain feedforward filter.

For multiple-input multiple-output (MIMO) spatial multiplexing systems the complexity of the maximum likelihood detector (MLD) can be prohibitively extensive when the number of transmitting antennas and constellation points is high. To simplify the MLD many linear and nonlinear techniques have been proposed. In this paper the principle of reduced state sequence detection, based on mapping by set partitioning, is applied to perform detection in MIMO systems. Simulation results are presented for the proposed suboptimal detection algorithm.

In digital radio transmission over frequency selective channels, the minimum mean-square error decision feedback equalizer (MMSE-DFE) is widely recognized as an efficient equalization scheme. In order to compute the coefficients of the feedforward and feedback filters of the MMSE-DFE, both the channel impulse response (CIR) and the variance of the noise have to be estimated. The estimate of the CIR is usually performed by the standard least-square method, where a known training sequence is employed. Then, the estimated CIR is used to evaluate the variance of the noise. The use of the two above estimates for MMSE-DFE is studied. In particular, an unbiased estimate of the variance of the noise is described.

In signal equalization, a technique that allows reduction of the number of states of the Viterbi detector is the delayed decision feedback sequence detector (DDFSD). In order to achieve good performance, it is essential to operate, before the DDFSD, an appropriate prefiltering of the received sequence. This paper is devoted to performance evaluation of the DDFSD when the feedforward filter of a minimum mean square error decision feedback equalizer is adopted as a prefilter. A truncated version of the union bound is used to approximate the bit error rate. The analysis includes a method for determining the error events that dominate the bound.

In signal equalization, a detection technique that allows reduction of the number of states of the Viterbi (1979) detector is the delayed decision feedback sequence detector (DDFSD). In order to achieve good performance, it is crucial to operate an appropriate prefiltering of the received sequence before the DDFSD. The main novelty of the paper is performance evaluation of the DDFSD when the feedforward filter of the minimum mean-square error decision feedback equalizer (DFE) is adopted as prefilter. The union upper bound is used to evaluate the probability of first error event and truncation of the sum appearing in the bound to the error sequences that dominate the performance is discussed. It is also shown that the feedforward filter of the minimum mean-square error DFE leads to maximum likelihood sequence detection with a minimum number of states, which seems to be a novel result.

In signal equalization, a technique that allows reduction of the number of states of the Viterbi (1979) detector is the delayed decision feedback sequence detector (DDFSD). In order to achieve good performance, it is crucial to operate an appropriate prefiltering of the received sequence before the DDFSD. This paper is devoted to performance evaluation of the DDFSD when the prefilter of a minimum mean square error decision feedback equalizer is adopted. The union upper bound is used to evaluate the probability of first-event error, and truncation of the sum to the error sequences that dominate the performance is discussed.

In signal equalization, a suboptimal technique for reducing the number of states of the Viterbi detector is the delayed decision feedback sequence detector (DDFSD). In order to achieve good performance a mean-square (MS) prefilter is employed before the DDFSD. This paper is devoted to investigate the sensitivity of the MS-DDFSD to a noisy estimate of the variance of the noise. Analytical performance evaluation is addressed. A truncated version of the union bound is used to approximate the bit error rate. The analysis includes a method for determining the terms that dominate the bound.

The paper deals with the design of suboptimal receivers for data transmission over frequency selective channels. The complexity of maximum likelihood sequence estimation (MLSE) turns out be exponential in the channel memory. Hence, when dealing with channels with long memory, suboptimal reception must be considered. Among the suboptimal methods, the delayed decision feedback sequence estimation (DDFSE) plays an important role. This receiver is based on a Viterbi processor where the channel memory is truncated. The memory truncation is compensated by a per-survivor decision feedback equalizer. The DDFSE was originally proposed by Duel-Hallen and Heegard (1989), where the whitened matched filter was considered as a front-end. Our contribution is to extend the principles of MLSE and DDFSE to the case where the mean-square whitened matched filter is adopted as a front-end. Simulation results show that our proposed design of the DDFSE gives substantial benefits when a severe frequency selective channel is considered.

A common model for distributed detection systems is that of several separated sensors each of which measures some observable, quantizes it, and communicates to a fusion center the quantized observation. The fusion center collects the quantized observations and takes the decision. The article deals with the design of the quantizers and of the fusion center under a rate constraint. The system of interest allows soft nonbreakpoint quantizers and nonindependent observations. Our finding is that locally optimal design of the distributed detection system is feasible via alternate minimization of the average misclassification risk.