Design and Validation of a Software Defined Radio Testbed for DVB-T Transmission

This paper describes the design and validation of a Software Defined Radio (SDR) testbed, which can be used for Digital Television transmission using the Digital Video Broadcasting - Terrestrial (DVB-T) standard. In order to generate a DVB-T-compliant signal with low computational complexity, we design an SDR architecture that uses the C/C++ language and exploits multithreading and vectorized instructions. Then, we transmit the generated DVB-T signal in real time, using a common PC equipped with multicore central processing units (CPUs) and a commercially available SDR modem board. The proposed SDR architecture has been validated using fixed TV sets, and portable receivers. Our results show that the proposed SDR architecture for DVB-T transmission is a low-cost low-complexity solution that, in the worst case, only requires less than 22% of CPU load and less than 170 MB of memory usage, on a 3.0 GHz Core i7 processor. In addition, using the same SDR modem board, we design an off-line software receiver that also performs time synchronization and carrier frequency offset estimation and compensation

Distributed Adaptive Learning of Graph Signals

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs.Comment: To appear in IEEE Transactions on Signal Processing, 201

EVM and Achievable Data Rate Analysis of Clipped OFDM Signals in Visible Light Communication

Orthogonal frequency division multiplexing (OFDM) has been considered for visible light communication (VLC) thanks to its ability to boost data rates as well as its robustness against frequency-selective fading channels. A major disadvantage of OFDM is the large dynamic range of its time-domain waveforms, making OFDM vulnerable to nonlinearity of light emitting diodes (LEDs). DC biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are two popular OFDM techniques developed for the VLC. In this paper, we will analyze the performance of the DCO-OFDM and ACO-OFDM signals in terms of error vector magnitude (EVM), signal-to-distortion ratio (SDR), and achievable data rates under both average optical power and dynamic optical power constraints. EVM is a commonly used metric to characterize distortions. We will describe an approach to numerically calculate the EVM for DCO-OFDM and ACO-OFDM. We will derive the optimum biasing ratio in the sense of minimizing EVM for DCO-OFDM. Additionally, we will formulate the EVM minimization problem as a convex linear optimization problem and obtain an EVM lower bound against which to compare the DCO-OFDM and ACO-OFDM techniques. We will prove that the ACO-OFDM can achieve the lower bound. Average optical power and dynamic optical power are two main constraints in VLC. We will derive the achievable data rates under these two constraints for both additive white Gaussian noise (AWGN) channel and frequency-selective channel. We will compare the performance of DCO-OFDM and ACO-OFDM under different power constraint scenarios

Opportunistic information-bottleneck for goal-oriented feature extraction and communication

The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features are maximally relevant to a specific learning or inference task. Despite its theoretical value, the IB is based on a functional optimization problem that admits a closed form solution only on specific cases (e.g., Gaussian distributions), making it difficult to be applied in most applications, where it is necessary to resort to complex and approximated variational implementations. To overcome this limitation, we propose an approach to adapt the closed-form solution of the Gaussian IB to a general task. Whichever is the inference task to be performed by a (possibly deep) neural-network, the key idea is to opportunistically design a regression sub-task, embedded in the original problem, where we can safely assume a (joint) multivariate normality between the sub-task’s inputs and outputs. In this way we can exploit a fixed and pre-trained neural network to process the input data, using a tunable number of features, to trade data-size and complexity for accuracy. This approach is particularly useful every time a device needs to transmit data (or features) to a server that has to fulfil an inference task, as it provides a principled way to extract the most relevant features for the task to be executed, while looking for the best trade-off between the size of the feature vector to be transmitted, inference accuracy, and complexity. Extensive simulation results testify the effectiveness of the proposed method and encourage to further investigate this research line

Observing and tracking bandlimited graph processes from sampled measurements

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and financial network signals are correlated in both domains. Hence, in this work, we develop a sampling theory for time varying graph signals, named graph processes, to observe and track a process described by a linear state-space model. We provide a mathematical analysis to highlight the role of the graph, process bandwidth, and sample locations. We also propose sampling strategies that exploit the coupling between the topology and the corresponding process. Numerical experiments corroborate our theory and show the proposed methods trade well the number of samples with accuracy

AI-driven ground robots: mobile edge computing and mmWave communications at work

The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios

Real-Time Generation of Standard-Compliant DVB-T Signals

This paper proposes and discusses two software implementations of the DVB-T modulator, using C++ and MATLAB, respectively. All the key features of the DVB-T standard are included. The C++ DVB-T modulator, incorporated into the Iris framework developed by Trinity College of Dublin, works in real time on an Intel Core i7 2.4 GHz CPU with the Iris testbed. The MATLAB-based DVB-T modulator is coupled with a receiver implementation with channel estimation, equalization, soft-output demapping and channel decoding. The validation step demonstrates that the proposed DVB-T software implementations generate standard-compliant DVB-T signals that are correctly received by commercially available TV sets and USB dongles. The software code for the Iris-based C++ modulator, and for the MATLAB-based modulator and receiver, has been made publicly available under the GNU license

The inhibition of FGF receptor 1 activity mediates sorafenib-induced antiproliferative effects in human mesothelioma tumor-initiating cells

Tumor-initiating cells (TICs), the subset of cells within tumors endowed with stem-like features, being highly resistant to conventional cytotoxic drugs, are the major cause of tumor relapse. The identification of molecules able to target TICs remains a significant challenge in cancer therapy. Using TIC-enriched cultures (MM1, MM3 and MM4), from 3 human malignant pleural mesotheliomas (MPM), we tested the effects of sorafenib on cell survival and the intracellular mechanisms involved. Sorafenib inhibited cell-cycle progression in all the TIC cultures, but only in MM3 and MM4 cells this effect was associated with induction of apoptosis via the down-regulation of Mcl-1. Although sorafenib inhibits the activity of several tyrosine kinases, its effects are mainly ascribed to Raf inhibition. To investigate the mechanisms of sorafenib-mediated antiproliferative activity, TICs were treated with EGF or bFGF causing, in MM3 and MM4 cells, MEK, ERK1/2, Akt and STAT3 phosphorylation. These effects were significantly reduced by sorafenib in bFGF-treated cells, while a slight inhibition occurred after EGF stimulation, suggesting that sorafenib effects are mainly due to FGFR inhibition. Indeed, FGFR1 phosphorylation was inhibited by sorafenib. A different picture was observed in MM1 cells, which, releasing high levels of bFGF, showed an autocrine activation of FGFR1 and a constitutive phosphorylation/activation of MEK-ERK1/2. A powerful inhibitory response to sorafenib was observed in these cells, indirectly confirming the central role of sorafenib as FGFR inhibitor. These results suggest that bFGF signaling may impact antiproliferative response to sorafenib of MPM TICs, which is mainly mediated by a direct FGFR targeting

A multidrug approach to modulate the mitochondrial metabolism impairment and relative oxidative stress in fanconi anemia complementation group a

Fanconi Anemia (FA) is a rare recessive genetic disorder characterized by aplastic anemia due to a defective DNA repair system. In addition, dysfunctional energy metabolism, lipid droplets accumulation, and unbalanced oxidative stress are involved in FA pathogenesis. Thus, to modulate the altered metabolism, Fanc-A lymphoblast cell lines were treated with quercetin, a flavonoid compound, C75 (4-Methylene-2-octyl-5-oxotetrahydrofuran-3-carboxylic acid), a fatty acid synthesis inhibitor, and rapamycin, an mTOR inhibitor, alone or in combination. As a control, isogenic FA cell lines corrected with the functional Fanc-A gene were used. Results showed that: (i) quercetin recovered the energy metabolism efficiency, reducing oxidative stress; (ii) C75 caused the lipid accumulation decrement and a slight oxidative stress reduction, without improving the energy metabolism; (iii) rapamycin reduced the aerobic metabolism and the oxidative stress, without increasing the energy status. In addition, all molecules reduce the accumulation of DNA double-strand breaks. Two-by-two combinations of the three drugs showed an additive effect compared with the action of the single molecule. Specifically, the quercetin/C75 combination appeared the most efficient in the mitochondrial and lipid metabolism improvement and in oxidative stress production reduction, while the quercetin/rapamycin combination seemed the most efficient in the DNA breaks decrement. Thus, data reported herein suggest that FA is a complex and multifactorial disease, and a multidrug strategy is necessary to correct the metabolic alterations