Design and Implementation of a Low Complexity Multiuser Detector for Hybrid CDMA Systems

In hybrid CDMA systems, multiuser detection (MUD) algorithms are adopted at the base station to reduce both multiple access and inter symbol interference by exploiting space-time (ST) signal processing techniques. Linear ST-MUD algorithms solve a linear problem where the system matrix has a block-Toeplitz shape. While exact inversion techniques impose an intolerable computational load, reduced complexity algorithms may be efficiently employed even if they show suboptimal behavior introducing performance degradation and nearfar effects. The block-Fourier MUD algorithm is generally considered the most effective one. However, the block-Bareiss MUD algorithm, that has been recently reintroduced, shows also good performance and low computational complexity comparing favorably with the block Fourier one. In this paper, both MUD algorithms will be compared, along with other well known ones, in terms of complexity, performance figures, hardware feasibility and implementation issues. Finally a short hardware description of the block-Bareiss and block Fourier algorithms will be presented along with the FPGA (Field Programmable Gate Array) implementation of the block-Fourier using standard VHDL (VHSIC Hardware Description Language) design

Leveraging MIMO-OFDM radio signals for device-free occupancy inference: system design and experiments

Abstract In device-free radio frequency (RF) body occupancy inference systems, RF signals encode information (e.g., body location, posture, activity) about moving targets (not instrumented) that alter the radio propagation in the surroundings of the RF link(s). Such systems are now getting more attention as they enable flexible location-based services for new smart scenarios (e.g., smart spaces, safety and security, assisted living) just using off-the-shelf wireless devices. The goal of this paper is to set the fundamental signal processing methods and tools for performance evaluation of passive occupancy inference problems that leverage on the analysis of physical layer (PHY) channel state information (CSI) obtained from multiple antennas (spatial domain) and carriers (frequency domain) jointly. To this aim, we consider here a multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) radio interface adopted in high-throughput WiFi networks such as IEEE 802.11n,ac. The proposed approach investigates at first relevant CSI features that are more sensitive to body presence; next, it proposes a space-frequency selection method based on principal component analysis (PCA). Considering an experimental case study with WiFi links, we show that the joint space- and frequency-domain processing of the radio signal quality indicators enable both detection and localization of two independent targets (i.e., human bodies) arbitrarily moving in the surroundings of the transmitter/receiver locations. Experiments are conducted using off-the-shelf WiFi devices configured to extract and process CSI over standard PHY preambles: performance analysis sets the best practices for system design and evaluation

Analog MIMO Radio-over-Copper: Prototype and Preliminary Experimental Results

Analog Multiple-Input Multiple-Output Radio-over-Copper (A-MIMO-RoC) is an effective all-analog FrontHaul (FH) architecture that exploits any pre-existing Local Area Network (LAN) cabling infrastructure of buildings to distribute Radio-Frequency (RF) signals indoors. A-MIMO-RoC, by leveraging a fully analog implementation, completely avoids any dedicated digital interface by using a transparent end-to-end system, with consequent latency, bandwidth and cost benefits. Usually, LAN cables are exploited mainly in the low-frequency spectrum portion, mostly due to the moderate cable attenuation and crosstalk among twisted-pairs. Unlike current systems based on LAN cables, the key feature of the proposed platform is to exploit more efficiently the huge bandwidth capability offered by LAN cables, that contain 4 twisted-pairs reaching up to 500 MHz bandwidth/pair when the length is below 100 m. Several works proposed numerical simulations that assert the feasibility of employing LAN cables for indoor FH applications up to several hundreds of MHz, but an A-MIMO-RoC experimental evaluation is still missing. Here, we present some preliminary results obtained with an A-MIMO-RoC prototype made by low-cost all-analog/all-passive devices along the signal path. This setup demonstrates experimentally the feasibility of the proposed analog relaying of MIMO RF signals over LAN cables up to 400 MHz, thus enabling an efficient exploitation of the LAN cables transport capabilities for 5G indoor applications.Comment: Part of this work has been accepted as a conference publication to ISWCS 201

Hidden Markov models for radio localization in mixed LOS/NLOS conditions

Abstract—This paper deals with the problem of radio localization of moving terminals (MTs) for indoor applications with mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions. To reduce false localizations, a grid-based Bayesian approach is proposed to jointly track the sequence of the positions and the sight conditions of the MT. This method is based on the assumption that both the MT position and the sight condition are Markov chains whose state is hidden in the received signals [hidden Markov model (HMM)]. The observations used for the HMM localization are obtained from the power-delay profile of the received signals. In ultrawideband (UWB) systems, the use of the whole power-delay profile, rather than the total power only, allows to reach higher localization accuracy, as the power-profile is a joint measurement of time of arrival and power. Numerical results show that the proposed HMM method improves the accuracy of localization with respect to conventional ranging methods, especially in mixed LOS/NLOS indoor environments. Index Terms—Bayesian estimation, hidden Markov models (HMM), mobile positioning, source localization, tracking algorithms

Dual-View Single-Shot Multibox Detector at Urban Intersections: Settings and Performance Evaluation

The explosion of artificial intelligence methods has paved the way for more sophisticated smart mobility solutions. In this work, we present a multi-camera video content analysis (VCA) system that exploits a single-shot multibox detector (SSD) network to detect vehicles, riders, and pedestrians and triggers alerts to drivers of public transportation vehicles approaching the surveilled area. The evaluation of the VCA system will address both detection and alert generation performance by combining visual and quantitative approaches. Starting from a SSD model trained for a single camera, we added a second one, under a different field of view (FOV) to improve the accuracy and reliability of the system. Due to real-time constraints, the complexity of the VCA system must be limited, thus calling for a simple multi-view fusion method. According to the experimental test-bed, the use of two cameras achieves a better balance between precision (68%) and recall (84%) with respect to the use of a single camera (i.e., 62% precision and 86% recall). In addition, a system evaluation in temporal terms is provided, showing that missed alerts (false negatives) and wrong alerts (false positives) are typically transitory events. Therefore, adding spatial and temporal redundancyincreases the overall reliability of the VCA system

A physics-informed generative model for passive radio-frequency sensing

Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) stray radiation received by wireless devices nearby. These wireless devices may be co-located members of a Wireless Local Area Network (WLAN) or even cellular devices connected with a Wide Area Network (WAN). Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging problems and Bayesian estimation, such as passive localization, RF tomography, and holography. Physics-informed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. Thus, GNNs can be used to simulate/reconstruct missing samples, or learn physics-informed data distributions. The paper discusses a Variational Auto-Encoder (VAE) technique and its adaptations to incorporate a relevant EM body diffraction method with applications to passive RF sensing and localization/tracking. The proposed EM-informed generative model is verified against classical diffraction-based EM body tools and validated on real RF measurements. Applications are also introduced and discussed

EM Model-Based Device-Free Localization of Multiple Bodies

In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting the Received Signal Strength (RSS) in a way that strongly depends on people location. The paper targets specifically the modelling of the effects on the electromagnetic (EM) field, and the related inference methods. A multiple-body diffraction model is exploited to predict the impact of these bodies on the RSS field, i.e., the multi-body-induced shadowing, in the form of an extra attenuation w.r.t. the reference scenario where no targets are inside the monitored area. Unlike almost all methods available in the literature, that assume multi-body-induced shadowing to sum linearly with the number of people co-present in the monitored area, the proposed model describes also the EM effects caused by their mutual interactions. As a relevant case study, the proposed EM model is exploited to predict and evaluate the effects due to two co-located bodies inside the monitored area. The proposed real-time localization and tracking method, exploiting both average and deviation of the RSS perturbations due to the two subjects, is compared against others techniques available in the literature. Finally, some results, based on experimental RF data collected in a representative indoor environment, are presented and discussed

A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning

Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers. Novel paradigms, such as federated learning (FL), are suitable for decentralized model training across devices or silos that simultaneously act as both data producers and learners. Unlike centralized learning (CL) techniques, relying on big-data fusion and analytics located in energy hungry data centers, in FL scenarios devices collaboratively train their models without sharing their private data. This article breaks down and analyzes the main factors that influence the environmental footprint of FL policies compared with classical CL/Big-Data algorithms running in data centers. The proposed analytical framework takes into account both learning and communication energy costs, as well as the carbon equivalent emissions; in addition, it models both vanilla and decentralized FL policies driven by consensus. The framework is evaluated in an industrial setting assuming a real-world robotized workplace. Results show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless systems characterized by low bit/Joule efficiency (50 kbit/Joule or lower). Consensus-driven FL does not require the parameter server and further reduces emissions in mesh networks (200 kbit/Joule). On the other hand, all FL policies are slower to converge when local data are unevenly distributed (often 2x slower than CL). Energy footprint and learning loss can be traded off to optimize efficiency.Comment: The work has been submitted to the IEEE for possible publicatio

Multitarget detection/tracking for monostatic ground penetrating radar: Application to pavement profiling

Monostatic ground penetrating radar (GPR) has proven to be a useful technique in pavement profiling. In road and highway pavements, layer thickness and permittivity of asphalt and concrete can be estimated by using an inverse scattering approach. Layer-stripping inversion refers to the iterative estimation of layer properties from amplitude and time of delay (TOD) of echoes after their detection. This method is attractive for realtime implementation, in that accuracy is improved by reducing false alarms. To make layer stripping useful, a multitarget detection/tracking (D/T) algorithm is proposed. It exploits the lateral continuity of echoes arising from a multilayered medium. Interface D/T means that both detection and tracking are employed simultaneously (not sequentially). For each scan, both detection of the target and tracking of the corresponding TOD of the backscattered echoes are based on the evaluated a posteriori probability density. The TOD is then estimated by using the maximum a posteriori (MAP) or the minimum mean square error (MMSE) criterion. The statistical properties of a scan are related to those of the neighboring ones by assuming, for the interface, a first-order Markov model. © 1999 IEEE