A subspace method for channel estimation in soft-iterative receivers

In this paper we propose a new soft method for the estimation of block-fading channels based on multi-block (MB) processing. The MB estimator [1] exploits the invariance of the subspace spanned by the multipath components of the channel and it estimates the channel subspace by sample averaging over a frame of blocks. Here the MB method is extended to incorporate also soft information, which is available in iterative (turbo) equalizers. The mean square error (MSE) of the soft-based estimate is evaluated analytically and validated by simulations. The comparison with the conventional training-based block-by-block estimate shows the benefits of the proposed approach on the turbo equalizer convergence

Implicit Cooperative Positioning in Vehicular Networks

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.Comment: 15 pages, 10 figures, in review, 201

Wireless home automation networks for indoor surveillance: technologies and experiments

The use of wireless technologies for critical surveillance and home automation introduces a number of opportunities as well as technological challenges. New emerging technologies give the opportunity to exploit the full potential of the internet of things paradigm by augmenting existing wired installations with smart wireless architectures. This work gives an overview of requirements, characteristics, and challenges of wireless home automation networks with special focus on intrusion detection systems. The proposed wireless network is based on several sensors that are deployed over a monitored area for detecting possible risky situations and triggering appropriate actions in response. The network needs to support critical traffic patterns with different characteristics and quality constraints. Namely, it should provide a periodic low-power monitoring service and, in case of intrusion detection, a real-time alarm propagation mechanism over inherently unreliable wireless links subject to fluctuations of the signal power. Following the guidelines introduced by recent standardization, this paper proposes the design of a wireless network prototype at 868 MHz which is able to satisfy the specifications of typical intrusion detection applications. A proprietary medium access control is developed based on the low-power SimpliciTI radio stack (Texas Instruments Incorporated, San Diego, CA, USA). Network performance is assessed by experimental measurements using a test-bed in an indoor office environment with severe multipath and nonline-of-sight propagation conditions. The measurement campaigns highlight the potential of the sub-GHz technology for cable replacing

Signalling Design in Sensor-Assisted mmWave Communications for Cooperative Driving

Millimeter-Wave (mmWave) Vehicle-To-Vehicle (V2V) communications are a key enabler for connected and automated vehicles, as they support the low-latency exchange of control signals and high-resolution imaging data for maneuvering coordination. The employment of mmWave V2V communications calls for Beam Alignment and Tracking (BAT) procedures to ensure that the antenna beams are properly steered during motion. The conventional beam sweeping approach is known to be unsuited for the high vehicular mobility and its large overhead reduces transmission efficiency. A promising solution to reduce BAT signalling foresees the integration of V2V communication systems with on-board vehicle sensors. We focus on a cooperative sensor-assisted architecture for mmWave V2V communications in line of sight, where vehicles exchange the estimate of antenna position and its uncertainty to compute the optimal beam direction and dimension. We analyze and compare different signalling strategies for sharing the information on antenna estimate, evaluating the tradeoff between signalling overhead and performance loss for different position and uncertainty encoding strategies. Main attention is given to differential quantization on both the antenna position and uncertainty. Analyses over realistic urban mobility trajectories suggest that differential approaches introduce a negligible performance loss while significantly reducing the BAT signalling communication overhead