Decision Fusion with Unknown Sensor Detection Probability

In this correspondence we study the problem of channel-aware decision fusion when the sensor detection probability is not known at the decision fusion center. Several alternatives proposed in the literature are compared and new fusion rules (namely 'ideal sensors' and 'locally-optimum detection') are proposed, showing attractive performance and linear complexity. Simulations are provided to compare the performance of the aforementioned rules.Comment: To appear in IEEE Signal Processing Letter

Low-complexity dominance-based Sphere Decoder for MIMO Systems

The sphere decoder (SD) is an attractive low-complexity alternative to maximum likelihood (ML) detection in a variety of communication systems. It is also employed in multiple-input multiple-output (MIMO) systems where the computational complexity of the optimum detector grows exponentially with the number of transmit antennas. We propose an enhanced version of the SD based on an additional cost function derived from conditions on worst case interference, that we call dominance conditions. The proposed detector, the king sphere decoder (KSD), has a computational complexity that results to be not larger than the complexity of the sphere decoder and numerical simulations show that the complexity reduction is usually quite significant

Slepian-based serial estimation of time-frequency variant channels for MIMO-OFDM systems

This paper proposes a low-complexity two dimensional channel estimator for MIMO-OFDM systems derived from a time-frequency variant channel estimator previously proposed. The estimator exploits both time and frequency correlations of the wireless channel via use of Slepian-basis expansions. The computational saving comes from replacing a two-dimensional Slepian-basis expansion with two serially concatenated one-dimensional Slepian-basis expansions. Performance in terms of Normalized Mean Square Error (NMSE) vs. Signal-to-Noise Ratio (SNR) have been analyzed via numerical simulations and compared with the original estimator. The analysis of the performance takes into account the impact of both system and channel parameters

Distributed Detection in Wireless Sensor Networks under Multiplicative Fading via Generalized Score-tests

In this paper, we address the problem of distributed detection of a non-cooperative (unknown emitted signal) target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things (IoT), one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The Fusion Center (FC) receives sensor bits via noisy Binary Symmetric Channels (BSCs) and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e. the target position xT) observable only under H1 hypothesis. Davies framework is exploited herein to design the generalized forms of Rao and Locally-Optimum Detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold-optimization is also proposed. Simulation results confirm the promising performance of our proposed approaches.acceptedVersio

Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario

In this work, we present a spatio-temporal decision fusion approach aimed at performing quickest detection of faults within an Oil and Gas subsea production system. Specifically, a sensor network collectively monitors the state of different pieces of equipment and reports the collected decisions to a fusion center. Therein, a spatial aggregation is performed and a global decision is taken. Such decisions are then aggregated in time by a post-processing center, which performs quickest detection of system fault according to a Bayesian criterion which exploits change-time statistical distributions originated by system components’ datasheets. The performance of our approach is analyzed in terms of both detection- and reliability-focused metrics, with a focus on (fast & inspection-cost-limited) leak detection in a real-world oil platform located in the Barents Sea.acceptedVersio

Energy Storage Solutions for Offshore Applications

Increased renewable energy production and storage is a key pillar of net-zero emission. The expected growth in the exploitation of offshore renewable energy sources, e.g., wind, provides an opportunity for decarbonising offshore assets and mitigating anthropogenic climate change, which requires developing and using efficient and reliable energy storage solutions offshore. The present work reviews energy storage systems with a potential for offshore environments and discusses the opportunities for their deployment. The capabilities of the storage solutions are examined and mapped based on the available literature. Selected technologies with the largest potential for offshore deployment are thoroughly analysed. A landscape of technologies for both short- and long-term storage is presented as an opportunity to repurpose offshore assets that are difficult to decarbonise. Keywords: energy storage; decarbonisation; offshore; batteries; hydrogen; ammonia; CAES; flywheel; supercapacitorpublishedVersio

Internet traffic modeling by means of Hidden Markov Models

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Massive MIMO meets decision fusion: Decode-and-fuse vs. decode-then-fuse

We study channel-aware decision fusion over a multiple-input multiple-output (MIMO) channel in the largearray regime at the decision-fusion center (DFC). Inhomogeneous large-scale fading between the sensors and the DFC is consider in addition to the small-scale fading, and pilot-based channel estimation is performed at the DFC. Linear processing techniques are analyzed in order to design low-complexity alternatives to the optimum log-likelihood ratio test (LLRT). Performance evaluation based on Monte Carlo simulations are presented

On the performance of iterative receivers for interfering MIMO-OFDM systems in measured channels

This paper investigates the gains harvested through base station cooperation in the up-link for a multi-user (MU) Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, operating in a real indoor environment. The base stations perform joint detection using an iterative receiver that carries out multi-user detection and channel estimation via soft information from the single-user decoders. Performance evaluation is carried out using real channels from an indoor dynamic dual MIMO link measurement campaign. The measured scenario represent a real life situation where two users communicate with two base stations, each with two antennas, in an environment resembling a shopping mall or an airport terminal. System performance is evaluated in terms of both Bit-Error Rate (BER) vs. Signal-to-Interference Ratio (SIR) and Cumulative Distribution Functions (CDF) for the instantaneous BER. Also, the impact of using soft information in the channel estimation is analyzed