Orthogonal Multiple Access with Correlated Sources: Feasible Region and Pragmatic Schemes

In this paper, we consider orthogonal multiple access coding schemes, where correlated sources are encoded in a distributed fashion and transmitted, through additive white Gaussian noise (AWGN) channels, to an access point (AP). At the AP, component decoders, associated with the source encoders, iteratively exchange soft information by taking into account the source correlation. The first goal of this paper is to investigate the ultimate achievable performance limits in terms of a multi-dimensional feasible region in the space of channel parameters, deriving insights on the impact of the number of sources. The second goal is the design of pragmatic schemes, where the sources use "off-the-shelf" channel codes. In order to analyze the performance of given coding schemes, we propose an extrinsic information transfer (EXIT)-based approach, which allows to determine the corresponding multi-dimensional feasible regions. On the basis of the proposed analytical framework, the performance of pragmatic coded schemes, based on serially concatenated convolutional codes (SCCCs), is discussed

Mathematical modeling of oxygen control in biocell composting plants

We propose an optimal control problem to determine the best aeration strategy for aerobic biodegradation in a composting cell. The goal is to minimize the deviation of the oxygen level from its reference value for the entire duration of the biodegradation process. The mathematical model includes several chemical phenomena, like the aerobic biodegradation of the soluble substrate by means of a bacterial biomass, the hydrolysis of insoluble substrate and the biomass decay. The oxygen and the optimal mechanical aeration time profiles are obtained and discussed. Finally, the plant performance is evaluated in absence and presence of external aeration by means of several specific indices

A minimum time control problem for aerobic degradation processes in biocell composting plants

We introduce a mathematical model for the composting process in biocells. The model includes several phenomena, like the aerobic biodegradation of the soluble substrate by means of a bacterial population, the hydrolysis of insoluble substrate, and the biomass decay. We investigate the best strategies to reduce substrate components in minimal time by controlling the effects of cell oxygen concentration on the degradation phenomenon. It is shown that singular controls are not optimal for this model and the optimal control time profiles are of bang or bang-bang type. The occurrence of switching curves is discussed. In the case of a bang-bang control we prove that optimal control profiles have a unique switching time and the corresponding switching curve is determined

Hybrid UWB-Inertial TDoA-based Target Tracking with Concentrated Anchors

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively

Acknowledgement to reviewers of JSAN in 2016

The editors of JSAN would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...

Decoding and fusion in sensor networks with noisy observations and communications

In this paper, we study how to combine decoding and fusion at the access point (AP) in sensor networks for decentralized binary detection. We consider a scenario where all sensors make noisy observations of the same spatially constant binary phenomenon and communicate to the AP through noisy communication links. Simple distributed channel coding strategies are used, either using repetition coding at each sensor (i.e., multiple observations) or distributed systematic block channel coding. In all cases, the system performance is analyzed separating or joining the decoding and fusion operations. As expected, the schemes with joint decoding and fusion show a significant performance improvement with respect to that of schemes with separate decoding and fusion. Our results suggest that the use of multiple observations is often the winning choice at practical values of the probability of decision error at the AP

Reduced-complexity decentralized detection of spatially non-constant phenomena

In this chapter, we study sensor networks with decentralized detection of a non-constant phenomenon, whose status might change independently from sensor to sensor. In particular, we consider binary phenomena characterized by a fixed number of status changes (from state ???0??? to state ???1???) across the sensors. This is realistic for sensor networking scenarios where abrupt spatial variations of the phenomenon under observation need to be estimated, e.g., an abrupt temperature increase, as could be the case in the presence of a fire in a specific zone of the monitored surface. In such scenarios, we derive the minimum mean square error (MMSE) fusion algorithm at the access point (AP). The improvement brought by the use of quantization at the sensors is investigated. Finally, we derive simplified (sub-optimum) fusion algorithms at the AP, with a computational complexity lower than that of schemes with MMSE fusion at the AP

Decoding and fusion in distributed detection schemes with unreliable communications

In the work presented here, we study how to combine decoding and fusion at the access point (AP) in sensor networks for distributed binary detection. We assume that all sensors make noisy observations of the same spatially constant binary phenomenon and communicate to the AP through noisy communication links. Simple distributed channel coding strategies are analyzed, either using repetition coding at each sensor (i.e., multiple observations) or distributed (network-wide) systematic block channel coding (possibly with local fusion in the presence of multiple observations). In the latter case, the use of a relay is proposed. In all cases, the system performance is analyzed separating or joining the decoding and fusion operations at the AP. As expected, the schemes with joint decoding and fusion show a significant performance improvement with respect to that of schemes with separate decoding and fusion

A simple information-theoretic analysis of clustered sensor networks with decentralized detection

In this letter, we present a simple information-theoretic framework to analyze clustered sensor networks with hierarchical multi-level majority-like fusion and decentralized detection. The sensor nodes observe a binary phenomenon and transmit their own data to an access point (AP), possibly through intermediate fusion centers (FCs). We investigate the impact of uniform and non-uniform clustering on the system performance, evaluated in terms of mutual information between the true phenomenon status and its estimate at the AP. Being the overall system binary-input binary-output (BIBO), it will be shown that the probability of decision error (Pe) is a specific function of the input-output mutual information (I). In other words, the network operational point lies over a specific Pe - I curve and depends on the network characteristics (e.g., topology, observation and communication noise levels, etc.)

Outage capacity analysis of the massive MIMO diversity channel

We consider the massive Multiple Input Multiple Output (MIMO) channel affected by independent and identically distributed Rayleigh fading, with linear processing at both transmitter and receiver sides to pursue full diversity, and analyze its outage capacity for large number of antennas. We first discuss the classical Single Input Multiple Output (SIMO) diversity channel that encompasses Maximal Ratio Combining (MRC) or Selection Combining (SC). For MRC, a numerical computation and a Gaussian Approximation (GA) are considered, whereas for SC an exact evaluation is presented. The analysis is then straightforwardly extended to the Multiple Input Single Output (MISO) diversity channel that encompasses Maximal Ratio Transmission (MRT) or transmit antenna selection. The general full diversity MIMO channel is finally considered, with optimal linear processing or simple antenna selection at both transmitter and receiver. If the number of antennas is sufficiently large on at least one side, the outage capacity of each considered diversity channel approaches that of a reference Additive White Gaussian Noise (AWGN) channel with properly defined Signal-to-Noise Ratio (SNR), which provides a performance benchmark. This conclusion is valid for large but realistic number of antennas compatible with the assumption of independent fading