Wavelet Packet Division Multiplexing (WPDM)-Aided Industrial WSNs

Industrial Internet-of-Things (IIoT) involve multiple groups of sensors, each group sending its observations on a particular phenomenon to a central computing platform over a multiple access channel (MAC). The central platform incorporates a decision fusion center (DFC) that arrives at global decisions regarding each set of phenomena by combining the received local sensor decisions. Owing to the diverse nature of the sensors and heterogeneous nature of the information they report, it becomes extremely challenging for the DFC to denoise the signals and arrive at multiple reliable global decisions regarding multiple phenomena. The industrial environment represents a specific indoor scenario devoid of windows and filled with different noisy electrical and measuring units. In that case, the MAC is modelled as a large-scale shadowed and slowly-faded channel corrupted with a combination of Gaussian and impulsive noise. The primary contribution of this paper is to propose a flexible, robust and highly noise-resilient multi-signal transmission framework based on Wavelet packet division multiplexing (WPDM). The local sensor observations from each group of sensors are waveform coded onto wavelet packet basis functions before reporting them over the MAC. We assume a multi-antenna DFC where the waveform-coded sensor observations can be separated by a bank of linear filters or a correlator receiver, owing to the orthogonality of the received waveforms. At the DFC we formulate and compare fusion rules for fusing received multiple sensor decisions, to arrive at reliable conclusions regarding multiple phenomena. Simulation results show that WPDM-aided wireless sensor network (WSN) for IIoT environments offer higher immunity to noise by more than 10 times over performance without WPDM in terms of probability of false detection

IoT Localization and Optimized Topology Extraction Using Eigenvector Synchronization

Internet-of-Things (IoT) devices are low size, weight and power (SWaP), low complexity and include sensors, meters, wearables and trackers. Transmitting information with high signal power is exacting on device battery life, therefore an efficient link and network configuration is absolutely crucial to avoid signal power enhancement in interference-rich environment and resorting to battery-life extending strategies. Efficient network configuration can also ensure fulfilment of network performance metrics like throughput, coding rate and spectral efficiency. We formulate a novel approach of first localizing the IoT nodes and then extracting the network topology for information exchange between the nodes (devices, gateway and sinks), such that overall network throughput is maximized. The nodes are localized using noisy measurements of a subset of Euclidean distances between two nodes. Realizable subsets of neighboring devices agree with their own position within the entire network graph through eigenvector synchronization. Using communication global graph-model-based technique, network topology is constructed in terms of transmit power allocation with the aim of maximizing spatial usage and overall network throughput. This topology extraction problem is solved using the concept of linear programming

Transmit Power Optimization of IoT Devices over Incomplete Channel Information

Efficient resource allocation (RA) strategies within massive and dense Internet of Things (IoT) networks is one of the major challenges in the deployment of IoT-network based smart ecosystems involving heterogeneous power-constrained IoT devices operating in varied radio and environmental conditions. In this paper, we focus on the transmit power minimization problem for IoT devices while maintaining a threshold channel throughput. The established optimization literature is not robust against the fast-fading channel and the interaction among different transmit signals in each instance. Besides, realistically, each IoT node possesses incomplete channel state information (CSI) on its neighbors, such as the channel gain being private information for the node itself. In this work, we resort to Bayesian game theoretic strategies for solving the transmit power optimization problem exploiting incomplete CSIs within massive IoT networks. We provide a steady discussion on the rationale for selecting the game theory, particularly the Bayesian scheme, with a graphical visualization of our formulated problem. We take advantage of the property of the existence and uniqueness of the Bayesian Nash equilibrium (BNE), which exhibits reduced computational complexity while optimizing transmit power and maintaining target throughput within networks comprised of heterogeneous devices

Classical Capacity of Arbitrarily Distributed Noisy Quantum Channels

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system's perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage

Space-Time Spreading Aided Distributed MIMO-WSNs

In this letter, we consider the plaguing, yet rarely handled problem of interference resulting from superposition of multiple sensor signals in time, when sent over a multiple access channel (MAC) in wireless sensor networks (WSNs). We propose space-time spreading (STS) of local sensor decisions before reporting them over a MAC to i) minimize interference and ii) reduce energy required for combating interference due to superposition of sensor decisions. Each sensor decision is encoded on appropriately indexed space-time block of fixed duration using dispersion vectors, such that a single sensor is activated over each space-time block while all the other sensors are silent. At the receive side of the reporting channel, we assume a multi-antenna decision fusion center (DFC), thereby representing a distributed multiple-input-multiple-output (MIMO) communication scenario. We formulate and compare optimum and sub-optimum fusion rules for fusing sensor decisions at the DFC to arrive at a reliable conclusion. Simulation results demonstrate gain in fusion performance with STS-aided transmission by 3 to 6 times over performance without STS

Compact Full-Duplex Amplify-and-Forward Relay Design for 5G Applications

This paper presents a compact circuit design for implementing full-duplex relays serving network architectures envisioned for 5G applications. The proposed design prevents the transmit signal from interfering with the received signal through signal inversion. Signal inversion is accomplished through a compact design based on parametric amplifier circuit which operates simultaneously in two different modes, re-transmit and demodulate. This design also has an added advantage of being capable of generating phase difference between the transmitted and received signals by controlling the local oscillator signal. The validity of the design is evaluated against an example Smart Grid architecture, where it is employed to function as amplify-and-forward full-duplex relays/repeaters for serving several communication links. Simulation results indicate that full-duplex mode outperforms half-duplex one in terms of average channel capacity as well as bit error rate irrespective of the position of the relays with respect to distance from source/destination

Agent-Based Modeling for Distributed Decision Support in an IoT Network

An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-based modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze, and compare various medium access control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high-level MAC protocol by using three different approaches: 1) centralized decision maker (DM); 2) DESYNC; and 3) decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple DMs in order to improve the information reporting error and spectrum utilization of the system

Semi-blind iterative joint channel estimation and K-best sphere decoding for MIMO

An efficient and high-performance semi-blind scheme is proposed for Multiple-Input Multiple-Output (MIMO) systems by iteratively combining channel estimation with K-Best Sphere Decoding (SD). To avoid the exponentially increasing complexity of Maximum Likelihood Detection (MLD) while achieving a near optimal MLD performance, K-best SD is considered to accomplish data detection. Semi-blind iterative estimation is adopted for identifying the MIMO channel matrix. Specifically, a training-based least squares channel estimate is initially provided to the K-best SD data detector, and the channel estimator and the data detector then iteratively exchange information to perform the decision-directed channel update and consequently to enhance the detection performance. The proposed scheme is capable of approaching the ideal detection performance obtained with the perfect MIMO channel state information

Cross-Layer Inference in WSN: From Methods to Experimental Validation

In this chapter, the fundamentals of distributed inference problem in wireless sensor networks (WSN) is addressed and the statistical theoretical foundations to several applications is provided. The chapter adopts a statistical signal processing perspective and focusses on distributed version of the binary-hypothesis test for detecting an event as correctly as possible. The fusion center is assumed to be equipped with multiple antennas collecting and processing the information. The inference problem that is solved, primarily concerns the robust detection of a phenomenon of interest (for example, environmental hazard, oil/gas leakage, forest fire). The presence of multiple antennas at both transmit and receive sides resembles a multiple-input-multiple-output (MIMO) system and allows for utilization of array processing techniques providing spectral efficiency, fading mitigation and low energy sensor adoption. The problem is referred to as MIMO decision fusion. Subsequently, both design and evaluation (simulated and experimental) of these fusion approaches is presented for this futuristic WSN set-up

Propagation Model and Performance Analysis for Mobility Constrained Indoor Wireless Environments

In an indoor wireless environment like an open office or laboratory, there are not enough large obstacles to reflect or refract the main waves contributed by the scattering clusters visited by the mobile user. Moreover, mobile WLAN users generally restrict their movements to a small area due to the inability of most WLAN standards to accommodate hand-offs. As a result, users visit at most one or two scattering clusters and experience only a handful of different shadowing values. This thesis proposes the first ever appropriate composite fading / shadowing channel model that characterizes the combination of small scale fading and large scale shadowing for users confined to small coverage areas in a large office environment described above. Based on a detailed indoor measurement campaign, a joint distribution called the Joint fading and Two-path Shadowing (JFTS) distribution is proposed that combines the Rician fading and the two waves with diffuse power (TWDP) shadowing models. This thesis also presents the first ever analysis of different performance metrics like outage probability, error rate performances and spectral efficiencies of existing high throughput communication techniques like error control coding, fixed and adaptive modulation in mobility constrained indoor wireless environment, where the propagation scenario can be appropriately characterized by the newly developed JFTS model. Performance evaluation is done both in presence or absence of perfect channel state information