Special Issue "Multi-Channel and Multi-Agent Signal Processing"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Prof. Dr. Angelo Coluccia
E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell'Innovazione, University of Salento, via Monteroni, 73100 Lecce, Italy
Interests: Array and (networked) multi-sensor signal processing, including sensing and estimation in wireless networks, detection, and localization. Relevant application fields are radar, (sensor, 5G, overlay, social, neural) networks, and smart systems (including intelligent transportation systems and smart cities)

Special Issue Information

Dear Colleagues,

Multi-channel and array signal processing is a well-established field with fundamental applications in wireless communications, radar/sonar, remote sensing, and medical imaging. Its focus is on signals from multiple sensors or channels, but often involving only a pair of entities (identifiable as transmitter and receiver in Shannon’s sense) under a point-to-point communication paradigm. On the other hand, many modern application contexts are networked, that is, interconnection of different devices or agents is possible and can be exploited to solve problems in a cooperative, possibly distributed way: Cooperation can improve performance and make solvable problems that are not such in a non-cooperative setting (e.g., due to the presence of several unknown parameters); distributed (in-network) processing enables the design of privacy-preserving and/or robust schemes, while taking advantage of the aggregate power of many devices, instead of a single node where all data need to be sent for centralized processing (which is, thus, conversely, a single point of failure and has very high computational, bandwidth, and energy demands).

Although research is very active on both multi-channel (single-link) and multi-agent (networked, i.e., multi-link) signal processing, the potential of combining both fields is still underexploited. A positive example is instead the recent trend in antenna array (MIMO) solutions for 5G cellular networks, in particular using device-to-device (D2D) communications for solving problems (e.g., user localization, coordinated control of autonomous systems, cooperative extended horizon in vehicular networks, smart factory) while improving throughput and reducing interference from multiple radio access; the latter is becoming, in fact, a bottleneck given the exponential growth of connected devices (smartphones, wearables, smart objects, etc.) as the Internet of Things (IoT) paradigm spreads out.

This Special Issue aims at promoting cross-fertilization between multi-channel/array processing techniques and multi-agent methodologies in order to provide advanced solutions for emerging application contexts.

Prof. Dr. Angelo Coluccia
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

The topics relevant to this Special Issue include but are not limited to:
  • Cooperative statistical signal processing and data fusion
  • Machine learning and artificial intelligence approaches to multi-channel and multi-agent signal processing
  • Cooperative positioning using multi-dimensional signals and multi-agent strategies
  • Sensing in ad-hoc networks and more generally graph signal processing
  • IoT-enabling advances in MIMO communications and 5G networks
  • Multi-agent array processing for radar, sonar, communications and medical imaging
  • Distributed sensing, detection, and estimation in cyber-physical systems
  • Applications of multi-channel signal processing in multi-agent contexts (social networks, smart agriculture, smart factory, smart grids, smart cities)

Published Papers (4 papers)

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Research

Open AccessArticle
An Accurate Probabilistic Model for TVWS Identification
Appl. Sci. 2019, 9(20), 4232; https://doi.org/10.3390/app9204232 - 10 Oct 2019
Abstract
Television White Spaces (TVWS)-based cognitive radio systems can improve spectrum efficiency by facilitating opportunistic usage of television broadcasting spectrum by secondary users without interfering with primary users. Previously applied models introduce missed detection errors, giving a limited estimation of the spectrum occupancy, which [...] Read more.
Television White Spaces (TVWS)-based cognitive radio systems can improve spectrum efficiency by facilitating opportunistic usage of television broadcasting spectrum by secondary users without interfering with primary users. Previously applied models introduce missed detection errors, giving a limited estimation of the spectrum occupancy, which does not correspond to the reality of its usage, hence resulting in a partial waste of this resource. Considering jointly parameters like false alarm probability and detection probability, this article proposes a probabilistic model that can identify TVWS with improved accuracy. The proposed model considers energy detection criteria, combined with simultaneous sensing of the noise and of the signal from primary users. In order to demonstrate the model effectiveness, a low-cost Mobile Spectrum Sensing Station prototype was designed, implemented, and subsequently mounted on a vehicle. More than eight million spatio-temporally variant data samples were collected by scanning the UHF-TV spectrum of 500–698 MHz in the city of Windsor, ON, Canada. Analysis of the collected data showed that the proposed model achieves an accuracy improvement of about 9.6% compared to existing models, demonstrating that TVWS vary with spatial displacement and increasing significantly in the rural areas. Even in the most crowded spectrum zone, about 28% of the channels are identified as TVWS, and this number increases to a maximum of 60% in less crowded regions in urban areas. We conclude that the proposed model improves the TVWS detection compared with other used models, and also that the elements considered in this research contribute to reduce the complexity of the mathematical calculations while maintaining the accuracy. A low-cost open-source sensing station has been designed and tested, which represents an operative and useful data source in this setting. Full article
(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)
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Open AccessArticle
Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
Appl. Sci. 2019, 9(19), 4187; https://doi.org/10.3390/app9194187 - 08 Oct 2019
Abstract
A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new [...] Read more.
A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm. Full article
(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)
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Open AccessArticle
Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
Appl. Sci. 2019, 9(19), 3951; https://doi.org/10.3390/app9193951 - 20 Sep 2019
Abstract
As density of a wireless LAN grows, per-user throughput degrades severely, deteriorating user experience. To improve service quality, it is important to increase system spectral efficiency. Controlling carrier-sense threshold is one of the key techniques to achieve the goal, because frequently transmissions are [...] Read more.
As density of a wireless LAN grows, per-user throughput degrades severely, deteriorating user experience. To improve service quality, it is important to increase system spectral efficiency. Controlling carrier-sense threshold is one of the key techniques to achieve the goal, because frequently transmissions are unnecessarily blocked by carrier sensing, even though these transmissions can take place without causing packet losses. Using high carrier-sense threshold and allowing nodes to transmit aggressively may increase the system throughput, but this approach can lead to unfair channel share and cause starvation for the edge nodes. In this paper, we propose a medium access control protocol where transmitters include the carrier-sense threshold required to protect its packet in the preamble. Nodes receiving the preamble only transmit concurrently, when they are confident that their own transmission as well as the on-going transmission will both be successfully received at the respective receivers. The simulation results show that this dual-threshold approach can achieve higher system throughput compared to using a single carrier-sense threshold, without penalizing edge nodes. Full article
(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)
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Open AccessArticle
Radar Application: Stacking Multiple Classifiers for Human Walking Detection Using Micro-Doppler Signals
Appl. Sci. 2019, 9(17), 3534; https://doi.org/10.3390/app9173534 - 28 Aug 2019
Abstract
We propose a stacking method for ensemble learning to distinguish micro-Doppler signals generated by human walking from background noises using radar sensors. We collected micro-Doppler signals caused by four types of background noise (line of sight (LoS), fan, snow and rain) and additionally [...] Read more.
We propose a stacking method for ensemble learning to distinguish micro-Doppler signals generated by human walking from background noises using radar sensors. We collected micro-Doppler signals caused by four types of background noise (line of sight (LoS), fan, snow and rain) and additionally considered micro-Doppler signals caused by human walking combined with these four types of background noise. We firstly verified the effectiveness of a fully connected deep neural network (DNN) to classify 8 types of signals. The average accuracy was 88.79% for the test set. Then, we propose a stacking method to combine two base classifiers of different structures. The average accuracy of the stacking method on the test set was 91.43%. Lastly, we designed a modified stacking method to reuse feature information stored at the previous stage and the average test accuracy increased to 95.62%. This result shows that the proposed stacking methods can be an effective approach to improve classifier’s accuracy in recognizing human walking using micro-Doppler signals with background noise. Full article
(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)
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