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Multi-Channel and Multi-Agent Signal Processing

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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: closed (30 September 2020) | Viewed by 27808

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Special Issue Editor

Prof. Dr. Angelo Coluccia

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Guest Editor

Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: radar detection and localization; wireless networks; multi-sensor; multi-agent signal processing; cyber-physical systems; smart devices; social networks
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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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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 (11 papers)

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Editorial

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4 pages, 185 KiB

Open AccessEditorial

Multi-Channel and Multi-Agent Signal Processing

by Angelo Coluccia

Appl. Sci. 2022, 12(4), 1851; https://doi.org/10.3390/app12041851 - 11 Feb 2022

Viewed by 1288

Abstract

Multi-channel and array signal processing is a well-established field with fundamental applications in wireless communications, radar/sonar, remote sensing, medical imaging, and more [...] Full article

(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)

Research

Jump to: Editorial, Review

15 pages, 3099 KiB

Open AccessArticle

A Dropout Compensation ILC Method for Formation Tracking of Heterogeneous Multi-Agent Systems with Loss of Multiple Communication Packets

by Yuzhou Wu, Jialu Zhang, Yu Ge, Zhichao Sheng and Yong Fang

Appl. Sci. 2020, 10(14), 4752; https://doi.org/10.3390/app10144752 - 10 Jul 2020

Cited by 6 | Viewed by 1621

Abstract

In this paper, the formation tracking problem for heterogeneous multi-agent systems with loss of multiple communication packets is considered using the iterative learning control (ILC) method. A dropout compensation ILC method is presented to construct effective distributed iterative learning protocols. The convergence conditions are given based on the frequency-domain analysis by using the general Nyquist stability criterion and Greshgorin’s disk theorem. The results show that the multi-agent system with different packet loss rate can achieve formation tracking without reducing the convergence speed. Numerical simulation results show the effectiveness of the proposed dropout compensation ILC method. Full article

(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)

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16 pages, 493 KiB

Open AccessArticle

An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems

by Woon-Sang Lee, Jae-Hyun Ro, Young-Hwan You, Duckdong Hwang and Hyoung-Kyu Song

Appl. Sci. 2020, 10(13), 4547; https://doi.org/10.3390/app10134547 - 30 Jun 2020

Cited by 5 | Viewed by 1646

Abstract

Recently, as the demand for data rate of users has increased, wireless communication systems have aimed to offer high throughput. For this reason, various techniques which guarantee high performance have been invented, such as massive multiple-input multiple-output (MIMO). However, the implementation of huge base station (BS) antenna array and decrease of reliability as the number of users increases are chief obstacles. In order to mitigate these problems, this paper proposes an adaptive precoder which provides high throughput and bit error rate (BER) performances to achieve the desired data rate in multi user (MU) MIMO downlink systems which have a practical BS antenna array (up to 16). The proposed scheme is optimized with a modified minimum mean square error (MMSE) criterion in order to improve BER gain and reduce data streams in order to obtain diversity gain at low signal to noise ratio (SNR). It is shown that the BER and throughput performances of the proposed scheme are improved. Full article

(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)

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22 pages, 6189 KiB

Open AccessArticle

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

by Qinhao Wu, Hongqiang Wang, Xiang Li, Bo Zhang and Jinlin Peng

Appl. Sci. 2019, 9(23), 5173; https://doi.org/10.3390/app9235173 - 28 Nov 2019

Cited by 16 | Viewed by 3737

Abstract

The networked unmanned aerial vehicle (UAV) radar system may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. This paper is devoted to quantifying and optimizing the anti-jamming performance of networked UAV radar systems in adversarial electromagnetic environments. Firstly, instead of using the conventional metric of signal-to-interference ratio (SIR), this paper explores use of the theory of radar information representation as the basis of evaluating the information acquisition capabilities of the networked UAV radar systems. Secondly, this paper proposes a modified Q-Learning method based on double greedy algorithm to optimize the anti-jamming performance of the networked UAV radar systems, through joint programming in the frequency-motion-antenna domain. Simulation results prove the effectiveness of the algorithm in two different networking scenarios. Full article

(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)

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9 pages, 934 KiB

Open AccessArticle

Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems

by Uzokboy Ummatov and Kyungchun Lee

Appl. Sci. 2019, 9(21), 4624; https://doi.org/10.3390/app9214624 - 30 Oct 2019

Cited by 2 | Viewed by 2373

Abstract

This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios. Full article

(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)

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9 pages, 955 KiB

Open AccessArticle

Energy-Efficient Hybrid Beamforming with Variable and Constant Phase Shifters

by Godwin Mruma Gadiel and Kyungchun Lee

Appl. Sci. 2019, 9(21), 4476; https://doi.org/10.3390/app9214476 - 23 Oct 2019

Cited by 4 | Viewed by 2458

Abstract

In this paper, the authors propose a novel partially connected hybrid beamforming (PC-HBF) architecture, which employs variable phase shifters (VPSs) and constant phase shifters (CPSs) for analog beamforming to harness the potential of these two types of phase shifters. In the proposed architecture, the system sum rate optimization to determine the analog precoders can be formulated as a combinatorial problem. However, its exact solution is intractable, and in massive multiple-input multiple-output systems, exhaustive search to solve the corresponding combinatorial problem is practically infeasible. To resolve this problem, we employ a greedy algorithm that provides a near-optimal solution with reduced complexity. The simulation results obtained herein show that by optimally combining VPSs and CPSs, the proposed architecture achieves performance close to that of the VPS-based PC-HBF architecture. Furthermore, its energy efficiency is up to

27.3 %

higher than that of the CPS-based fully connected HBF scheme. Full article

(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)

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25 pages, 9588 KiB

Open AccessArticle

An Accurate Probabilistic Model for TVWS Identification

by Danilo Corral-De-Witt, Sabbir Ahmed, Faroq Awin, José Luis Rojo-Álvarez and Kemal Tepe

Appl. Sci. 2019, 9(20), 4232; https://doi.org/10.3390/app9204232 - 10 Oct 2019

Cited by 8 | Viewed by 2204

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 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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16 pages, 893 KiB

Open AccessArticle

Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

by Rang Liu, Hongqi Fan and Huaitie Xiao

Appl. Sci. 2019, 9(19), 4187; https://doi.org/10.3390/app9194187 - 08 Oct 2019

Cited by 11 | Viewed by 2582

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 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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18 pages, 871 KiB

Open AccessArticle

Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs

by Jungmin So and Joosang Lee

Appl. Sci. 2019, 9(19), 3951; https://doi.org/10.3390/app9193951 - 20 Sep 2019

Cited by 6 | Viewed by 2898

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 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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14 pages, 7108 KiB

Open AccessArticle

Radar Application: Stacking Multiple Classifiers for Human Walking Detection Using Micro-Doppler Signals

by Jihoon Kwon and Nojun Kwak

Appl. Sci. 2019, 9(17), 3534; https://doi.org/10.3390/app9173534 - 28 Aug 2019

Cited by 8 | Viewed by 3147

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 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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Review

Jump to: Editorial, Research

22 pages, 2606 KiB

Open AccessFeature PaperReview

Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods

by Slavisa Tomic, Marko Beko, Luís M. Camarinha-Matos and Luís Bica Oliveira

Appl. Sci. 2020, 10(1), 272; https://doi.org/10.3390/app10010272 - 30 Dec 2019

Cited by 18 | Viewed by 3094

Abstract

Remarkable progress in radio frequency and micro-electro-mechanical systems integrated circuit design over the last two decades has enabled the use of wireless sensor networks with thousands of nodes. It is foreseen that the fifth generation of networks will provide significantly higher bandwidth and faster data rates with potential for interconnecting myriads of heterogeneous devices (sensors, agents, users, machines, and vehicles) into a single network (of nodes), under the notion of Internet of Things. The ability to accurately determine the physical location of each node (stationary or moving) will permit rapid development of new services and enhancement of the entire system. In outdoor environments, this could be achieved by employing global navigation satellite system (GNSS) which offers a worldwide service coverage with good accuracy. However, installing a GNSS receiver on each device in a network with thousands of nodes would be very expensive in addition to energy constraints. Besides, in indoor or obstructed environments (e.g., dense urban areas, forests, and canyons) the functionality of GNSS is limited to non-existing, and alternative methods have to be adopted. Many of the existing alternative solutions are centralized, meaning that there is a sink in the network that gathers all information and executes all required computations. This approach quickly becomes cumbersome as the number of nodes in the network grows, creating bottle-necks near the sink and high computational burden. Therefore, more effective approaches are needed. As such, this work presents a survey (from a signal processing perspective) of existing distributed solutions, amalgamating two radio measurements, received signal strength (RSS) and angle of arrival (AOA), which seem to have a promising partnership. The present article illustrates the theory and offers an overview of existing RSS-AOA distributed solutions, as well as their analysis from both localization accuracy and computational complexity points of view. Finally, the article identifies potential directions for future research. Full article

(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)

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