Low-complexity soft ML detection for generalized spatial modulation

[EN] Generalized Spatial Modulation (GSM) is a recent Multiple-Input Multiple-Output (MIMO) scheme, which achieves high spectral and energy efficiencies. Specifically, soft-output detectors have a key role in achiev-ing the highest coding gain when an error-correcting code (ECC) is used. Nowadays, soft-output Maxi-mum Likelihood (ML) detection in MIMO-GSM systems leads to a computational complexity that is un-feasible for real applications; however, it is important to develop low-complexity decoding algorithms that provide a reasonable computational simulation time in order to make a performance benchmark available in MIMO-GSM systems. This paper presents three algorithms that achieve ML performance. In the first algorithm, different strategies are implemented, such as a preprocessing sorting step in order to avoid an exhaustive search. In addition, clipping of the extrinsic log-likelihood ratios (LLRs) can be incor-porating to this algorithm to give a lower cost version. The other two proposed algorithms can only be used with clipping and the results show a significant saving in computational cost. Furthermore clipping allows a wide-trade-off between performance and complexity by only adjusting the clipping parameter.Acknowledgements This work has been partially supported by Spanish Ministry of Science, Innovation and Universities and by European Union through grant RTI2018-098085-BC41 (MCUI/AEI/FEDER) , by GVASimarro, MA.; García Mollá, VM.; Martínez Zaldívar, FJ.; Gonzalez, A. (2022). Low-complexity soft ML detection for generalized spatial modulation. Signal Processing. 196:1-12. https://doi.org/10.1016/j.sigpro.2022.10850911219

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Proceedings of the Third International Mobile Satellite Conference (IMSC 1993)

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial cellular communications services. While the first and second International Mobile Satellite Conferences (IMSC) mostly concentrated on technical advances, this Third IMSC also focuses on the increasing worldwide commercial activities in Mobile Satellite Services. Because of the large service areas provided by such systems, it is important to consider political and regulatory issues in addition to technical and user requirements issues. Topics covered include: the direct broadcast of audio programming from satellites; spacecraft technology; regulatory and policy considerations; advanced system concepts and analysis; propagation; and user requirements and applications

A Framework for Generic and Energy Efficient Context Recognition for Personal Mobile Devices

The advancements in the field of mobile computing over the last decade have enabled the scientific community to expedite the theoretical and experimental work to achieve the vision of ubiquitous computing. As ubiquitous computing aims to provide seamless and distraction free task support to its users, one of the essential pieces of information required by the ubiquitous computing systems to do so is the context of its users. Context of a user can be defined as the information that describes the task the user is performing and the environment in which the user is currently present. Among various platforms that are commonly used to determine user's context, the personal mobile devices like smart phones stand out as one of the most widely used and widely evaluated ones. However, despite numerous advantages that are provided by modern day personal mobile devices, such as high computational and communication capabilities, variety of on-board sensors to capture raw data related to user's motion and environment, high resolution displays to enable interaction with other services and systems, these devices suffer from limited battery resources. In contrast to the advancements in other domains, the advancements in the battery domain have not been up to the mark. Consequently, the context recognition applications developed for these devices suffer from the trade-off between achieving accuracy and longevity of other device's basic operations. As a result, most of the existing context recognition applications for these devices are fine tuned for specific context types and thereby lacks generality. The situation gets worse when a number of context recognition applications are executed simultaneously, thus competing for limited resources and consuming the device's battery additively. To address the aforementioned issues, this thesis provides a generic and energy efficient context recognition framework for personal mobile devices. The main contribution consists of a generic framework to support development of context recognition applications supported by algorithms to achieve their energy efficient execution. The proposed framework consists of two systems namely the component system and the activation system. The component system allows developers to create context recognition applications using a component abstraction. This enables runtime analysis of applications' structures to adopt our novel energy efficiency mechanism. The activation system uses a state machine abstraction to allow context dependent activation of context recognition configurations pertaining to relevant user's tasks such that only needed configurations are executed to determine only the relevant context characteristics, thereby enabling energy efficiency. The activation system also provides generic applicability of four different energy efficiency techniques, already used in different existing systems but mostly for specific context characteristics. To aid rapid prototyping, both systems are equipped with off-line development tools. The tools include graphical editors and a component tool-kit. The graphical editors allow developers to create component configurations used by the component system and state machines used by the activation system. These editors enable developers to create component configurations and state machines by simply dragging, dropping and connecting different models used in our component and state machine abstractions. These tools also provide validation and code generation utilities. In addition to the graphical editor, the framework provides a component tool-kit which consists of a number of already implemented sensing, preprocessing and classification components which can be re-used in new applications. In order to provide the energy efficient execution of context recognition applications, the thesis introduces a novel energy efficiency technique called configuration folding. Configuration folding analyses structures of simultaneously executing context recognition applications to identify redundant functionalities between them and as an output produces a single redundancy free context recognition configuration which holds the structural integrity of all applications. Consequently, the overall energy expenditure is reduced compared to the original expenditure when redundant functionalities are not removed. The experimental evaluation of configuration folding on test applications shows energy savings between 13 and 48 %. The thesis also investigates optimization possibilities in configuration folding in case the redundant functionalities between the applications differ in parametrization. Towards this end, the thesis identifies commonly used parameters in context recognition applications and defines relations between them. Finally, an extended version of configuration folding is introduced to handle the differences in parametrization. The evaluation of the extended version of configuration folding on test scenarios shows energy saving of up to 45%. The contributions in this thesis have been evaluated extensively. The framework has been used in number of European Commission (EC) projects and in student projects and theses at the University of Duisburg-Essen, Germany. Using the component system and the activation system, a number of applications have been developed in those projects. Some of these applications include crowd density estimation in buses, bus ride detections, navigation application for buses in Madrid, user movement detection, user localization, fall detection application etc. Moreover, the component system, the activation system and the configuration folding technique have been published in different prestigious conferences and workshops

Approximate message passing detector based upon probability sorting for large-scale GSM systems

Large-scale generalized spatialmodulation(GSM) technology has great potential in constructing low-complexity massive multiple-input multiple-output wireless systems, with a simplified structure which employs only a fewradio frequency (RF) chains. However, detection is a very complex problem for large-scale GSM systems. In this correspondence paper, we have proposed an innovative probability sorting based approximate message passing (PS-AMP) detector. Specifically, in the proposed scheme, an approximate message passing method is used to obtain the probability for activation of each transmit antenna, and a method based upon probability sorting is developed to ascertain the signal search space. Simulation results demonstrate that the proposed PS-AMP detector exhibits a better bit error rate performance with a reduced complexity compared to its conventional counterparts

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al