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008			200417s2020 si a o |||| 0|eng d
020			_a9789811538704
024	7		_a10.1007/978-981-15-3870-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5102.9 _b2020 EB
100	1		_aShi, Yuanming _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n2012051903 _1http://viaf.org/viaf/256091928 _9675811
245	1	0	_aLow-overhead Communications in IoT Networks : _bStructured Signal Processing Approaches / _cYuanming Shi, Jialin Dong, Jun Zhang
250			_aFirst edition
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2020
300			_a1 recurso en línea (XIV, 152 páginas) _b350 ilustraciones, 19 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aEngineering (Springer-11647)
505	0		_aChapter 1. Introduction -- Chapter 2. Sparse Linear Model -- Chapter 3. Blind Demixing -- Chapter 4. Sparse Blind Demixing -- Chapter 5. Shuffled Linear Regression -- Chapter 6. Learning Augmented Methods -- Chapter 7. Conclusions and Discussions -- Chapter 8. Appendix. .
520	3		_aThe recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.
988			_aSpringer_Engineering_23062020
650		7	_aProceso digital de señales _2embne _9150609
650		7	_aInternet de los objetos _2embne _9483083
700	1		_aDong, Jialin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/281340669 _9675812
700	1		_aZhang, Jun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/nr00035394 _1http://viaf.org/viaf/262417912 _9675813
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729
776	0	8	_iPrinted edition: _z9789811538698
776	0	8	_iPrinted edition: _z9789811538711
776	0	8	_iPrinted edition: _z9789811538728
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-3870-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b09/2020 _dz _eo _zSI