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000931672 019__ $$a1152531017$$a1153068133$$a1153110164$$a1153163730$$a1153944081$$a1154487060
000931672 020__ $$a9789811538704$$q(electronic book)
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000931672 020__ $$z9789811538698
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000931672 0247_ $$a10.1007/978-981-15-3
000931672 035__ $$aSP(OCoLC)on1152053543
000931672 035__ $$aSP(OCoLC)1152053543$$z(OCoLC)1152531017$$z(OCoLC)1153068133$$z(OCoLC)1153110164$$z(OCoLC)1153163730$$z(OCoLC)1153944081$$z(OCoLC)1154487060
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000931672 049__ $$aISEA
000931672 050_4 $$aTK5102.9
000931672 08204 $$a621.382/2$$223
000931672 1001_ $$aShi, Yuanming.
000931672 24510 $$aLow-overhead communications in IoT networks :$$bstructured signal processing approaches /$$cYuanming Shi, Jialin Dong, Jun Zhang.
000931672 260__ $$aSingapore :$$bSpringer,$$c2020.
000931672 300__ $$a1 online resource (164 pages)
000931672 336__ $$atext$$btxt$$2rdacontent
000931672 337__ $$acomputer$$bc$$2rdamedia
000931672 338__ $$aonline resource$$bcr$$2rdacarrier
000931672 504__ $$aIncludes bibliographical references.
000931672 506__ $$aAccess limited to authorized users.
000931672 520__ $$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.
000931672 588__ $$aDescription based on print version record.
000931672 650_0 $$aSignal processing$$xDigital techniques.
000931672 650_0 $$aInternet of things.
000931672 7001_ $$aDong, Jialin.
000931672 7001_ $$aZhang, Jun.
000931672 77608 $$iPrint version:$$aShi, Yuanming$$tLow-Overhead Communications in IoT Networks : Structured Signal Processing Approaches$$dSingapore : Springer Singapore Pte. Limited,c2020$$z9789811538698
000931672 852__ $$bebk
000931672 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-3870-4$$zOnline Access$$91397441.1
000931672 909CO $$ooai:library.usi.edu:931672$$pGLOBAL_SET
000931672 980__ $$aEBOOK
000931672 980__ $$aBIB
000931672 982__ $$aEbook
000931672 983__ $$aOnline
000931672 994__ $$a92$$bISE