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008 120828s2013 gw | s |||| 0|eng d
020 _a9783642316920
_9978-3-642-31692-0
024 7 _a10.1007/978-3-642-31692-0
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aHunger, Raphael.
_eauthor.
245 1 0 _aAnalysis and Transceiver Design for the MIMO Broadcast Channel
_h[electronic resource] /
_cby Raphael Hunger.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aX, 322 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aFoundations in Signal Processing, Communications and Networking,
_x1863-8538 ;
_v8
505 0 _aSystem Models -- Dualities for the MIMO BC and the MIMO MAC with Linear Transceivers -- Rate Duality with Nonlinear Interference Cancelation -- Matrix-Based Gradient-Projection Algorithm -- MIMO BC Transceiver Design with Interference Cancelation -- Asymptotic High Power Analysis of the MIMO BC -- Description of the Quality of Service Feasibility Region.
520 _aThis book deals with the optimization-based joint design of the transmit and receive filters in   MIMO broadcast channel in which the user terminals may be equipped with several antenna elements. Furthermore, the maximum performance of the system in the high power regime as well as the set of all feasible quality-of-service requirements is analyzed. First, a fundamental duality is derived that holds between the MIMO broadcast channel and virtual MIMO multiple access channel. This duality construct allows for the efficient solution of problems originally posed in the broadcast channel in the dual domain where a possibly hidden convexity can often be revealed. On the basis of the established duality result, the gradient-projection algorithm is introduced as a tool to solve constrained optimization problems to global optimality under certain conditions. The gradient-projection tool is then applied to solving the weighted sum rate maximization problem which is a central optimization that arises in any network utility maximization. In the high power regime, a simple characterization of the obtained performance becomes possible due to the fact that the weighted sum rate utility converges to an affine asymptote in the logarithmic power domain. We find closed form expressions for these asymptotes which allows for a quantification of the asymptotic rate loss that linear transceivers have to face with respect to dirty paper coding. In the last part, we answer the fundamental question of feasibility in quality-of-service based optimizations with inelastic traffic that features strict delay constraints. Under the assumption of linear transceivers, not every set of quality-of-service requirements might be feasible making the power minimization problem with given lower bound constraints on the rate for example infeasible  in these cases. We derive a complete description of the quality-of-service feasibility region for  arbitrary channel matrices.
650 0 _aEngineering.
650 0 _aElectrical engineering.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aCommunications Engineering, Networks.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642316913
830 0 _aFoundations in Signal Processing, Communications and Networking,
_x1863-8538 ;
_v8
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-31692-0
912 _aZDB-2-ENG
942 _2Dewey Decimal Classification
_ceBooks
999 _c46075
_d46075