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Analysis and Transceiver Design for the MIMO Broadcast Channel (Hardcover, 2013 ed.)
Loot Price: R2,830
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Analysis and Transceiver Design for the MIMO Broadcast Channel (Hardcover, 2013 ed.)
Series: Foundations in Signal Processing, Communications and Networking, 8
Expected to ship within 10 - 15 working days
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This 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.
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