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Title: On Gaussian MIMO BC-MAC duality with multiple transmit covariance constraints
Authors: Zhang, L.
Zhang, R. 
Liang, Y.-C. 
Xin, Y.
Poor, H.V.
Keywords: Beamforming
broadcast channels
multiple antennas
wireless systems
Issue Date: Apr-2012
Citation: Zhang, L., Zhang, R., Liang, Y.-C., Xin, Y., Poor, H.V. (2012-04). On Gaussian MIMO BC-MAC duality with multiple transmit covariance constraints. IEEE Transactions on Information Theory 58 (4) : 2064-2078. ScholarBank@NUS Repository.
Abstract: Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit sum-power constraint. An alternative approach is based on minimax duality, which can be applied to the case of the sum-power constraint or per-antenna power constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance constraint (LTCC) case, which includes sum-power and per-antenna power constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance constraints in the MIMO BC. © 2006 IEEE.
Source Title: IEEE Transactions on Information Theory
ISSN: 00189448
DOI: 10.1109/TIT.2011.2177760
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