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Please use this identifier to cite or link to this item : http://hdl.handle.net/2078.1/84115
This paper establishes three kinds of mean-square-error (MSE) uplink-downlink duality for multiple-input multiple-output (MIMO) systems. Our duality is established for the practically relevant scenario where the noise vector of each mobile station (MS) is a zero-mean circularly symmetric complex Gaussian (ZMCSCG) random variable with arbitrary covariance matrix. As an application example of our duality, we examine the linear transceiver design for the weighted sum MSE minimization constrained with a total base station (BS) power problem for the downlink multiuser MIMO systems. To solve this problem, first we establish the MSE uplink-downlink duality. Then, we formulate the power allocation part of the equivalent problem in the uplink channel as a Geometric Programming (GP). Finally, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. The proposed duality maintains the easier-to-handle mathematical structure of MSE-based problems in the uplink channel and generalizes the existing MSE uplink-downlink duality. Furthermore, by utilizing our duality, we exploit the hidden convexity of the sum MSE minimization constrained with a total BS power problem in the downlink channel.
|Publication Date :||2011|
|Document type :||Communication à un colloque (Conference Paper) - (Présentation orale avec comité de sélection)|
|Conference :||"2011 45th Annual Conference on Information Sciences and Systems (CISS2011)", Baltimore, MD, USA (du 23/03/2011 au 25/03/2011)|
|Source :||"2011 45th Annual Conference on Information Sciences and Systems (CISS2011)"- 1-6 (ISBN : 978-1-4244-9846-8)|
|Publisher :||IEEE (Piscataway, NJ, USA)|
|Publication status :||Publié|
|Subject :||Theoretical or Mathematical/ covariance matrices ; Gaussian channels ; Multiple-input multiple-output system ; Noise vector;mobile station ; Zero-mean circularly symmetric complex Gaussian randomvariable ; ZMCSCG ; Linear transceiver design ; Base station ; Downlinkmultiuser MIMO system ; Power allocation ; Geometric programming;optimization technique ; Uplink channel/ B6250 Radio links and equipmentB0260 Optimisation techniquesB0290H Linear algebra (numerical analysis)B0290F Interpolation and function approximation (numerical analysis) ; Geometric programming ; Meansquare error methods ; MIMO communication ; MIMO systems ; Multiuserchannels ; Radio transceivers/ mean square error uplink-downlink duality ; Arbitrary noise covariancematrix ; MSE|
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