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Luc Vandendorpe  One of the best experts on this subject based on the ideXlab platform.

robust sum mse optimization for downlink multiuser mimo systems with arbitrary Power Constraint generalized duality approach
IEEE Transactions on Signal Processing, 2012CoAuthors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper considers linear minimum meansquareerror (MMSE) transceiver design problems for downlink multiuser multipleinput multipleoutput (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum meansquareerror (MSE) minimization problems. The problems are examined for the generalized scenario where the Power Constraint is per BS, per BS antenna, per user or per symbol, and the noise vector of each MS is a zeromean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel duality based iterative solution. Each of these problems is solved as follows. First, we establish a novel sum average meansquareerror (AMSE) duality. Second, we formulate the Power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. To solve robust sum MSE minimization constrained with per BS antenna and per BS Power problems, we have established novel downlinkuplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol Power problems, we have established novel downlinkinterference duality. For the total BS Power constrained robust sum MSE minimization problem, the current duality is established by modifying the Constraint function of the dual uplink channel problem. And, for the robust sum MSE minimization with per BS antenna and per user (symbol) Power Constraint problems, our duality are established by formulating the noise covariance matrices of the uplink and interference channels as fixed point functions, respectively. We also show that our sum AMSE duality are able to solve other sum MSEbased robust design problems. Computer simulations verify the robustness of the proposed robust designs compared to the nonrobust/naive designs.

Robust Sum MSE optimization for downlink multiuser MIMO systems with arbitrary Power Constraint: Generalized duality approach
IEEE Transactions on Signal Processing, 2012CoAuthors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper considers linear minimum meansquare error (MMSE) transceiver design problems for downlink multiuser multipleinput multipleoutput (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum meansquareerror (MSE) minimization problems. The problems are examined for the generalized scenario where the Power Constraint is per BS, per BS antenna, per user or per symbol, and the noise vector of each MS is a zeromean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel duality based iterative solution. Each of these problems is solved as follows. First, we establish a novel sum average meansquare error (AMSE) duality. Second, we formulate the Power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. To solve robust sum MSE minimization constrained with per BS antenna and per BS Power problems, we have established novel downlinkuplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol Power problems, we have established novel downlinkinterference duality. For the total BS Power constrained robust sum MSE minimization problem, the current duality is established by modifying the Constraint function of the dual uplink channel problem. And, for the robust sum MSE minimization with per BS antenna and per user (symbol) Power Constraint problems, our duality are established by formulating the noise covariance matrices of the uplink and interference channels as fixed point functions, respectively.
Petre Stoica  One of the best experts on this subject based on the ideXlab platform.

mimo transmit beamforming under uniform elemental Power Constraint
IEEE Transactions on Signal Processing, 2007CoAuthors: Xiayu Zheng, Yao Xie, Petre StoicaAbstract:We consider multiinput multioutput (MIMO) transmit beamforming under the uniform elemental Power Constraint. This is a nonconvex optimization problem, and it is usually difficult to find the optimal transmit beamformer. First, we show that for the multiinput singleoutput (MISO) case, the optimal solution has a closedform expression. Then we propose a cyclic algorithm for the MIMO case which uses the closedform MISO optimal solution iteratively. The cyclic algorithm has a low computational complexity and is locally convergent under mild conditions. Moreover, we consider finiterate feedback methods needed for transmit beamforming. We propose a simple scalar quantization method, as well as a novel vector quantization method. For the latter method, the codebook is constructed under the uniform elemental Power Constraint and the method is referred as VQUEP. We analyze VQUEP performance for the MISO case. Specifically, we obtain an approximate expression for the average degradation of the receive signaltonoise ratio (SNR) caused by VQUEP. Numerical examples are provided to demonstrate the effectiveness of our proposed transmit beamformer designs and the finiterate feedback techniques.

MIMO transmit beamforming under uniform elemental Power Constraint
2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications, 2007CoAuthors: Xiayu Zheng, Yao Xie, Petre StoicaAbstract:We consider multiinput multioutput (MIMO) transmit beamforming under the uniform elemental Power Constraint. This is a nonconvex optimization problem, and it is usually difficult to find the optimal transmit beamformer. First, we show that for the multiinput singleoutput (MISO) case, the optimal solution has a closedform expression. Then we propose a cyclic algorithm for the MIMO case which uses the closed form MISO optimal solution iteratively. The cyclic algorithm has a low computational complexity and is locally convergent under mild conditions. Moreover, we consider finiterate feedback methods needed for transmit beamforming. We propose a novel vector quantization method, where the codebook is constructed under the uniform elemental Power Constraint and the method is referred as VQUEP. Numerical examples are provided to demonstrate the effectiveness of our proposed transmit beamformer designs and the finiterate feedback technique.
Tadilo Endeshaw Bogale  One of the best experts on this subject based on the ideXlab platform.

robust sum mse optimization for downlink multiuser mimo systems with arbitrary Power Constraint generalized duality approach
IEEE Transactions on Signal Processing, 2012CoAuthors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper considers linear minimum meansquareerror (MMSE) transceiver design problems for downlink multiuser multipleinput multipleoutput (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum meansquareerror (MSE) minimization problems. The problems are examined for the generalized scenario where the Power Constraint is per BS, per BS antenna, per user or per symbol, and the noise vector of each MS is a zeromean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel duality based iterative solution. Each of these problems is solved as follows. First, we establish a novel sum average meansquareerror (AMSE) duality. Second, we formulate the Power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. To solve robust sum MSE minimization constrained with per BS antenna and per BS Power problems, we have established novel downlinkuplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol Power problems, we have established novel downlinkinterference duality. For the total BS Power constrained robust sum MSE minimization problem, the current duality is established by modifying the Constraint function of the dual uplink channel problem. And, for the robust sum MSE minimization with per BS antenna and per user (symbol) Power Constraint problems, our duality are established by formulating the noise covariance matrices of the uplink and interference channels as fixed point functions, respectively. We also show that our sum AMSE duality are able to solve other sum MSEbased robust design problems. Computer simulations verify the robustness of the proposed robust designs compared to the nonrobust/naive designs.

Robust Sum MSE optimization for downlink multiuser MIMO systems with arbitrary Power Constraint: Generalized duality approach
IEEE Transactions on Signal Processing, 2012CoAuthors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper considers linear minimum meansquare error (MMSE) transceiver design problems for downlink multiuser multipleinput multipleoutput (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum meansquareerror (MSE) minimization problems. The problems are examined for the generalized scenario where the Power Constraint is per BS, per BS antenna, per user or per symbol, and the noise vector of each MS is a zeromean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel duality based iterative solution. Each of these problems is solved as follows. First, we establish a novel sum average meansquare error (AMSE) duality. Second, we formulate the Power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. To solve robust sum MSE minimization constrained with per BS antenna and per BS Power problems, we have established novel downlinkuplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol Power problems, we have established novel downlinkinterference duality. For the total BS Power constrained robust sum MSE minimization problem, the current duality is established by modifying the Constraint function of the dual uplink channel problem. And, for the robust sum MSE minimization with per BS antenna and per user (symbol) Power Constraint problems, our duality are established by formulating the noise covariance matrices of the uplink and interference channels as fixed point functions, respectively.
Venkat Anantharam  One of the best experts on this subject based on the ideXlab platform.

a geometric analysis of the awgn channel with a sigma rho Power Constraint
IEEE Transactions on Information Theory, 2016CoAuthors: Varun Jog, Venkat AnantharamAbstract:In this paper, we consider the additive white Gaussian noise (AWGN) channel with a Power Constraint called the $(\sigma , \rho )$ Power Constraint, which is motivated by energy harvesting communication systems. Given a codeword, the Constraint imposes a limit of $\sigma + k \rho $ on the total Power of any $k\geq 1$ consecutive transmitted symbols. Such a channel has infinite memory and evaluating its exact capacity is a difficult task. Consequently, we establish an $n$ letter capacity expression and seek bounds for the same. We obtain a lower bound on capacity by considering the volume of $ {\mathcal{ S}}_{n}(\sigma , \rho ) \subseteq \mathbb {R}^{n}$ , which is the set of all length $n$ sequences satisfying the $(\sigma , \rho )$ Power Constraints. For a noise Power of $\nu $ , we obtain an upper bound on capacity by considering the volume of $ {\mathcal{ S}}_{n}(\sigma , \rho ) \oplus B_{n}(\sqrt {n\nu })$ , which is the Minkowski sum of $ {\mathcal{ S}}_{n}(\sigma , \rho )$ and the $n$ dimensional Euclidean ball of radius $\sqrt {n\nu }$ . We analyze this bound using a result from convex geometry known as Steiner’s formula, which gives the volume of this Minkowski sum in terms of the intrinsic volumes of $ {\mathcal{ S}}_{n}(\sigma , \rho )$ . We show that as the dimension $n$ increases, the logarithm of the sequence of intrinsic volumes of $\{ {\mathcal{ S}}_{n}(\sigma , \rho )\}$ converges to a limit function under an appropriate scaling. The upper bound on capacity is then expressed in terms of this limit function. We derive the asymptotic capacity in the low and highnoise regime for the $(\sigma , \rho )$ Power constrained AWGN channel, with strengthened results for the special case of $\sigma = 0$ , which is the amplitude constrained AWGN channel.

a geometric analysis of the awgn channel with a sigma rho Power Constraint
arXiv: Information Theory, 2015CoAuthors: Varun Jog, Venkat AnantharamAbstract:In this paper, we consider the AWGN channel with a Power Constraint called the $(\sigma, \rho)$Power Constraint, which is motivated by energy harvesting communication systems. Given a codeword, the Constraint imposes a limit of $\sigma + k \rho$ on the total Power of any $k\geq 1$ consecutive transmitted symbols. Such a channel has infinite memory and evaluating its exact capacity is a difficult task. Consequently, we establish an $n$letter capacity expression and seek bounds for the same. We obtain a lower bound on capacity by considering the volume of ${\cal S}_n(\sigma, \rho) \subseteq \mathbb{R}^n$, which is the set of all length $n$ sequences satisfying the $(\sigma, \rho)$Power Constraints. For a noise Power of $\nu$, we obtain an upper bound on capacity by considering the volume of ${\cal S}_n(\sigma, \rho) \oplus B_n(\sqrt{n\nu})$, which is the Minkowski sum of ${\cal S}_n(\sigma, \rho)$ and the $n$dimensional Euclidean ball of radius $\sqrt{n\nu}$. We analyze this bound using a result from convex geometry known as Steiner's formula, which gives the volume of this Minkowski sum in terms of the intrinsic volumes of ${\cal S}_n(\sigma, \rho)$. We show that as the dimension $n$ increases, the logarithm of the sequence of intrinsic volumes of $\{{\cal S}_n(\sigma, \rho)\}$ converges to a limit function under an appropriate scaling. The upper bound on capacity is then expressed in terms of this limit function. We derive the asymptotic capacity in the low and high noise regime for the $(\sigma, \rho)$Power constrained AWGN channel, with strengthened results for the special case of $\sigma = 0$, which is the amplitude constrained AWGN channel.
Xiayu Zheng  One of the best experts on this subject based on the ideXlab platform.

mimo transmit beamforming under uniform elemental Power Constraint
IEEE Transactions on Signal Processing, 2007CoAuthors: Xiayu Zheng, Yao Xie, Petre StoicaAbstract:We consider multiinput multioutput (MIMO) transmit beamforming under the uniform elemental Power Constraint. This is a nonconvex optimization problem, and it is usually difficult to find the optimal transmit beamformer. First, we show that for the multiinput singleoutput (MISO) case, the optimal solution has a closedform expression. Then we propose a cyclic algorithm for the MIMO case which uses the closedform MISO optimal solution iteratively. The cyclic algorithm has a low computational complexity and is locally convergent under mild conditions. Moreover, we consider finiterate feedback methods needed for transmit beamforming. We propose a simple scalar quantization method, as well as a novel vector quantization method. For the latter method, the codebook is constructed under the uniform elemental Power Constraint and the method is referred as VQUEP. We analyze VQUEP performance for the MISO case. Specifically, we obtain an approximate expression for the average degradation of the receive signaltonoise ratio (SNR) caused by VQUEP. Numerical examples are provided to demonstrate the effectiveness of our proposed transmit beamformer designs and the finiterate feedback techniques.

MIMO transmit beamforming under uniform elemental Power Constraint
2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications, 2007CoAuthors: Xiayu Zheng, Yao Xie, Petre StoicaAbstract:We consider multiinput multioutput (MIMO) transmit beamforming under the uniform elemental Power Constraint. This is a nonconvex optimization problem, and it is usually difficult to find the optimal transmit beamformer. First, we show that for the multiinput singleoutput (MISO) case, the optimal solution has a closedform expression. Then we propose a cyclic algorithm for the MIMO case which uses the closed form MISO optimal solution iteratively. The cyclic algorithm has a low computational complexity and is locally convergent under mild conditions. Moreover, we consider finiterate feedback methods needed for transmit beamforming. We propose a novel vector quantization method, where the codebook is constructed under the uniform elemental Power Constraint and the method is referred as VQUEP. Numerical examples are provided to demonstrate the effectiveness of our proposed transmit beamformer designs and the finiterate feedback technique.