MMSE Optimization with Per-Base-Station Power Constraints for Network MIMO Systems

Shi, Shuhui; Schubert, Martin; Vučić, Nikola; Boche, Holger

doi:10.1109/icc.2008.771

Cited by 48 publications

(75 citation statements)

References 19 publications

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“…In the downlink channel, since the precoders of all users are jointly coupled, downlink transceiver design problems have more complicated mathematical structure than that of the dual uplink problems [4], [5]. In [6], we have demonstrated that for a per BS antenna power constrained sum MSE-based problems, the duality approach of [6] requires less total BS power than the approach of [7]. Due to these reasons, duality approach for solving downlink problems has received a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

Weighted Sum Rate Optimization for Downlink Multiuser MIMO Coordinated Base Station Systems: Centralized and Distributed Algorithms

Bogale

Vandendorpe

2012

IEEE Trans. Signal Process.

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Abstract-This paper considers weighted sum rate maximization constrained with a per base station (BS) antenna power problem for multiuser multiple-input multiple-output (MIMO) systems. For this problem, we propose new downlink-uplink duality based solution. We solve the problem as follows. First, by introducing additional optimization variables, we reformulate our problem into an equivalent problem that incorporates a weighted sum mean-square-error (MSE) term. Second, we establish novel weighted sum MSE duality. The duality is established by modifying the input covariance matrix of the dual uplink problem, and formulating the noise covariance matrix of the uplink channel as a fixed point function. Third, we optimize the introduced variables and powers in the downlink channel by a Geometric Program (GP) method. Fourth, using the duality result and the solution of GP, we apply alternating optimization technique to solve the original downlink problem. In our simulation results, we have observed that the proposed duality based solution utilizes less power than that of existing algorithms.

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Section: Introductionmentioning

confidence: 99%

Weighted Sum Rate Optimization for Downlink Multiuser MIMO Coordinated Base Station Systems: Centralized and Distributed Algorithms

Bogale

Vandendorpe

2012

IEEE Trans. Signal Process.

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“…of (44) can be found exactly like that of (26). As a result, with the duality of this section, we can solve P4 iteratively with Algorithm I.…”

Section: B Sum Amse Transfer (From Interference To Downlink Channel)mentioning

confidence: 95%

“…Moreover, (37) can be solved exactly like that of (26) and the solution of (37) satisfies (32). As a result, one can apply the duality of this section to solve P3 by Algorithm I.…”

Section: B Sum Amse Transfer (From Interference To Downlink Channel)mentioning

confidence: 99%

See 1 more Smart Citation

Robust Sum MSE Optimization for Downlink Multiuser MIMO Systems With Arbitrary Power Constraint: Generalized Duality Approach

Bogale

Vandendorpe

2012

IEEE Trans. Signal Process.

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Abstract-This paper considers linear minimum meansquare-error (MMSE) transceiver design problems for downlink multiuser multiple-input multiple-output (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum mean-square-error (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 zero-mean 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 downlink-uplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol power problems, we have established novel downlink-interference 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 MSE-based robust design problems. Computer simulations verify the robustness of the proposed robust designs compared to the non-robust/naive designs.

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“…Then the large scale channels of the ith user are modeled as, We consider per-user-power-constraint (PUPC) and each user has identical power constraint. While per-BS-powerconstraint (PBPC) [13], [14] is more proper in the context, PUPC leads to tractable analysis [15]. Moreover, it is worth to note that in multi-carrier systems, when we perform PUPC on each sub-carrier, the PBPC over all carriers will be automatically satisfied due to the averaging over multiple carriers.…”

Section: System Modelmentioning

confidence: 99%

User grouping based on large scale channel information in multi-cell downlink cooperative multi-antenna systems

Sun¹,

Yang²,

Li³

et al. 2010

Proceedings of the 5th International ICST Conference on Communications and Networking in China

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Abstract-In this paper, we study low feedback user grouping in downlink cooperative transmission multi-antenna systems. Users in the same group are served on the same frequency and time resource by multiple cooperative BSs using the zeroforcing precoder. We first derive the user's average received signal to interference and noise ratio (SINR). It shows that each user's SINR is an increasing function of the large scale signal to interference ratio (SIR) of users in neighbor cells, which can be obtained using the large scale channel information. We then propose a novel fair user grouping algorithm based on the large scale SIR. Simulation results show that our algorithm achieves better cell edge throughput compared with existing methods with minor reduction of cell throughput.

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MMSE Optimization with Per-Base-Station Power Constraints for Network MIMO Systems

Cited by 48 publications

References 19 publications

Weighted Sum Rate Optimization for Downlink Multiuser MIMO Coordinated Base Station Systems: Centralized and Distributed Algorithms

Weighted Sum Rate Optimization for Downlink Multiuser MIMO Coordinated Base Station Systems: Centralized and Distributed Algorithms

Robust Sum MSE Optimization for Downlink Multiuser MIMO Systems With Arbitrary Power Constraint: Generalized Duality Approach

User grouping based on large scale channel information in multi-cell downlink cooperative multi-antenna systems

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