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Register a new userRobust Linear Processing for Downlink Multiuser MIMO System With Imperfectly Known Channel
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Added by
Xiaochuan Zhao
Publication date
2008
fields
Informatics Engineering
and research's language is
English
Authors
Pengfei Ma
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This paper proposes a roust downlink multiuser MIMO scheme that exploits the channel mean and antenna correlations to alleviate the performance penalty due to the mismatch between the true and estimated CSI.
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We consider a MIMO fading broadcast channel where the fading channel coefficients are constant over time-frequency blocks that span a coherent time $times$ a coherence bandwidth. In closed-loop systems, channel state information at transmitter (CSIT) is acquired by the downlink training sent by the base station and an explicit feedback from each user terminal. In open-loop systems, CSIT is obtained by exploiting uplink training and channel reciprocity. We use a tight closed-form lower bound on the ergodic achievable rate in the presence of CSIT errors in order to optimize the overall system throughput, by taking explicitly into account the overhead due to channel estimation and channel state feedback. Based on three time-frequency block models inspired by actual systems, we provide some useful guidelines for the overall system optimization. In particular, digital (quantized) feedback is found to offer a substantial advantage over analog (unquantized) feedback.
This paper proposes a joint transmitter-receiver design to minimize the weighted sum power under the post-processing signal-to-interference-and-noise ratio (post-SINR) constraints for all subchannels. Simulation results demonstrate that the algorithm can not only satisfy the post-SINR constraints but also easily adjust the power distribution among the users by changing the weights accordingly. Hence the algorithm can be used to alleviates the adjacent cell interference by reducing the transmitting power to the edge users without performance penalty.
In this paper, we investigate the robust linear precoder design for three dimensional (3D) massive multi-input multi-output (MIMO) downlink with uniform planar array (UPA) and imperfect channel state information (CSI). In practical massive MIMO with UPAs, the number of antennas in each column or row is usually limited. The straightforward extension of the conventional DFT based beam domain channel model widely used in massive MIMO with uniform linear arrays (ULAs) can not apply. To overcome this issue, we establish a new beam domain channel model by using sampled steering vectors. Then, a novel method to obtain the beam domain channel power matrices and the instantaneous beam domain channel coefficients is proposed, and an a posteriori beam domain channel model which includes the channel aging and the spatial correlation is established. On the basis of the a posteriori channel model, we consider the robust precoder design with the expected weighted sum-rate maximization under a total power constraint. By viewing the power constraint as a Riemannian manifold, we transform the constrained optimization problem into an unconstrained optimization problem on the Riemannian manifold. Then, we derive an iterative algorithm to obtain the optimal precoders by setting the Riemannian gradient of the objective function to zero. Furthermore, we propose a low complexity robust precoder design by replacing the expected rates in the objective function with their upper bounds. Simulation results show that the proposed precoders can achieve significant performance gain than the widely used regularized zero forcing (RZF) precoder and signal to leakage noise ratio (SLNR) precoder.
Joint user selection (US) and vector precoding (US-VP) is proposed for multiuser multiple-input multiple-output (MU-MIMO) downlink. The main difference between joint US-VP and conventional US is that US depends on data symbols for joint US-VP, whereas conventional US is independent of data symbols. The replica method is used to analyze the performance of joint US-VP in the large-system limit, where the numbers of transmit antennas, users, and selected users tend to infinity while their ratios are kept constant. The analysis under the assumptions of replica symmetry (RS) and 1-step replica symmetry breaking (1RSB) implies that optimal data-independent US provides nothing but the same performance as random US in the large-system limit, whereas data-independent US is capacity-achieving as only the number of users tends to infinity. It is shown that joint US-VP can provide a substantial reduction of the energy penalty in the large-system limit. Consequently, joint US-VP outperforms separate US-VP in terms of the achievable sum rate, which consists of a combination of vector precoding (VP) and data-independent US. In particular, data-dependent US can be applied to general modulation, and implemented with a greedy algorithm.
In this paper, the design of robust linear precoders for the massive multi-input multi-output (MIMO) downlink with imperfect channel state information (CSI) is investigated. The imperfect CSI for each UE obtained at the BS is modeled as statistical CSI under a jointly correlated channel model with both channel mean and channel variance information, which includes the effects of channel estimation error, channel aging and spatial correlation. The design objective is to maximize the expected weighted sum-rate. By combining the minorize-maximize (MM) algorithm with the deterministic equivalent method, an algorithm for robust linear precoder design is derived. The proposed algorithm achieves a stationary point of the expected weighted sum-rate maximization problem. To reduce the computational complexity, two low-complexity algorithms are then derived. One for the general case, and the other for the case when all the channel means are zeros. For the later case, it is proved that the beam domain transmission is optimal, and thus the precoder design reduces to the power allocation optimization in the beam domain. Simulation results show that the proposed robust linear precoder designs apply to various mobile scenarios and achieve high spectral efficiency.
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