Channel State Information Mismatch Research Articles

Performance Analysis of Interference Alignment Under CSI Mismatch

With interference alignment (IA), the achievable degrees of freedom (DoFs) in wireless networks can be linearly scaled up with the number of users. However, to attain the full DoF, the availability of perfect network channel state information (CSI) is mandatory, which is not pragmatic in general. In this paper, we quantify the performance of IA under CSI mismatch where the variance of the CSI measurement error depends on the signal-to-noise ratio (SNR). We show that when this error variance is proportional to the inverse of the SNR, the full DoF is achievable, and an upper bound on asymptotic mean loss in sum rate compared with the perfect CSI case is derived. We also derive a bound on the achievable DoF when the CSI error variance is proportional to the inverse of the SNR to a power of a constant. Furthermore, we investigate the effect of CSI mismatch on the performance of the maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. We show that with perfect CSI, the Max-SINR algorithm outperforms interference leakage minimization algorithms, but with the presence of imperfect CSI, its comparative improvement becomes negligible. We then propose an adaptive Max-SINR algorithm that can notably improve the performance of the original Max-SINR algorithm under CSI mismatch.

A Novel CSI Feedback Method for Dynamic SU/MU MIMO Adaptation

Channel state information (CSI) reporting at one mobile station (MS) often targets maximizing the throughput of one single link, hence is optimized for single-user multi-input multi-output (SU-MIMO) transmission at base station (BS). However, the system wide throughput may not be maximized when BS is performing multi-user multi-input multi-output (MU-MIMO) transmission utilizing the SU-MIMO optimized CSI reports, and vice versa. This CSI mismatch can cause system throughput degradation. In this paper, a novel CSI feedback method is proposed which can switch between SU-MIMO optimized CSI reporting mode and MU-MIMO optimized CSI reporting mode when instructed by BS's signaling. System level simulation results show when BS is performing MU-MIMO transmission, significant throughput gain can be obtained if MS feeds back MU-MIMO optimized CSI reports instead of SU-MIMO optimized CSI reports. With the instruction of the BS signaling to dynamic indicate MS to switch between SU-MIMO and MU-MIMO optimized CSI reporting modes, the SU-MIMO performance is not compromised either.

Optimized One-Way Relaying Strategy With Outdated CSI Quantization for Spatial Multiplexing

This correspondence considers a MIMO relay downlink system using precoding and limited feedback. Since the conventional precoding matrices are directly obtained by treating the quantized channel state information (CSI) feedback as real CSI, we propose an optimized relay precoding strategy by taking both effects of channel quantization error and feedback delay into account. Conditioned on the outdated CSI quantization available at the relay station, the relay precoding is optimized via minimizing an expected mean-square error (MSE) criterion over CSI mismatches. By using Karush-Kuhn-Tucker (KKT) conditions, a closed-form solution to the relay design is achieved with comparable computational complexity relative to the conventional approach. Numerical results verify the effectiveness of the proposed scheme.

Practical robust uniform channel decomposition scheme for CSI mismatch in limited feedback MIMO systems

Uniform channel decomposition (UCD) has been proven to be optimal in bit error rate (BER) performance and strictly capacity lossless when perfect channel state information (CSI) is assumed to be available at both the transmitter and receiver side. In practice, CSI can be obtained by channel estimation at receiver and conveyed to transmitter via a limited-rate feedback channel. In such case, the implementation of traditional UCD by treating the imperfect CSI as perfect CSI cause significant performance degradation due to inevitable channel estimation error and vector quantization error. To overcome this problem, a practical robust UCD scheme was proposed in this paper, which includes two steps, firstly, a matching architecture was proposed to eliminate the mismatch between CSI at receiver (CSIR) and CSI at transmitter (CSIT), secondly, an MMSE based robust UCD scheme considering channel estimation error and vector quantization error as an integral part of the design was derived. Simulation results show that the proposed practical robust UCD scheme is capable of improving the BER performance greatly in the context of channel estimation error and vector quantization error compared with the traditional UCD scheme.

Robust linear receivers for multiaccess space-time block-coded MIMO systems: a probabilistically constrained approach

Traditional multiuser receiver algorithms developed for multiple-input-multiple-output (MIMO) wireless systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations, the exact CSI may be unavailable because of channel estimation errors and/or outdated training. In this paper, we address the problem of robustness of multiuser MIMO receivers against imperfect CSI and propose a new linear technique that guarantees the robustness against CSI errors with a certain selected probability. The proposed receivers are formulated as probabilistically constrained stochastic optimization problems. Provided that the CSI mismatch is Gaussian, each of these problems is shown to be convex and to have a unique solution. The fact that the CSI mismatch is Gaussian also enables to convert the original stochastic problems to a more tractable deterministic form and to solve them using the second-order cone programming approach. Numerical simulations illustrate an improved robustness of the proposed receivers against CSI errors and validate their better flexibility as compared with the robust multiuser MIMO receivers based on the worst case designs.

Robust transmit eigen beamforming based on imperfect channel state information

Transmit beamforming is a powerful technique for enhancing the performance and increasing the throughput of wireless communication systems that employ multiple antennas at the transmitter. A major drawback of most existing transmit beamforming techniques is that they require nearly perfect knowledge of the channel at the transmitter, which is typically not available in practice. Transmitter designs that address the imperfect channel state information (CSI) problem commonly use statistical models for the channel and/or mismatch between the presumed and actual transmitter CSI. Since these approaches are model based, they can suffer from mismodeling. In this paper, a more robust framework is proposed in which no statistical assumptions are made about the CSI mismatch or the channel. The goal is to design a transmitter that has the best performance under the worst-case CSI mismatch. The transmitter designed herein achieves this goal for all CSI mismatches below a certain threshold level. The proposed design combines beamforming along the eigenvectors of the (deterministic) autocorrelation of the channel matrix perceived by the transmitter and power loading across those beams. While the power-loading algorithm resembles conventional water-filling to some degree, it explicitly incorporates robustness to the CSI mismatch, and the water level can be determined in a simple systematic way.

Robust linear receivers for space-time block coded multiaccess MIMO systems with imperfect channel state information

Recently, several linear receiver algorithms have been developed for space-time block-coded multiaccess multiple-input multiple-output (MIMO) wireless communication systems. All these techniques are based on the assumption that the channel state information (CSI) at the receiver side is perfect. However, in practical situations, the available CSI may be imperfect because of channel estimation errors and/or outdated training. In this paper, we develop new robust linear receiver techniques for joint space-time decoding and interference rejection in multiaccess MIMO systems that use orthogonal space-time block codes and erroneous CSI. The proposed receivers are based on worst-case performance optimization. They are shown to provide a substantially improved robustness against CSI mismatches as compared with the existing linear multiaccess MIMO receivers.

Tomlinson–Harashima Precoding With Partial Channel Knowledge

We consider minimum mean-square error Tomlinson-Harashima (MMSE-TH) precoding for time-varying frequency-selective channels. We assume that the receiver estimates the channel and sends the channel state information (CSI) estimate to the transmitter through a lossless feedback channel that introduces a certain delay. Thus, the CSI mismatch at the receiver is due to estimation errors, while the CSI mismatch at the transmitter is due to both estimation errors and channel time variations. We exploit a priori statistical channel knowledge, and we derive an optimal TH precoder, adopting a Bayesian approach. We use simulations to compare the performance of the so-derived TH precoder with that of the same-complexity MMSE decision-feedback equalizer (DFE). We observe that for low signal-to-noise ratios (SNRs) and sufficiently slow channel time variations, the optimal TH precoder outperforms the DFE, while at high SNR, the opposite happens.