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robust precoding for Massive MIMO LEO Satellite Integrated Communication and Localization Systems

IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 21-25页

作者： Zhu, Yongxiang You, Li Zhou, Huibin Jin, Zhenzhou Kong, Qingfu Gao, Xiqi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China

Low earth orbit (LEO) satellite networks combined with massive multiple-input multiple-output (MIMO) are expected to support ubiquitous integrated communication and localization (ICAL) with enhanced gains. Ensuring robust localization performance with prior angle uncertainty for the target is essential for ICAL systems. In this letter, we present a robust precoding framework to tackle this issue. We first derive the energy efficiency for communication and the worst-case Cram & eacute;r-Rao bound (CRB) for localization. Subsequently, we develop a multi-objective framework aimed at simultaneously operating communication and localization for massive MIMO LEO satellite systems. Simulation results illustrate that the proposed scheme can attain satisfactory target angle estimation performance with prior angle uncertainty while guaranteeing the performance of communication.

关键词： Satellites Location awareness Low earth orbit satellites precoding Vectors Optimization Massive MIMO Uncertainty Signal to noise ratio Interference LEO satellite ICAL CRB massive MIMO robust precoding

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robust precoding for Massive MIMO LEO Satellite Localization Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3434-3438页

作者： Zhu, Yongxiang You, Li Kong, Qingfu Seco-Granados, Gonzalo Gao, Xiqi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China Univ Autonoma Barcelona Dept Telecommun & Syst Engn Barcelona 08193 Spain

Low earth orbit (LEO) satellite networks combined with massive multiple-input multiple-output (MIMO) technology are expected to support ubiquitous localization with enhanced gains. This paper investigates robust precoding for massive MIMO LEO satellite localization systems under imperfect prior knowledge on user terminals' position. Specifically, we first characterize the signal propagation properties, and derive the squared position error bound (SPEB) to evaluate the localization performance. Then, under imperfect prior position knowledge, we formulate a worst-case sum SPEB minimization problem and propose a codebook-based robust precoding scheme. Simulation results verify the effectiveness of the proposed robust precoding scheme for massive MIMO LEO satellite localization.

关键词： Satellites Low earth orbit satellites Location awareness precoding OFDM Massive MIMO Symbols LEO satellite massive MIMO localization robust precoding SPEB

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Feedback Design With VQ-VAE for robust precoding in Multi-User FDD Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 390-394页

作者： Turan, Nurettin Baur, Michael Li, Jianqing Utschick, Wolfgang Tech Univ Munich TUM Sch Computat Informat & Technol D-80333 Munich Germany

In this letter, we propose a vector quantized-variational autoencoder (VQ-VAE)-based feedback scheme for robust precoder design in multi-user frequency division duplex (FDD) systems. We demonstrate how the VQ-VAE can be tailored to specific propagation environments, focusing on systems with low pilot overhead, which is crucial in massive multiple-input multiple-output (MIMO) systems. Extensive simulations with real-world measurement data show that the proposed feedback scheme outperforms state-of-the-art autoencoder (AE)-based compression schemes and conventional Discrete Fourier transform (DFT) codebook-based schemes. These improvements enable the deployment of systems with fewer feedback bits or pilots.

关键词： Vectors Covariance matrices Decoding Signal to noise ratio Discrete Fourier transforms Transmitting antennas Quantization (signal) precoding Data models Training robust precoding VQ-VAE machine learning feedback measurement data

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robust precoding With Limited Feedback Design Based on precoding MSE for MU-MISO Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第6期60卷 3101-3111页

作者： Joham, Michael Castro, Paula M. Utschick, Wolfgang Castedo, Luis Tech Univ Munich Associate Inst Signal Proc D-80290 Munich Germany Univ A Coruna Dept Elect & Syst La Coruna 15071 Spain

For the separation of the signals in the vector broadcast channel (BC), some information about the channel state is necessary at the transmitter. In many cases, this channel state information (CSI) must be fed back from the receivers to the transmitter. We jointly design the channel estimators and the quantizers at the receivers together with the precoder at the transmitter based on a precoder-centric criterion, i.e., the minimization of a mean square error (MSE) metric appropriate for the precoder design. This is in contrast to our previous works, where the quantizer design was based on a CSI MSE metric, i.e., based on the minimization of the MSE between the true channel and the channel recovered by the transmitter using a feedback channel. Interestingly, the estimators resulting from this joint formulation are independent of the used codebook. The codebook entries are the employed precoders. Therefore, each receiver feeds back the index of a set of precoders and the intersection of the sets gives the appropriate precoder. Since the quantizers of the different receivers have to work separately, the metric for the computation of the partition cells cannot be expressed as a simple squared error depending on the quantizer output. The proposed system based on a joint optimization clearly outperforms previous designs with separate optimization of feedback and precoding.

关键词： Bayesian approach feedback channel imperfect CSI precoding MSE metric robust precoding

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robust precoding for HF Skywave Massive MIMO

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第10期22卷 6691-6705页

作者： Yu, Xianglong Gao, Xiqi Lu, An-An Zhang, Jinlin Wu, Hebing Li, Geoffrey Ye Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Huawei Technol Co Ltd Shanghai 201206 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

In this paper, we investigate the robust precoding with imperfect channel state information (CSI) for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization (MM). Furthermore, we develop a low-complexity BDRP design with an ergodic sum-rate upper bound, simplifying the MM based design algorithm. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

关键词： Massive MIMO HF skywave communications robust precoding beam structured precoding imperfect CSI

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robust precoding for 3D Massive MIMO Configuration With Matrix Manifold Optimization

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第5期21卷 3423-3437页

作者： Wang, Chen Lu, An-An Gao, Xiqi Ding, Zhi Southeast Univ Natl Mobile Commun Res Lab NCRL Nanjing 210096 Peoples R China Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting with a posteriori channel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we design an iterative algorithm and present its convergence analysis. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

关键词： precoding Manifolds Optimization Massive MIMO Channel models Wireless communication Three-dimensional displays 3D massive MIMO robust precoding matrix manifold optimization generalized eigenvalue problem

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robust precoding Design for Massive MIMO: An Efficient Fractional Programming-Based Approach

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IEEE COMMUNICATIONS LETTERS 2022年第12期26卷 2999-3003页

作者： Lin, Tian Zhu, Yu Fudan Univ Sch Informat Sci & Technol Key Lab Informat Sci Electromagnet Waves MoE Shanghai Peoples R China

Due to the effects of channel aging and estimation errors, perfect instantaneous channel state information (CSI) is unavailable at a base station. Therefore, robust precoding under imperfect CSI is important for practical communications. In this letter, we propose an efficient robust precoding design for massive multiple-input multiple-output systems. Based on the fractional programming technique, we transform the original non-convex optimization problem into a much more tractable equivalent problem, which can be iteratively solved by alternating optimization. Since all variables are updated via closed-form optimal solutions, the proposed algorithm is guaranteed to converge to a locally optimal point. Simulation results reveal that the proposed robust precoding algorithm has fast convergence and achieves significant performance improvement over the conventional ones.

关键词： robust precoding fractional programming alternating minimization massive MIMO

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robust precoding for HF Skywave Massive MIMO With Slepian Transform

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第6期72卷 3752-3765页

作者： Song, Linfeng Shi, Ding Gan, Lu Gao, Xiqi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Brunel Univ London Dept Elect & Elect Engn London UB8 3PH England

In this paper, we address robust precoding in high-frequency (HF) skywave massive multiple-input multiple-output (MIMO) systems with imperfect channel state information (CSI). We first employ a sparse beam based a posteriori channel model and demonstrate that robust precoding can be efficiently solved in the Slepian transform domain with a large number of base station (BS) antennas. Next, we introduce two Slepian transform based robust precoding methods, including a joint approach that leverages inverse fast Fourier transform (IFFT) for reduced complexity with a large number of user terminals (UTs). We then establish a local optimum for the Slepian transform domain robust precoder (STRP) design using the majorization minimization (MM) algorithm, taking advantages of HF skywave massive MIMO channel sparsity and Slepian sequence properties. Further, two distinct designs are presented: separate STRP (SSTRP) and joint STRP (JSTRP). Simulation results confirm the effectiveness of proposed robust precoders, showcasing their excellent ergodic sum-rate performance and low complexity.

关键词： Transforms precoding Massive MIMO Symbols Complexity theory Channel models OFDM HF skywave communications robust precoding Slepian transform

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robust precoding for 3D Massive MIMO with Riemannian Manifold Optimization

Robust Precoding for 3D Massive MIMO with Riemannian Manifol...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Wang, Chen Lu, An-An Gao, Xiqi Ding, Zhi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Moutain Labs Nanjing 211100 Peoples R China Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

ISBN: (纸本)9781665442664

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting with a posteriori channel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we present an iterative algorithm to design the precoder. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

关键词： 3D massive MIMO robust precoding matrix manifold optimization generalized eigenvalue problem

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robust precoding Design for Coarsely Quantized MU-MIMO Under Channel Uncertainties-V0

Robust Precoding Design for Coarsely Quantized MU-MIMO Under...

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IEEE International Conference on Communications (IEEE ICC)

作者： Chu, Lei Wen, Fei Qiu, Robert Caiming Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA

ISBN: (纸本)9781538680889

Recently, multi-user multiple input multiple output (MU-MIMO) systems with low-resolution digital-to-analog converters (DACs) has received considerable attention, owing to the capability of dramatically reducing the hardware cost. Besides, it has been shown that the use of low-resolution DACs enable great reduction in power consumption while maintain the performance loss within acceptable margin, under the assumption of perfect knowledge of channel state information (CSI). In this paper, we investigate the precoding problem for the coarsely quantized MU-MIMO system without such an assumption. The channel uncertainties are modeled to be a random matrix with finite second-order statistics. By leveraging a favorable relation between the multi-bit DACs outputs and the single-bit ones, we first reformulate the original complex precoding problem into a nonconvex binary optimization problem. Then, using the S-procedure lemma, the nonconvex problem is recast into a tractable formulation with convex constraints and finally solved by the semidefinite relaxation (SDR) method. Compared with existing representative methods, the proposed precoder is robust to various channel uncertainties and is able to support a MU-MIMO system with higher-order modulations, e.g., 16QAM.

关键词： MU-MIMO low-resolution DACs robust precoding

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