2026· IEEE Transactions on Wireless Communications· Vol 25, pp. 20639-20650· 0 citations· 38 references
Computer Science
Abstract
In time-division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, users send pilot sequences for channel state estimation in a fixed time interval, which leads to pilot redundancy if users undergo longer coherence time than the prescribed channel estimation interval, and consequently reduces the net sum spectral efficiency of the systems. In this paper, we propose an adaptive channel estimation scheme for TDD massive MIMO systems, where base stations can reuse the aged channel state information (CSI) by exploiting the temporal correlation inherent in channel aging. We introduce a Fisher transformation method for temporal correlation estimation to determine the CSI estimation interval. We also derive the closed-form expressions of the spectral efficiency with channel aging effect and design a threshold to control the channel aging error introduced by CSI reuse. Numerical results demonstrate that our proposed adaptive scheme yields significant performance gains in spectral efficiency across various communication scenarios.
Stable downlink precoding in time-division duplex (TDD) multi-user multi-input multi-output (MU-MIMO) systems critically depends on the reliability of channel state information (CSI) at the transmitter. In mobile environments, Doppler-induced channel variation, estimation noise, and pilot contamination distort the channel structure, often leading to ill-conditioned matrices and unstable linear precoding. This paper proposes a Doppler-aware CSI reliability framework in which the condition number of sounding reference signal (SRS)-based channel estimates is used as a per-snapshot indicator of spatial robustness. Instead of relying solely on instantaneous CSI, the proposed approach selects the better-conditioned CSI between current and previously estimated channel realizations prior to downlink transmission, thereby improving robustness under mobility. System-level simulations compliant with 5G New Radio specifications are conducted under fixed, increasing, and decreasing Doppler scenarios using clustered delay line (CDL) channel models. Results show consistent gains in throughput and block error rate (BLER), approaching the performance of the perfect channel state information at transmitter (CSIT). Among the considered schemes, regularized zero forcing (RZF) precoding exhibits higher robustness to CSI degradation, while block diagonalization (BD) is more sensitive to Doppler variations. Overall, the proposed method provides a lightweight and standard-compatible solution for improving MU-MIMO precoding reliability in time-varying channels.
L. Bharti, Adarsh Ravi, Hamza Bouchebbah et al.· International Conference on...· 0 citations
A unified comparative analysis of three widely used spatially correlated centralized scattering channel models in a multi-cell massive MIMO system suggests that M-MMSE is preferable in strongly correlated environments, while lower-complexity schemes such as RZF provide a favorable trade-off in scenarios with milder spatial correlation.
Waleed A. Ali, M. M. Zayed· Wireless networks· 0 citations
A time-evolving RCE model is developed that characterizes the joint effects of residual phase mismatches, residual carrier frequency offsets, and oscillator phase noise, and a Gauss--Legendre quadrature-based weighted minimum mean square error (WMMSE) optimization framework is developed.
Mingjun Sun, Xidong Mu, Shaochuan Wu et al.· 0 citations
Wideband time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) precoding requires channel state information (CSI) that is both frequency-selective and temporally fresh. In practical fifth-generation New Radio networks, uplink sounding reference signal (SRS) resources are often sparsely allocated and configured with frequency hopping to reduce pilot overhead and support multi-user multiplexing. As a result, SRS measurements over different frequency segments are obtained at different sounding occasions and stitched at the next-generation NodeB (gNB), leading to frequency-asynchronous SRS observations with frequency-dependent CSI aging. This letter proposes a hop-aware CSI fusion framework for frequency-asynchronous SRS in TDD massive MIMO systems. The proposed method reconstructs physical resource block-resolution CSI by combining quantized subband CSI feedback with stitched SRS observations at the gNB. To explicitly model frequency asynchrony and observation reliability, the decoder incorporates per-physical resource block measurement age and uplink signal-to-noise ratio (SNR) as side information, and further introduces precoding resource group (PRG)-level residual gating to adaptively regulate SRS-driven refinement under stale or unreliable frequency segments. In addition, a compact subband feedback representation augmented with intra-subband variation features is designed to enhance frequency selectivity under limited feedback payload. Simulation results show that the proposed framework consistently improves PRG-level squared generalized cosine similarity across different SRS periodicities, feedback payload sizes, and user mobilities.
Ziqiang Ye, Yuan Zhong, Yulan Gao et al.· IEEE Wireless Communications...· 0 citations
In Orthogonal Time Frequency Space (OTFS) systems, in order to support high-speed mobility and multi-user compatibility, and to reconstruct the entire two-dimensional delay-Doppler (DD) channel response from limited observations in strong interference environments, pilot structures with extensive guard intervals are required for channel estimation. This leads to high pilot overhead and low information transmission efficiency. To address this issue, a pilot-sharing-based channel estimation method for OTFS systems is proposed. This method targets high-speed railway long-tunnel scenarios and leverages the correlation between adjacent users’ channels to enable cooperative sharing and reuse of pilot information, thereby effectively reducing the overall system pilot overhead. Firstly, a correlation-based shared pilot structure is designed, allowing the saved delay-Doppler resource grid elements under low pilot overhead to be reallocated for data transmission. Then, a decision-feedback-based channel estimation algorithm is designed, which significantly enhances the overall resource utilization and effective data transmission rate of the system while ensuring channel estimation accuracy. Simulation results demonstrate that, in a delay-Doppler domain grid of $32\times 32$ with a subcarrier spacing of 15 kHz and under Quadrature Phase Shift Keying (QPSK) modulation, the proposed method reduces the pilot overhead of the considered two-user system by 50% compared to existing approaches while keeping the bit error rate (BER) at a low level. This results in a relative increase of 19.05% in the system’s throughput. Moreover, at a mean square error (MSE) of $1.5\times 10^{-4}$ , the proposed algorithm achieves a performance gain of about 2 dB compared to conventional schemes.
Xiaotian Li, Zitian Zhao, Jiameng Pei et al.· IEEE Open Journal of the Com...· 0 citations
Massive Multiple-Input Multiple-Output (mMIMO) is a key enabling technology for beyond fifth-generation (B5G) and sixth-generation (6G) wireless systems; however, its performance critically depends on accurate channel state information (CSI). Conventional minimum mean square error (MMSE) channel estimators are typically derived under the assumption of spatially uncorrelated fading, which rarely holds in practical deployments due to antenna coupling, limited spacing, and structured propagation environments. As a result, spatial correlation can significantly degrade estimation accuracy. This paper investigates uplink channel estimation for mMIMO systems under spatially correlated channels. First, an analytical characterization of the effect of correlation on the traditional MMSE estimator is presented. Then, two correlation-aware estimation techniques are developed. The first approach introduces a regularized MMSE formulation to improve robustness in correlated scenarios. The second approach proposes a pre-whitening-based MMSE estimator that exploits channel covariance information to transform the correlated channel into an equivalent white channel prior to estimation. Closed-form solutions for the optimal filters are derived, and their complexity is calculated. The results of the simulation verify that the pre-whitening based estimator outperforms the conventional and regularized MMSE estimators over a wide interval for the value of the signal to noise ratio (SNR) and the correlation coefficient.
Muhammad Ahsan Shaikh, S. Rehman, Halar Mustafa· Romanian Journal of Informat...· 0 citations
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