In 6G and future communication systems, the adoption of large scale antenna arrays and high frequency bands is an inevitable trend. This leads to most users operating in the near-field region, where higher angular resolution and sensitivity to distance changes make user mobility effects fundamentally different from those in the far-field. In this work, a systematic study of near-field channel dynamics under user mobility is investigated. We begin by analyzing the Doppler characteristics associated with both line of sight (LoS) and multipath components, and demonstrate that notable Doppler shifts can occur even at low user velocities. In particular, the LoS component exhibits antenna dependent Doppler shifts, highlighting the spatially non-uniform nature of Doppler effects in near-field scenarios. Based on this, we derive a closed form expression for the coherence time in scattering-free environments, which reveals its dependence on antenna aperture, wavelength, user motion, etc. Unlike far-field LoS channels where Doppler effects can be compensated by a phase rotation, the near-field counterpart exhibits antenna dependent Doppler shifts, resulting in a finite coherence time even without scattering. For multipath scenarios, where the near-field channel is inherently sparse, we establish upper and lower bounds for the temporal correlation function. Furthermore, to track time-varying channels, we propose an array segmentation strategy and formulate a state-space model of the near-field channel in the dictionary domain. The proposed estimation framework comprises an expectation maximization (EM) stage using Kalman filtering with Rauch-Tung-Striebel smoothing (KF-RTSS) for virtual channel statistics and a fixed-point iteration for parameter refinement, followed by a Kalman filtering stage for channel prediction. A detailed complexity analysis is also provided. Finally, extensive simulations validate our theoretical analysis and show that the proposed method accurately captures near-field channel dynamics, offering robust performance across different angles, distances, and signal to noise ratios.

The employment of extremely large antenna arrays and high-frequency signaling makes future communication systems likely to operate in the near-field region, where the conventional planar wave assumption is invalid. Instead, a spherical wave assumption which provides both the user angle and distance information is more accurate. Spherical wave-based channel representation and estimation is still under investigation. In this work, we propose a distance-parameterized angular-domain sparse model to represent the near-field channel, followed by a joint dictionary learning and sparse recovery based channel estimation algorithm. The proposed sparse representation model overcomes challenges such as the storage burden and high dictionary coherence that arise in the existing polar-domain method. Simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the polar-domain method in channel estimation error and pave the way to efficient and accurate modeling in the near-field regime.

The employment of extremely large antenna arrays and high-frequency signaling makes future 6G wireless communications likely to operate in the near-field region. In this case, the spherical wave assumption which takes into account both the user angle and distance is more accurate than the conventional planar one that is only related to the user angle. Therefore, the conventional planar wave based far-field channel model as well as its associated estimation algorithms need to be reconsidered. Here we first propose a distance-parameterized angular-domain sparse model to represent the near-field channel. In this model, the user distance is included in the dictionary as an unknown parameter, so that the number of dictionary columns depends only on the angular space division. This is different from the existing polar-domain near-field channel model where the dictionary is constructed on an angle-distance two-dimensional (2D) space. Next, using this model, joint dictionary learning and sparse recovery based channel estimation methods are proposed for both line of sight (LoS) and multi-path settings. To further demonstrate the effectiveness of the suggested algorithms, recovery conditions and computational complexity are studied. Our analysis shows that with the decrease of distance estimation error in the dictionary, the support set of the angular-domain sparse vector can be exactly recovered under certain Restricted Isometry Property (RIP) based conditions. The high storage burden and dictionary coherence issues that arise in the polar-domain 2D representation are also well addressed. Finally, simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the existing polar-domain method in channel estimation error.

For the estimation of real-valued Gaussian signals, the unscented Kalman filter (UKF) can provide a state estimate with second-order accuracy. However, when a general complex-valued system is considered, a direct extension of UKF from the real domain to the complex domain is inadequate, since the complementary covariance information associated with general improper complex-valued signals has been systematically ignored. To this end, in this work, we propose a general complex-valued unscented Kalman filter (GCUKF) algorithm which can be applied for both proper and improper signals. This is achieved by first proposing a novel sigma points selection scheme for the general complex-valued case, followed by a modified state update method to fully utilize both the innovation and its conjugate. A rigorous MSE analysis illustrates the superiority of the proposed state update method, and simulations support the analysis.

Direct-conversion receivers (DCRs) have been adopted in wideband communication systems owing to their simple structure and low cost, however, their operation is affected by amplitude and phase mismatches between their analog inphase (I) and quadrature (Q) branches, as well as the discrepancy of low-pass filter coefficients between these two channels. In this paper, a blind adaptive frequency-dependent I/Q imbalance compensator is proposed, which exploits the complex properness (second-order circularity) of ideal constellation mappings to provide more enhanced insight into the problem setting within the proposed compensator. This serves as a basis for a novel full second-order performance assessment framework, which is established through a joint consideration of the weight error covariance and complementary covariance in both the transient and steady-state stages. This conjoint analysis is further shown to facilitate accurate quantification of the overall mirror-frequency interference attenuation capability of the proposed compensator. Simulation results in an orthogonal frequency division multiplexing (OFDM) transmission system demonstrate the excellent performance of the proposed compensator.

Complex least mean square (CLMS) based adaptive computation of discrete orthogonal transforms has been extensively investigated in the literature. However, all of these results provide only a means for the calculation of either forward orthogonal transforms or their inverse orthogonal transforms, separately. In this work, a way to simultaneously calculate the discrete Fourier transform (DFT) and the inverse DFT (IDFT) is established via the widely linear (WL) signal processing framework. We show that by appropriately selecting the input vector and adaptation speed of the widely linear complex least mean square (WL-CLMS), the resulting spectrum analyzer is capable of simultaneously performing DFT and IDFT of the signal to be Fourier analyzed in both the block-based and online manners.