Integrated sensing and communication (ISAC) enables reliable transmission by sensing targets in scattering environment. However, a single base station (BS) struggles to accurately detect three-dimension (3D) moving targets. Meanwhile, asynchronicity caused by spatially separated transceivers introduces timing offsets (TOs) and carrier frequency offsets (CFOs), which impair the accuracy of sensing parameter. To address this, this paper studies an orthogonal frequency division multiplexing (OFDM) networked ISAC system, where multiple base stations (BSs) cooperatively sense multiple unmanned aerial vehicles (UAV) through a joint active and passive sensing framework. The sensing signals received from the BSs are transmitted through a backhaul-limited link to a fusion center (FC) for sensing parameter estimation. In this context, we propose a novel cooperative sensing scheme for UAV parameter estimation based on quantized signals. The joint active and passive sensing problem is formulated as a problem with imperfect parameters (such as TO and CFO). An efficient algorithm is proposed to solve the resulting problem. Simulation results indicate that the proposed cooperative sensing design achieves higher estimation accuracy than other baselines. The results also confirm the performance advantage of multi-BS cooperative sensing over conventional single-BS sensing.

With the advancement of wireless communications toward higher user densities and increasingly complex environments, near-field communication has emerged as a critical research focus. Supporting multi-user access in this regime with manageable complexity remains a key challenge, particularly due to the coupling between the spherical wavefront effect and the spatial non-stationarity (SnS) property, which complicates the exploitation of spatial correlation among user channels. To address this, we propose a unified multi-user channel model that incorporates joint support to capture the structured sparsity shared across users. Additionally, we introduce a two-dimensional (2D) Markov prior to model both local sparsity pattern continuity across adjacent grid points and joint burst sparsity in the shared support structure. Based on this, we develop a spatial information-sharing-aided framework that alternately estimates model parameters. Specifically, an inverse-free variational Bayesian inference (IF-VBI) algorithm is employed in the channel estimation module to avoid high-dimensional matrix inversion while enabling information exchange among users via joint support. In the common grid update module, joint updates across users are performed to achieve a full spatial-domain optimum, whereas in the joint visible region (VR) matrix detection module, the user-sharing structure is exploited to decouple and efficiently solve the VR matrix. Simulation results validate the effectiveness of the proposed approach, demonstrating improved estimation accuracy and computational efficiency in multi-user near-field extremely large-scale multiple-input-multiple-output (XL-MIMO) systems.

Integrated sensing and communication (ISAC) has drawn great attention for its capacity to simultaneously support wireless communication and environmental sensing. However, in dynamic ISAC systems, environmental mobility poses critical challenges in both dynamic channel estimation and continuous sensing parameter tracking across multiple time slots under severe Doppler effect. To address these challenges with a unified framework, we propose a novel base station-user cooperative sensing approach for joint dynamic channel estimation and sensing parameter tracking, including target/scatterer locations and actual velocities, which is formulated as a maximum a posterior (MAP) problem. In cases where some moving radar targets also serve as communication scatterers, the spatial overlap induces an underlying partially common sparsity between the location-domain sensing and communication channels. Based on this, we develop a two-dimensional Markov Model (2D-MM) based on dynamic location grids to capture the spatio-temporal correlations and partially common sparsity, thereby enhancing both sensing and communication performance. To alleviate the high-complexity matrix inverse in the E-step of sparse Bayesian inference, we propose a dynamic subspace-constrained variational Bayesian inference (D-SCVBI) algorithm with the aid of prior information obtained from the two-dimensional discrete Fourier transform (2D-DFT) localization and state evolution model. Finally, simulations show that the proposed D-SCVBI algorithm attains remarkable performance gains over various baselines.

With the increase of carrier frequencies and the expansion of large antenna array sizes, wireless communication systems are progressively entering the near-field region. Characterized by spherical wave characteristics, near-field channels exhibit more complex spatial structures and higher parameter dimensions. To mitigate these issues, we propose a near-field channel tracking framework based on a Markov model-based prior. Specifically, by leveraging the spherical wave propagation characteristics, we construct a three-dimensional polar-domain sparse representation, i.e., azimuth, elevation, and distance dimensions, enabling high-resolution channel modeling. Incorporating a Markov model-based prior, we can effectively track variations in spherical wave channels. For channel estimation, we develop a Turbo subspace-constrained variational Bayesian inference (Turbo-SC-VBI) algorithm that avoids high-dimensional matrix inversion, significantly reducing computational complexity. Numerical results demonstrate that the proposed method achieves superior channel tracking performance, with lower pilot overhead and acceptable computational complexity.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems enable extraordinarily high data rates and necessitate precise channel state information (CSI) for effective beamforming. To explore the structural sparsity feature, we propose an advanced low-rank matrix factorization technique for channel estimation. Our algorithm treats channel estimation as a problem of low-rank matrix completion by exploiting the simultaneous sparsity and low-rank nature of the channel. First, we decompose the channel matrix into two low-rank factor matrices, refining them through projected gradient descent to satisfy the constraints of low-rankness and sparsity simultaneously. For improved results, we introduce a deep unfolding network, named Deep Unfolding Low-Rank Factorization Net for Channel Estimation (DULRF-CE), which integrates the low-rank matrix factorization model into the neural network design. This network includes a module capable of nonlinear sparse transformations and a denoising module, giving it the ability to learn a more accurate low-rank decomposition and a sparser channel representation. Our experimental results show that the DULRF-CE algorithm provides a significant advance, offering up to 4.3 dB improvement in average normalized mean square error performance over existing matrix-completion-based channel estimation techniques in various measurement scenarios.

Most existing downlink channel estimation methods rely on channel sparsity in the angular domain to reduce pilot overhead for massive multiple-input multiple-output (MIMO) systems. Compared with channel estimation methods based on predefined basis or offline dictionary learning, in this paper, we design an online two-timescale joint uplink/downlink dictionary learning and channel estimation (TTS-JDLCE) algorithm for dynamic scenarios, where the channel sparsity and angle reciprocity between uplink and downlink transmissions are both exploited to reduce the pilot overhead. The downlink channel estimation is constructed as a TTS stochastic optimization problem with a constraint coupled by the long-term dictionary and short-term sparse channel representations. Treating the dictionary as a learnable parameter, the proposed algorithm can capture dynamic spatial information for enhancing performance. By introducing a relaxed TTS primal-dual decomposition (PDD) framework, the original problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems. Besides, the deep unfolding technique is employed to extract gradient information from short-term problems, which circumvents the non-closed form and non-convexity of long-term subproblem by constructing a convex surrogate problem. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gains over various baselines.

In this paper, we design a two-timescale approach for joint uplink/downlink (UL/DL) dictionary learning and channel estimation (TTS-JDLCE) for frequency division multiplexing (FDD) massive multiple-input multiple-output (MIMO) systems in dynamic scenarios. With channel sparsity and angle reciprocity between UL and DL transmissions, the joint UL/DL dictionary is regarded as a learnable parameter to capture dynamic spatial information at a slower timescale than instantaneous downlink channel estimation. By introducing the primal-dual decomposition (PDD) framework, the original non-convex two-timescale stochastic optimization problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems, and then solved in a divide-and-conquer manner, which can converge to the stationary solutions of the original problem over time. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gain over various baselines.

In massive multiple-input multiple-output (MIMO) systems employing hybrid analog-digital precoder, there are two commonly used antenna structures that have their own pros and cons, namely, the fully-connected antenna structure (FCAS) and the partially-connected antenna structure (PCAS). The FCAS achieves better spectrum efficiency (SE) even with reduced radio frequency (RF) chains, but the hardware cost and power consumption are still grievous. Contrarily, the PCAS has less hardware cost and power consumption, but suffers from severe performance loss. In this paper, by combining the advantages of both structures, we first propose a sparse adaptive antenna structure (SAAS) for the implementation of the hybrid precoder, which can jointly control the on/off state of all phase shifters (PS) and RF chains through a switch network. Then, a sparse hybrid precoding (SHP) optimization problem based on the proposed SAAS is established aiming at minimizing the total power consumption under individual average data rate requirements. To tackle the challenging non-smooth non-convex stochastic optimization (NSO) emerged with the SHP design and reduce the power consumption of PSs and RF chains, we propose a sparse smooth approximation based an online algorithm to find a stationary point of the NSO problem and establish its convergence. Simulations verify that the proposed antenna structure and algorithm achieve a better balance between power consumption and system throughput than the existing schemes.