Channel knowledge map (CKM) has emerged as a crucial technology for next-generation communication, enabling the construction of high-fidelity mappings between spatial environments and channel parameters via electromagnetic information analysis. Traditional CKM construction methods like ray tracing are computationally intensive. Recent studies utilizing neural networks (NNs) have achieved efficient CKM generation with reduced computational complexity and real-time processing capabilities. Nevertheless, existing research predominantly focuses on single-antenna systems, failing to address the beamforming requirements inherent to MIMO configurations. Given that appropriate precoding vector selection in MIMO systems can substantially enhance user communication rates, this paper presents a TransUNet-based framework for constructing CKM, which effectively incorporates discrete Fourier transform (DFT) precoding vectors. The proposed architecture combines a UNet backbone for multiscale feature extraction with a Transformer module to capture global dependencies among encoded linear vectors. Experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) deep learning (DL) approaches, yielding a 17% improvement in RMSE compared to RadioWNet. The code is publicly accessible at https://github.com/github-whh/TransUNet.

Underwater acoustic communication (UWA) faces significant coverage challenges due to the depth-varying sound speed gradients and the presence of sound shadow zones. Acoustic reconfigurable intelligent surface (RIS) is promising as an enabler to enhance acoustic signal quality and reliability. In this paper, we propose a novel full-phase-range acoustic RIS with effective acoustic beamforming scheme. Electrical unit parameters are carefully designed with Tonpilz hardware and equivalent circuit architecture. The reflective magnitude-phase coupling is analytically modelled by dual-quadratic expression. Furthermore, we propose one Majorization-Minimization (MM)-based acoustic RIS beamforming scheme, where alternative maximization approach is coordinated with fractional programming and MM methods to achieve the convex relaxation and near-optimal solutions.

In the double-sided near-field extremely large-scale multi-input multi-output (XL-MIMO) systems, due to the spherical-wavefront propagation, the line-of-sight (LoS) path exhibits multiple independent propagation components, leading to a channel rank greater than one. In contrast, the non-line-of-sight (NLoS) path is typically dominated by a single propagation component. Consequently, the unified modeling of mixed LoS and NLoS paths remains unresolved, particularly when with non-parallel and non-coplanar uniform linear arrays (ULAs) at the transceivers. Furthermore, there exist bottlenecks in the beamforming codeword design and low-overhead estimation in double-sided near-field communications. In this paper, we present a unified channel model to characterize both LoS and NLoS paths in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Apart from the transmitter (Tx)-side and receiver (Rx)-side separate response vectors, an additional Tx/Rx-coupled term with a Vandermonde windowing pattern is studied for the XL-MIMO LoS path. Two codebook-based beamfocusing schemes are proposed, which are termed as the beamspace-projection and diagonal-decomposition schemes. The achievable spectral efficiency and average power are theoretically analyzed in closed form. Under this framework, we further propose a low-overhead unified LoS/NLoS orthogonal matching pursuit (UOMP) algorithm for XL-MIMO channel estimation, which is then extended via 3-stage multiple-measurement-vector (3S-MMV) for complexity reduction. Last, simulation results demonstrate the superiority of the proposed strategies in both beamforming codeword selection and sparse channel estimation.

Traditional channel acquisition faces significant limitations due to ideal model assumptions and scalability challenges. A novel environment-aware paradigm, known as channel twinning, tackles these issues by constructing radio propagation environment semantics using a data-driven approach. In the spotlight of channel twinning technology, a radio map is recognized as an effective region-specific model for learning the spatial distribution of channel information. However, most studies focus on static channel map construction, with only a few collecting numerous channel samples and using deep learning for radio map prediction. In this paper, we develop a novel dynamic radio map twinning framework with a substantially small dataset. Specifically, we present an innovative approach that employs dynamic mode decomposition (DMD) to model the evolution of the dynamic channel gain map as a dynamical system. We first interpret dynamic channel gain maps as spatio-temporal video stream data. The coarse-grained and fine-grained evolving modes are extracted from the stream data using a new ensemble DMD (Ens-DMD) algorithm. To mitigate the impact of noisy data, we design a median-based threshold mask technique to filter the noise artifacts of the twin maps. With the proposed DMD-based radio map twinning framework, numerical results are provided to demonstrate the low-complexity reproduction and evolution of the channel gain maps. Furthermore, we consider four radio map twin performance metrics to confirm the superiority of our framework compared to the baselines.

Though meaningful progress has been achieved for mobile wireless networks with the development of orthogonal frequency division multiplexing (OFDM) modulation, the reliability and transmission efficiency of underwater acoustic (UWA) communications are still limited, which cannot support the ever-increasing requirements of underwater applications. The major barrier lies in the wideband time-varying channels with extremely large time scales. Since the time dilation or contraction of baseband signals cannot be ignored in UWA communications, the orthogonality between subcarriers of OFDM is destroyed more significantly than in narrowband high-mobility channels, which leads to severe performance degradation. To solve this problem, a novel multicarrier modulation scheme referred to as the orthogonal hyperbolic frequency division multiplexing (OHFDM) modulation is proposed in this paper inspired by the scale-invariance of hyperbolic frequency signals, where a series of orthogonal narrowband hyperbolic frequency subcarriers (HFSs) is adopted to load data symbols. The input-output relation is then characterized by jointly processing the carrier and subcarrier signals, and selecting the appropriate sampling time of the output of matched filters at the receiver corresponding to the time scale of the wideband time-varying channel. The analysis reveals that the approximate orthogonality can be guaranteed, i.e., much smaller inter-carrier-interference (ICI) than OFDM systems, which enhances the system reliability and reduces the processing complexity at the receiver notably. The robustness of the proposed OHFDM modulation when path-specific scales are involved is also confirmed theoretically in this paper. Simulation results demonstrate that the proposed OHFDM modulation outperforms OFDM in terms of bit error rate (BER) under typical UWA channels with large time scales.

Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimension beyond conventional beamspace. In this paper, we first present one concise closed-form channel formulation for extremely large-scale multiple-input multiple-output (XL-MIMO). All line-of-sight (LoS) and non-line-of-sight (NLoS) paths, far-field and near-field scenarios, and XL-MIMO and XL-MISO channels are unified under the framework, where additional Vandermonde windowing matrix is exclusively considered for LoS path. Under this framework, we further propose one low-complexity unified LoS/NLoS orthogonal matching pursuit (XL-UOMP) algorithm for XL-MIMO channel estimation. The simulation results demonstrate the superiority of the proposed algorithm on both estimation accuracy and pilot consumption.

Beam squint effect causes severe performance degradation for wideband beamforming inside Internet of thing (IoT) communication, and the true-time-delay (TTD) line has been regarded as a promising enabler to address this issue. However, beam training becomes a challenging puzzle in TTD-aided transceivers, where enormous beam directions bring about unacceptable training overhead, especially for the overhead-efficient IoT devices. In this paper, based on a joint delay-phase beamforming structure, we provide enhanced frequency-scanning-based training schemes for remarkable overhead reduction. Simultaneous beams pointing to different physical directions over a set of OFDM subcarriers can be generated to reduce overhead. The pointing directions can be flexibly controlled following two subcarrier-angular mapping policies: forward-pairing and backward-pairing. Besides, the power leakage problem is retrieved via the compressive phase retrieval (CPR) method to avoid beam mismatch. Furthermore, we adopt it into the multiuser scenario and propose a frequency-scanning-based simultaneous multi-user beam training (FS-MBT) scheme. The different subcarriers’ pencil beams illuminate a broad angular sector, while several sectors are merged into multi-finger wide beams for simultaneous multi-user training. Analytical and numerical results demonstrate the proposed schemes’ superiority over existing methods in both single-user and multi-user scenarios.

Over-the-air computation (AirComp) is considered a transformative enabler for next-generation artificial intelligence (AI) services and wireless data aggregation via the electromagnetic waveform-superposition property of wireless multi-access channels (MAC). However, the conventional distortion metric, minimum square error (MSE), is imperfect and not universally applicable in specific AirComp scenarios in a low-signal-to-noise ratio (SNR) regime and under power budget constraint. Conversely, the average discriminant gain is studied for task-oriented AirComp AI services like classification but with inaccurate performance indication. To solve these problems, this work establishes a novel framework for AirComp systems from the mutual information (MI) perspective. First, we categorize the AirComp model into two distinct classes based on the source (sensing) data independence, namely diverse-targets (DT) AirComp and homogeneous-target (HT) AirComp. Both categories with different inputs like classical Gaussian and classification-based Gaussian mixture model (GMM), can be unified and assessed via MI criterion. Next, for the DT AirComp system, we introduce a novel MI-aided AirComp beamforming scheme employing majorization-minimization (MM) relaxation. As for the HT AirComp, we present a heuristic successive approximation (SA)-based beamforming method considering complex GMM inputs. We also provide the feedback and update protocol for AirComp tracking. Simulations validate the superior performance on AirComp throughput and task-oriented metrics such as classification accuracy with our proposed MI-aided beamforming schemes.

Extremely large-scale multiple-input multiple-output (XL-MIMO) promises to provide ultrahigh data rates in millimeter-wave (mmWave) and Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition and beamforming. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose two low-overhead hierarchical beam training schemes for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Then we point out three critical criteria for XL-MIMO hierarchical beam training. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Thirdly, given the imperfect coverage and overlapping of spatial-chirp beams, we further design an enhanced hierarchical training codebook via manifold optimization and alternative minimization. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

XL-MIMO promises to provide ultrahigh data rates in Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose one low-overhead hierarchical beam training scheme for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with the hybrid precoder employed have been regarded as a reliable option to raise the capacity and coverage of the cellular network. Extensive studies have demonstrated that the capacity achieved by the hybrid precoder with fixed architectures cannot satisfy the requirement of the ever-increasing data traffic. However, the performance of the hybrid precoding can be further promoted by exploiting the flexibility of the precoding structure, where the adaptively-connected architecture can provide a critical enhancement. In this paper, we investigate the adaptively-connected structure and propose a near-optimal hybrid precoding scheme based on the Hungarian algorithm. Simulation results demonstrate the significant advantage of the proposed scheme over existing hybrid precoding algorithms.

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation for RIS-aided systems is still a critical challenge due to the excessive amount of parameters in the cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we analyze and exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Furthermore, a novel on-grid Multi-user Triple-Structured-Compressive-Sensing simultaneous orthogonal matching pursuit (MTSCS-SOMP) algorithm along with an enhanced super-resolution (gridless) generalized iterative reweighted (MTSCS-IR) scheme are successively proposed. The former is practical and can be easily employed with low computational complexity, and the latter is further proposed to handle the severe power leakage problem encountered in mmWave channel estimations. Besides, we extend the sparsity property and algorithms from uniform linear array (ULA) configuration to uniform planar array (UPA), by transforming cascaded channel from matrix to tensor. Simulation results show that our approaches can significantly reduce pilot overhead over 50% and achieve enhanced performance on estimation accuracy.

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation in RIS-aided system is still a critical challenge due to the excessive amount of parameters in cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Then a novel multi-user joint estimation algorithm is proposed. Simulation results show that our approach can significantly reduce pilot overhead in both ULA and UPA scenarios.