In wideband near-field arrays, frequency-dependent array responses cause wavefronts at different frequencies to deviate from that at the center frequency, producing beam squint and degrading multi-user performance. True-time-delay (TTD) circuits can realign the frequency dependence but require large delay ranges and intricate calibration, limiting scalability. Another line of work explores one- and two-dimensional array geometries, including linear, circular, and concentric circular, that exhibit distinct broadband behaviors such as different beam-squint sensitivities and focusing characteristics. These observations motivate adapting the array layout to enable wideband-friendly focusing and enhance multi-user performance without TTD networks. We propose a movable antenna (MA) aided architecture based on hierarchical sub-connected hybrid beamforming (HSC-HBF) in which antennas are grouped into tiles and only the tile centers are repositioned, providing slow geometric degrees of freedom that emulate TTD-like broadband focusing while keeping hardware and optimization complexity low. We show that the steering vector is inherently frequency dependent and that reconfiguring tile locations improves broadband focusing. Simulations across wideband near-field scenarios demonstrate robust squint suppression and consistent gains over fixed-layout arrays, achieving up to 5\% higher sum rate, with the maximum improvement exceeding 140\%.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems provide enhanced coverage and capacity for next-generation wireless networks. However, CF-mMIMO systems face significant challenges in downlink power allocation (PA) due to imperfect channel state information (CSI), severe multi-user interference (MUI), and high computational complexity. To address these issues, rate-splitting multiple access (RSMA) is adopted as a robust interference management strategy. Accordingly, this paper proposes an unsupervised and scalable graph neural network (GNN) framework for PA in rate-splitting CF-mMIMO (RS-CF-mMIMO) systems, relying exclusively on large-scale fading (LSF) coefficients without instantaneous CSI. To resolve the dimensionality mismatch in dynamic networks, we introduce a slice-based adaptive layer that projects variable-dimension features into a fixed latent space. This mechanism enables a unified model to generalize across diverse topologies without retraining. Within this architecture, the sum spectral efficiency (SE) is maximized under per-AP power constraints, assuming maximum-ratio precoding for common streams and regularized zero-forcing precoding for private streams. We also derive a weighted minimum mean-square error-alternating direction method of multipliers (WMMSE-ADMM) algorithm as a performance upper bound. Extensive simulations verify that the proposed GNN framework achieves near-optimal SE and outperforms unsupervised deep neural networks (DNNs) across diverse system sizes and pilot assignment schemes. Furthermore, the scalable variant maintains robust performance while reducing the trainable parameter count by over 57% relative to DNNs and decreasing inference latency by up to three orders of magnitude compared with WMMSE-ADMM.

Rate splitting multiple access (RSMA) is regarded as a crucial and powerful physical layer (PHY) paradigm for nextgeneration communication systems. Particularly, users employ successive interference cancellation (SIC) to decode part of the interference while treating the remainder as noise. However, conventional RSMA systems rely on fixed-position antenna arrays, limiting their ability to fully exploit spatial diversity. This constraint reduces beamforming gain and significantly impairs RSMA performance. To address this problem, we propose a movable antenna (MA)-aided RSMA scheme that allows the antennas at the base station (BS) to dynamically adjust their positions. Our objective is to maximize the system sum rate of common and private messages by jointly optimizing the MA positions, beamforming matrix, and common rate allocation. To tackle the formulated non-convex problem, we apply fractional programming (FP) and develop an efficient two-stage, coarse-to-fine-grained searching (CFGS) algorithm to obtain high-quality solutions. Numerical results demonstrate that, with optimized antenna adjustments, the MA-enabled system achieves substantial performance and reliability improvements in RSMA over fixedposition antenna setups.

Movable antennas (MAs) have shown significant potential in improving the performance of integrated sensing and communication (ISAC) systems. However, their application in integrated and cost-effective full-duplex (FD) monostatic systems remains underexplored. To bridge this research gap, we develop an MA-ISAC model within an FD monostatic framework, where the self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. This model enables antenna position optimization for maximizing the weighted sum of communication capacity and sensing mutual information. The resulting optimization problem is non-convex making it challenging to solve optimally. To address this, we employ the fractional programming (FP) method and propose an alternating optimization (AO) algorithm that jointly optimizes the beamforming and antenna positions at the transceivers. Specifically, closed-form solutions for the transmit and receive beamforming matrices are derived using the Karush-Kuhn-Tucker (KKT) conditions, and a novel coarse-to-fine grained searching (CFGS) approach is used to determine high-quality sub-optimal antenna positions. Numerical results demonstrate that with strong self-interference cancellation (SIC) capabilities, MAs significantly enhance the overall performance and reliability of the ISAC system when utilizing our proposed algorithm, compared to conventional fixed-position antenna designs.

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios with doubly selective channels. However, the existing channel estimation schemes for OTFS usually rely on high-power pilots, which cause the issue of high peak-to-average power ratio (PAPR). In this paper, a channel estimation scheme for OTFS is designed, utilizing the Zadoff-Chu (ZC) sequence as the pilot without any guard symbols to reduce the PAPR effectively. Furthermore, an iterative ZC-sequence-based estimation algorithm is proposed. It can accurately estimate channels with integer and fractional Doppler shifts, irrespective of whether ideal or rectangular waveforms are employed. The proposed scheme performs the channel estimation using a correlation-based method. The correlation’s interference, caused by the data, pilot, and noise, is mitigated by performing channel estimation and data detection alternately. Simulation results show that it has a significantly lower PAPR in the time domain and superior channel estimation performance with both integer and fractional Doppler.

The symbol-level precoding (SLP) technique can enhance the performance in multi-user wireless communication systems because of its ability to convert harmful multi-user interference (MUI) into beneficial ones. However, the tremendous computational complexity of conventional symbol-level precoding designs severely hinders practical implementations. This paper proposes an SLP design scheme based on unsupervised learning in a multiple-input multiple-output (MIMO) downlink system. In the SLP design scheme, the loss function is first designed to improve performance by pushing the received signal further away from the decision boundaries into a constructive region. An efficient symbol-level precoding network (SLP-Net) is introduced to optimize the SLP under the power constraint and adapt amplitude phase modulation. Numerical results highlight that the optimized SLP design scheme provides meaningful performance gain in the MIMO downlink system. The proposed SLP design scheme can be an efficient technology to perform better in the future 6G.

Multi-User Multiple-Input Multiple-Output (MU-MIMO) antenna arrays are considered a crucial technology for future wireless communication systems. However, precoding for MU-MIMO meets significant challenges. To tackle this issue, this paper introduces a novel star decentralized precoding algorithm, aiming to decentralize part of the precoded calculations from the central unit (CU) to the decentralized units (DUs) and reduce the computational complexity of the CU. Then, we apply the Zero Forcing Tomlinson-Harashima precoding (ZF-THP) algorithm to star decentralized baseband processing (DBP) for enhanced transmission rate, and this algorithm has the same performance as centralized precoding but with reduced CU complexity. Furthermore, we propose the star decentralized minimum mean square error THP (sDMMSE-THP) algorithm to enhance system performance further. Extensive simulation data validate the effectiveness of our proposed scheme.

For low earth orbit (LEO) satellite communications, the robustness to high mobility and the low peak-to-average power ratio (PAPR) are two essential requirements. Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios with doubly selective channels. However, the existing channel estimation schemes for OTFS usually contain high-power pilots, which cause high PAPR, while the current PAPR reduction schemes for OTFS usually ignore the impacts on channel estimation. This work proposes a novel channel estimation scheme utilizing a Zadoff-Chu (ZC) sequence as the pilot, with which the PAPR can be effectively reduced by proper pilot alignment. Then, an iterative ZC-sequence-based estimation algorithm is proposed, which adopts a correlation-based algorithm and a message passing (MP) algorithm to detect the pilot and data alternately. Simulation results show that the proposed scheme has a significantly lower PAPR in the time domain and superior channel estimation performance.

As the communication demand increases and base stations (BSs) are deployed more densely, the traditional fiber backhaul will increase the cost of network construction and limit the scalability of the network. Especially for mountainous areas and suburban rural areas, using wireless backhaul links instead of the traditional fiber backhaul is an attractive scheme. Compared with traditional integrated access and backhaul (IAB) networks, this paper studies resource allocation in two-hop IAB networks based on layer division multiplexing (LDM) and multi-connection (MC). IAB-donor obtains data from the core network (CN) and transmits it to the corresponding UE groups through the two-hop IAB network. Since the resource allocation problem is intractable by conventional algorithms, a distributed and parameterized framework is proposed. Simulation results show that the proposed two-hop IAB resource allocation algorithm based on LDM and MC can achieve higher capacity than traditional IAB networks.

Non-Terrestrial Networks (NTNs) are widely recognized as a potential solution to achieve ubiquitous connections of Narrow Bandwidth Internet of Things (NB-IoT). In order to adopt NTNs in NB-IoT, one of the main challenges is the uplink synchronization of Narrowband Physical Random Access procedure which refers to the estimation of time of arrival (ToA) and carrier frequency offset (CFO). Due to the large propagation delay and Doppler shift in NTNs, traditional estimation methods for Terrestrial Networks (TNs) can not be applied in NTNs directly. In this context, we design a two stage ToA and CFO estimation scheme including coarse estimation and fine estimation based on abrupt change point detection (CPD) of phase series with machine learning. Our method achieves high estimation accuracy of ToA and CFO under the low signal-noise ratio (SNR) and large Doppler shift conditions and extends the estimation range without enhancing Random Access preambles.