Two-Stage IQ Imbalance Estimation and Compensation for AFDM Systems

Affine frequency division multiplexing (AFDM) is an emerging chirp-based multicarrier waveform with strong diversity in doubly selective channels, but practical systems suffer from transmitter and receiver IQ imbalance, causing image interference and performance degradation. This paper proposes a two-stage IQ imbalance estimation and compensation method for AFDM systems. First, a preamble-assisted iterative algorithm estimates the time-invariant IQ imbalance parameters by exploiting their slowly time-varying nature. Then, a joint channel estimation and data detection scheme combines basis expansion model (BEM)-based channel estimation with an improved LMMSE detector for interference suppression. Simulations show rapid convergence and near-ideal BER performance.

Performance Analysis of AFDM Under In-Phase and Quadrature Imbalance at Receiver

Affine Frequency Division Multiplexing (AFDM) is a chirp-based multicarrier waveform that achieves full diversity in doubly selective channels while requiring reduced pilot overhead. It is regarded as a highly promising candidate for sixth-generation (6G) mobile communication waveforms in high-mobility scenarios. However, AFDM deployment remains subject to hardware impairments, particularly the in-phase and quadrature (IQ) imbalance commonly encountered in direct conversion transceivers. This paper investigates the impact of receiver IQ imbalance on the bit error rate (BER) performance of AFDM systems. A mathematical model of AFDM under receiver IQ imbalance is first established, where the resulting inter-carrier interference (ICI) in the discrete affine Fourier transform (DAFT) domain is explicitly characterized. Moreover, a closed-form expression for the BER is derived under the influence of receiver IQ imbalance in an M-QAM-AFDM system over an AWGN channel. Numerical simulation results validate the accuracy of the theoretical analysis, while also indicating that under identical IQ imbalance conditions, AFDM exhibits more pronounced BER degradation compared to OFDM. The results provide fundamental insights into the sensitivity of AFDM to receiver IQ imbalance and offer guidance for practical system design.

Rotatable Antenna-Enabled Near-Field Integrated Sensing and Communication

In this paper, we propose leveraging rotatable antennas (RAs) to enhance near-field communication and sensing by exploiting a new orientation-domain spatial degree-of-freedom (DoF) provided by element-wise antenna rotation. Specifically, we investigate an RA-enabled near-field integrated sensing and communication (ISAC) system with sub-connected hybrid beamforming, where each transmit RA can independently adjust its boresight direction under a practical rotation constraint. A spherical-wave channel model incorporating orientation-dependent antenna gains is established to characterize multi-user communication and target sensing in the presence of clutters. Based on this model, a weighted communication-sensing utility maximization problem is formulated by jointly optimizing the receive beamformer, digital beamformer, analog beamformer, and RA boresight directions. To solve the resulting non-convex problem, an alternating optimization algorithm is developed by combining fractional programming, Riemannian optimization, and a spherical-cap Frank--Wolfe-based boresight update. To further understand the impact of RA rotation on near-field sensing, we derive a closed-form root Cramer--Rao bound (RCRB) expression. Simulation results demonstrate the convergence and effectiveness of the proposed algorithm. It is shown that the RA-enabled hybrid design can match or even outperform the fully-digital FPA benchmark in some regimes, indicating that the orientation-domain DoF introduced by element-wise rotation can compensate for limited RF chains. The RCRB and beampattern results further show that RA rotation improves off-broadside sensing accuracy, enhances range-domain focusing, and suppresses same-angle clutters in the near field.

Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition

Standard Self-Supervised Learning (SSL) for Automatic Modulation Recognition (AMR) struggles with ineffective isotropic augmentations, spectral instability, and semantic drift. To address these challenges, we propose Dynamic-Consistency Contrastive Learning (DyCo-CL), a geometry-aware framework that couples Virtual Adversarial Augmentation (VAA) with a semantic consistency loss. We provide a theoretical analysis indicating that this strategy acts as an implicit spectral regularizer for the encoder, enabling stable manifold exploration. Complementing this, our Signal-Adaptive Swin Backbone with fixed-window attention improves structural stability by constraining attention locality, while a Hybrid Knowledge Fusion module anchors representations with physical priors. Experiments on RML benchmarks show that DyCo-CL achieves a 6.27% accuracy gain in 1-shot settings over prior methods.

Score-Based Conditional Flow Models for MIMO Receiver Design with Superimposed Pilots

Accurate channel state information (CSI) is vital for multiple-input multiple-output (MIMO) systems. However, superimposed pilots (SIP), which reduce overhead, introduce severe pilot contamination and data interference, complicating joint channel estimation and data detection. This paper proposes a conditional flow matching receiver (CFM-Rx), an unsupervised generative framework that learns directly from received signals, eliminating the need for labeled data and improving adaptability across diverse system settings. By leveraging flow-based generative modeling, CFM-Rx enables deterministic, low-latency inference and exploits model invertibility to capture the bidirectional nature of signal propagation. This framework unifies flow matching with score-based diffusion modeling via a moment-consistent ordinary differential equation (ODE), replacing stochastic differential equation (SDE) sampling with a deterministic and efficient process. Furthermore, it integrates receiver-side priors to ensure stable, data-consistent inference. Extensive simulation results across various MIMO configurations demonstrate that CFM-Rx consistently outperforms conventional estimators and state-of-the-art data-driven receivers, achieving notable gains in channel estimation accuracy and symbol detection robustness, particularly under severe pilot contamination.

Spectral Efficiency Maximization for DMA-enabled Multiuser MISO with Statistical CSI

Dynamic metasurface antennas (DMAs) offer the potential to achieve large-scale antenna arrays with low power consumption and reduced hardware costs, making them a promising technology for future communication systems. This paper investigates the spectral efficiency (SE) of DMA-enabled multiuser multiple-input single-output (MISO) systems in both uplink and downlink transmissions, using only statistical channel state information (CSI) to maximize the ergodic sum rate of multiple users. For the uplink system, we consider two decoding rules: minimum mean square error (MMSE) with and without successive interference cancellation (SIC). For both decoders, we derive closed-form surrogates to substitute the original expressions of ergodic sum rate and formulate tractable optimization problems for designing DMA weights. Then, a weighted MMSE (WMMSE)-based algorithm is proposed to maximize the ergodic sum rate. For the downlink system, we derive an approximate expression for the ergodic sum rate and formulate a hybrid analog/digital beamforming optimization problem that jointly optimizes the digital precoder and DMA weights. A penalty dual decomposition (PDD)-based algorithm is proposed by leveraging the fractional programming framework. Numerical results validate the accuracy of the derived surrogates and highlight the superiority of the proposed algorithms over baseline schemes. It is shown that these algorithms are effective across various DMA settings and are particularly well-suited for system design in fast time-varying channels.

Uplink Sum Rate Maximization for Pinching Antenna-Assisted Multiuser MISO

This article investigates the application of pinching-antenna systems (PASS) in multiuser multiple-input single-output (MISO) communications. Two sum-rate maximization problems are formulated under minimum mean square error (MMSE) decoding, with and without successive interference cancellation (SIC). To address the joint optimization of pinching antenna locations and user transmit powers, a fractional programming-based approach is proposed. Numerical results validate the effectiveness of the proposed method and show that PASS can significantly enhance uplink sum-rate performance compared to conventional fixed-antenna designs.

DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation

The method of training language models based on domain datasets has obtained significant achievements in the task of generating scientific paper abstracts. However, such models face problems of generalization and expensive training costs. The use of large language models (LLMs) to solve the task of generating paper abstracts saves the cost of model training. However, due to the hallucination problem of LLM, it is often necessary to improve the reliability of the results through multi-round query prompt approach such as Graph of Thoughts (GoT), which also brings additional reasoning costs. In this paper, we propose a Dynamic Graph of Thought (DGoT). It not only inherits the advantages of the existing GoT prompt approach, but also dynamically adjust the graph structure according to data characteristics while reducing model reasoning cost. Experimental results show that our method's cost-effectiveness in abstract generation tasks is only 43.7% to 56.4% of other multi-round query prompt approaches. Our code is available at https://github.com/JayceNing/DGoT.

Sum-Rate Optimization for RIS-Aided Multiuser Communications with Movable Antenna

Reconfigurable intelligent surface (RIS) is known as a promising technology to improve the performance of wireless communication networks, which has been extensively studied. Movable antenna (MA) is a novel technology that fully exploits the antenna position for enhancing the channel capacity. In this paper, we propose a new RIS-aided multiuser communication system with MAs. The sum-rate is maximized by jointly optimizing the beamforming, the reflection coefficient (RC) values of RIS and the positions of MAs. A fractional programming-based iterative algorithm is proposed to solve the formulated non-convex problem, considering three assumptions for the RIS. Numerical results are presented to verify the effectiveness of the proposed algorithm and the superiority of the proposed MA-based system in terms of sum-rate.

Secure Antenna Selection and Beamforming in MIMO Systems

This work proposes a novel joint design for multiuser multiple-input multiple-output wiretap channels. The base station exploits a switching network to connect a subset of its antennas to the available radio frequency chains. The switching network and transmit beamformers are jointly designed to maximize the weighted secrecy sum-rate for this setting. The principal design problem reduces to an NP-hard mixed-integer non-linear programming. We invoke the fractional programming technique and the penalty dual decomposition method to develop a tractable iterative algorithm that effectively approximates the optimal design. Our numerical investigations validate the effectiveness of the proposed algorithm and its superior performance compared with the benchmark.