arXiv subjects

Dazhi He

Publications and source records attributed to Dazhi He.

At least 19 recordsLinked to original sources

arXiv2026

Spatial Semantic Communication: When Semantic Transmission Meets Index Modulation

Current digital semantic communication systems have primarily focused on maintaining compatibility with conventional constellation-based modulation. In contrast, index modulation (IM) represents a more spectrally and energy-efficient alternative by exploiting additional dimensions for information conveyance. Recognizing this potential, this paper bridges the gap between IM and semantic communications by proposing a novel spatial semantic communication (SSC) system leveraging cutting-edge fluid antenna-IM (FA-IM) technology. Compatible with existing joint source-channel coding (JSCC) architectures, the proposed SSC system employs the residual quantization (RQ) approach to discretize analog semantic features for subsequent digital IM transmission. Notably, the proposed SSC system synergizes RQ and IM via a semantic-aware stream splitting scheme, which ensures that critical semantic information undergoes less severe channel fading, thereby further optimizing semantic transmission performance. Simulation results validate that the proposed SSC system effectively integrates the high fidelity of RQ, the reliability of semantic-aware splitting, and the spatial efficiency of FA-IM, thereby providing a robust solution for future digital semantic transmission. The open source code is available at: https://github.com/gxh1106/SSC.

cs.IT↗

arXiv2026

MCRB and MSE Analysis for Parameter Estimation in AFDM-ISAC Systems

Affine frequency division multiplexing (AFDM) is a promising waveform for integrated sensing and communication (ISAC). In AFDM systems, the complex gains, delays, and Doppler shifts are commonly estimated from the AFDM symbols carrying pilots and data simultaneously. In practice, however, the unknown data symbols and data-pilot coupling interference may render the estimator mismatched to the true signal model. In this paper, we systematically characterize the parameter-estimation performance of AFDM-ISAC systems under practical model misspecification. The main contributions are threefold. First, we extend the Cram\'er-Rao bound (CRB) for a general observation model that treats the data symbols as unknown, which generalizes existing AFDM CRB analyses and serves as the matched benchmark for the subsequent analysis. Second, we identify two practical sources of misspecification, namely a covariance mismatch caused by insufficient pilot-data isolation and a combined covariance-and-mean mismatch caused by sequential single-target estimation, and derive the corresponding misspecified CRB (MCRB). Third, we characterize the pseudotrue parameters under different levels of prior knowledge, analyze the resulting estimation bias, and establish a lower bound (LB) on the mean square error (MSE). Simulation results validate the derived bounds and show that, under model misspecification, the CRB is overly optimistic while the MCRB and LB faithfully characterize the achievable accuracy. The comparison further reveals how these bounds vary with the pilot length and pilot power, providing useful guidance for pilot configuration.

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arXiv2026

Low-Complexity Hybrid Precoding for Cell-Free Massive MU-MIMO ISAC Systems

Integrated sensing and communication (ISAC) in cell-free (CF) massive multi-user multiple-input multiple-output (MU-MIMO) system is a promising architecture for high-rate communications and high-accuracy multi-target sensing. However, centralized coordination among distributed access points (APs) incurs substantial fronthaul overhead and computation complexity. This paper proposes a low-complexity hybrid precoding framework for CF massive MU-MIMO ISAC systems with partially-connected architectures at the APs. By applying hybrid architecture at the APs, the proposed framework converts the original high-dimensional channel information into a low-dimensional effective channel, enabling digital precoding over the compressed channel domain and thereby substantially reducing both fronthaul overhead and baseband computational complexity. We formulate the joint hybrid precoding design as an ergodic sum-rate (ESR) maximization problem with position error bound (PEB) constraints to ensure multi-target sensing accuracy. An efficient alternating optimization (AO)-based solver is then developed, where the PEB constraint is reformulated into tractable convex constraints, while the digital-domain optimization is carried out over the reduced-dimensional effective channel and the analog precoding is refined on the constant-modulus manifold. For dynamic user topology, we further propose multi-branch (MB) rate-splitting (RS) minimum mean-square-error Tomlinson-Harashima precoding (MMSE-THP) update algorithm that combines multi-branch ordering with recursive MMSE-THP matrix updates, enabling common and private digital precodings to be refreshed without repeated full matrix recomputation. Simulation results demonstrate that the proposed scheme achieves high ESR and accurate multi-target sensing while reducing computational complexity by 87.02\% compared with conventional baselines.

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arXiv2026

Scalable GNN-Based Power Allocation for Rate-Splitting Cell-Free Massive MIMO Systems

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.

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arXiv2026

Phased Ultra Massive Array (PUMA)

This paper proposes a novel multiple-access framework, termed the phased ultra massive antenna array (PUMA), which exploits the distinctive spatial flexibility of fluid antenna systems (FAS) at the user equipment (UE). Building upon fluid antenna multiple access (FAMA) and compact ultra-massive antenna array (CUMA), PUMA incorporates a phased array for signal aggregation. This architecture enables the UE to inherently mitigate co-user interference within the spatial domain without necessitating channel state information (CSI) for precoding at the base station (BS) or complex interference cancellation at each UE. A primary advantage of PUMA lies in its hardware efficiency: by implementing phase shifting and signal combining in the analog domain, it achieves high antenna gain while requiring only a minimal number of radio-frequency (RF) chains, potentially a single RF chain. Comprehensive theoretical analysis of the achievable data rate is provided, complemented by extensive simulations that validate the framework. The results demonstrate that PUMA markedly outperforms FAMA and CUMA architectures, particularly for UEs with a single RF chain, offering a robust and scalable solution for interference-insensitive massive connectivity in sixth-generation (6G) systems.

cs.IT↗

arXiv2026

Low-Complexity Soft-Feedback Detector for AFDM Systems

Affine frequency division multiplexing (AFDM), an emerging multi-carrier modulation scheme, has garnered significant attention due to its resilience to Doppler shifts and capability to achieve full diversity in doubly dispersive channels. However, existing data detection algorithms for AFDM systems face a significant trade-off between computational complexity and accuracy. In this paper, a novel low-complexity data detection scheme, termed the soft-feedback detector (SFD), is proposed. Particularly, building upon a maximum ratio combining (MRC) estimator framework, the SFD leverages the a priori symbol distribution to mitigate error propagation during iterative detection. Specifically, soft-decision feedback is incorporated as extrinsic information derived from the log-likelihood ratios of the transmitted symbols. As a result, the proposed detector significantly enhances detection accuracy while maintaining low computational complexity. Simulation results demonstrate that the SFD consistently outperforms benchmark decision-feedback detectors. In particular, compared with the conventional MRC detector, the proposed scheme achieves approximately a 3 dB signal-to-noise ratio (SNR) gain at the bit error rate (BER) of $10^{-3}$.

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arXiv2026

Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models

Reconfigurable intelligent surface (RIS) has been recognized as a promising technology for next-generation wireless communications. However, the performance of RIS-assisted systems critically depends on accurate channel state information (CSI). To address this challenge, this letter proposes a novel channel estimation method for RIS-aided millimeter-wave (mmWave) systems based on diffusion models (DMs). Specifically, the forward diffusion process of the original signal is formulated to model the received signal as a noisy observation within the framework of DMs. Subsequently, the channel estimation task is formulated as the reverse diffusion process, and a sampling algorithm based on denoising diffusion implicit models (DDIMs) is developed to enable effective inference. Furthermore, a lightweight neural network, termed BRCNet, is introduced to replace the conventional U-Net, significantly reducing the number of parameters and computational complexity. Extensive experiments conducted under various scenarios demonstrate that the proposed method consistently outperforms existing baselines.

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arXiv2025

MA-Aided Hierarchical Hybrid Beamforming for Multi-User Wideband Beam Squint Mitigation

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\%.

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arXiv2025

Preconditioned Conjugate Gradient for MIMO-AFDM System

Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high mobility communications. A significant challenge in MIMO-AFDM systems is the multi-user interference (MUI), which can be effectively addressed by employing precoding techniques. However, the complexity introduced by AFDM makes the precoding process computationally expensive and challenging. To overcome this issue, We combine AFDM channel sparse property and using Preconditioned Conjugate Gradient (PCG) method to iteratively process the precoding, thereby reducing the complexity of the precoding design. Simulation results demonstrate that the proposed sparsification approach, coupled with the PCG method, achieving quite precoding performance while significantly reducing computational complexity. This makes the application of AFDM more feasible and efficient for high-mobility communication scenarios, paving the way for its broader implementation in next-generation communication systems.

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arXiv2025

Fluid Antenna-Empowered Receive Spatial Modulation

Fluid antenna (FA), as an emerging antenna technology, fully exploits spatial diversity. This paper integrates FA with the receive spatial modulation (RSM) scheme and proposes a novel FA-empowered RSM (FA-RSM) system. In this system, the transmitter is equipped with an FA that simultaneously activates multiple ports to transmit precoded signals. We address three key challenges in the FA-RSM system: port selection, theoretical analysis, and detection. First, for port selection, an optimal algorithm from a capacity maximization perspective are proposed, followed by two low-complexity alternatives. Second, for theoretical analysis, performance evaluation metrics are provided for port selection, which demonstrate that increasing the number of activated ports enhances system performance. Third, regarding detection, two low-complexity detectors are proposed. Simulation results confirm that the FA-RSM system significantly outperforms the conventional RSM system. The proposed low-complexity port selection algorithms facilitate minimal performance degradation. Moreover, while activating additional ports improves performance, the gain gradually saturates due to inherent spatial correlation, highlighting the importance of effective port selection in reducing system complexity and cost. Finally, both proposed detectors achieve near-optimal detection performance with low computational complexity, emphasizing the receiver-friendly nature of the FA-RSM system.

cs.IT↗

arXiv2025

ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probablity. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of $20$ dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB.

cs.IT↗

arXiv2025

Movable Antenna Aided Full-Duplex ISAC System with Self-Interference Mitigation

Movable antenna (MA) has shown significant potential for improving the performance of integrated sensing and communication (ISAC) systems. In this paper, we model an MA-aided ISAC system operating in a communication full-duplex mono-static sensing framework. The self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. We develop an optimization problem to maximize the weighted sum of downlink and uplink communication rates alongside the mutual information relevant to the sensing task. To address this highly non-convex problem, we employ the fractional programming (FP) method and propose an alternating optimization (AO)-based algorithm that jointly optimizes the beamforming, user power allocation, and antenna positions at the transceivers. Given the sensitivity of the AO-based algorithm to the initial antenna positions, a PSO-based algorithm is proposed to explore superior sub-optimal antenna positions within the feasible region. Numerical results indicate that the proposed algorithms enable the MA system to effectively leverage the antenna position flexibility for accurate beamforming in a complex ISAC scenario. This enhances the system's self-interference cancellation (SIC) capabilities and markedly improves its overall performance and reliability compared to conventional fixed-position antenna designs.

cs.IT↗

arXiv2025

AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model

Designing a 6G-oriented universal model capable of processing multi-modal data and executing diverse air interface tasks has emerged as a common goal in future wireless systems. Building on our prior work in communication multi-modal alignment and telecom large language model (LLM), we propose a scalable, task-aware artificial intelligence-air interface multi-modal universal model (AI2MMUM), which flexibility and effectively perform various physical layer tasks according to subtle task instructions. The LLM backbone provides robust contextual comprehension and generalization capabilities, while a fine-tuning approach is adopted to incorporate domain-specific knowledge. To enhance task adaptability, task instructions consist of fixed task keywords and learnable, implicit prefix prompts. Frozen radio modality encoders extract universal representations and adapter layers subsequently bridge radio and language modalities. Moreover, lightweight task-specific heads are designed to directly output task objectives. Comprehensive evaluations demonstrate that AI2MMUM achieves SOTA performance across five representative physical environment/wireless channel-based downstream tasks using the WAIR-D and DeepMIMO datasets.

cs.LG↗

arXiv2025

Joint Antenna Position and Beamforming Optimization with Self-Interference Mitigation in MA-ISAC System

Movable antennas (MAs) have demonstrated significant potential in enhancing the performance of integrated sensing and communication (ISAC) systems. However, the application in the integrated and cost-effective full-duplex (FD) monostatic systems remains underexplored. To address this research gap, we develop an MA-ISAC model within a monostatic framework, where the self-interference channel is modeled in the near field and characterized by antenna position vectors. This model allows us to investigate the use of MAs with the goal of 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 overcome this, we employ fractional programming (FP) to propose an alternating optimization (AO) algorithm that jointly optimizes the beamforming and antenna positions for both 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 search (CFGS) approach is employed to determine the 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.

cs.IT↗

arXiv2025

Addressing the Curse of Scenario and Task Generalization in AI-6G: A Multi-Modal Paradigm

Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm...

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arXiv2025

Fluid Antenna-Aided Robust Secure Transmission for RSMA-ISAC Systems

This paper leverages fluid antenna (FA) and rate-splitting multiple access (RSMA) to enhance the physical layer security (PLS) of an integrated sensing and communication (ISAC) system. We consider a practical multi-user multi-input single-output (MU-MISO) system, where a base station (BS) equipped with fixed position antennas (FPAs) employs RSMA to communicate with multiple single-FA users, while an eavesdropping target may potentially wiretap the signals. The system adopts a novel rate splitting (RS) scheme, where the common layer stream serves a dual purpose: it conveys valid data to legitimate users (LUs) while simultaneously generating jamming signals to confuse potential eavesdroppers. We establish the problem and propose the optimization algorithm under two conditions: perfect and imperfect channel state information (CSI) conditions. Specifically, under perfect the CSI condition, we address the non-convex optimization problem by proposing an alternating optimization (AO) algorithm, which decomposes the problem into two subproblems: beamforming matrix optimization and the adjustment of FA positions. For beamforming optimization, we utilize semidefinite programming (SDP) and successive convex approximation (SCA) to convert the problem into a more tractable convex form. Given a fixed beamforming matrix, SCA is applied to handle the surrogate upper bound of the constraints. In the case of imperfect CSI, the continuous nature of CSI errors leads to an infinite number of constraints. To overcome this challenge, we propose an AO-based algorithm that incorporates the S-Procedure and SCA to obtain a high-quality beamforming matrix and effective FA positions. Extensive simulation results demonstrate that the proposed FA-aided RSMA-ISAC system significantly enhances security compared to traditional FPA-based and SDMA-based systems.

cs.IT↗

arXiv2025

Efficient Precoding in XL-MIMO-AFDM System

This paper explores the potential of affine frequency division multiplexing (AFDM) to mitigate the multiuser interference (MUI) problem by employing time-domain precoding in extremely-large-scale multiple-input multiple-output (XL-MIMO) systems. In XL-MIMO systems, user mobility significantly improves network capacity and transmission quality. Meanwhile, the robustness of AFDM to Doppler shift is enhanced in user mobility scenarios, which further improves the system performance. However, the multicarrier nature of AFDM has attracted much attention, and it leads to a significant increase in precoding complexity. However, the serious problem is that the multicarrier use of AFDM leads to a sharp increase in precoding complexity. Therefore, we employ efficient precoding randomized Kaczmarz (rKA) to reduce the complexity overhead. Through simulation analysis, we compare the performance of XL-MIMO-AFDM and XL-MIMO orthogonal frequency division multiplexing (XL-MIMO-OFDM) in mobile scenarios, and the results show that our proposed AFDM-based XL-MIMO precoding design can be more efficient.

cs.DC↗

arXiv2025

Downlink OFDM-FAMA in 5G-NR Systems

Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division multiplexing (OFDM) framework, referred to as OFDM-FAMA, and evaluate its performance in broadband multipath channels. We first design the OFDM-FAMA system, taking into account 5G channel coding and OFDM modulation. Then the system's achievable rate is analyzed, and an algorithm to approximate the FAS configuration at each user is proposed based on the rate. Extensive link-level simulation results reveal that OFDM-FAMA can significantly improve the multiplexing gain over the OFDM system with fixed-position antenna (FPA) users, especially when robust channel coding is applied and the number of radio-frequency (RF) chains at each user is small.

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