arXiv subjects

Ying-Chang Liang

Publications and source records attributed to Ying-Chang Liang.

At least 19 recordsLinked to original sources

arXiv2026

Polarization-Aware Rotatable Antennas for RIS-Empowered Symbiotic Radios

This paper investigates a dual-polarized reconfigurable intelligent surface (DP-RIS)-empowered symbiotic radio (SR) system with rotatable antennas (RAs). By reconfiguring antenna orientations, RAs can steer their radiation patterns toward desired directions, thereby effectively mitigating the double-fading effect in RIS-assisted cascaded links. However, antenna rotation not only changes the radiation direction but also alters the local polarization bases, which may result in polarization mismatch and degrade the achievable gain. This motivates a joint spatial-polarization design that simultaneously exploits directional radiation gain and polarization matching. Specifically, we formulate a transmit power minimization problem that jointly optimizes digital beamforming, RA rotations, transceiver polarization states, and DP-RIS phase shifts, subject to the primary and secondary rate requirements as well as interference temperature constraints for non-SR users. To solve this non-convex problem, we develop an alternating optimization algorithm that integrates semidefinite programming, difference-of-convex programming, and Riemannian conjugate gradient methods. Moreover, to reduce hardware and computational complexity for practical deployment, we propose two low-complexity RA designs, namely a subarray-wise shared rotation design and a discrete rotation codebook design. Simulation results show that the proposed polarization-aware RA design significantly reduces the required transmit power compared with fixed-orientation and polarization-unaware benchmark schemes. Moreover, both the proposed low-complexity RA designs achieve comparable performance with reduced rotation complexity.

eess.SP↗

arXiv2026

Simultaneous Indoor and Outdoor Coverage with Conformal Intelligent Omni-Surface

Seamless indoor--outdoor coverage conventionally relies on coordinated outdoor macro cells and indoor small cells, incurring high deployment and backhaul costs. Intelligent omni-surfaces (IOSs) provide a promising alternative by enabling simultaneous reflection and transmission across building boundaries. Enabled by recent advances in conformal antennas, conformal IOSs (CIOSs) mounted on building facades offer a flexible and deployment-friendly solution for integrated indoor--outdoor coverage. This paper develops a stochastic-geometry framework to evaluate the downlink performance of CIOS-assisted networks serving both indoor and outdoor users. Specifically, a building-dependent spatial model is established where randomly located buildings are modeled via a Boolean scheme with CIOSs mounted on their facades. Unlike planar IOS deployment, conformal integration introduces geometry dependent visibility constraints, which are captured through the effective numbers of participating elements defined by visible arc metrics for reflection and transmission. Incorporating these constraints, coverage probability expressions are derived under three CIOS operating protocols. Numerical results show that, compared with planar IOS, CIOS improves the successful connection probabilities of the reflected and transmitted links by up to 75% and 125%, respectively, while the overall coverage gain remains dependent on blockage conditions because curvature reduces the effective serving aperture.

eess.SP↗

arXiv2026

Mixture-of-Experts Diffusion Models for Adaptive Massive MIMO Channel Estimation via Variational Bayesian Inference

Channel estimation is essential to massive multiple-input multiple-output (MIMO) systems. While recent generative model-based approaches using lightweight diffusion models (DMs) have achieved superior performance, they typically rely on a single data-driven prior, which limits their adaptability to varying channel distributions in real-world scenarios. To address this deficiency, we propose a mixture-of-experts (MoE) diffusion model (DM) framework combined with variational Bayesian inference. Specifically, our approach employs multiple pre-trained DMs, with each trained on a specific type of propagation channels. We then propose a probabilistic graphical model in which the channel is modeled as a latent variable drawn from one of these candidate generative priors with a certain probability. By integrating variational Bayesian inference with DM-based data priors, the underlying channel along with the expert indicator variable are jointly inferred, thus enabling automatic model adaptation for channel estimation. The effectiveness of our approach is evaluated on 3GPP CDL channels. Simulation results demonstrate that our proposed approach achieves a clear performance improvement over the standard DM-based method that employs a single prior trained on aggregated data from all channel types, particularly when the channel samples from different propagation environments are imbalanced.

eess.SP↗

arXiv2026

TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks

Adapting large AI models (LAMs) to personalized edge data is challenging because wireless devices have limited memory, computation, and uplink capacity. Federated fine-tuning preserves data privacy but still requires each device to host the full model, while split learning reduces device memory at the cost of heavy activation transmission. This paper proposes TSFLora, a token-compressed split fine-tuning framework for communication-efficient LAM adaptation at the edge. TSFLora combines attention-guided token selection, token merging, low-bit activation quantization, and LoRA-based adaptation within a split federated training pipeline. The key idea is to compress the intermediate token sequence before transmission so that the system reduces both uplink traffic and server-side processing without changing the frozen backbone. Experiments on ViT models over CIFAR-10, CIFAR-100, and TinyImageNet show that TSFLora achieves up to \textbf{6.8$\times$} communication reduction and \textbf{41\%} memory saving while maintaining competitive accuracy.

cs.DC↗

arXiv2026

Collaborative Spectrum Sensing in Cognitive and Intelligent Wireless Networks: An Artificial Intelligence Perspective

Artificial intelligence (AI) has become a key enabler for next-generation wireless communication systems, offering powerful tools to cope with the increasing complexity, dynamics, and heterogeneity of modern wireless environments. To illustrate the role and impact of AI in wireless communications, this paper takes collaborative spectrum sensing (CSS) in cognitive and intelligent wireless networks as a representative application and surveys recent advances from an AI perspective. We first introduce the fundamentals of CSS, including the general framework, classical detector design, fusion strategies and evaluation metrics. Then, we present an overview of the state-of-the-art research on AI-driven CSS, classified into three categories according to learning paradigms: discriminative deep learning (DL), generative DL models, and deep reinforcement learning (DRL). Building on this, we explore AI-empowered semantic communication (SemCom) as a paradigm-shifting solution for CSS. By extracting and transmitting task-relevant features, SemCom upgrades CSS from a computation-centric approach to a highly efficient joint communication and computation framework. Both single-user and multi-user SemCom scenarios are elaborated in detail. Finally, we discuss limitations, open challenges, and future research directions at the intersection of AI and wireless communication.

eess.SP↗

arXiv2026

On the Capacity Region of Additive-Multiplicative MAC with Heterogeneous Input Constraints

This paper characterizes the capacity region of a two-user additive-multiplicative multiple access channel (AM-MAC) under heterogeneous input constraints. This model captures the fundamental limits of symbiotic radio, where an active primary transmitter (PT) conveys information via active transmission subject to an average power constraint, while a passive backscatter device (BD) modulates signals through backscattering under a peak amplitude constraint. Our main results are threefold. Firstly, we prove that the sum-rate capacity equals the PT's point-to-point capacity, achieved when the PT employs Gaussian signaling and the BD acts as a pure reflector to assist the PT's transmission. Secondly, to achieve the BD's maximum achievable rate, the PT must adopt a constant-envelope signaling strategy, while the optimal BD distribution exhibits a concentric-circle structure with a uniform phase. Thirdly, for the remaining boundary points, we establish that the optimal PT signal consists of a continuous uniform phase and a discrete amplitude, whereas the optimal BD distribution is fully discrete. Finally, numerical results are provided to characterized the capacity region by solving a specialized nonlinear optimization problem. To demonstrate the practical implications, we also characterize an baseline rate pair and evaluate the overall performance of the AM-MAC.

cs.IT↗

arXiv2026

Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study

Vehicular edge intelligence (VEI) is vital for future intelligent transportation systems. However, traditional centralized learning in dynamic vehicular networks faces significant communication overhead and privacy risks. Split federated learning (SFL) offers a distributed solution but is often hindered by substantial communication bottlenecks from transmitting high-dimensional intermediate features and can present label privacy concerns. Semantic communication offers a transformative approach to alleviate these communication challenges in SFL by focusing on transmitting only task-relevant information. This paper leverages the advantages of semantic communication in the design of SFL, and presents a case study the semantic communication-enhanced U-Shaped split federated learning (SC-USFL) framework that inherently enhances label privacy by localizing sensitive computations with reduced overhead. It features a dedicated semantic communication module (SCM), with pre-trained and parameter-frozen encoding/decoding units, to efficiently compress and transmit only the task-relevant semantic information over the critical uplink path from vehicular users to the edge server (ES). Furthermore, a network status monitor (NSM) module enables adaptive adjustment of the semantic compression rate in real-time response to fluctuating wireless channel conditions. The SC-USFL framework demonstrates a promising approach for efficiently balancing communication load, preserving privacy, and maintaining learning performance in resource-constrained vehicular environments. Finally, this paper highlights key open research directions to further advance the synergy between semantic communication and SFL in the vehicular network.

cs.LG↗

arXiv2026

Polarization-aware Reconfigurable Antenna Aided Wireless Communications

Reconfigurable antennas (RAs) have emerged as a promising technology for future wireless networks, offering additional flexibility for wireless communications. Among existing designs, rotatable antennas are particularly effective in improving directional gain via boresight alignment only. However, conventional rotatable RAs often overlook a critical physical coupling: the mechanical rotation inevitably alters the radiated polarization orientation, potentially leading to polarization mismatch. To address this challenge, we investigate a novel RA architecture that simultaneously supports 3D rotation and polarization state reconfiguration, ensuring alignment in both spatial and polarization domains. To quantify the performance gains, we analyze a simplified single-user LoS scenario to compare the optimized rotatable design against a fixed scheme. This analysis attributes the performance improvement to three aspects: directional and projection gain arising from boresight steering, polarization direction alignment gain enabled by roll adjustment, and polarization state matching gain provided by polarization reconfiguration. Furthermore, for general multipath multi-user systems, we formulate a joint power minimization problem by optimizing digital beamforming alongside rotation and polarization designs, subject to rate and hardware constraints. To solve the resulting non-convex problem efficiently, we develop an alternating optimization framework, where the digital beamforming is solved via semidefinite relaxation and difference-of-convex techniques, while the rotation and polarization designs are updated using Riemannian conjugate gradient on their respective manifolds. Simulation results demonstrate that the proposed RA outperforms both rotation-only and boresight-only benchmarks, achieving lower transmit power under the same rate constraints by joint spatial-polarization design.

eess.SP↗

arXiv2025

Noncoherent Detection of Constant-Envelope Signals for Mobile Edge Applications -- Optimum Detectors and Intelligent Decision Rule

Constant-envelope signals are widely used in mobile edge applications and wireless communication systems for their hardware-friendly design, energy efficiency, and reliability. However, reliable detection with simple, power-efficient receivers remains challenging. Coherent methods offer superior performance but require complex synchronization, increasing complexity and power use. Noncoherent detection is simpler, avoiding synchronization, but traditional approaches rely on in-phase and quadrature-phase (IQ) demodulators for signal magnitudes and assume energy detectors without theoretical justification. This paper proposes a framework for optimal detection using a bandpass-filter envelope-detector (BFED) with Bayes criterion and generalized likelihood ratio test (GLRT) under unknown amplitudes. Using modified Bessel function approximations, we show the optimal detector shifts based on SNR: in the low-SNR regime, we rigorously prove for the first time that the well-known energy detector (ED) is the Bayesian-optimal solution, thus providing a firm theoretical foundation for its widespread use; in high-SNR regimes, a novel amplitude detector (AD) compares estimated amplitude to noise deviation, leading to a simple yet optimal detection strategy. For unknown SNR, a reliability-based intelligent decision (RID) rule adaptively selects detectors, leveraging their strengths across SNR ranges. Simulations confirm energy and amplitude detectors minimize errors in their domains, with RID providing robust gains. The proposed framework provides a rigorous theoretical foundation and enables low-complexity implementations for resource-constrained, interference-limited mobile edge applications, including wireless sensor networks (WSNs) and Internet of Things (IoT) systems.

eess.SP↗

arXiv2025

Communication-Pipelined Split Federated Learning for Foundation Model Fine-Tuning in UAV Networks

Deploying foundation models (FMs) on uncrewed aerial vehicles (UAVs) promises broad ``low-altitude economy'' applications. Split federated learning (SFL)-based fine-tuning leverages distributed data while keeping raw data local and reduces client-side burden by partitioning the model between client and server. However, the per-round training latency is dominated by stragglers. Training paradigms featuring parallel gradient transmission (GT) allocate dedicated portions of downlink communication resources to each client. They may leave resources idle and suffer from prolonged GT latency, especially in UAV networks, where the communication latency typically far exceeds the computation latency. To address this, we propose a sequential GT paradigm, where the server dedicates all downlink resources for the current GT. We further propose communication-pipelined SFL (CPSFL), characterized by downlink GT priority scheduling and intra-round asynchronous training. We investigate CPSFL-based LoRA fine-tuning of FMs in UAV networks and formulate an optimization problem to minimize a weighted sum of per-round training latency and worst-case client energy consumption by optimizing the split point selection (SPS) and the computing and communication resource allocation (CCRA) (the uplink bandwidth allocation and the server computing frequency allocation). To solve this problem, we develop an attention-based deep reinforcement learning (DRL) framework, where the base station agent decides the split point and the CCRA in each round by leveraging previous round information, including UAV trajectories. Simulation results show that the proposed DRL-based CPSFL scheme outperforms the parallel GT benchmarks, the ablation variants, the fixed CCRA scheme, while approaching the best fixed-SPS scheme.

cs.IT↗

arXiv2025

Deep Neural Network-Driven Adaptive Filtering

This paper proposes a deep neural network (DNN)-driven framework to address the longstanding generalization challenge in adaptive filtering (AF). In contrast to traditional AF frameworks that emphasize explicit cost function design, the proposed framework shifts the paradigm toward direct gradient acquisition. The DNN, functioning as a universal nonlinear operator, is structurally embedded into the core architecture of the AF system, establishing a direct mapping between filtering residuals and learning gradients. The maximum likelihood is adopted as the implicit cost function, rendering the derived algorithm inherently data-driven and thus endowed with exemplary generalization capability, which is validated by extensive numerical experiments across a spectrum of non-Gaussian scenarios. Corresponding mean value and mean square stability analyses are also conducted in detail.

stat.ML↗

arXiv2025

Satellite-Assisted Low-Altitude Economy Networking: Concepts, Applications, and Opportunities

The low-altitude economy (LAE) is a new economic paradigm that leverages low-altitude vehicles (LAVs) to perform diverse missions across diverse areas. To support the operations of LAE, it is essential to establish LAE networks that enable LAV management and communications.Existing studies mainly reuse terrestrial networks to construct LAE networks. However, the limited coverage of terrestrial networks poses challenges for serving LAVs in remote areas. Besides, efficient LAV operations also require support such as localization and navigation, which terrestrial networks designed for communications cannot fully provide. Due to ubiquitous coverage and diverse functions, satellites are a promising technology to support LAVs. Therefore, this article investigates satellite-assisted LAE networking. First, we introduce an overview of LAE and satellites, discussing their features, applications, and architectures. Next, we investigate opportunities for satellites to assist LAE from aspects of communication, control, and computation. As all assistance depends on reliable satellite-LAV communications, we propose a satellite-assisted LAE framework to tackle issues caused by the severe path loss and high dynamics in satellite-assisted LAE networks.The case study demonstrates that the distributed MIMO architecture efficiently reduces the required transmission power and extends service duration, while the two-timescale optimization scheme balances the performance and control signaling overheads. Specifically, the proposed framework comprises distributed satellite MIMO, distributed LAV MIMO, and a two-timescale optimization scheme.

cs.NI↗

arXiv2025

Near-Field Multiuser Localization Based on Extremely Large Antenna Array with Limited RF Chains

Extremely large antenna array (ELAA) not only effectively enhances system communication performance but also improves the sensing capabilities of communication systems, making it one of the key enabling technologies in 6G wireless networks. This paper investigates the multiuser localization problem in an uplink Multiple Input Multiple Output (MIMO) system, where the base station (BS) is equipped with an ELAA to receive signals from multiple single-antenna users. We exploit analog beamforming to reduce the number of radio frequency (RF) chains. We first develop a comprehensive near-field ELAA channel model that accounts for the antenna radiation pattern and free space path loss. Due to the large aperture of the ELAA, the angular resolution of the array is high, which improves user localization accuracy. However, it also makes the user localization problem highly non-convex, posing significant challenges when the number of RF chains is limited. To address this issue, we use an array partitioning strategy to divide the ELAA channel into multiple subarray channels and utilize the geometric constraints between user locations and subarrays for probabilistic modeling. To fully exploit these geometric constraints, we propose the array partitioning-based location estimation with limited measurements (APLE-LM) algorithm based on the message passing principle to achieve multiuser localization. We derive the Bayesian Cramer-Rao Bound (BCRB) as the theoretical performance lower bound for our formulated near-field multiuser localization problem. Extensive simulations under various parameter configurations validate the proposed APLE-LM algorithm. The results demonstrate that APLE-LM achieves superior localization accuracy compared to baseline algorithms and approaches the BCRB at high signal-to-noise ratio (SNR).

eess.SP↗

arXiv2025

A Derivative-Free Position Optimization Approach for Movable Antenna Multi-User Communication Systems

Movable antennas (MAs) have emerged as a disruptive technology in wireless communications for enhancing spatial degrees of freedom through continuous antenna repositioning within predefined regions, thereby creating favorable channel propagation conditions. In this paper, we study the problem of position optimization for MA-enabled multi-user MISO systems, where a base station (BS), equipped with multiple MAs, communicates with multiple users each equipped with a single fixed-position antenna (FPA). To circumvent the difficulty of acquiring the channel state information (CSI) from the transmitter to the receiver over the entire movable region, we propose a derivative-free approach for MA position optimization. The basic idea is to treat position optimization as a closed-box optimization problem and calculate the gradient of the unknown objective function using zeroth-order (ZO) gradient approximation techniques. Specifically, the proposed method does not need to explicitly estimate the global CSI. Instead, it adaptively refines its next movement based on previous measurements such that it eventually converges to an optimum or stationary solution. Simulation results show that the proposed derivative-free approach is able to achieve higher sample and computational efficiencies than the CSI estimation-based position optimization approach, particularly for challenging scenarios where the number of multi-path components (MPCs) is large or the number of pilot signals is limited.

eess.SP↗

arXiv2025

Wireless large AI model: shaping the AI-empowered future of 6G and beyond

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is a wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, explaining its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges and discuss pivotal future research directions.

cs.IT↗

arXiv2025

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning

Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations across various industries. Yet, their practical deployment on resource-limited mobile edge devices is hindered by critical challenges such as data privacy, constrained resources, and high overhead costs. Addressing this gap, this paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM). By partitioning the training load between edge devices and servers using a split learning paradigm, SFLAM can facilitate the operation of large models on devices and significantly lowers the memory requirements on edge devices. Additionally, SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency while concurrently reducing energy consumption and communication latency. A theoretical analysis exploring the latency-energy trade-off is presented, and the framework's efficacy is validated via comprehensive simulations. The findings indicate that SFLAM achieves superior performance in terms of learning efficiency and scalability compared to conventional methods, thereby providing a valuable approach for enabling advanced AI services in resource-constrained scenarios.

cs.LG↗

arXiv2025

AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions

Federated learning (FL) can fully leverage large-scale terminal data while ensuring privacy and security, and is considered as a distributed alternative for the centralized machine learning. However, the issue of data heterogeneity poses limitations on FL's performance. To address this challenge, artificial intelligence-generated content (AIGC) which is an innovative data synthesis technique emerges as one potential solution. In this article, we first provide an overview of the system architecture, performance metrics, and challenges associated with AIGC-assistant FL system design. We then propose the Generative federated learning (GenFL) architecture and present its workflow, including the design of aggregation and weight policy. Finally, using the CIFAR10 and CIFAR100 datasets, we employ diffusion models to generate dataset and improve FL performance. Experiments conducted under various non-independent and identically distributed (non-IID) data distributions demonstrate the effectiveness of GenFL on overcoming the bottlenecks in FL caused by data heterogeneity. Open research directions in the research of AIGC-assisted FL are also discussed.

cs.LG↗

arXiv2025

User Association and Coordinated Beamforming in Cognitive Aerial-Terrestrial Networks: A Safe Reinforcement Learning Approach

Cognitive aerial-terrestrial networks (CATNs) offer a solution to spectrum scarcity by sharing spectrum between aerial and terrestrial networks. However, aerial users (AUs) experience significant interference from numerous terrestrial base stations (BSs). To alleviate such interference, we investigate a user association and coordinated beamforming (CBF) problem in CATN, where the aerial network serves as the primary network sharing its spectrum with the terrestrial network. Specifically, we maximize the sum rate of the secondary terrestrial users (TUs) under the interference temperature constraints of the AUs. Traditional iterative optimization schemes are impractical due to their high computational complexity and information exchange overhead. Although deep reinforcement learning (DRL) based schemes can address these challenges, their performance is sensitive to the weights of the weighted penalty terms for violating constraints in the reward function. Motivated by these issues, we propose a safe DRL-based user association and CBF scheme for CATN, eliminating the need for training multiple times to find the optimal penalty weight before actual deployment. Specifically, the CATN is modeled as a networked constrained partially observable Markov game. Each TU acts as an agent to choose its associated BS, and each BS acts as an agent to decide its beamforming vectors, aiming to maximize the reward while satisfying the safety constraints introduced by the interference constraints of the AUs. By exploiting a safe DRL algorithm, the proposed scheme incurs lower deployment expenses than the penalty-based DRL schemes since only one training is required before actual deployment. Simulation results show that the proposed scheme can achieve a higher sum rate of TUs than a two-stage optimization scheme while the average received interference power of the AUs is generally below the threshold.

cs.IT↗