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Zhi Ding

Publications and source records attributed to Zhi Ding.

At least 19 recordsLinked to original sources

Blockchain-based Proportional Fair Scheduling for Multi-Operator O-RAN

The openness and disaggregation of Open radio access network (O-RAN) facilitate resource sharing and coordination across networks, creating new demands for efficient and trustworthy cross-operator scheduling. However, such scheduling is beyond the scope and capability of conventional proportional fair scheduling (PFS), which lacks mechanisms for establishing trust among independent operators. To fulfill this gap, we propose the blockchain-based proportional fair scheduling (BC-PFS) that enables trustworthy inter-network coordination and resource pooling across operators in O-RAN. Specifically, we design four core smart contracts including registration, status reporting, scheduling, and settlement contracts with corresponding Solidity implementations to ensure trustworthy on-chain execution. Theoretically, to evaluate the BC-PFS performance, we develop an analytical framework to derive the user average throughput via both probabilistic and ordinary differential equation (ODE) approaches, and provide a simplified closed-form solution. Based on the above performance assessment, we quantify the pooling effect in O-RAN achieved through trustworthy cross-operator collaboration via BC-PFS, and point out that this effect grows monotonically in both the numbers of operator networks and users. Simulations validate the theoretical analysis and show the performance of the BC-PFS in O-RAN.

cs.NI

Structured-Sparsity-Aware Joint User Activity Detection and Channel Estimation for OTFS-Based Grant-Free Random Access

Grant-free random access (GFRA) is a promising solution for massive machine-type communications (mMTC) in future wireless networks. However, reliable user activity detection and channel estimation are critical challenges, particularly when orthogonal time-frequency space (OTFS) modulation is integrated with GFRA to address doubly selective channels induced by high mobility. In this paper, we propose an OTFS-based GFRA framework that exploits the inherent structured sparsity of delay-Doppler channels. By adopting a basis expansion model (BEM), we formulate joint user activity detection and channel estimation as a structured compressive sensing problem. A bi-level sparsity structure is identified, consisting of common sparsity across multiple receive antennas and activation sparsity across mMTC users. To effectively leverage this structure, we construct a two-layer factor graph and develop a structured sparsity expectation propagation (SS-EP) algorithm for efficient Bayesian inference. Simulation results demonstrate that the proposed scheme significantly outperforms existing benchmarks.

cs.IT

LiTCom: A Lightweight Transmitter and Inference-Capable Receiver Framework for 6G Uplink

This paper introduces LiTCom, a lightweight transmitter and inference-capable receiver framework, designed to enable robust 6G uplink communication under low signal-to-noise (SNR) conditions. It embraces the resource asymmetry between edge devices and the network infrastructure. LiTCom simplifies transmitter design by applying basic low-pass filtering for source coding and minimal channel coding, significantly reducing the processing complexity. The receiver employs large-scale generative artificial intelligence (GenAI) models to infer high semantic-fidelity content from highly distorted and degraded signals beyond traditional decoding capabilities. Furthermore, efficient power allocation strategies are developed by exploiting data importance to improve system performance, which is measured by the introduced quality of experience (QoE) metric. Simulation results validate the effectiveness of the proposed LiTCom framework and the lightweight coding design. Compared with the 5G NR-like baseline (using JPEG source coding and LDPC channel coding) and the Deep-JSCC baseline, LiTCom achieves SNR gains up to 8 dB and 2.5 dB, respectively, while reducing over 95% transmitter-side computations.

eess.SP

RadioKMoE: Knowledge-Guided Radiomap Estimation with Kolmogorov-Arnold Networks and Mixture-of-Experts

Radiomap serves as a vital tool for wireless network management and deployment by providing powerful spatial knowledge of signal propagation and coverage. However, increasingly complex radio propagation behavior and surrounding environments pose strong challenges for radiomap estimation (RME). In this work, we propose a knowledge-guided RME framework that integrates Kolmogorov-Arnold Networks (KAN) with Mixture-of-Experts (MoE), namely RadioKMoE. Specifically, we design a KAN module to predict an initial coarse coverage map, leveraging KAN's strength in approximating physics models and global radio propagation patterns. The initial coarse map, together with environmental information, drives our MoE network for precise radiomap estimation. Unlike conventional deep learning models, the MoE module comprises expert networks specializing in distinct radiomap patterns to improve local details while preserving global consistency. Experimental results in both multi- and single-band RME demonstrate the enhanced accuracy and robustness of the proposed RadioKMoE in radiomap estimation.

cs.CV

Resi-VidTok: An Efficient and Decomposed Progressive Tokenization Framework for Ultra-Low-Rate and Lightweight Video Transmission

Real-time transmission of video over wireless networks remains highly challenging, even with advanced deep models, particularly under severe channel conditions such as limited bandwidth and weak connectivity. In this paper, we propose Resi-VidTok, a Resilient Tokenization-Enabled framework designed for ultra-low-rate and lightweight video transmission that delivers strong robustness while preserving perceptual and semantic fidelity on commodity digital hardware. By reorganizing spatio--temporal content into a discrete, importance-ordered token stream composed of key tokens and refinement tokens, Resi-VidTok enables progressive encoding, prefix-decodable reconstruction, and graceful quality degradation under constrained channels. A key contribution is a resilient 1D tokenization pipeline for video that integrates differential temporal token coding, explicitly supporting reliable recovery from incomplete token sets using a single shared framewise decoder--without auxiliary temporal extractors or heavy generative models. Furthermore, stride-controlled frame sparsification combined with a lightweight decoder-side interpolator reduces transmission load while maintaining motion continuity. Finally, a channel-adaptive source--channel coding and modulation scheme dynamically allocates rate and protection according to token importance and channel condition, yielding stable quality across adverse SNRs. Evaluation results indicate robust visual and semantic consistency at channel bandwidth ratios (CBR) as low as 0.0004 and real-time reconstruction at over 30 fps, demonstrating the practicality of Resi-VidTok for energy-efficient, latency-sensitive, and reliability-critical wireless applications.

cs.IT

Leveraging Bi-Directional Channel Reciprocity for Robust Ultra-Low-Rate Implicit CSI Feedback with Deep Learning

Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high environmental robustness. First, a novel bi-directional correlation enhancement module is proposed to strengthen the correlation between uplink and downlink CSI eigenvector matrices. This module projects highly correlated uplink and downlink channel matrices into their respective eigenspaces, effectively reducing redundancy for ultra-low-rate feedback. Second, an innovative input format alignment module is designed to maintain consistent data distributions at both encoder and decoder sides without extra transmission overhead, thereby enhancing robustness against environmental variations. Finally, we develop an efficient transformer-based implicit CSI feedback network to exploit angular-delay domain sparsity and bi-directional correlation for ultra-low-rate CSI compression. Simulation results demonstrate successful reduction of the feedback overhead by 85% compared with the state-of-the-art method and robustness against unseen environments.

eess.SP

TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding

Recent advancements in generative artificial intelligence have introduced groundbreaking approaches to innovating next-generation semantic communication, which prioritizes conveying the meaning of a message rather than merely transmitting raw data. A fundamental challenge in semantic communication lies in accurately identifying and extracting the most critical semantic information while adapting to downstream tasks without degrading performance, particularly when the objective at the receiver may evolve over time. To enable flexible adaptation to multiple tasks at the receiver, this work introduces a novel semantic communication framework, which is capable of jointly capturing task-specific information to enhance downstream task performance and contextual information. Through rigorous experiments on popular image datasets and computer vision tasks, our framework shows promising improvement compared to existing work, including superior performance in downstream tasks, better generalizability, ultra-high bandwidth efficiency, and low reconstruction latency.

cs.AI

Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration

Semantic communications represent a new paradigm of next-generation networking that shifts bit-wise data delivery to conveying the semantic meanings for bandwidth efficiency. To effectively accommodate various potential downstream tasks at the receiver side, one should adaptively convey the most critical semantic information. This work presents a novel task-adaptive semantic communication framework based on diffusion models that is capable of dynamically adjusting the semantic message delivery according to various downstream tasks. Specifically, we initialize the transmission of a deep-compressed general semantic representation from the transmitter to enable diffusion-based coarse data reconstruction at the receiver. The receiver identifies the task-specific demands and generates textual prompts as feedback. Integrated with the attention mechanism, the transmitter updates the semantic transmission with more details to better align with the objectives of the intended receivers. Our test results demonstrate the efficacy of the proposed method in adaptively preserving critical task-relevant information for semantic communications while preserving high compression efficiency.

cs.CL

AGI-Driven Generative Semantic Communications: Principles and Practices

Semantic communications leverage artificial intelligence (AI) technologies to extract semantic information for efficient data delivery, thereby significantly reducing communication cost. With the evolution towards artificial general intelligence (AGI), the increasing demands for AGI services pose new challenges to semantic communications. In this context, an AGI application is typically defined on a general-sense task, covering a broad, even unforeseen, set of objectives, as well as driven by the need for a human-friendly interface in forms (e.g., videos, images, or text) easily understood by human users.In response, we introduce an AGI-driven communication paradigm for supporting AGI applications, called generative semantic communication (GSC). We first describe the basic concept of GSC and its difference from existing semantic communications, and then introduce a general framework of GSC based on advanced AI technologies including foundation models and generative models. Two case studies are presented to verify the advantages of GSC. Finally, open challenges and new research directions are discussed to stimulate this line of research and pave the way for practical applications.

cs.AI

Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications

Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE.

cs.LG

BagChain: A Dual-functional Blockchain Leveraging Bagging-based Distributed Learning

This work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized learning. BagChain integrates blockchain with distributed machine learning by replacing the computationally costly hash operations in proof-of-work with machine-learning model training. BagChain utilizes individual miners' private data samples and limited computing resources to train potentially weak base models, which may be very weak, and further aggregates them into strong ensemble models. Specifically, we design a three-layer blockchain structure associated with the corresponding generation and validation mechanisms to enable distributed machine learning among uncoordinated miners in a permissionless and open setting. To reduce computational waste due to blockchain forking, we further propose the cross fork sharing mechanism for practical networks with lengthy delays. Extensive experiments illustrate the superiority and efficacy of BagChain when handling various machine learning tasks on both independently and identically distributed (IID) and non-IID datasets. BagChain remains robust and effective even when facing constrained local computing capability, heterogeneous private user data, and sparse network connectivity.

cs.DC

A Unifying View of OTFS and Its Many Variants

High mobility environment leads to severe Doppler effects and poses serious challenges to the conventional physical layer based on the widely popular orthogonal frequency division multiplexing (OFDM). The recent emergence of orthogonal time frequency space (OTFS) modulation, along with its many related variants, presents a promising solution to overcome such channel Doppler effects. This paper aims to clearly establish the relationships among the various manifestations of OTFS. Among these related modulations, we identify their connections, common features, and distinctions. Building on existing works, this work provides a general overview of various OTFS-related detection schemes and performance comparisons. We first provide an overview of OFDM and filter bank multi-carrier (FBMC) by demonstrating OTFS as a precoded FBMC through the introduction of inverse symplectic finite Fourier transform (ISFFT). We explore the relationship between OTFS and related modulation schemes with similar characteristics. We provide an effective channel model for high-mobility channels and offer a unified detection representation. We provide numerical comparisons of power spectrum density (PSD) and bit error rate (BER) to underscore the benefit of these modulation schemes in high-mobility scenarios. We also evaluate various detection schemes, revealing insights into their efficacies. We discuss opportunities and challenges for OTFS in high mobility, setting the stage for future research and development in this field.

eess.SP

Efficient Algorithm Design of Dynamic Spectrum Access by Whittle Index

Dynamic spectrum access problem is an important problem that allows a wireless sub-network to use channels temporarily unoccupied by the parent network for minimizing the spectrum waste. Previous work has shown that the sequential channel allocation problem for the sub-network can be formulated within the restless multi-armed bandits (RMAB) framework. The objective is to maximize the expected long-term return over an infinite horizon while minimizing interference to the parent network. Different from the previous work that exploits a binary feedback (e.g., ACK/NAK) to compensate for sensing errors, we leverage the finer and more robust channel quality indicator (CQI) feedback to update the information state (belief vector) of the sub-network. However, the implementation of CQI-based observation model yields significantly more complex belief transition behaviors in an infinite state space and worsens the curse of dimensionality of dynamic programming. To overcome this challenge, we dive into the rich structures of the value functions and obtain tight bounds on their derivatives. These results lead to the proof of optimality of threshold policies on a single-channel problem with subsidy and subsequently a closed-form channel index function using an iterative method to approximate the well-known Whittle index policy, which offers a low-complexity solution for ranking the currently available channels whose states are never directly observable. Through extensive numerical studies, we demonstrate the superior performance and robustness of our proposed algorithm.

math.OC

LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency

The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) remarkably efficient multimedia information transmissions. The concept of GO-COMS leverages advanced artificial intelligence (AI) tools to address the rising demand for bandwidth efficiency in applications, such as edge computing and Internet-of-Things (IoT). Unlike traditional communication systems focusing on source data accuracy, GO-COMs provide intelligent message delivery catering to the special needs critical to accomplishing downstream tasks at the receiver. In this work, we present a novel GO-COM framework, namely LaMI-GO that utilizes emerging generative AI for better quality-of-service (QoS) with ultra-high communication efficiency. Specifically, we design our LaMI-GO system backbone based on a latent diffusion model followed by a vector-quantized generative adversarial network (VQGAN) for efficient latent embedding and information representation. The system trains a common feature codebook the receiver side. Our experimental results demonstrate substantial improvement in perceptual quality, accuracy of downstream tasks, and bandwidth consumption over the state-of-the-art GOCOM systems and establish the power of our proposed LaMI-GO communication framework.

cs.LG

Diff-GO$^\text{n}$: Enhancing Diffusion Models for Goal-Oriented Communications

The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GO-COM, this work introduces a novel noise-restricted diffusion-based GO-COM (Diff-GO$^\text{n}$) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing high-quality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO$^\text{n}$ well-suited for real-time communications and downstream applications.

eess.IV

Fast Adaptation for Deep Learning-based Wireless Communications

The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications.

cs.NI

Analysis of Channel Uncertainty in Trusted Wireless Services via Repeated Interactions

The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications; however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results.

cs.NI

Plug-in UL-CSI-Assisted Precoder Upsampling Approach in Cellular FDD Systems

Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency-Division Duplexing (FDD). Most cellular wireless communication systems employ codebook-based precoder designs, which offer advantages such as simpler, more efficient feedback mechanisms and reduced feedback overhead. Common codebook-based approaches include Type II and eType II precoding methods defined in the 3GPP standards. Feedback in these systems is typically standardized per subband (SB), allowing user equipment (UE) to select the optimal precoder from the codebook for each SB, thereby reducing feedback overhead. However, this subband-level feedback resolution may not suffice for frequency-selective channels. This paper addresses this issue by introducing an uplink CSI-assisted precoder upsampling module deployed at the gNodeB. This module upsamples SB-level precoders to resource block (RB)-level precoders, acting as a plug-in compatible with existing gNodeB or base stations.

eess.SP