arXiv subjects

Zilong Liu

Publications and source records attributed to Zilong Liu.

At least 19 recordsLinked to original sources

arXiv2026

Probabilistic Symbol-Level Precoding based Affine Frequency Division Multiplexing Transmission

Affine frequency division multiplexing (AFDM) has recently gained significant attention due to its robustness against time-frequency doubly selective channel fading. However, the high computational complexity at the receiver poses a critical challenge for practical deployment. To overcome this issue, we propose a probabilistic symbol-level precoding (SLP)-based AFDM transmission framework, in which the processing burden in downlink transmission is shifted from the user to the base station (BS), enabling direct symbol detection without channel estimation or equalization at the receiver. In the proposed framework, the BS exploits the uplink channel state information (CSI) to design the downlink transmit waveform based on uplink-downlink channel reciprocity. In particular, we innovatively introduce a probabilistic SLP technology by explicitly characterizing the likelihood of symbol detection errors under noise perturbations. Specifically, the transmitted symbols are optimized to minimize the likelihood that the received symbols fall into erroneous decision regions, where the resulting error-probability minimization problem is subsequently approximated as a second order cone programming (SOCP) problem by exploiting the monotonicity of the objective function. Simulation results show that the proposed probabilistic SLP-based scheme achieves performance comparable to that of conventional AFDM receivers, whilst enjoying significant reduction of computational complexity at the receiver end. These results demonstrate the effectiveness and practical potential of the proposed approach.

eess.SP↗

arXiv2026

Joint PAPR and OOBE Reduction for AFDM via Chirp Parameter Tuning

This paper addresses the joint reduction of the peak-to-average power ratio (PAPR) and out-of-band emissions (OOBE) in affine frequency division multiplexing (AFDM) systems by selecting the pre-chirp parameter c2. While existing approaches typically optimize either PAPR or OOBE independently, the proposed method jointly considers both metrics. To this end, a weighted cost function combining PAPR and OOBE is introduced to evaluate the trade-off between the two objectives. A pre-chirp selection scheme, inspired by the selected mapping (SLM) technique, is then employed to identify the optimal c2 value from a finite set of candidates, yielding a Pareto-optimal operating point within a discrete set. Simulation results demonstrate that the proposed approach simultaneously reduces both PAPR and OOBE compared with conventional AFDM. Moreover, its performance remains close to that of methods specifically optimized for a single objective, with only about a 1 dB degradation in PAPR reduction and a 2-3 dB degradation in OOBE suppression.

eess.SP↗

arXiv2026

LMM Modality Transfer: A Pre-requisite for Autonomous GIS Agents

AI models are becoming increasingly adept at understanding and processing spatial information, thereby facilitating agentic problem-solving in spatial tasks and workflows. However, most of the research on their spatial capabilities (e.g., spatial reasoning) has focused on the textual modality as input and output. This contrasts with the human approach to GIS workflows, where text and visual modalities are often used together, interchangeably, and in a complementary manner. Thus, to truly achieve an automated GIS analysis pipeline or carry out human-designed GIS workflows, AI models --- Large Multimodal Models (LMMs) in particular --- need to be able to seamlessly transition between image- and text-based modalities that are traditionally used in such workflows. We present a modality transfer task that (1) asks an LMM to first describe an input image of colored squares in a regular grid, and (2) asks a new LMM instance to re-generate an image of the original spatial scene using the textual description output by the former model. This task quantifies the ability of LMMs to transfer spatial information between image and text modalities. Ultimately, by examining the modality transfer capability of LMMs through the lens of spatial information theory, this work highlights a critical bottleneck: achieving strong and robust geospatial understanding in LMMs requires rigorous, multi-modal alignment. Our results indicate that recent LMMs (here from OpenAI) still struggle with modality transfer, when tasked with re-generating an image of a simple spatial grid of color squares.

cs.AI↗

arXiv2026

SCMA Inspired Sparse Vector Coding: An Enhanced URLLC Transmission Scheme

Sparsity is inherently exploited in sparse code multiple access (SCMA) and sparse vector coding (SVC), yet the interaction between these two has not been explored before. It is intriguing to ask if one can be used to improve the other, and vice versa. In this work, we present a novel SCMA inspired SVC scheme, called SCMA-SVC, for enhanced ultra-reliable low-latency communications. Our key idea is to exploit the sparse pattern and multidimensional constellation nature of SCMA, with which one is able to further enlarge the minimum Euclidean distance (MED) of the corresponding SVC codebooks. Such an innovation allows us to harvest the multiuser coding gain and the constellation shaping gain which are pertinent to SCMA. Moreover, by applying random phase rotations to the sparse vectors, it is shown that the proposed SCMA-SVC achieves full diversity order over Rayleigh fading channels. Under maximum likelihood (ML) decoding, the proposed SCMA-SVC demonstrates remarkable error rate performances over both Gaussian and Rayleigh fading channels. Additionally, we develop a low-complexity decoder that exploits the structural sparsity of SCMA-SVC while maintaining near-ML performance. Simulation results demonstrate that the proposed SCMA-SVC achieves significantly improved reliability over the existing SVC variants.

cs.IT↗

arXiv2026

Frame-Based AFDM-ISAC Waveform Design With Chirp-Enabled Pulse Compression

This paper proposes an Affine frequency division multiplexing (AFDM)-empowered integrated sensing and communications (ISAC) design, referred to as AFDM-ISAC. We first design a novel AFDM-ISAC frame structure that consists of both ISAC and pure data symbols. Each ISAC symbol consists of a single chirp subcarrier for both sensing and channel estimation, while the remaining subcarriers are allocated for communication. Building upon this structure, we present an analog-domain sensing receiver that down-mixes the received echo with a local chirp to fully exploit \textit{chirp compression} gains avoiding the need for full-duplex hardware. In addition, a sensing fusion algorithm, guided by AFDM modulation parameters, is further proposed in the digital domain. Leveraging the distinct features of the proposed AFDM-ISAC frame, we present a low-complexity channel estimation scheme for high mobility channels based on a generalized complex exponential basis expansion model (GCE-BEM), along with an optimal power allocation strategy between pilot and data symbols. Moreover, to support frame-based AFDM communications, a GCE-BEM-based Kalman filter is also employed for robust intra-frame channel estimation.

eess.SP↗

arXiv2026

Spatially Coupled Sparse Code Multiple Access (SC-SCMA): A Spectral Graph Approach

This paper presents a spatially coupled sparse code multiple access (SC-SCMA) framework to overcome the performance and scalability limitations of conventional SCMA systems. By analyzing the pairwise error probability associated to multi-user error patterns, we show that spatial coupling projects the superimposed SCMA codewords into a higher-dimensional effective signal space, leading to a strictly improved minimum Euclidean distance (MED) compared with conventional SCMA, while simultaneously enhancing the coding gain through global message propagation and the diversity gain through inter-block resource spreading. Such a distance gain is shown to be governed by the effective access dimensionality (EAD) induced by the coupled factor graph. With the aid of spectral graph theory, we establish a direct relationship between the spectral gap of the factor graph and a lower bound on the EAD, providing a computable structural metric that guarantees MED improvement under various error patterns. Building upon these theoretical insights, we introduce a low-complexity structure-aware codebook design approach, including a spectral-gap-oriented construction of spatially coupled factor matrices and a localized codebook optimization strategy that exploits the dominant error-inducing local user group. Simulation results validate the analysis and demonstrate that the proposed SC-SCMA consistently outperforms conventional SCMA in overloaded massive access channels.

eess.SP↗

arXiv2026

ARKD: Adaptive Reinforcement Learning-Guided Bidirectional KL Divergence Distillation for Text Generation

Knowledge distillation (KD) is a key technique for compressing Large Language Models (LLMs), yet methods relying on a single KL objective often fail to balance primary distribution fitting with long-tail probability modeling, limiting both generation quality and generalization. To address this, we analyze the complementary roles of forward and reverse KL divergence (FKL/RKL) in distribution alignment from theoretical and empirical perspectives. We then propose a reinforcement-learning-based adaptive KL-weighted distillation framework, in which a policy network dynamically assigns weights to FKL and RKL based on teacher-student distributional characteristics, guided by immediate reward signals to achieve dual alignment on principal and long-tail modes. Extensive experiments demonstrate consistent improvements across Rouge-L and BertScore metrics, surpassing greedy heuristics by 0.4-0.6 points and outperforming other baseline methods on diverse benchmarks.

cs.CL↗

arXiv2026

Priority Random Access and Power Control for NOMA-ALOHA in Heterogeneous mMTC

This paper presents a novel priority random access (PRA) non-orthogonal multiple access assisted ALOHA, called PRA-NA, to provide access priority for machine-type devices (MTDs) with different delay requirements (i.e., delay-sensitive and delay-tolerant). We first introduce a received power level model that incorporates imperfect channel state information and imperfect successive interference cancellation to study the impact of practical non-ideal channel conditions. Two PRA strategies including fixed PRA-NA (FPRA-NA) and adaptive PRA-NA (APRA-NA) are then designed to reduce the average access delay of delay-sensitive MTDs in heterogeneous massive machine-type communications. Subsequently, the throughputs of both the FPRA-NA and APRA-NA strategies are analyzed to demonstrate their effectiveness. Moreover, to improve the energy efficiency of random access, we introduce an enhanced user barring algorithm (EUBA) to carry out power control. It is shown that our proposed EUBA can not only alleviate the user overload problem, but also reduce the average transmit power of MTDs. By extending it to the proposed PRA-NA schemes, we demonstrate via extensive simulation results that the random access performances in terms of throughput, access delay, and energy efficiency can be significantly improved over the conventional NOMA-ALOHA.

eess.SP↗

arXiv2026

Robust SCMA Codebook Design: A Hardware-Aware Autoencoder Approach

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access scheme which is transmitted over orthogonal frequency division multiplexing (OFDM) to exploit multicarrier diversity. In practice, however, carrier frequency offset (CFO) and phase noise (PN) may disrupt the subcarrier orthogonality in OFDM-SCMA systems. Addressing this research problem from a new SCMA codebook design angle, we propose a hardware-aware end-to-end autoencoder that embeds differentiable CFO and Wiener PN layers into the training loop. Simulations show that the proposed codebook effectively suppresses the bit error floors caused by CFO and PN without requiring real-time phase tracking.

cs.IT↗

arXiv2026

Towards Standardizing Affine Frequency Division Multiplexing (AFDM) for Future Wireless Networks

Affine frequency division multiplexing~(AFDM) has emerged as a compelling waveform candidate for future wireless networks, owing to its strong resilience to doubly selective channels and its ability to enable the seamless integration of communication and sensing functionalities. Against this context, this article provides a systematic study of AFDM from a standardization perspective. We first introduce the principles of AFDM and discuss the major considerations involved in waveform standardization. We then examine the backwards compatibility of AFDM with 4G/5G multi-numerology frameworks and their anticipated evolution, frequency-modulated continuous-wave (FMCW) radar waveforms, and long-range (LoRa) modulation, demonstrating that AFDM can be incorporated into legacy processing chains with limited modification. Key standardization-critical capabilities are further discussed, including multiple-antenna and multi-user support, and peak-to-average power ratio (PAPR). Finally, we investigate the potential of AFDM in several emerging scenarios, including non-terrestrial networks~(NTN), integrated sensing and communications (ISAC), vehicle-to-everything (V2X), and underwater acoustic (UWA) communications, whereby severe delay-Doppler dispersion places stringent demands on waveform robustness. Through these explorations, it is shown that that AFDM represents a timely and compelling technology for future wireless networks.

eess.SP↗

arXiv2026

One-hot Coding-based URA with RFFI-Enabled Message Authentication

Unsourced random access (URA) has emerged as a promising paradigm for enabling massive connectivity in Internet-of-Things (IoT) networks. However, since URA transmissions do not contain device identifiers, the receiver may not associate decoded messages with their originating devices, introducing a security vulnerability: forged messages may be decoded as legitimate. To address this problem, this paper proposes a one-hot coding (OHC)-based URA framework that enables message authentication while preserving the unsourced transmission principle. Specifically, distinct messages are mapped onto orthogonal channel uses via an OHC-based common codebook and transmitted using on-off keying modulation. The resulting orthogonal channel structure enables radio-frequency fingerprint identification to authenticate received signals by exploiting device-specific hardware impairments, thereby authenticating decoded messages without introducing an additional authentication payload. Analytical expressions for the per-user probability of error and the probability of successful spoofing are derived. Numerical results demonstrate that the proposed scheme enables secure URA transmission while maintaining reliable communication performance in ultra-short-payload IoT scenarios.

eess.SP↗

arXiv2026

Geographic Bias and Diversity in AI Evaluation

Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its various forms. This underscores the widespread concerns across AI researchers that model outputs, e.g., from generative AI, may encode structural distributional imbalances (stemming from training data or model design) that may amplify social inequality or introduce systemic distortions across application domains ranging from biodiversity to disaster mitigation. Yet, relatively little work has investigated the geographical nature of bias or developed measurable benchmarks for what it means for (generative) AI to be unbiased. In this chapter, we investigate this issue through a literature review. As foundation models are reshaping the landscape of bias research, we examine work spanning both the pre-generative AI and generative AI periods. First, we identify a range of geographic biases. These biases span from representation bias in the training data and regional disparities in the factual recall of language models to the tendency of generative AI to over-proportionally favor prototypical places (called defaults). Then, we showcase how recent studies address the latter bias by evaluating geographic diversity in the outputs of generative AI across various cognitive levels, parameter settings, and output modalities.

cs.CY↗

arXiv2026

Assessing the Geographic Diversity of AI's Platial Representations in Image Generation

(Gen)AI diversity is not merely an ethical issue. From the perspective of geographic information science (GIScience), it could be interpreted as a function of uncertainty and as a form of cognitive bias, embedded in AI outputs. Recent work has sought to develop information-theoretic diversity measures and apply them to evaluate AI-chatbot outputs in a geographic context. As the AI ecosystem to which we are exposed on a daily basis becomes rapidly multimodal, we believe it is important to examine geographic diversity across various modalities. Focusing on images, this paper aims to fill this research gap. First, we select the GPT and DALL-E models as state-of-the-art examples and point out how assessing their geographic diversity involves various stages, including prompt revision and image generation. Then, taking inspiration from species diversity measures in ecological research, we incorporate similarity weighting into the measurement of geographic diversity. Next, we demonstrate how to evaluate geographic diversity in image generation through a case study. Our analysis reveals several counterintuitive findings. For instance, older models can exhibit greater geographic diversity despite producing lower-quality images, and prompt revision yields greater geographic diversity than image generation. At the same time, we observe explicit model homogeneity underlying the lack of geographic diversity, as the selected models consistently depict the same prototypical geo-specific feature or similar features. This is concerning, as it risks producing stereotypical representations of places.

cs.CY↗

arXiv2026

Toward Low-Altitude Embodied Intelligence: A Sensing-Communication-Computation-Control Closed-Loop Perspective

The rapid growth of the low-altitude economy drives increasingly autonomous unmanned aerial vehicle (UAV) operations, giving rise to low-altitude embodied intelligence (LAEI), in which sensing, communication, computation, and control (SC$^3$) are tightly integrated to enable closed-loop interaction, ensuring timely, effective, and safe responses in complex or unknown environments. This article systematically explores the LAEI networks, from its fundamental architecture to the diverse scenarios that it can support. We examine key enabling techniques that sustain timely information exchange and effective decision feedback within the $\text{SC}^3$ closed loop. A representative low-altitude UAV mission in an unknown urban area is presented as a case study, where the UAV provides communication services and performs environmental sensing to inform closed-loop control, illustrating how coordinated $\text{SC}^3$ capabilities enable efficient and responsive operation. By identifying major challenges and outlining future research directions, this work serves as a cornerstone for developing next-generation low-altitude intelligent systems.

eess.SY↗

arXiv2026

Enabling Flexible AFDM-ISAC Design: When Ambiguity Shaping Meets PAPR Control

Affine frequency division multiplexing (AFDM) has emerged as an enabling waveform for integrated sensing and communication (ISAC) due to its intrinsic chirp signaling nature. Nevertheless, the practicality of AFDM-ISAC systems needs to address two major technical challenges, i.e., high ambiguity function (AF) sidelobes and high peak-to-average power ratio (PAPR). By exploiting the reserved chirp-subcarrier (RCS) symbols and per-subcarrier pre-chirp parameters, we develop a flexible and unified AFDM waveform design framework for AF shaping and PAPR control. The proposed framework supports three tunable modes: AF shaping via weighted integrated sidelobe level (ISL) minimization, PAPR minimization, and joint AF shaping and PAPR control under a prescribed PAPR constraint. To solve the formulated nonconvex problem and to accommodate the discrete-phase constraints on the pre-chirp parameters, a joint ISL-PAPR discrete-phase majorization-minimization (JIPD-MM) algorithm is developed. Simulation results verify the effectiveness of the proposed framework under all the three design modes, with the benchmark comparisons conducted at similar effective spectral efficiencies. It is shown that the resulting weighted-ISL and PAPR gains lead to improved weak-target detectability in multi-target scenarios and lower bit error rate (BER) under power-amplifier (PA) nonlinearity.

eess.SP↗

arXiv2026

Joint Phase Noise and Off-Grid Channel Estimation for AFDM Systems via Sparse Bayesian Learning

In practical affine frequency division multiplexing (AFDM) systems, the intricate coupling of oscillator phase noise (PN) and off-grid fractional shifts traps conventional estimators in a severe high-SNR error floor. To address these challenges, we propose a joint PN and channel estimation method based on sparse Bayesian learning (JPNCE-SBL). Specifically, a reduced-rank subspace projection is first introduced to capture the dominant eigen-energy of the Wiener PN process. Concurrently, a dynamic grid evolution strategy is designed to iteratively eliminate off-grid errors without requiring computationally prohibitive global grid densification. Both components are integrated into a unified Expectation-Maximization (EM) framework, where the channel and PN estimates are jointly updated at each iteration to prevent error propagation. Simulation results demonstrate that JPNCE-SBL significantly outperforms existing benchmarks in both NMSE and BER, closely approaching the perfect channel state information case under practical PN conditions.

eess.SP↗

arXiv2026

On the Scalability of Quasi-Complementary Sequence Sets: Quadratic and Cubic Laws

This work is concerned with the fundamental scaling laws of quasi-complementary sequence sets (QCSSs) by understanding how large the set size (denoted by $M$) can grow with the flock size ($K$) and the sequence length ($N$). We first establish a geometric framework that transforms a QCSS into a complex unit-norm codebook, through which and by exploiting the density thresholds of the codebooks, certain polynomial upper bounds of the QCSS set size are obtained. Sharp quadratic and cubic scaling laws are then introduced. Specifically, we show that asymptotically optimal QCSSs with tightness factor $\rho=1$ satisfy $M \le (1+o(1))K^2N$, while asymptotically near-optimal QCSSs satisfy $M \le (1+o(1))K^3N^2$ for $\rho < {(1+\sqrt{5})}/{2}$. To validate these upper bounds, we further propose explicit additive-character and mixed-character based constructions for QCSSs that achieve $M = K^2N + K$ and $M = K^3N^2 + 2K^2N + K$, respectively, thereby showing that the quadratic and cubic scaling laws are asymptotically tight. Our proposed constructions admit flexible parameter choices, and their maximum correlation estimates are shown to be tight through explicit extremal examples. Additionally, it is conjectured that the cubic scaling law is universal for all $1<\rho\le 2$, i.e., any asymptotically near-optimal QCSSs should satisfy $M \le (1+o(1))K^3N^2$. This identifies a fundamental cubic barrier for QCSS scalability.

math.CO↗

arXiv2026

A Novel Low-Complexity Dual-Domain Expectation Propagation Detection Aided AFDM for Future Communications

This paper presents a dual-domain low-complexity expectation propagation (EP) detection framework for affine frequency division multiplexing (AFDM) systems. By analyzing the structural properties of the effective channel matrices in both the time and affine frequency (AF) domains, our key observation is the domain-specific quasi-banded sparsity patterns, including AF-domain sparsity under frequency-selective channels and time-domain sparsity under doubly-selective channels. Based on these observations, we develop an AF-domain EP (EP-AF) detector for frequency-selective channels and a time-domain EP (EP-T) detector for doubly-selective channels, respectively. By performing iterative inference in the time domain using the Gaussian approximation, the proposed EP-T detector avoids inverting the dense channel matrix in the AF domain. Furthermore, the proposed EP-AF and EP-T detectors leverage the aforementioned quasi-banded sparsity of the AF domain and time domain channel matrices, respectively, to reduce the complexity of matrix inversion from cubic to linear order. Simulation results demonstrate that the proposed low-complexity EP-AF detector achieves nearly identical error rate performance to its conventional counterpart, while the proposed low-complexity EP-T detector offers an attractive trade-off between detection performance and complexity.

eess.SP↗