arXiv subjects

Zilong Liu

Publications and source records attributed to Zilong Liu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Low Complexity Weight Flexible Decoding Schemes of Linear Block Code for 6G xURLLC

Low complexity error correction code is a key enabler for next generation ultra-reliable low-latency communications (xURLLC) in six generation (6G). Against this background, this paper proposes a decoding scheme for linear block code by leveraging certain interesting properties of dual codewords. It is found that dual codewords with flexible weights can provide useful decoding information for the locations and magnitudes of error bits, which yielding higher reliability performance. In addition, two decoding schemes are proposed, in which one directly utilizes intrinsic information for iterative decoding, and the other combines prior channel information with intrinsic information for decoding. Both schemes are implemented using vector multiplication and real-number comparisons, making them easy to implement in hardware. Simulation results demonstrate the validness of our study.

eess.SP↗

arXiv2025

Orthogonal Chirp Delay-Doppler Division Multiplexing (CDDM) Modulation for High Mobility Communications

This paper proposes a novel multi-carrier modulation framework for high-mobility communication scenarios. Our key idea lies in spreading data symbols across the delay-Doppler (DD) domain through orthogonal chirp-Zak transform (CZT). To enable efficient signal multiplexing, the proposed modulation scheme employs a transmitter signal that maintains orthogonality with the inherent resolution characteristics of the DD plane. Termed as Orthogonal Chirp Delay-Doppler Division Multiplexing (CDDM), we demonstrate a synergistic integration of chirp waveform properties with the channel structure of the DD domain, thereby achieving advantages with both lower computational efficiency and improved detection performance. We introduce a novel CZT-based superimposed sparse pilot structure to enable simultaneous estimation of delay-Doppler shifts and channel coefficients. For enhanced performance, we further develop an embedded pilot scheme that demonstrates channel estimation performance comparable to that of Orthogonal Delay-Doppler Division Multiplexing (ODDM) systems. Simulation results demonstrate that CDDM achieves significant bit error rate (BER) improvements over existing modulation schemes , under perfect channel state information (CSI), as well as superior out-of-band emissions (OOBE). Further, for the imperfect CSI case, the proposed CZT-based superimposed pilot scheme leads to significantly reduced normalized mean square error (NMSE), whilst attaining equivalent estimation accuracy to that of ODDM with lower computational complexity.

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arXiv2025

On the Robustness of AFDM and OTFS Against Passive Eavesdroppers

We investigate the robustness of affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) waveforms against passive eavesdroppers performing brute-force demodulation to intercepted signals, under the assumption that eavesdroppers have no knowledge of chirp parameters (in AFDM) or the delay-Doppler grid configuration (in OTFS), such that they must search exhaustively over possible demodulation matrices. Analytical results show that the brute-force complexity scales as $\mathcal{O}(\sqrt{N})$ for OTFS and $\mathcal{O}(N^2)$ for AFDM, where $N$ is the number of subcarriers, indicating that AFDM has superior resilience over OTFS. Bit error rate (BER) simulations confirm the analysis by showing that, with AFDM, the signal remains nearly undecodable at the eavesdropper, while OTFS allows partial signal recovery under equivalent conditions.

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arXiv2025

Optimal Real-time Communication in 6G Ultra-Massive V2X Mobile Networks

This paper introduces a novel cooperative vehicular communication algorithm tailored for future 6G ultra-massive vehicle-to-everything (V2X) networks leveraging integrated space-air-ground communication systems. Specifically, we address the challenge of real-time information exchange among rapidly moving vehicles. We demonstrate the existence of an upper bound on channel capacity given a fixed number of relays, and propose a low-complexity relay selection heuristic algorithm. Simulation results verify that our proposed algorithm achieves superior channel capacities compared to existing cooperative vehicular communication approaches.

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arXiv2025

Generalized Spatial Modulation Aided Affine Frequency Division Multiplexing

Generalized spatial modulation-aided affine frequency division multiplexing (GSM-AFDM) is conceived for reliable multiple-input multiple-output (MIMO) communications over doubly selective channels. \textcolor{black}{We commence by proposing several low-complexity detectors for large-scale GSM-AFDM systems to meet the diverse requirements of heterogeneous receiver designs in terms of detection complexity and reliability.} Specifically, we introduce the linear minimum mean square error (LMMSE) equalizer-based maximum likelihood detector (LMMSE-MLD). By exploiting the GSM properties, we then derive the LMMSE-based transmit-antenna activation pattern (TAP) check-based log-likelihood ratio detector (LMMSE-TC-LLRD). In addition, we propose a pair of new detectors, namely the greedy residual check detector (GRCD) and the reduced space check detector (RSCD). We also derive a bit error rate (BER) upper-bound by considering the MLD. \textcolor{black}{Our analytical results are also available for multiple-input multiple-output (MIMO)-AFDM, since MIMO-AFDM can be regarded as a special case of the proposed GSM-AFDM.} Our simulation results demonstrate that 1) the BER upper bound derived is tight for moderate to high signal-to-noise ratios (SNRs), \textcolor{black}{2) the proposed GSM-AFDM achieves lower BER than its conventional orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) and AFDM counterparts. Specifically, at a BER of $10^{-4}$ and a velocity of $540$ km/h, the proposed GSM-AFDM is capable of attaining about $6$ dB SNR gain compared to GSM-OFDM,} and 3) the conceived detectors strike a compelling trade-off between the BER and complexity.

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arXiv2025

Energy Detection over Composite $κ-μ$ Shadowed Fading Channels with Inverse Gaussian Distribution in Ultra mMTC Networks

This paper investigates the characteristics of energy detection (ED) over composite $κ$-$μ$ shadowed fading channels in ultra machine-type communication (mMTC) networks. We have derived the closed-form expressions of the probability density function (PDF) of signal-to-noise ratio (SNR) based on the Inverse Gaussian (\emph{IG}) distribution. By adopting novel integration and mathematical transformation techniques, we derive a truncation-based closed-form expression for the average detection probability for the first time. It can be observed from our simulations that the number of propagation paths has a more pronounced effect on average detection probability compared to average SNR, which is in contrast to earlier studies that focus on device-to-device networks. It suggests that for 6G mMTC network design, we should consider enhancing transmitter-receiver placement and antenna alignment strategies, rather than relying solely on increasing the device-to-device average SNR.

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arXiv2025

Whose Truth? Pluralistic Geo-Alignment for (Agentic) AI

AI (super) alignment describes the challenge of ensuring (future) AI systems behave in accordance with societal norms and goals. While a quickly evolving literature is addressing biases and inequalities, the geographic variability of alignment remains underexplored. Simply put, what is considered appropriate, truthful, or legal can differ widely across regions due to cultural norms, political realities, and legislation. Alignment measures applied to AI/ML workflows can sometimes produce outcomes that diverge from statistical realities, such as text-to-image models depicting balanced gender ratios in company leadership despite existing imbalances. Crucially, some model outputs are globally acceptable, while others, e.g., questions about Kashmir, depend on knowing the user's location and their context. This geographic sensitivity is not new. For instance, Google Maps renders Kashmir's borders differently based on user location. What is new is the unprecedented scale and automation with which AI now mediates knowledge, expresses opinions, and represents geographic reality to millions of users worldwide, often with little transparency about how context is managed. As we approach Agentic AI, the need for spatio-temporally aware alignment, rather than one-size-fits-all approaches, is increasingly urgent. This paper reviews key geographic research problems, suggests topics for future work, and outlines methods for assessing alignment sensitivity.

cs.AI↗

arXiv2025

Non-Orthogonal AFDM: A Promising Spectrum-Efficient Waveform for 6G High-Mobility Communications

This paper proposes a spectrum-efficient nonorthogonal affine frequency division multiplexing (AFDM) waveform for reliable high-mobility communications in the upcoming sixth-generation (6G) mobile systems. Our core idea is to introduce a compression factor to enable controllable subcarrier overlapping in chirp-based AFDM modulation. To mitigate intercarrier interference (ICI), we introduce linear precoding at the transmitter and an iterative detection scheme at the receiver. Simulation results demonstrate that these techniques can effectively reduce interference and maintain robust bit error rate (BER) performance even under aggressive compression factors and high-mobility channel conditions. The proposed non-orthogonal AFDM waveform offers a promising solution for next-generation wireless networks, balancing spectrum efficiency and Doppler resilience in highly dynamic environments.

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arXiv2025

Local Ambiguity Shaping for Doppler-Resilient Sequences Under Spectral and PAPR Constraints

This paper focuses on designing Doppler-resilient sequences with low local Ambiguity Function (AF) sidelobes, subject to certain spectral and Peak-to-Average Power Ratio (PAPR) constraints. To achieve this, we propose two distinctoptimization algorithms: (i) an Alternating Minimization (AM) algorithm for superior Weighted Peak Sidelobe Level (WPSL) minimization, and (ii) a low-complexity Augmented Lagrangian-assisted Majorization Minimization (ALaMM) algorithm with effective WPSL suppression. The proposed schemes hold great potential for sequence design in future 6G and integrated sensing and communication applications, supporting robust sensing under spectral coexistence constraints in high-mobility scenarios.

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arXiv2025

Generalized Arlery-Tan-Rabaste-Levenshtein Lower Bounds on Ambiguity Function and Their Asymptotic Achievability

This paper presents generalized Arlery-Tan-Rabaste-Levenshtein lower bounds on the maximum aperiodic ambiguity function (AF) magnitude of unimodular sequences under certain delay-Doppler low ambiguity zones (LAZ). Our core idea is to explore the upper and lower bounds on the Frobenius norm of the weighted auto- and cross-AF matrices by introducing two weight vectors associated with the delay and Doppler shifts, respectively. As a second major contribution, we demonstrate that our derived lower bounds are asymptotically achievable with selected Chu sequence sets by analyzing their maximum auto- and cross- AF magnitudes within certain LAZ.

cs.IT↗

arXiv2025

Joint Sparse Graph for Enhanced MIMO-AFDM Receiver Design

Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high-mobility communications. This paper is devoted to enhanced receiver design for multiple input and multiple output AFDM (MIMO-AFDM) systems. Firstly, we introduce a unified variational inference (VI) approach to approximate the target posterior distribution, under which the belief propagation (BP) and expectation propagation (EP)-based algorithms are derived. As both VI-based detection and low-density parity-check (LDPC) decoding can be expressed by bipartite graphs in MIMO-AFDM systems, we construct a joint sparse graph (JSG) by merging the graphs of these two for low-complexity receiver design. Then, based on this graph model, we present the detailed message propagation of the proposed JSG. Additionally, we propose an enhanced JSG (E-JSG) receiver based on the linear constellation encoding model. The proposed E-JSG eliminates the need for interleavers, de-interleavers, and log-likelihood ratio transformations, thus leading to concurrent detection and decoding over the integrated sparse graph. To further reduce detection complexity, we introduce a sparse channel method by approaximating multiple graph edges with insignificant channel coefficients into a single edge on the VI graph. Simulation results show the superiority of the proposed receivers in terms of computational complexity, detection and decoding latency, and error rate performance compared to the conventional ones.

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arXiv2025

Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the ``modulating'' zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions.

cs.IT↗

arXiv2025

Waveform and Filter Design for Integrated Sensing and Communication Against Signal-dependent Modulated Jamming

This paper focuses on an integrated sensing and communication (ISAC) system in the presence of signal-dependent modulated jamming (SDMJ). Our goal is to suppress jamming while carrying out simultaneous communications and sensing. We minimize the integrated sidelobe level (ISL) of the mismatch filter output for the transmitted waveform and the integrated level (IL) of the mismatch filter output for the jamming, under the constraints of the loss in-processing gain (LPG) and the peak-to-average power ratio (PAPR) of the transmitted waveform. Meanwhile, the similarity constraint is introduced for information-bearing transmit waveform. We develop a decoupled majorization minimization (DMM) algorithm to solve the proposed multi-constrained optimization problem. In contrast to the existing approaches, the proposed algorithm transforms the difficult optimization problem involving two variables into two parallel sub-problems with one variable, thus significantly speeding up the convergence rate. Furthermore, fast Fourier transform (FFT) is introduced to compute the closed-form solution of each sub-problem, giving rise to a greatly reduced computation complexity. Simulation results demonstrate the capabilities of the proposed ISAC system which strikes a proper trade-off among sensing and jamming suppression.

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arXiv2025

Flag Sequence Set Design for Low-Complexity Delay-Doppler Estimation

This paper studies Flag sequences for low-complexity delay-Doppler estimation by exploiting their distinctive peak-curtain ambiguity functions (AFs). Unlike the existing Flag sequence designs that are limited to prime lengths and periodic auto-AFs, we aim to design Flag sequence sets of arbitrary lengths with low (nontrivial) periodic/aperiodic auto- and cross-AFs. Since every Flag sequence consists of a Curtain sequence and a Peak sequence, we first investigate the algebraic design of Curtain sequence sets of arbitrary lengths. Our proposed design gives rise to novel Curtain sequence sets with ideal curtain auto-AFs and zero/near-zero cross-AFs within the delay-Doppler zone of operation. Leveraging these Curtain sequence sets, two optimization problems are formulated to minimize the weighted integrated masked sidelobe level (WImSL) of the Flag sequence set. Accelerated parallel partially majorization-minimization algorithms are proposed to jointly optimize the transmit Flag sequences and symmetric/asymmetric reference sequences stored in the receiver. Simulations demonstrate that our proposed Flag sequences lead to improved WImSL and peak-to-max-masked-sidelobe ratio compared with the existing Flag sequences. Additionally, our Flag sequences under the Flag method exhibit Mean Squared Errors that approach the Cramér-Rao lower bound and the sampling bound at high signal-to-noise power ratios.

cs.IT↗

arXiv2025

The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment

Global challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to and integration of diverse datasets, many of which are geospatial. Over the past few years, a growing number of (geo)portals have been developed to address this need. However, most existing (geo)portals are stacked by separated or sparsely connected data "silos" impeding effective data consolidation. A new way of sharing and reusing geospatial data is therefore urgently needed. In this work, we introduce KnowWhereGraph, a knowledge graph-based data integration, enrichment, and synthesis framework that not only includes schemas and data related to human and environmental systems but also provides a suite of supporting tools for accessing this information. The KnowWhereGraph aims to address the challenge of data integration by building a large-scale, cross-domain, pre-integrated, FAIR-principles-based, and AI-ready data warehouse rooted in knowledge graphs. We highlight the design principles of KnowWhereGraph, emphasizing the roles of space, place, and time in bridging various data "silos". Additionally, we demonstrate multiple use cases where the proposed geospatial knowledge graph and its associated tools empower decision-makers to uncover insights that are often hidden within complex and poorly interoperable datasets.

cs.DB↗

arXiv2025

Joint Beamforming Design for Integrated Sensing and Communication Systems with Hybrid-Colluding Eavesdroppers

In this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive eavesdropper (PE) collude to intercept the confidential information. To ensure the accuracy of sensing while preventing the eavesdropping, a base station transmits a signal consisting of information symbols and sensing waveform, in which the sensing waveform can be also used as artificial noise to interfere with eavesdroppers. Under this setup, we propose an alternating optimization-based two stage scheme (AO-TSS) for improving the sensing and communication performance. In the first stage, based on the assumptions that the perfect channel state information (CSI) of the AE and statistical CSI of the PE are known, the communication and sensing beamforming problem is formulated with the objective of minimizing the weighted sum of the beampattern matching mean squared error (MSE) and cross-correlation, subject to the secure transmission constraint. To tackle the non-convexity, we propose a semi-definite relaxation (SDR) algorithm and a reduced-complexity zero-forcing (ZF) algorithm. Then, the scenarios are further extended to more general cases with imperfect AE CSI and unknown PE CSI. To further improve the communication performance, the second-stage problem is developed to optimize the secrecy rate threshold under the radar performance constraint. Finally, numerical results demonstrate the superiority of the proposed scheme in terms of sensing and secure communication.

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arXiv2025

Phase Noise Resilient Codebook Design for Sparse Code Multiple Access

Sparse code multiple access (SCMA) is a promising technique for future machine type communication systems due to its superior spectral efficiency and capability for supporting massive connectivity. This paper proposes a novel class of sparse codebooks to improve the error rate performance of SCMA in the presence of phase noise (PN). Specifically, we first analyze the error rate performance of SCMA impaired by looking into the pair-wise error probability. Then, a novel codebook design metric, called minimum PN metric (MPNM), is proposed. In addition, to design PN resilient codebooks, we propose a novel pulse-amplitude modulation (PAM)-based low projection mother constellation (LP-MC), called LP-PAM. The codebooks for different users are obtained by rotating and scaling the MC, where the phase rotation angles and scaling factors for different users are optimized by maximizing the proposed MPNM. Numerical results show that the proposed PNCBs have larger MPNM values and achieve improved error rate performance than the-state-of-the-art codebooks.

cs.IT↗

arXiv2024

Sparse Zero Correlation Zone Arrays for Training Design in Spatial Modulation Systems

This paper presents a novel training matrix design for spatial modulation (SM) systems, by introducing a new class of two-dimensional (2D) arrays called sparse zero correlation zone (SZCZ) arrays. An SZCZ array is characterized by a majority of zero entries and exhibits the zero periodic auto- and cross-correlation zone properties across any two rows. With these unique properties, we show that SZCZ arrays can be effectively used as training matrices for SM systems. Additionally, direct constructions of SZCZ arrays with large ZCZ widths and controllable sparsity levels based on 2D restricted generalized Boolean functions (RGBFs) are proposed. Compared with existing training schemes, the proposed SZCZ-based training matrices have larger ZCZ widths, thereby offering greater tolerance for delay spread in multipath channels. Simulation results demonstrate that the proposed SZCZ-based training design exhibits superior channel estimation performance over frequency-selective fading channels compared to existing alternatives.

cs.IT↗