arXiv subjects

Tony Quek

Publications and source records attributed to Tony Quek.

12 recordsLinked to original sources

arXiv2025

Wireless Video Semantic Communication with Decoupled Diffusion Multi-frame Compensation

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework with decoupled diffusion multi-frame compensation (DDMFC), abbreviated as WVSC-D, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC-D first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, DDMFC is proposed to generate compensated current semantic frame by a two-stage conditional diffusion process. With both the reference frame transmission and DDMFC frame compensation, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC-D over other DL-based methods e.g. DVSC about 1.8 dB in terms of PSNR.

cs.MM↗

arXiv2025

Multi-hop Parallel Image Semantic Communication for Distortion Accumulation Mitigation

Existing semantic communication schemes primarily focus on single-hop scenarios, overlooking the challenges of multi-hop wireless image transmission. As semantic communication is inherently lossy, distortion accumulates over multiple hops, leading to significant performance degradation. To address this, we propose the multi-hop parallel image semantic communication (MHPSC) framework, which introduces a parallel residual compensation link at each hop against distortion accumulation. To minimize the associated transmission bandwidth overhead, a coarse-to-fine residual compression scheme is designed. A deep learning-based residual compressor first condenses the residuals, followed by the adaptive arithmetic coding (AAC) for further compression. A residual distribution estimation module predicts the prior distribution for the AAC to achieve fine compression performances. This approach ensures robust multi-hop image transmission with only a minor increase in transmission bandwidth. Experimental results confirm that MHPSC outperforms both existing semantic communication and traditional separated coding schemes.

cs.IT↗

arXiv2025

Sovereign AI for 6G: Towards the Future of AI-Native Networks

The advent of Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), and Large Telecom Models (LTM) significantly reshapes mobile networks, especially as the telecom industry transitions from 5G's cloud-centric to AI-native 6G architectures. This transition unlocks unprecedented capabilities in real-time automation, semantic networking, and autonomous service orchestration. However, it introduces critical risks related to data sovereignty, security, explainability, and regulatory compliance especially when AI models are trained, deployed, or governed externally. This paper introduces the concept of `Sovereign AI' as a strategic imperative for 6G, proposing architectural, operational, and governance frameworks that enable national or operator-level control over AI development, deployment, and life-cycle management. Focusing on O-RAN architecture, we explore how sovereign AI-based xApps and rApps can be deployed Near-RT and Non-RT RICs to ensure policy-aligned control, secure model updates, and federated learning across trusted infrastructure. We analyse global strategies, technical enablers, and challenges across safety, talent, and model governance. Our findings underscore that Sovereign AI is not just a regulatory necessity but a foundational pillar for secure, resilient, and ethically-aligned 6G networks.

cs.NI↗

arXiv2025

Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks.

eess.IV↗

arXiv2025

Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RAN

The transition to 6G is expected to bring significant advancements, including much higher data rates, enhanced reliability and ultra-low latency compared to previous generations. Although 6G is anticipated to be 100 times more energy efficient, this increased efficiency does not necessarily mean reduced energy consumption or enhanced sustainability. Network sustainability encompasses a broader scope, integrating business viability, environmental sustainability, and social responsibility. This paper explores the sustainability requirements for 6G and proposes Open RAN as a key architectural solution. By enabling network diversification, fostering open and continuous innovation, and integrating AI/ML, Open RAN can promote sustainability in 6G. The paper identifies high energy consumption and e-waste generation as critical sustainability challenges and discusses how Open RAN can address these issues through softwarisation, edge computing, and AI integration.

cs.NI↗

arXiv2025

DiffPO: Diffusion-styled Preference Optimization for Efficient Inference-Time Alignment of Large Language Models

Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. In this paper, we propose a novel approach, Diffusion-styled Preference Optimization (\model), which provides an efficient and policy-agnostic solution for aligning LLMs with humans. By directly performing alignment at sentence level, \model~avoids the time latency associated with token-level generation. Designed as a plug-and-play module, \model~can be seamlessly integrated with various base models to enhance their alignment. Extensive experiments on AlpacaEval 2, MT-bench, and HH-RLHF demonstrate that \model~achieves superior alignment performance across various settings, achieving a favorable trade-off between alignment quality and inference-time latency. Furthermore, \model~demonstrates model-agnostic scalability, significantly improving the performance of large models such as Llama-3-70B.

cs.CL↗

arXiv2024

Consistent and Repeatable Testing of O-RAN Distributed Unit (O-DU) across Continents

Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE's mission to achieve more intelligent, open, virtualized and fully interoperable mobile networks, O-RAN Open Testing and Integration Centers (OTICs) play a key role in accelerating the adoption of O-RAN specifications based on rigorous testing and validation. One theme in the recent O-RAN Global PlugFest Spring 2024 focused on demonstrating consistent and repeatable Open Fronthaul testing in multiple labs. To respond to this topic, in this paper, we present a detailed analysis of the testing methodologies and results for O-RAN Distributed Unit (O-DU) in O-RAN across two OTICs. We identify key differences in testing setups, share challenges encountered, and propose best practices for achieving repeatable and consistent testing results. Our findings highlight the impact of different deployment technologies and testing environments on performance and conformance testing outcomes, providing valuable insights for future O-RAN implementations.

cs.NI↗

arXiv2024

Consistent and Repeatable Testing of mMIMO O-RU across labs: A Japan-Singapore Experience

Open Radio Access Networks (RAN) aim to bring a paradigm shift to telecommunications industry, by enabling an open, intelligent, virtualized, and multi-vendor interoperable RAN ecosystem. At the center of this movement, O-RAN ALLIANCE defines the O-RAN architecture and standards, so that companies around the globe can use these specifications to create innovative and interoperable solutions. To accelerate the adoption of O-RAN products, rigorous testing of O-RAN Radio Unit (O-RU) and other O-RAN products plays a key role. O-RAN ALLIANCE has approved around 20 Open Testing and Integration Centres (OTICs) globally. OTICs serve as vendor-neutral platforms for providing the testing and integration services, with the vision that an O-RAN product certified in any OTIC is accepted in other parts of the world. To demonstrate the viability of such a certified-once-and-use-everywhere approach, one theme in the O-RAN Global PlugFest Spring 2024 is to demonstrate consistent and repeatable testing for the open fronthaul interface across multiple labs. Towards this, Japan OTIC and Asia Pacific OTIC in Singapore have teamed up together with an O-RU vendor and Keysight Technology. Our international team successfully completed all test cases defined by O-RAN ALLIANCE for O-RU conformance testing. In this paper, we share our journey in achieving this outcome, focusing on the challenges we have overcome and the lessons we have learned through this process.

cs.NI↗

arXiv2024

Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and intelligence. This paper presents the 6G-GOALS approach to goal-oriented and semantic communications for AI-Native 6G Networks. The proposed approach incorporates semantic, pragmatic, and goal-oriented communication into AI-native technologies, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner, thereby optimizing bandwidth, latency, energy, and electromagnetic field (EMF) radiation. The focus is on distilling data to its most relevant form and terse representation, aligning with the source's intent or the destination's objectives and context, or serving a specific goal. 6G-GOALS builds on three fundamental pillars: i) AI-enhanced semantic data representation, sensing, compression, and communication, ii) foundational AI reasoning and causal semantic data representation, contextual relevance, and value for goal-oriented effectiveness, and iii) sustainability enabled by more efficient wireless services. Finally, we illustrate two proof-of-concepts implementing semantic, goal-oriented, and pragmatic communication principles in near-future use cases. Our study covers the project's vision, methodologies, and potential impact.

eess.SP↗

arXiv2023

Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy

We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it remains an open problem whether such discrete-valued mechanisms provide any privacy protection. In this paper, we study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP). More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms, including the binomial noise and the binomial mechanisms that are proposed for privacy preservation, and the sign-based methods that are proposed for data compression, in closed-form expressions. We further investigate the amplification in privacy by sparsification and propose a ternary stochastic compressor. By leveraging compression for privacy amplification, we improve the existing methods by removing the dependency of accuracy (in terms of mean square error) on communication cost in the popular use case of distributed mean estimation, therefore breaking the three-way tradeoff between privacy, communication, and accuracy. Finally, we discuss the Byzantine resilience of the proposed mechanism and its application in federated learning.

cs.CR↗

arXiv2023

Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity

Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. To alleviate the concern, various gradient compression methods have been proposed, and sign-based algorithms are of surging interest. However, SIGNSGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. Error feedback has been proposed to address the non-convergence issue. Nonetheless, it requires the workers to locally keep track of the compression errors, which renders it not suitable for FL since the workers may not participate in the training throughout the learning process. In this paper, we propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of SIGNSGD while further improving communication efficiency. Moreover, the local update scheme is further incorporated to improve the learning performance, and the convergence of the proposed method is established. The effectiveness of the proposed scheme is validated through experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets.

cs.LG↗

arXiv2022

ISAC-Enabled Beam Alignment for Terahertz Networks: Scheme Design and Coverage Analysis

As a key pillar technology for the future 6G networks, Terahertz (THz) communications can provide high-capacity transmissions, but suffers from severe propagation loss and line-of-sight (LoS) blockage that limits the network coverage. Narrow beams are required to compensate for the loss, but they in turn bring in beam misalignment challenge and degrade the THz network coverage. The high sensing resolution of THz signals enables integrated sensing and communications (ISAC) technology to assist the LoS blockage and user mobility-induced beam misalignment, enhancing THz network coverage. Based on the 5G beam management, we propose a joint synchronization signal block (SSB) and reference signal (RS)-based sensing (JSRS) scheme to assist beam alignment. JSRS enables a predict-and-prevent procedure that provides early interventions for timely beam switches. To maximize performance of JSRS, we provide an optimal sensing signal insertion and time-to-frequency allocation to improve the joint range and velocity resolutions. We derive the coverage probability of the JSRS-enabled network to evaluate its abilities in beam misalignment reduction and coverage enhancement. The expression also instructs the network density deployment and beamwidth selection. Numerical results show that the JSRS scheme is effective and highly compatible with the 5G air interface. Averaged in the tested urban use cases, JSRS achieves near-ideal performance and reduces around 80% of beam misalignment, and enhances the coverage probability by about 75%, compared to the network with 5G-required positioning ability.

cs.IT↗