arXiv subjects

Erwu Liu

Publications and source records attributed to Erwu Liu.

At least 19 recordsLinked to original sources

arXiv2026

Task-oriented Framework for Communication-Efficient Federated Learning: From Isolated Optimization to Holistic Synergy

Communication bottlenecks remain a primary obstacle to the large-scale deployment of federated learning (FL). This article proposes a comprehensive framework for building communication-efficient FL, founded on three fundamental pillars: model compression, client selection, and resource allocation. We first survey state-of-the-art techniques for each pillar, specifically elucidating how quantization, pruning, and low-rank approximation reduce payloads; how intelligent client schedulers exploit heterogeneity; and how emerging communication paradigms such as Integrated Sensing and Communication (ISAC) and Over-the-Air Computation (AirComp) redefine bandwidth and energy utilization. Subsequently, these insights are unified through a task-oriented design philosophy that couples strategy selection with cross-layer, multi-objective optimization. To validate the proposed framework, we present an autonomous driving case study with two complementary experiments: a task-oriented client scheduling strategy that improves object detection accuracy under the same communication time budget, and a joint quantization-bandwidth optimization that further reduces total training time under dynamic networks. Together, the experiments demonstrate the advantages of holistic task-oriented design for real-world FL deployment.

eess.SP↗

arXiv2026

A Survey of Decentralized Physical Infrastructure Network,Research Directions, and Open Challenges

The Decentralized Physical Infrastructure Network (DePIN) represents a transformative paradigm that redefines the construction, operation, and governance of Information and Communication Technology (ICT) infrastructure in the Web 3.0 era. DePIN integrates physical resources, such as networking equipment, storage, and computing power, with decentralized digital governance, forming a self-incentivized ecosystem that is collaboratively built, shared, and governed by the community. It provides a foundational framework for future communication networks, facilitating decentralized edge intelligence, efficient resource sharing, and trustworthy coordination among heterogeneous devices. Focusing on the feasibility of this emerging paradigm, this paper examines the technology landscape in the pre-DePIN era and gaps between existing methodologies and the forthcoming decentralized infrastructure for Web 3.0. It provides a systematic and comprehensive survey of the background, core characteristics, technical architecture, and applications of DePIN across various vertical domains. The paper analyzes the DePIN technology stack from six layers: physical infrastructure, blockchain, interaction, trust, incentive, and application, with special attention to their cross-layer feedback loops, implementation readiness, and deployment limitations. To further bridge conceptual analysis and practical deployment, we propose a DePIN feasibility assessment framework covering technical, governance, and economic dimensions. Moreover, we highlight promising research directions, providing insights and guidance for further exploration and deployment of DePIN.

eess.SP↗

arXiv2026

Effective Range and Optimal Frequency of Through-the-Earth Magnetic Induction Communication

Magnetic induction communication (MIC) is a promising technology for through-the-earth (TTE) communication. Previous studies on the MIC range have often overlooked the impact of eddy losses caused by underground materials. For TTE MIC, significant eddy losses complicate the analysis of the effective MIC range, which is vital for optimizing performance but has never been addressed in the literature. Accounting for the conductivity and permittivity of the underground medium, this paper derives the effective MIC range in TTE MIC, along with a closed-from expression that predicts the optimal carrier frequency to maximize this range. Finite element simulations validate the analysis, demonstrating that the optimal carrier frequency can significantly enhance the MIC range. It is also revealed that optimizing the antenna radius is effective in extending the MIC range for TTE and vehicle MIC applications.

eess.SY↗

arXiv2026

Cooperative MARL-Based Energy-Efficient Power Control for Two-Hop Relaying Networks

In this paper, we study a cooperative game in the cooperative communication network, where each relay makes decisions autonomously and aims to achieve the same optimization objective of maximizing energy efficiency. We consider the non-ideal situation where instantaneous channel state information (CSI) is difficult to obtain and only partially observable outdated CSI is available. To solve this game problem, we define a delayed reward-based state-action value function and propose a multi-agent deep Q network learning framework. Then, we prove analytically that utilities obtained by game-theoretic approaches with the instantaneous CSI serve as upper bounds for those of the proposed method. Simulation results reveal that our approach considerably outperforms its potential alternatives and is only about 5.2% away from the optimal solution.

eess.SY↗

arXiv2026

Effect of Antenna Deployment on Achievable Rate in Cooperative Magnetic Induction Communication

Magnetic Induction (MI) communication can be applied in some through-the-earth scenarios such as mines and underground rivers. To increase the transmission rate of MI communications, we propose a cooperative MI (CMI) scheme with an amplify-and-forward (AF) relay. Different from existing studies, we mainly focus on the relay with arbitrary antenna position and orientation (antenna deployment, AD). We derive the closed-form expression of CMI achievable data rate gain (CMG) for the relay and the closed-form expression of CMI channel bandwidth. Simulations reveal that a relay with appropriate AD could yield a significant increase in the achievable rate for MI systems.

eess.SP↗

arXiv2026

Antenna Optimization for Decode-and-Forward Relay in Magnetic Induction Communications

Magnetic Induction (MI) communication is effective in underground tunnels for emergency rescue vehicle due to the small-size antenna. It can highly benefit from a cooperative decode-and-forward (DF) relay to achieve a higher data rate. However, its channel gain is extremely position-and-orientation-selective. The unreachable space increases the complexity of the antenna deployment. To find the best antenna position and orientation (PO) of the relay achieving the higher data rate, this paper formulates the optimization problem of the relay MI antenna PO with tunnel constraints. To solve the problem more quickly, we propose to use geometric modeling to eliminate the tunnel constraints and develop a random-search algorithm achieving a fast convergence and excellent global search ability. Simulations show that the proposed algorithm can quickly converge to one optimum which signifies a noticeable improvement of data rate for vehicle MI systems with weak signals.

eess.SP↗

arXiv2025

Joint Beamforming and Position Design for Movable Antenna Assisted LEO ISAC Systems

Low earth orbit (LEO) satellite-assisted integrated sensing and communications (ISAC) systems have been extensively studied to achieve ubiquitous connectivity. However, the severe signal attenuation and limited transmit power at LEO satellites can degrade ISAC performance. To address this issue, this paper investigated movable antenna (MA)-assisted LEO ISAC systems. We derive the communication signal-to-interference-plus-noise ratio (SINR) and the sensing squared position error bound (SPEB) for evaluating the ISAC performance. Then, we jointly optimize the transmit beamforming and the MA positions to minimize the SPEB under the SINR constraints, total transmit power constraint, and several inherent physical constraints of the MA array. We first simplify the complex problem using the semidefinite relaxation (SDR). Then, we present a novel alternating optimization (AO)-based algorithm to decouple the original problem into two subproblems, consequently convexified and solved. Simulations demonstrate the convergence and effectiveness of the proposed algorithm. Better trade-off between communication and sensing performance, and at least 25% gain in sensing performance are achieved, compared to the benchmarks.

eess.SP↗

arXiv2025

Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey

Magnetic induction (MI) communication (MIC) has emerged as a promising candidate for underground communication networks due to its excellent penetration capabilities. Integration with Space-Air-Ground-Underground (SAGUI) networks in next-generation mobile communication systems requires a well-defined network architecture. A recent discovery in MIC research, MI fast fading, remains in its early stages and presents unique challenges. This paper provides a comprehensive survey on through-the-earth (TTE) MIC, covering MI applications, channel modeling, point-to-point MIC design, relay techniques, network frameworks, and emerging technologies. We compare various MIC applications to highlight TTE-specific challenges and review the principles of channel modeling, addressing both MI slow fading and MI fast fading, along with its potential impact on existing MIC theories. We conduct a fine-grained decomposition of MI channel power gain into four distinct physical parameters, and propose a novel geometric model to analyze MI fast fading. We also summarize MI relay techniques, examine crosstalk effects in relay and high-density networks, and explore key research tasks within the OSI framework for a holistic MI network protocol in SAGUI. To bridge the gaps identified, we propose a MIC framework that supports TCP/IP and Linux, enabling full implementation of existing and emerging MIC solutions. This framework empowers researchers to leverage Linux resources and deep learning platforms for accelerated development of MIC in SAGUI networks. Remaining research challenges, open issues, and promising novel techniques are further identified to advance MIC research.

eess.SY↗

arXiv2025

LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments

Federated Learning (FL) is a distributed machine learning paradigm based on protecting data privacy of devices, which however, can still be broken by gradient leakage attack via parameter inversion techniques. Differential privacy (DP) technology reduces the risk of private data leakage by adding artificial noise to the gradients, but detrimental to the FL utility at the same time, especially in the scenario where the data is Non-Independent Identically Distributed (Non-IID). Based on the impact of heterogeneous data on aggregation performance, this paper proposes a Lightweight Adaptive Privacy Allocation (LAPA) strategy, which assigns personalized privacy budgets to devices in each aggregation round without transmitting any additional information beyond gradients, ensuring both privacy protection and aggregation efficiency. Furthermore, the Deep Deterministic Policy Gradient (DDPG) algorithm is employed to optimize the transmission power, in order to determine the optimal timing at which the adaptively attenuated artificial noise aligns with the communication noise, enabling an effective balance between DP and system utility. Finally, a reliable aggregation strategy is designed by integrating communication quality and data distribution characteristics, which improves aggregation performance while preserving privacy. Experimental results demonstrate that the personalized noise allocation and dynamic optimization strategy based on LAPA proposed in this paper enhances convergence performance while satisfying the privacy requirements of FL.

cs.LG↗

arXiv2025

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments

The aggregation efficiency and accuracy of wireless Federated Learning (FL) are significantly affected by resource constraints, especially in heterogeneous environments where devices exhibit distinct data distributions and communication capabilities. This paper proposes a clustering strategy that leverages prior knowledge similarity to group devices with similar data and communication characteristics, mitigating performance degradation from heterogeneity. On this basis, a novel Cluster- Aware Multi-round Update (CAMU) strategy is proposed, which treats clusters as the basic units and adjusts the local update frequency based on the clustered contribution threshold, effectively reducing update bias and enhancing aggregation accuracy. The theoretical convergence of the CAMU strategy is rigorously validated. Meanwhile, based on the convergence upper bound, the local update frequency and transmission power of each cluster are jointly optimized to achieve an optimal balance between computation and communication resources under constrained conditions, significantly improving the convergence efficiency of FL. Experimental results demonstrate that the proposed method effectively improves the model performance of FL in heterogeneous environments and achieves a better balance between communication cost and computational load under limited resources.

cs.LG↗

arXiv2024

Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning

One of the biggest challenges of building artificial intelligence (AI) model in the healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausting, costly, and sometimes impossible. In this paper, we propose a framework for global healthcare modelling using datasets from multi-continents (Europe, North America, and Asia) without sharing the local datasets, and choose glucose management as a study model to verify its effectiveness. Technically, blockchain-enabled federated learning is implemented with adaptation to meet the privacy and safety requirements of healthcare data, meanwhile, it rewards honest participation and penalizes malicious activities using its on-chain incentive mechanism. Experimental results show that the proposed framework is effective, efficient, and privacy-preserving. Its prediction accuracy consistently outperforms models trained on limited personal data and achieves comparable or even slightly better results than centralized training in certain scenarios, all while preserving data privacy. This work paves the way for international collaborations on healthcare projects, where additional data is crucial for reducing bias and providing benefits to humanity.

cs.LG↗

arXiv2024

A Mathematical Theory of Hyper-simplex Fractal Network for Blockchain: Part I

Blockchain technology holds promise for Web 3.0, but scalability remains a critical challenge. Here, we present a mathematical theory for a novel blockchain network topology based on fractal N-dimensional simplexes. This Hyper-simplex fractal network folds one-dimensional data blocks into geometric shapes, reflecting both underlying and overlaying network connectivities. Our approach offers near-infinite scalability, accommodating trillions of nodes while maintaining efficiency. We derive the mathematical foundations for generating and describing these network topologies, proving key properties such as node count, connectivity patterns, and fractal dimension. The resulting structure facilitates a hierarchical consensus mechanism and enables deterministic address mapping for rapid routing. This theoretical framework lays the groundwork for next-generation blockchain architectures, potentially revolutionizing large-scale decentralized systems. The Part I work was conducted between March and September 2024.

cs.NI↗

arXiv2024

Blockchain-Enabled IoV: Secure Communication and Trustworthy Decision-Making

The Internet of Vehicles (IoV), which enables interactions between vehicles, infrastructure, and the environment, faces challenges in maintaining communication security and reliable automated decisions. This paper introduces a decentralized framework comprising a primary layer for managing inter-vehicle communication and a sub-layer for securing intra-vehicle interactions. By implementing blockchain-based protocols like Blockchain-integrated Secure Authentication (BiSA) and Decentralized Blockchain Name Resolution (DBNR), the framework ensures secure, decentralized identity management and reliable data exchanges, thereby supporting safe and efficient autonomous vehicle operations.

cs.CR↗

arXiv2024

Blockchain Based Zero-Knowledge Proof of Location in IoT

With the development of precise positioning technology, a growing number of location-based services (LBSs) facilitate people's life. Most LBSs require proof of location (PoL) to prove that the user satisfies the service requirement, which exposes the user's privacy. In this paper, we propose a zero-knowledge proof of location (zk-PoL) protocol to better protect the user's privacy. With the zk-PoL protocol, the user can choose necessary information to expose to the server, so that hierarchical privacy protection can be achieved. The evaluation shows that the zk-PoL has excellent security to resist main attacks, moreover the computational efficiency is independent of input parameters and the zk-PoL is appropriate to delay-tolerant LBSs.

cs.CR↗

arXiv2024

Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement Learning

This paper aims to balance performance and cost in a two-hop wireless cooperative communication network where the source and relays have contradictory optimization goals and make decisions in a distributed manner. This differs from most existing works that have typically assumed that source and relay nodes follow a schedule created implicitly by a central controller. We propose that the relays form an alliance in an attempt to maximize the benefit of relaying while the source aims to increase the channel capacity cost-effectively. To this end, we establish the trade problem as a Stackelberg game, and prove the existence of its equilibrium. Another important aspect is that we use multi-agent reinforcement learning (MARL) to approach the equilibrium in a situation where the instantaneous channel state information (CSI) is unavailable, and the source and relays do not have knowledge of each other's goal. A multi-agent deep deterministic policy gradient-based framework is designed, where the relay alliance and the source act as agents. Experiments demonstrate that the proposed method can obtain an acceptable performance that is close to the game-theoretic equilibrium for all players under time-invariant environments, which considerably outperforms its potential alternatives and is only about 2.9% away from the optimal solution.

eess.SY↗

arXiv2024

Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network

The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments. In the traditional MI communication, there is no fast-fading channel since the MI channel is treated as a quasi-static channel. However, for the vehicle (mobile) MI (VMI) communication, the unpredictable antenna vibration brings the remarkable fast-fading. As such fast-fading cannot be modeled by the central limit theorem, it differs radically from other wireless fast-fading channels. Unfortunately, few studies focus on this phenomenon. In this paper, using a novel space modeling based on the electromagnetic field theorem, we propose a 3-dimension model of the VMI antenna vibration. By proposing ``conjugate pseudo-piecewise functions'' and boundary $p(x)$ distribution, we derive the cumulative distribution function (CDF), probability density function (PDF) and the expectation of the VMI fast-fading channel. We also theoretically analyze the effects of the VMI fast-fading on the network throughput, including the VMI outage probability which can be ignored in the traditional MI channel study. We draw several intriguing conclusions different from those in wireless fast-fading studies. For instance, the fast-fading brings more uniformly distributed channel coefficients. Finally, we propose the power control algorithm using the non-cooperative game and multiagent Q-learning methods to optimize the throughput of the cellular VMI network. Simulations validate the derivation and the proposed algorithm.

eess.SP↗

arXiv2024

BeerReview: A Blockchain-enabled Peer Review Platform

In an era of increasing concerns over intellectual property rights, traditional peer review systems face challenges including plagiarism, malicious attacks, and unauthorized data access. BeerReview, a blockchain-enabled peer review platform, offers a robust solution, enabling experts and scholars to participate actively in the review process without concerns about plagiarism or security threats. Following the completion of its alpha testing, BeerReview demonstrates the potential for expanded deployment. This platform offers improved convenience and more robust intellectual property protection within the peer review process with open source initiative.

cs.DC↗

arXiv2024

Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments

Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence. This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication and data heterogeneity in FL. This is achieved by defining a new signal-to-noise ratio (SNR) matrix and information quantity matrix to capture the communication and data heterogeneity, respectively. The celebrated affinity propagation algorithm is leveraged to iteratively refine the clustering of clients based on the newly defined matrices effectively enhancing model aggregation in heterogeneous environments. The convergence analysis and experimental results show that the DSC strategy can improve the convergence rate of wireless FL and demonstrate superior accuracy in heterogeneous environments compared to classical clustering methods.

cs.LG↗