arXiv subjects

Erwu Liu

Publications and source records attributed to Erwu Liu.

At least 37 records · Page 2Linked to original sources

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

BeACONS: A Blockchain-enabled Authentication and Communications Network for Scalable IoV

This paper introduces a novel blockchain-enabled authentication and communications network for scalable Internet of Vehicles, which aims to bolster security and confidentiality, diminish communications latency, and reduce dependence on centralised infrastructures like Certificate Authorities and Public Key Infrastructures by leveraging Blockchain-enabled Domain Name Services and Blockchain-enabled Mutual Authentication. The proposed network is structured into a primary layer, consisting of Road Side Units and edge servers as servers of Blockchain-enabled Domain Name Services for managing inter-vehicle communications identities, and a sub-layer within each vehicle for intra-vehicle communications via the Blockchain-enabled Mutual Authentication Protocol. This design facilitates secure connections across vehicles by coordinating between the layers, significantly improving communications security and efficiency. This study also evaluates Road Side Unit availability against the random distribution of Road Side Units along the route of different vehicles. The proposed model presents a novel pathway towards a decentralised, secure, and efficient Internet of Vehicles ecosystem, contributing to the advancement of autonomous and trustworthy vehicular networks.

cs.DC↗

arXiv2024

SNR Maximization and Localization for UAV-IRS-Assisted Near-Field Systems

This letter introduces a novel unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) structure into near-field localization systems to enhance the design flexibility of IRS, thereby obtaining additional performance gains. Specifically, a UAV-IRS is utilized to improve the harsh wireless environment and provide localization possibilities. To improve the localization accuracy, a joint optimization problem considering UAV position and UAV-IRS passive beamforming is formulated to maximize the receiving signal-to-noise ratio (SNR). An alternative optimization algorithm is proposed to solve the complex non-convex problem leveraging the projected gradient ascent (PGA) algorithm and the principle of minimizing the phase difference of the receiving signals. Closed-form expressions for UAV-IRS phase shift are derived to reduce the algorithm complexity. In the simulations, the proposed algorithm is compared with three different schemes and outperforms the others in both receiving SNR and localization accuracy.

eess.SP↗

arXiv2024

A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning

The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users, which takes advantage of the superposition characteristics of wireless channels. The Minimum Mean Square Error (MMSE) based serial interference cancellation (SIC) technology is used to recover the gradient of each terminal node one by one at the receiving end. In this paper, the gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors. On this basis, we designed the SER-based device selection mechanism (SER-DSM) to ensure that the learning performance is not affected by users with bad communication conditions, while accommodating as many users as possible to participate in the learning process, which is inclusive to a certain extent. The experiments show the influence of multi-bit quantization of gradient on FL and the necessity and superiority of the proposed SER-based device selection mechanism.

cs.IT↗

arXiv2024

Blockchain for Finance: A Survey

As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point-to-point transactions, and decentralized autonomous organizations. In this paper, we focus on blockchain-based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third-party verification by using consensus-based verification. We investigate the 12 most popular blockchain platforms and elaborate on 6 platforms that are related to finance, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain-based securities trading applications. We gather numerous practical applications of blockchain-based securities trading and categorize them into four distinct categories. For each category, we introduce a typical example and explain how blockchain contributes to solving the key problems faced by FinTech companies and researchers. Finally, we provide interesting observations ranging from mainstream blockchain-based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance.

cs.CR↗

arXiv2023

When Web 3.0 Meets Reality: A Hyperdimensional Fractal Polytope P2P Ecosystems

Web 3.0 opens the world of new existence of the crypto-network-entity, which is independently defined by the public key pairs for entities and the connection to the Web 3.0 cyberspace. In this paper, we first discover a spacetime coordinate system based on fractal polytope in any dimensions with discrete time offered by blockchain and consensus. Second, the novel network entities and functions are defined to make use of hyperdimensional deterministic switching and routing protocols and blockchain-enabled mutual authentication. In addition to spacetime network architecture, we also define a multi-tier identity scheme which extends the native Web 3.0 crypto-network-entity to outer cyber and physical world, offering legal-compliant anonymity and linkability to all derived identifiers of entities. In this way, we unify the holistic Web 3.0 network based on persistent spacetime and its entity extension to our cyber and physical world.

cs.NI↗

arXiv2022

Beamforming Design and Performance Evaluation for Reconfigurable Intelligent Surface Assisted Wireless Communication Systems With Non-Ideal Hardware

Reconfigurable intelligent surface (RIS) can effectively control the wavefront of the impinging signals and has emerged as a cost-effective promising solution to improve the spectrum and energy efficiency of wireless systems. Most existing researches on RIS assume that the hardware operations are perfect. However, both physical transceiver and RIS suffer from inevitable hardware impairments in practice, which can lead to severe system performance degradation and increase the complexity of beamforming optimization. Consequently, the existing researches on RIS, including channel estimation, beamforming optimization, spectrum and energy efficiency analysis, etc., cannot directly apply to the case of hardware impairments. In this paper, by taking hardware impairments into consideration, we conduct the joint transmit and reflect beamforming optimization, and reevaluate the system performance. First, we characterize the closed-form estimators of direct and cascaded channels in both single-user and multi-user cases, and analyze the impact of hardware impairments on channel estimation accuracy. Then, the optimal transmit beamforming solution is derived, and a gradient descent method-based algorithm is also proposed to optimize the reflect beamforming. Moreover, we analyze the three types of asymptotic channel capacities with respect to the transmit power, the antenna number, and the reflecting element number. Finally, in terms of the system energy consumption, we analyze the power scaling law and the energy efficiency. Our experimental results also reveal an encouraging phenomenon that the RIS-assisted wireless system with massive reflecting elements can achieve both high spectrum and energy efficiency without the need for massive antennas and without allocating too many resources to optimize the reflect beamforming.

cs.IT↗

arXiv2022

Reinforcement Learning Based Robust Policy Design for Relay and Power Optimization in DF Relaying Networks

In this paper, we study the outage minimization problem in a decode-and-forward cooperative network with relay uncertainty. To reduce the outage probability and improve the quality of service, existing researches usually rely on the assumption of both exact instantaneous channel state information (CSI) and environmental uncertainty. However, it is difficult to obtain perfect instantaneous CSI immediately under practical situations where channel states change rapidly, and the uncertainty in communication environments may not be observed, which makes traditional methods not applicable. Therefore, we turn to reinforcement learning (RL) methods for solutions, which do not need any prior knowledge of underlying channel or assumptions of environmental uncertainty. RL method is to learn from the interaction with communication environment, optimize its action policy, and then propose relay selection and power allocation schemes. We first analyse the robustness of RL action policy by giving the lower bound of the worst-case performance, when RL methods are applied to communication scenarios with environment uncertainty. Then, we propose a robust algorithm for outage probability minimization based on RL. Simulation results reveal that compared with traditional RL methods, our approach has better generalization ability and can improve the worst-case performance by about 6% when evaluated in unseen environments.

cs.IT↗

arXiv2021

Asymptotic Achievability of the Cramér-Rao Lower Bound of Channel Estimation for Reconfigurable Intelligent Surface Aided Communication Systems

To achieve the joint active and passive beamforming gains in the reconfigurable intelligent surface assisted millimeter wave system, the reflected cascade channel needs to be accurately estimated. Many strategies have been proposed in the literature to solve this issue. However, whether the Cramér-Rao lower bound (CRLB) of such estimation is achievable still remains uncertain. To fill this gap, we first convert the channel estimation problem into a sparse signal recovery problem by utilizing the properties of discrete Fourier transform matrix and Kronecker product. Then, a joint typicality based estimator is utilized to carry out the signal recovery task. We show that, through both mathematical proofs and numerical simulations, the solution proposed in this letter can in fact asymptotically achieve the CRLB.

cs.IT↗

arXiv2021

Deep Deterministic Policy Gradient for Relay Selection and Power Allocation in Cooperative Communication Network

Perfect channel state information (CSI) is usually required when considering relay selection and power allocation in cooperative communication. However, it is difficult to get an accurate CSI in practical situations. In this letter, we study the outage probability minimizing problem based on optimizing relay selection and transmission power. We propose a prioritized experience replay aided deep deterministic policy gradient learning framework, which can find an optimal solution by dealing with continuous action space, without any prior knowledge of CSI. Simulation results reveal that our approach outperforms reinforcement learning based methods in existing literatures, and improves the communication success rate by about 4%.

cs.IT↗

arXiv2021

Joint Location and Communication Study for Intelligent Reflecting Surface Aided Wireless Communication System

Intelligent reflecting surface (IRS) is a novel burgeoning concept, which possesses advantages in enhancing wireless communication and user localization, while maintaining low hardware cost and energy consumption. Herein, we establish an IRS-aided mmWave-MIMO based joint localization and communication system (IMM-JLCS), and probe into its performance evaluation and optimization design. Specifically, first, we provide the signal, channel and estimation error models, and contrive the working process of the IMM-JLCS in detail. Then, by configuring appropriate IRS phase shifts, we derive the closed-form expressions of the Cramer-Rao Lower Bound (CRLB) of the position/orientation estimation errors and the effective achievable data rate (EADR), with respect to the time allocation ratio of the beam alignment and localization stage (BALS). Subsequently, we investigate the trade-off between the two performance metrics, for which we propose a joint optimization algorithm. Finally, we carry out simulations and comparisons to view the trade-off and validate the effectiveness of the proposed algorithm, in the presence of distinct levels of estimation uncertainty and user mobility. Our results demonstrate that the proposed algorithm can find the joint optimal solution for the position/orientation estimation accuracy and EADR, with its optimization performance being robust to slight localization or channel estimation errors and user mobility.

cs.IT↗

arXiv2021

Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles

The rapid increase of the data scale in Internet of Vehicles (IoV) system paradigm, hews out new possibilities in boosting the service quality for the emerging applications through data sharing. Nevertheless, privacy concerns are major bottlenecks for data providers to share private data in traditional IoV networks. To this end, federated learning (FL) as an emerging learning paradigm, where data providers only send local model updates trained on their local raw data rather than upload any raw data, has been recently proposed to build a privacy-preserving data sharing models. Unfortunately, by analyzing on the differences of uploaded local model updates from data providers, private information can still be divulged, and performance of the system cannot be guaranteed when partial federated nodes executes malicious behavior. Additionally, traditional cloud-based FL poses challenges to the communication overhead with the rapid increase of terminal equipment in IoV system. All these issues inspire us to propose an autonomous blockchain empowered privacy-preserving FL framework in this paper, where the mobile edge computing (MEC) technology was naturally integrated in IoV system.

cs.CR↗

arXiv2021

Passive Beamforming Design and Channel Estimation for IRS Communication System with Few-Bit ADCs

Utilizing intelligent reflecting surface (IRS) was proven to be efficient in improving the energy efficiency for wireless networks. In this paper, we investigate the passive beamforming and channel estimation for IRS assisted wireless communications with low-resolution analog-to-digital converters (ADCs) at the receiver. We derive the approximate achievable rate by using the Bussgang theorem. Based on the derived analytical achievable rate expression, we maximize the achievable rate by using semidefinite programming (SDP), branch-and-bound (BB), and gradient-based approaches. A maximum likelihood (ML) estimator is then proposed for channel estimation by considering the $\mathrm{1}$-bit quantization ADC. Numerical result shows that the proposed beamforming design and channel estimation method significantly outperforms the existing methods.

cs.IT↗

arXiv2021

Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network

Cooperative communication is an effective approach to improve spectrum utilization. In order to reduce outage probability of communication system, most studies propose various schemes for relay selection and power allocation, which are based on the assumption of channel state information (CSI). However, it is difficult to get an accurate CSI in practice. In this paper, we study the outage probability minimizing problem subjected to a total transmission power constraint in a two-hop cooperative relay network. We use reinforcement learning (RL) methods to learn strategies for relay selection and power allocation, which do not need any prior knowledge of CSI but simply rely on the interaction with communication environment. It is noted that conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further propose a hierarchical reinforcement learning (HRL) framework and training algorithm. A key difference from other RL-based methods in existing literatures is that, our proposed HRL approach decomposes relay selection and power allocation into two hierarchical optimization objectives, which are trained in different levels. With the simplification of search space, the HRL approach can solve the problem of sparse reward, while the conventional RL method fails. Simulation results reveal that compared with traditional DRL method, the HRL training algorithm can reach convergence 30 training iterations earlier and reduce the outage probability by 5% in two-hop relay network with the same outage threshold.

eess.SY↗

arXiv2020

Achievable Rate Analysis and Phase Shift Optimization on Intelligent Reflecting Surface with Hardware Impairments

Intelligent reflecting surface (IRS) is envisioned as a promising hardware solution to hardware cost and energy consumption in the fifth-generation (5G) mobile communication network. It exhibits great advantages in enhancing data transmission, but may suffer from performance degradation caused by inherent hardware impairment (HWI). For analysing the achievable rate (ACR) and optimizing the phase shifts in the IRS-aided wireless communication system with HWI, we consider that the HWI appears at both the IRS and the signal transceivers. On this foundation, first, we derive the closed-form expression of the average ACR and the IRS utility. Then, we formulate optimization problems to optimize the IRS phase shifts by maximizing the signal-to-noise ratio (SNR) at the receiver side, and obtain the solution by transforming non-convex problems into semidefinite programming (SDP) problems. Subsequently, we compare the IRS with the conventional decode-and-forward (DF) relay in terms of the ACR and the utility. Finally, we carry out simulations to verify the theoretical analysis, and evaluate the impact of the channel estimation errors and residual phase noises on the optimization performance. Our results reveal that the HWI reduces the ACR and the IRS utility, and begets more serious performance degradation with more reflecting elements. Although the HWI has an impact on the IRS, it still leaves opportunities for the IRS to surpass the conventional DF relay, when the number of reflecting elements is large enough or the transmitting power is sufficiently high.

cs.IT↗

arXiv2020

Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time

Route planning is important in transportation. Existing works focus on finding the shortest path solution or using metrics such as safety and energy consumption to determine the planning. It is noted that most of these studies rely on prior knowledge of road network, which may be not available in certain situations. In this paper, we design a route planning algorithm based on deep reinforcement learning (DRL) for pedestrians. We use travel time consumption as the metric, and plan the route by predicting pedestrian flow in the road network. We put an agent, which is an intelligent robot, on a virtual map. Different from previous studies, our approach assumes that the agent does not need any prior information about road network, but simply relies on the interaction with the environment. We propose a dynamically adjustable route planning (DARP) algorithm, where the agent learns strategies through a dueling deep Q network to avoid congested roads. Simulation results show that the DARP algorithm saves 52% of the time under congestion condition when compared with traditional shortest path planning algorithms.

cs.LG↗

arXiv2020

Reconfigurable Intelligent Surface Aided Wireless Localization

The advantages of millimeter-wave and large antenna arrays technologies for accurate wireless localization received extensive attentions recently. However, how to further improve the accuracy of wireless localization, even in the case of obstructed line-of-sight, is largely undiscovered. In this paper, the reconfigurable intelligent surface (RIS) is introduced into the system to make the positioning more accurate. First, we establish the three-dimensional RIS-assisted wireless localization channel model. After that, we derive the Fisher information matrix and the Cramer-Rao lower bound for evaluating the estimation of absolute mobile station position. Finally, we propose an alternative optimization method and a gradient decent method to optimize the reflect beamforming, which aims to minimize the Cramer-Rao lower bound to obtain a more accurate estimation. Our results show that the proposed methods significantly improve the accuracy of positioning, and decimeter-level or even centimeter-level positioning can be achieved by utilizing the RIS with a large number of reflecting elements.

cs.IT↗

arXiv2020

Channel Estimation and Power Scaling Law of Large Reflecting Surface with Non-Ideal Hardware

Large reflecting surface (LRS) has emerged as a new solution to improve the energy and spectrum efficiency of wireless communication system. Most existing studies were conducted with an assumption of ideal hardware, and the impact of hardware impairments receives little attention. However, the non-negligible hardware impairments should be taken into consideration when we evaluate the system performance. In this paper, we consider an LRS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis. First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. After that, we analyze the power scaling law and reveal that if the base station (BS) has perfect channel state information, the transmit power of user can be made inversely proportional to the number of BS antennas and the square of the number of reflecting elements with no reduction in performance; If the BS has imperfectly estimated channel state information, to achieve the same performance, the transmit power of user can be made inversely proportional to the square-root of the number of BS antennas and the square of the number of reflecting elements.

cs.IT↗