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Yonghui Li

Publications and source records attributed to Yonghui Li.

At least 73 records · Page 4Linked to original sources
arXiv2024

Meta Reinforcement Learning for Resource Allocation in Multi-Antenna UAV Network with Rate Splitting Multiple Access

Unmanned aerial vehicles (UAVs) with multiple antennas have recently been explored to improve capacity in wireless networks. However, the strict energy constraint of UAVs, given their simultaneous flying and communication tasks, renders the exploration of energy-efficient multi-antenna techniques indispensable for UAVs. Meanwhile, lens antenna subarray (LAS) emerges as a promising energy-efficient solution that has not been previously harnessed for this purpose. In this paper, we propose a LAS-aided multi-antenna UAV to serve ground users in the downlink transmission of the terahertz (THz) band, utilizing rate splitting multiple access (RSMA) for effective beam division multiplexing. We formulate an optimization problem of maximizing the total system spectral efficiency (SE). This involves optimizing the UAV's transmit beamforming and the common rate of RSMA. By recasting the optimization problem into a Markov decision process (MDP), we propose a deep deterministic policy gradient (DDPG)-based resource allocation mechanism tailored to capture problem dynamics and optimize its variables. Moreover, given the UAV's frequent mobility and consequential system reconfigurations, we fortify the trained DDPG model with a meta-learning strategy, enhancing its adaptability to system variations. Numerically, more than 20\% energy efficiency gain is achieved by our proposed LAS-aided multi-antenna UAV equipped with 4 lenses, compared to a single-lens UAV. Simulations also demonstrate that at a signal-to-noise (SNR) of 10 dB, the incorporation of RSMA results in a 22\% SE enhancement over conventional orthogonal beam division multiple access. Furthermore, the overall system SE improves by 27\%, when meta-learning is employed for fine-tuning the conventional DDPG method in literature.

eess.SP↗
arXiv2024

Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approach

In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise. A discussion of the quasi-static Rayleigh fading channel is presented herein to enhance the realism of the underlying assumption in a real-world context. To achieve a desirable performance, the proposed control scheme leverages the predictive capabilities of a direct multiple shooting MPC, employs a compensation strategy to mitigate the impact of wireless channel imperfections. Instead of feeding noisy measurements into the MPC, we employ an Extended Kalman Filter (EKF) to mitigate the influence of measurement noise and process disturbances. Finally, we implement the proposed MPC algorithm on a simulated Unmanned Ground Vehicle (UGV) and conduct a series of experiments to evaluate the performance of our control scheme across various scenarios. Through our simulation results and comparative analyses, we have substantiated the effectiveness and improvements brought about by our approach through the utilization of multiple metrics.

eess.SY↗
arXiv2024

Meta Reinforcement Learning for Resource Allocation in Aerial Active-RIS-assisted Networks with Rate-Splitting Multiple Access

Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional methods deploying passive RIS on UAVs, this study delves into the efficacy of an aerial active RIS (AARIS). Specifically, the downlink transmission of an AARIS network is investigated, where the base station (BS) leverages rate-splitting multiple access (RSMA) for effective interference management and benefits from the support of an AARIS for jointly amplifying and reflecting the BS's transmit signals. Considering both the non-trivial energy consumption of the active RIS and the limited energy storage of the UAV, we propose an innovative element selection strategy for optimizing the on/off status of RIS elements, which adaptively and remarkably manages the system's power consumption. To this end, a resource management problem is formulated, aiming to maximize the system energy efficiency (EE) by jointly optimizing the transmit beamforming at the BS, the element activation, the phase shift and the amplification factor at the RIS, the RSMA common data rate at users, as well as the UAV's trajectory. Due to the dynamicity nature of UAV and user mobility, a deep reinforcement learning (DRL) algorithm is designed for resource allocation, utilizing meta-learning to adaptively handle fast time-varying system dynamics. Simulations indicate that incorporating an active RIS at the UAV leads to substantial EE gain, compared to passive RIS-aided UAV. We observe the superiority of the RSMA-based AARIS system in terms of EE, compared to existing approaches adopting non-orthogonal multiple access (NOMA).

cs.IT↗
arXiv2024

A Constrained Deep Reinforcement Learning Optimization for Reliable Network Slicing in a Blockchain-Secured Low-Latency Wireless Network

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tampering attacks in NS networks, we propose a novel optimization framework for reliable NS resource allocation in a blockchain-secured low-latency wireless network, where trusted base stations (BSs) with high reputations are selected for blockchain management and NS service-provisioning. For such a blockchain-secured network, we consider that the latency is measured by the summation of blockchain management and NS service-provisioning, whilst the NS reliability is evaluated by the BS denial-of-service (DoS) probability. To satisfy the requirements of both the latency and reliability, we formulate a constrained computing resource allocation optimization problem to minimize the total processing latency subject to the BS DoS probability. To efficiently solve the optimization, we design a constrained deep reinforcement learning (DRL) algorithm, which satisfies both latency and DoS probability requirements by introducing an additional critic neural network. The proposed constrained DRL further solves the issue of high input dimension by incorporating feature engineering technology. Simulation results validate the effectiveness of our approach in achieving reliable and low-latency NS service-provisioning in the considered blockchain-secured wireless network.

eess.SP↗
arXiv2023

Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications

This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper. To improve the privacy and prevent eavesdropping attacks, we propose a user scheduling algorithm to schedule users satisfying an instantaneous minimum secrecy rate constraint. Based on this, we optimize the bandwidth allocations with three algorithms namely iterative search (IvS), GNN-based supervised learning (GNN-SL), and GNN-based unsupervised learning (GNN-USL). We present a computational complexity analysis which shows that GNN-SL and GNN-USL can be more efficient compared to IvS which is limited by the bandwidth block size. Numerical simulation results highlight that our proposed GNN-based resource allocations can achieve a comparable sum secrecy rate compared to IvS with significantly lower computational complexity. Furthermore, we observe that the GNN approach is more robust to uncertainties in the eavesdropper's channel state information, especially compared with the best channel allocation scheme.

cs.IT↗
arXiv2023

Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks.} {We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability}. {We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to} validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and {MEC} resource allocation algorithms.

cs.LG↗
arXiv2023

Machine learning approach to analyze heavy quark diffusion coefficient in relativistic heavy-ion collisions

The diffusion coefficient of heavy quarks in the deconfined medium is examined in this research using a deep convolutional neural network (CNN) trained with data from relativistic heavy ion collisions involving heavy flavor hadrons. The CNN is trained using observables such as the nuclear modification factor $R_{AA}$ and the elliptic flow $v_2$ of non-prompt $J/ψ$ from B-hadron decay in different centralities, where B meson evolutions are calculated using the Langevin equation and the Instantaneous Coalescence Model. The CNN outputs the parameters characterizing the temperature and momentum dependence of the heavy quark diffusion coefficient. By inputting the experimental data of non-prompt $J/ψ$ $(R_{AA}, v_2)$ from various collision centralities into multiple channels of the well-trained network, we derive the values of the diffusion coefficient parameters. Additionally, We evaluate the uncertainty in determining the diffusion coefficient by taking into account the uncertainties present in the experimental data $(R_{AA}, v_2)$, which serve as inputs to the deep neural network.

nucl-th↗
arXiv2023

When Distributed Consensus Meets Wireless Connected Autonomous Systems: A Review and A DAG-based Approach

The connected and autonomous systems (CAS) and auto-driving era is coming into our life. To support CAS applications such as AI-driven decision-making and blockchain-based smart data management platform, data and message exchange/dissemination is a fundamental element. The distributed message broadcast and forward protocols in CAS, such as vehicular ad hoc networks (VANET), can suffer from significant message loss and uncertain transmission delay, and faulty nodes might disseminate fake messages to confuse the network. Therefore, the consensus mechanism is essential in CAS with distributed structure to guaranteed correct nodes agree on the same parameter and reach consistency. However, due to the wireless nature of CAS, traditional consensus cannot be directly deployed. This article reviews several existing consensus mechanisms, including average/maximum/minimum estimation consensus mechanisms that apply on quantity, Byzantine fault tolerance consensus for request, state machine replication (SMR) and blockchain, as well as their implementations in CAS. To deploy wireless-adapted consensus, we propose a Directed Acyclic Graph (DAG)-based message structure to build a non-equivocation data dissemination protocol for CAS, which has resilience against message loss and unpredictable forwarding latency. Finally, we enhance this protocol by developing a two-dimension DAG-based strategy to achieve partial order for blockchain and total order for the distributed service model SMR.

cs.NI↗
arXiv2023

Simulations of the Visible, Infrared and Terahertz Properties of Doped Cyclo[18]carbon Molecules Using Tuned Exchange-Correlation Potential

Cyclocarbon molecules are critical in understanding the carbon structure formation and the nature of the interaction between carbon atoms. In cyclocarbons, light elements such as H, O and N may interplay with rings to form doped cyclocarbon molecules. Such molecules show unique optical properties that have never been reported before. In this study, density functional theory with a tuned PBE functional (39% HF exchange) is employed to study the ground and excited states of a cyclo[18] carbon molecule (C18) and its doped variants C18M (M = H, Be, B, N, and O). The doping is shown to either make the UV-Vis spectra of C18 blue- or red-shifted depending on the spin brought by the dopant. Furthermore, introducing extra-atoms is found to cause the emergence of a new set of infrared modes in the structure under investigation. Finally, applying the molecular dynamic simulations enables one to observe the terahertz characteristics of C18M with frequencies up to 1.5 THz due to the propagation of a particular pattern along the carbon ring.

cond-mat.mes-hall↗
arXiv2023

Functional Group Induced Transformations in Stacking and Electron Structure in Mo2CTx/NiS Heterostructures

The two-dimensional transition metal carbide/nitride family (MXenes) has garnered significant attention due to their highly customizable surface functional groups. Leveraging modern material science techniques, the customizability of MXenes can be enhanced further through the construction of associated heterostructures. As indicated by recent research, the Mo2CTx/NiS heterostructure has emerged as a promising candidate exhibiting superior physical and chemical application potential. The geometrical structure of Mo2CTx/NiS heterostructure is modeled and 6 possible configurations are validated by Density Functional Theory simulations. The variation in functional groups leads to structural changes in Mo2CTx/NiS interfaces, primarily attributed to the competition between van der Waals and covalent interactions. The presence of different functional groups results in significant band fluctuations near the Fermi level for Ni and Mo atoms, influencing the role of atoms and electron's ability to escape near the interface. This, in turn, modulates the strength of covalent interactions at the MXenes/NiS interface and alters the ease of dissociation of the MXenes/NiS complex. Notably, the Mo2CO2/NiS(P6_3/mmc) heterostructure exhibits polymorphism, signifying that two atomic arrangements can stabilize the structure. The transition process between these polymorphs is also simulated, further indicating the modulation of the electronic level of properties by a sliding operation.

cond-mat.mes-hall↗
arXiv2023

Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach

For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance. Semantics here is a measure of the usefulness of information transmissions. Semantic-aware transmission scheduling of a large system often involves a large decision-making space, and the optimal policy cannot be obtained by existing algorithms effectively. In this paper, we first investigate the fundamental properties of the optimal semantic-aware scheduling policy and then develop advanced deep reinforcement learning (DRL) algorithms by leveraging the theoretical guidelines. Our numerical results show that the proposed algorithms can substantially reduce training time and enhance training performance compared to benchmark algorithms.

cs.LG↗
arXiv2023

Hybrid NOMA assisted Integrated Sensing and Communication via RIS

This paper investigates the optimization of reconfigurable intelligent surface (RIS) in an integrated sensing and communication (ISAC) system. \red{To meet the demand of growing number of devices, power domain non-orthogonal multiple access (NOMA) is considered. However, traditional NOMA with a large number of devices is challenging due to large decoding delay and propagation error introduced by successive interference cancellation (SIC). Thus, OMA is integrated into NOMA to support more devices}. We formulate a max-min problem to optimize the sensing beampattern \red{with constraints on communication rate}, through joint power allocation, active beamforming and RIS phase shift design. To solve the non-convex problem with a non-smooth objective function, we propose a low complexity alternating optimization (AO) algorithm, where a closed form expression for the intra-cluster power allocation (intra-CPA) is derived, and penalty and successive convex approximation (SCA) methods are used to optimize the beamforming and phase shift design. Simulation results show the effectiveness of the proposed algorithm in terms of improving minimum beampattern gain (MBPG) compared with other baselines. Furthermore, the trade-off between sensing and communication is analyzed and demonstrated in the simulation results.

eess.SP↗
arXiv2023

Efficient Near Maximum-Likelihood Reliability-Based Decoding for Short LDPC Codes

In this paper, we propose an efficient decoding algorithm for short low-density parity check (LDPC) codes by carefully combining the belief propagation (BP) decoding and order statistic decoding (OSD) algorithms. Specifically, a modified BP (mBP) algorithm is applied for a certain number of iterations prior to OSD to enhance the reliability of the received message, where an offset parameter is utilized in mBP to control the weight of the extrinsic information in message passing. By carefully selecting the offset parameter and the number of mBP iterations, the number of errors in the most reliable positions (MRPs) in OSD can be reduced by mBP, thereby significantly improving the overall decoding performance of error rate and complexity. Simulation results show that the proposed algorithm can approach the maximum-likelihood decoding (MLD) for short LDPC codes with only a slight increase in complexity compared to BP and a significant decrease compared to OSD. Specifically, the order-(m-1) decoding of the proposed algorithm can achieve the performance of the order-m OSD.

cs.IT↗
arXiv2023

Task-Oriented Metaverse Design in the 6G Era

As an emerging concept, the Metaverse has the potential to revolutionize the social interaction in the post-pandemic era by establishing a digital world for online education, remote healthcare, immersive business, intelligent transportation, and advanced manufacturing. The goal is ambitious, yet the methodologies and technologies to achieve the full vision of the Metaverse remain unclear. In this paper, we first introduce the three infrastructure pillars that lay the foundation of the Metaverse, i.e., human-computer interfaces, sensing and communication systems, and network architectures. Then, we depict the roadmap towards the Metaverse that consists of four stages with different applications. To support diverse applications in the Metaverse, we put forward a novel design methodology: task-oriented design, and further review the challenges and the potential solutions. In the case study, we develop a prototype to illustrate how to synchronize a real-world device and its digital model in the Metaverse by task-oriented design, where a deep reinforcement learning algorithm is adopted to minimize the required communication throughput by optimizing the sampling and prediction systems subject to a synchronization error constraint.

cs.NI↗
arXiv2023

Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services

Generative AI (GAI) models have been rapidly advancing, with a wide range of applications including intelligent networks and mobile AI-generated content (AIGC) services. Despite their numerous applications and potential, such models create opportunities for novel security challenges. In this paper, we examine the challenges and opportunities of GAI in the realm of the security of intelligent network AIGC services such as suggesting security policies, acting as both a ``spear'' for potential attacks and a ``shield'' as an integral part of various defense mechanisms. First, we present a comprehensive overview of the GAI landscape, highlighting its applications and the techniques underpinning these advancements, especially large language and diffusion models. Then, we investigate the dynamic interplay between GAI's spear and shield roles, highlighting two primary categories of potential GAI-related attacks and their respective defense strategies within wireless networks. A case study illustrates the impact of GAI defense strategies on energy consumption in an image request scenario under data poisoning attack. Our results show that by employing an AI-optimized diffusion defense mechanism, energy can be reduced by 8.7%, and retransmission count can be decreased from 32 images, without defense, to just 6 images, showcasing the effectiveness of GAI in enhancing network security.

cs.CR↗
arXiv2023

Spin Cooperated Catalytic Activities in Mn-N4 based Single-atom Nanozyme: Mechanisms and a Brief Charge-spin Model

Although developing artificial enzymes has made great progress, there is still a gap between artificial enzymes and natural enzymes in catalytic performance. Designing and constructing efficient artificial biocatalysts is extremely desirable because of their high stability, low cost and easy storage. Here, we report a synthesized amino-functionalized graphene quantum dots-based manganese single atom catalyst (SAC) Mn-N4, which exhibits POD-, CAT, SOD-like activities, especially the superior SOD-like activity. Recent studies have reported Mn-based SAzymes, however, the multi-enzyme mimicking catalytic mechanisms for Mn-N4 are not comprehensive and in-depth enough. Therefore, we combine density functional theory (DFT) calculations and machine learning (ML) to validate the performance of the multi-enzyme mimicking activities. The DFT simulations show that Mn-N4 owns a highly effective SOD in the "one-side adsorption" with a very low energy barrier of 0.077 eV, which can be attributed to variation of the preferred spin states of Mn-O2.- system and its "spin flip-collection lock" in the SOD-like catalytic procedure. Furthermore, spin related charge distributions on Mn-N4 configurations by machine learning (ML) analysis suggest that the pattern of spin and natural charge/valence electron distribution will exhibit similarity in the structures of multiple intermediate steps of multi-enzyme mimicking activities. This work not only puts forward the catalytic mechanisms of Mn-N4 SAzymes, but also provides essential guidance for future design of highly performance artificial enzymes.

cond-mat.mes-hall↗
arXiv2023

Matrix Factorization Based Blind Bayesian Receiver for Grant-Free Random Access in mmWave MIMO mMTC

Grant-free random access is promising for massive connectivity with sporadic transmissions in massive machine type communications (mMTC), where the hand-shaking between the access point (AP) and users is skipped, leading to high access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we explore the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging our recently developed unitary approximate message passing based matrix factorization (UAMP-MF) algorithm, we design a message passing based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme.

cs.IT↗
arXiv2023

Design of a Reconfigurable Intelligent Surface-Assisted FM-DCSK-SWIPT Scheme with Non-linear Energy Harvesting Model

In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted frequency-modulated (FM) differential chaos shift keying (DCSK) scheme with simultaneous wireless information and power transfer (SWIPT), called RIS-FM-DCSK-SWIPT scheme, for low-power, low-cost, and high-reliability wireless communication networks. In particular, the proposed scheme is developed under a non-linear energy-harvesting (EH) model which can accurately characterize the practical situation. The proposed RIS-FM-DCSK-SWIPT scheme has an appealing feature that it does not require channel state information, thus avoiding the complex channel estimation. We further derive the closed-form theoretical expressions for the energy shortage probability and bit error rate (BER) of the proposed scheme over the multipath Rayleigh fading channel. In addition, we investigate the influence of key parameters on the performance of the proposed transmission scheme in two different scenarios, i.e., RIS-assisted access point (RIS-AP) and dual-hop communication (RIS-DH). Finally, we carry out various Monte-Carlo experiments to verify the accuracy of the theoretical derivation, illustrate the performance advantage of the proposed scheme, and give some design insights for future study.

cs.IT↗