arXiv subjects

Zhu Han

Publications and source records attributed to Zhu Han.

At least 361 records · Page 20Linked to original sources

arXiv2021

Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation, and has shown faster convergence than gradient descent based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IIoT networks. We provide convergence properties for proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has $O(\frac{1}{k}) +O(\frac{1}{M})$ convergence rate where $k$ and $ M$ are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.

cs.LG↗

arXiv2021

Intelligent Omni-Surface: Ubiquitous Wireless Transmission by Reflective-Transmissive Metasurface

Intelligent reflecting surface (IRS), which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface, has been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, IOS is an important instance of reconfigurable intelligent surface (RIS) that is capable to provide service coverage to the mobile users (MUs) in a reflective and a transmissive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS perform beamforming jointly, to improve the received power of multiple MUs on both sides of the IOS, through different reflective/transmissive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to solve the resulting non-convex program efficiently. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.

eess.SY↗

arXiv2021

HAP-Reserved Communications in Space-Air-Ground Integrated Networks

Terrestrial communication networks have experienced significant development in recent years by providing emerging services for ground users. However, one critical challenge raised is to provide full coverage (especially in dense high-rise urban environments) for ground users due to scarce network resources and limited coverage. To meet this challenge, we propose a high altitude platform (HAP)-reserved ground-air-space (GAS) transmission scheme, which combines with the ground-to-space (G2S) transmission scheme to strengthen the terrestrial communication and save the transmission power. To integrate the two transmission schemes, we propose a transmission control strategy. Wherein, the ground user decides its transmission scheme, i.e., switches between the GAS link transmission and the G2S link transmission with a probability. We then maximize the overall throughput and derive the optimal probability that a ground user adopts the GAS transmission scheme. Numerical results demonstrate the superiority of the proposed transmission control strategy.

cs.NI↗

arXiv2021

Minimizing Delay in Network Function Visualization with Quantum Computing

Network function virtualization (NFV) is a crucial technology for the 5G network development because it can improve the flexibility of employing hardware and reduce the construction of base stations. There are vast service chains in NFV to meet users' requests, which are composed of a sequence of network functions. These virtual network functions (VNFs) are implemented in virtual machines by software and virtual environment. How to deploy VMs to process VNFs of the service chains as soon as possible when users' requests are received is very challenging to solve by traditional algorithms on a large scale. Compared with traditional algorithms, quantum computing has better computational performance because of quantum parallelism. We build an integer linear programming model of the VNF scheduling problem with the objective of minimizing delays and transfer it into the quadratic unconstrained binary optimization (QUBO) model. Our proposed heuristic algorithm employs a quantum annealer to solve the model. Finally, we evaluate the computational results and explore the feasibility of leveraging quantum computing to solve the VNFs scheduling problem.

eess.SY↗

arXiv2021

Dispersed Federated Learning: Vision, Taxonomy, and Future Directions

The ongoing deployment of the Internet of Things (IoT)-based smart applications is spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been a significant recent interest in the concept of federated learning. Federated learning offers on-device, privacy-preserving machine learning without the need to transfer end-devices data to a third party location. However, federated learning still has privacy concerns due to sensitive information inferring capability of the aggregation server using end-devices local learning models. Furthermore, the federated learning process might fail due to a failure in the aggregation server (e.g., due to a malicious attack or physical defect). Other than privacy and robustness issues, federated learning over IoT networks requires a significant amount of communication resources for training. To cope with these issues, we propose a novel concept of dispersed federated learning (DFL) that is based on the true decentralization. We opine that DFL will serve as a practical implementation of federated learning for various IoT-based smart applications such as smart industries and intelligent transportation systems. First, the fundamentals of the DFL are presented. Second, a taxonomy is devised with a qualitative analysis of various DFL schemes. Third, a DFL framework for IoT networks is proposed with a matching theory-based solution. Finally, an outlook on future research directions is presented.

cs.DC↗

arXiv2021

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Third, we propose two IoT use cases of dispersed federated learning that can offer better privacy preservation than federated learning. Finally, we present several open research challenges with their possible solutions.

cs.NI↗

arXiv2021

Converged Reconfigurable Intelligent Surface and Mobile Edge Computing for Space Information Networks

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area.

cs.NI↗

arXiv2021

When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the traditional FL framework heavily relies on a single central server and may fall apart if such a server behaves maliciously. To address this single point of failure issue, this work investigates a blockchain assisted decentralized FL (BLADE-FL) framework, which can well prevent the malicious clients from poisoning the learning process, and further provides a self-motivated and reliable learning environment for clients. In detail, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. In addition, we investigate the unique issues in this framework and provide analytical and experimental results to shed light on possible solutions.

cs.NI↗

arXiv2021

Edge-assisted Democratized Learning Towards Federated Analytics

A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the summary of model performances across the training devices. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, which often results in learning models with poor generalization, i.e., an ability to handle new/unseen data, for real-world applications. Moreover, a hierarchical FL structure with distributed computing platforms demonstrates incoherent model performances at different aggregation levels. Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability. In this work, we adopt the novel democratized learning (Dem-AI) principles and designs to meet these objectives. Firstly, we show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn, as a practical framework to empower generalization capability in support of FA. Secondly, we validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions by leveraging the distributed computing infrastructure. The distributed edge computing servers construct regional models, minimize the communication loads, and ensure distributed data analytic application's scalability. To that end, we adhere to a near-optimal two-sided many-to-one matching approach to handle the combinatorial constraints in Edge-DemLearn and solve it for fast knowledge acquisition with optimization of resource allocation and associations between multiple servers and devices. Extensive simulation results on real datasets demonstrate the effectiveness of the proposed methods.

cs.LG↗

arXiv2021

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy server, and external attacks. To address this issue, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts the trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training of the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds K, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients. Based on MNIST and Fashion-MNIST datasets, we show that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than 5%, and the optimized K based on the upper bound can effectively minimize the loss function.

cs.LG↗

arXiv2021

A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars

Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.

cs.NI↗

arXiv2021

AI-Assisted MAC for Reconfigurable Intelligent Surface-Aided Wireless Networks: Challenges and Opportunities

Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve the physical layer performance, including the wireless coverage, achievable data rate and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate upon centralized, distributed and hybrid artificial-intelligence (AI)-assisted MAC architectures in RIS-aided multi-user communications systems. Finally, we discuss some challenges, perspectives and potential applications of RISs as they are related to MAC design.

cs.NI↗

arXiv2021

Reconfigurable Intelligent Surface-Assisted Aerial-Terrestrial Communications via Multi-Task Learning

The aerial-terrestrial communication system constitutes an efficient paradigm for supporting and complementing terrestrial communications. However, the benefits of such a system cannot be fully exploited, especially when the line-of-sight (LoS) transmissions are prone to severe deterioration due to complex propagation environments in urban areas. The emerging technology of reconfigurable intelligent surfaces (RISs) has recently become a potential solution to mitigate propagation-induced impairments and improve wireless network coverage. Motivated by these considerations, in this paper, we address the coverage and link performance problems of the aerial-terrestrial communication system by proposing an RIS-assisted transmission strategy. In particular, we design an adaptive RIS-assisted transmission protocol, in which the channel estimation, transmission strategy, and data transmission are independently implemented in a frame. On this basis, we formulate an RIS-assisted transmission strategy optimization problem as a mixed-integer non-linear program (MINLP) to maximize the overall system throughput. We then employ multi-task learning to speed up the solution to the problem. Benefiting from multi-task learning, the computation time is reduced by about four orders of magnitude. Numerical results show that the proposed RIS-assisted transmission protocol significantly improves the system throughput and reduces the transmit power.

cs.NI↗

arXiv2021

Latency-sensitive Service Delivery with UAV-Assisted 5G Networks

In this letter, a novel framework to deliver critical spread out URLLC services deploying unmanned aerial vehicles (UAVs) in an out-of-coverage area is developed. To this end, the resource optimization problem, i.e., resource blocks (RBs) and power allocation, and optimal UAV deployment strategy are studied for UAV-assisted 5G networks to jointly maximize the average sum-rate and minimize the transmit power of UAV while satisfying the URLLC requirements. To cope with the sporadic URLLC traffic problem, an efficient online URLLC traffic prediction model based on Gaussian Process Regression (GPR) is proposed which derives optimal URLLC scheduling and transmit power strategy. The formulated problem is revealed as a mixed-integer nonlinear programming (MINLP), which is solved following the introduced successive minimization algorithm. Finally, simulation results are provided to show our proposed solution approach's efficiency.

cs.NI↗

arXiv2021

Reconfigurable Intelligent Surface-Assisted MAC for Wireless Networks: Protocol Design, Analysis, and Optimization

Reconfigurable intelligent surface (RIS) is a promising reflective radio technology for improving the coverage and rate of future wireless systems by reconfiguring the wireless propagation environment. The current work mainly focuses on the physical layer design of RIS. However, enabling multiple devices to communicate with the assistance of RIS is a crucial challenging problem. Motivated by this, we explore RIS-assisted communications at the medium access control (MAC) layer and propose an RIS-assisted MAC framework. In particular, RISassisted transmissions are implemented by pre-negotiation and a multi-dimension reservation (MDR) scheme. Based on this, we investigate RIS-assisted single-channel multi-user (SCMU) communications. Wherein the RIS regarded as a whole unity can be reserved by one user to support the multiple data transmissions, thus achieving high efficient RIS-assisted connections at the user. Moreover, under frequency-selective channels, implementing the MDR scheme on the RIS group division, RISassisted multi-channel multi-user (MCMU) communications are further explored to improve the service efficiency of the RIS and decrease the computation complexity. Besides, a Markov chain is built based on the proposed RIS-assisted MAC framework to analyze the system performance of SCMU/MCMU. Then the optimization problem is formulated to maximize the overall system capacity of SCMU/MCMU with energy-efficient constraint. The performance evaluations demonstrate the feasibility and effectiveness of each

cs.NI↗

arXiv2021

Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches

A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of the RIS and enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable to provide NOMA with artificial controlled channel conditions, which can be seen as a beneficial operation condition to obtain NOMA gains. To optimize the sum rate of all users, a deep deterministic policy gradient (DDPG) algorithm is invoked to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. In order to improve the efficiency and effectiveness of agent training for the DDPG agents, a federated learning (FL) concept is adopted to enable multiple agents to simultaneously explore similar environments and exchange experiences. We also proved that with the same random exploring policy, the FL armed deep reinforcement learning (DRL) agents can theoretically obtain a reward gain compare to the independent agents. Our simulation results indicate that the mobile RIS scheme can significantly outperform the fixed RIS paradigm, which provides about three times data rate gain compare to the fixed RIS paradigm. Moreover, the NOMA scheme is capable to achieve a gain of 42% in contrast with the OMA scheme in terms of sum rate. Finally, the multi-cell simulation proved that the FL enhanced DDPG algorithm has a superior convergence rate and optimization performance than the independent training framework.

cs.NI↗

arXiv2021

Spatial Equalization Before Reception: Reconfigurable Intelligent Surfaces for Multi-path Mitigation

Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous reflection, have appeared as a promising method to enhance wireless systems. In this paper, we propose to use an RIS as a spatial equalizer to address the well-known multi-path fading phenomenon. By introducing some controllable paths artificially against the multi-path fading through the RIS, we can perform equalization during the transmission process instead of at the receiver, and thus all the users can share the same equalizer. Unlike the beamforming application of the RIS, which aims to maximize the received energy at receivers, the objective of the equalization application is to reduce the inter-symbol interference (ISI), which makes phase shifts at the RIS different. To this end, we formulate the phase shift optimization problem and propose an iterative algorithm to solve it. Simulation results show that the multi-path fading effect can be eliminated effectively compared to benchmark schemes.

cs.IT↗

arXiv2021

Reconfigurable Intelligent Surfaces in 6G: Reflective, Transmissive, or Both?

Reconfigurable intelligent surfaces (RISs) have attracted wide interest from industry and academia since they can shape the wireless environment into a desirable form with a low cost. In practice, RISs have three types of implementations: 1) reflective, where signals can be reflected to the users on the same side of the base station (BS), 2) transmissive, where signals can penetrate the RIS to serve the users on the opposite side of the BS, and 3) hybrid, where the RISs have a dual function of reflection and transmission. However, existing works focus on the reflective type RISs, and the other two types of RISs are not well investigated. In this letter, a downlink multi-user RIS-assisted communication network is considered, where the RIS can be one of these types. We derive the system sum-rate, and discuss which type can yield the best performance under a specific user distribution. Numerical results verify our analysis.

cs.IT↗