Wei Ni

The Commonwealth Scientific and Industrial Research Organisation | CSIRO · Data61

Indoor wireless networks connect an increasing number of people to the Internet. Unfortunately, spectrum is limited, and the increasing traffic leads to increased interference. We propose a novel network architecture that reconfigures topologies and frequency bands, adapting to changing indoor traffic demands. The key idea is to have fixed antennas...

Small cells are an emerging approach to improving hotspots throughput in cellular networks. Unfortunately, they cannot be deployed in a large scale under current cellular architectures, because of a severe interference problem and inefficient use of spectrum. We propose a new small-cell architecture which reconfigures topologies and frequency bands...

Integrating artificial intelligence (AI) with mobile edge computing, edge intelligence (EI) has emerged as a new paradigm for 5G and beyond 5G (B5G) systems. The integration of EI and heterogeneous networks (e.g., mobile and wireless local area networks) also raises new concerns about security and privacy. This article examines two important securi...

With excellent flexibility, unmanned aerial vehicles (UAVs) can act as airborne computing servers to assist smart terminals (STs) with their computationally-intense and delay-sensitive tasks. This paper presents a new UAV-assisted edge computing framework, which jointly optimizes the trajectory and CPU frequency of a fixed-wing UAV, and the offload...

Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. F...

Cloud-of-clouds storage can enhance the data security and reliability of online applications by encrypting, encoding, and distributing user data across multiple clouds. Fast transferring large volumes of data through networks with limited bandwidths remains a practical challenge, especially in the event of disaster backup. To address this, we model...

Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/transmitter of a multi-task semantic communica...

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical e...

Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clients’ concern about their priva...

In the upcoming 6G era, inclusive intelligent services(IISs) that rely on integrated communications and AI arithmetic will become the norm. These services require efficient distributed intelligent learning or reasoning. However, with the proliferation of complex applications, providing differentiated and customized services through effective networ...

Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed...

In this paper, a novel joint energy and age of information (AoI) optimization framework for IoT devices in a non-stationary environment is presented. In particular, IoT devices that are distributed in the real-world are required to efficiently utilize their computing resources so as to balance the freshness of their data and their energy consumptio...

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...

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This p...

This letter presents a new over-the-air federated learning (OTA-FL) system supported by a user-centric cell-free (UCCF) network. We propose a two-level hierarchical deep reinforcement learning (HDRL) framework that minimizes mean squared error (MSE) derived from convergence analysis by jointly optimizing AP-device association (ADA) and power contro...

Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. In real-world applications like FinTech and Smart Manufacturing, transactional data, often in tabular form, are generated and analyzed for insight generation. However, such datasets typically contain sensitive person...

Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable interest recently. This paper delves into the problem of identifying and protecting critical information of entity connections for individual participants in a graph based on...

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from...

The exploitation of radio channels’ inherent randomness for generating secret keys within a vehicular platoon offers a promising approach to securing communications in dynamic and unpredictable environments. The channel-based key generation leverages the fact that the physical characteristics of the radio channel, such as fading, shadowing, and mul...

As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. As the decoding specification of CAN is a proprietary black-box maintained by Original Equipment Manufacturers (OEMs), conducting related research and industry developmen...

Unmanned aerial vehicles (UAVs) have demonstrated success in delivering goods, but their delivery distances are limited due to their finite battery capacity. While roadside charging stations can replenish the battery, they cause delays and prevent timely delivery. In this paper, we present a novel UAV charging scheduling and speed control framework...

As small base stations (SBSs) are densely deployed to meet the explosive communication demands, the increases in energy consumption and carbon emissions call for the green and sustainable design of next-generation cellular networks. In this paper, we jointly optimize user association and resource allocation for a wireless network powered by a combi...

The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences within an intelligent digital ecosystem where artificial intelligence (AI) plays a crucial role in network management and data analysis. This advancement seeks to enable im...

This paper proposes a novel hierarchical methodology to planning safe UAV trajectories in complex environments. We start by improving a canonical hybrid A* in relation to high memory requirements, performance degradation, and the low efficiency customarily observed in the initial global trajectory suggested by the planner. Then, the Marden theorem...

A cloud network schedules diverse tasks to multi-access edge computing (MEC) or cloud platforms within dynamic industrial Internet of Things (IIoT). The scheduling is influenced by the diverse intents of different parties, including the time-sensitive nature of device-generated tasks and the energy efficiency of servers. The complexity of this prob...

Radio simultaneous localization and mapping (SLAM) is challenging due to multipath propagation. While line-of-sight (LoS) and first-order non-LoS (NLoS) paths, referred to as NLoS-1 paths, play a critical role in SLAM, no existing techniques can effectively separate them from high-order NLoS paths, i.e., NLoS- n paths ( n ≥ 2). This paper present...

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum...

Accurate estimation of the State of Charge (SoC) of Li-Ion batteries is crucial for secure and efficient energy consumption in electric vehicles (EVs). Traditional SoC estimation methods often require expert knowledge of battery chemistry and suffer from limited accuracy due to complex non-linear battery behaviour. Owing to the model-free nature an...

To satisfy the requirements of content distribution in computation-intensive and delay-sensitive services, this paper presents a novel joint task offloading and content caching (JTOCC) scheme in multi-cell multi-carrier non-orthogonal multiple-access (MCMC-NOMA)-assisted cloud-edge-terminal cooperation networks. Based on queuing theory, we formulat...

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and ident...

The emerging sixth-generation (6G) systems aim to integrate machine learning (ML) capabilities into the network architecture. Open Radio Access Network (O-RAN) is a paradigm that supports this vision. However, deep integration of 6G edge intelligence and O-RAN can face challenges in efficient execution of ML tasks due to finite link bandwidth and d...

Powering mobile edge computing (MEC) with a hybrid supply of smart grid (SG) and renewable energy source (RES) offers an opportunity to utilize clean energy and cut down energy expenses under two-way energy transactions. We propose two-timescale online resource allocation and energy management (TSRE) for MEC with a hybrid power supply, adapting to...

Rapid advances in Machine Learning (ML) have triggered new trends in Autonomous Vehicles (AVs). ML algorithms play a crucial role in interpreting sensor data, predicting potential hazards, and optimizing navigation strategies. However, achieving full autonomy in cluttered and complex situations, such as intricate intersections, diverse sceneries, v...

As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. As the decoding specification of CAN is a proprietary black-box maintained by Original Equipment Manufacturers (OEMs), conducting related research and industry developmen...

Unlearning in various learning frameworks remains challenging, with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework, which enables fully parallel unlearning among models exhibiting inheritance. A key enabler is the new Unified Model Inheritance Graph (UMIG),...

Meeting the diverse quality-of-service (QoS) requirements in ultra-dense Internet of Things (IoT) networks operating under varying network loads is challenging. Moreover, latency-critical IoT applications cannot afford excessive control signaling overheads caused by centralized access control methods. A distributed network access approach can poten...

The use of legitimate unmanned aerial vehicles (UAVs) to surveil and track misbehaved UAVs can serve a crucial role in public safety and security. This paper proposes a new deep reinforcement learning (DRL)-based online control scheme for visual-based UAV-on-UAV tracking and monitoring, where a solar-powered, fixed-wing UAV tracks a suspicious UAV...

Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedule...

Unmanned Aerial Vehicles (UAVs) provide agile and safe solutions to communication relay networks, offering improved throughput. However, their modeling and control present challenges, and real-world deployment is hindered by the gap between simulation and reality. Moreover, enhancing situational awareness is critical. Several works in the literatur...

Federated learning (FL) can suffer from communication bottlenecks when deployed in mobile networks, limiting participating clients and deterring FL convergence. In this context, the impact of practical air interfaces with discrete modulation schemes on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggre...

Signal-dependent noise and atmospheric turbulence have hindered the implementation of photon-counting systems for ultraviolet (UV) free-space optical (FSO) communications, despite the great potential of the systems in low-power signal applications. This paper designs a new downlink, multi-user, photon-counting UV FSO communication system that minim...

Integrated sensing and communication (ISAC) is one of the key technologies enabling the sixth-generation (6G) communication systems. Assisted by unmanned aerial vehicles (UAVs), terrestrial ISAC systems can take advantage of the UAVs’ flexibility and line-of-sight. In this letter, we propose a new ISAC system enabled by a cellular-connected UAV, wh...

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This p...

The emerging concept of channel twinning (CT) has great potential to become a key enabler of ubiquitous connectivity in next-generation (xG) wireless systems. By fusing multimodal sensor data, CT advocates a high-fidelity and low-overhead channel acquisition paradigm, which is promising to provide accurate channel prediction in cross-domain and hig...

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...

This paper studies the potential of tightly coupling the Internet-of-Things (IoT) and smart grids for effective management of energy. A new approach is presented to minimize energy costs for IoT devices and edge servers, and reduce reliance on non-renewable energy by diversifying power supply. Rechargeable batteries at end devices are considered fo...

Artificial Intelligence (AI) has advanced significantly in various domains like healthcare, finance, and cybersecurity, with successes such as DeepMind's medical imaging and Tesla's autonomous vehicles. As telecommunications transition from 5G to 6G, integrating AI is crucial for complex demands like data processing, network optimization, and secur...

This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex graph structures, our approach ade...

Recent attacks on federated learning (FL) can introduce malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This paper proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in federated learning. The LayerCAM-AE puts forth a new Layer Class Activation Map...

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from...

In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each utilizing a different energy source: grid power...

Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable interest recently. This paper delves into the problem of identifying and protecting critical information of entity connections for individual participants in a graph based on...

Federated learning (FL) is a distributed machine learning paradigm with high efficiency and low communication load, only transmitting parameters or gradients of network. However, the non-independent and identically distributed (Non-IID) data characteristic has a negative impact on this paradigm. Furthermore, the heterogeneity of communication quali...