56 research outputs found
Holographic-Type Communication for Digital Twin: A Learning-based Auction Approach
Digital Twin (DT) technologies, which aim to build digital replicas of
physical entities, are the key to providing efficient, concurrent simulation
and analysis of real-world objects. In displaying DTs, Holographic-Type
Communication (HTC), which supports the transmission of holographic data such
as Light Field (LF), can provide an immersive way for users to interact with
Holographic DTs (HDT). However, it is challenging to effectively allocate
interactive and resource-intensive HDT services among HDT users and providers.
In this paper, we integrate the paradigms of HTC and DT to form a HTC for DT
system, design a marketplace for HDT services where HDT users' and providers'
prices are evaluated by their valuation functions, and propose an auction-based
mechanism to match HDT services using a learning-based Double Dutch Auction
(DDA). Specifically, we apply DDA and train an agent acting as the auctioneer
to adjust the auction clock dynamically using Deep Reinforcement Learning
(DRL), aiming to achieve the best market efficiency. Simulation results
demonstrate that the proposed learning-based auctioneer can achieve
near-optimal social welfare at halved auction information exchange cost of the
baseline method.Comment: 6 page
AI-Generated Network Design: A Diffusion Model-based Learning Approach
The future networks pose intense demands for intelligent and customized
designs to cope with the surging network scale, dynamically time-varying
environments, diverse user requirements, and complicated manual configuration.
However, traditional rule-based solutions heavily rely on human efforts and
expertise, while data-driven intelligent algorithms still lack interpretability
and generalization. In this paper, we propose the AIGN (AI-Generated Network),
a novel intention-driven paradigm for network design, which allows operators to
quickly generate a variety of customized network solutions and achieve
expert-free problem optimization. Driven by the diffusion model-based learning
approach, AIGN has great potential to learn the reward-maximizing trajectories,
automatically satisfy multiple constraints, adapt to different objectives and
scenarios, or even intelligently create novel designs and mechanisms unseen in
existing network environments. Finally, we conduct a use case to demonstrate
that AIGN can effectively guide the design of transmit power allocation in
digital twin-based access networks.Comment: 7 pages, 3 figure
Entangled Pair Resource Allocation under Uncertain Fidelity Requirements
In quantum networks, effective entanglement routing facilitates remote
entanglement communication between quantum source and quantum destination
nodes. Unlike routing in classical networks, entanglement routing in quantum
networks must consider the quality of entanglement qubits (i.e., entanglement
fidelity), presenting a challenge in ensuring entanglement fidelity over
extended distances. To address this issue, we propose a resource allocation
model for entangled pairs and an entanglement routing model with a fidelity
guarantee. This approach jointly optimizes entangled resources (i.e., entangled
pairs) and entanglement routing to support applications in quantum networks.
Our proposed model is formulated using two-stage stochastic programming, taking
into account the uncertainty of quantum application requirements. Aiming to
minimize the total cost, our model ensures efficient utilization of entangled
pairs and energy conservation for quantum repeaters under uncertain fidelity
requirements. Experimental results demonstrate that our proposed model can
reduce the total cost by at least 20\% compared to the baseline model.Comment: 6 pages and 6 figure
Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference for Mobile AIGC Services
Aiming at achieving artificial general intelligence (AGI) for Metaverse,
pretrained foundation models (PFMs), e.g., generative pretrained transformers
(GPTs), can effectively provide various AI services, such as autonomous
driving, digital twins, and AI-generated content (AIGC) for extended reality.
With the advantages of low latency and privacy-preserving, serving PFMs of
mobile AI services in edge intelligence is a viable solution for caching and
executing PFMs on edge servers with limited computing resources and GPU memory.
However, PFMs typically consist of billions of parameters that are computation
and memory-intensive for edge servers during loading and execution. In this
article, we investigate edge PFM serving problems for mobile AIGC services of
Metaverse. First, we introduce the fundamentals of PFMs and discuss their
characteristic fine-tuning and inference methods in edge intelligence. Then, we
propose a novel framework of joint model caching and inference for managing
models and allocating resources to satisfy users' requests efficiently.
Furthermore, considering the in-context learning ability of PFMs, we propose a
new metric to evaluate the freshness and relevance between examples in
demonstrations and executing tasks, namely the Age of Context (AoC). Finally,
we propose a least context algorithm for managing cached models at edge servers
by balancing the tradeoff among latency, energy consumption, and accuracy
Game Theoretic Resource Allocation in Media Cloud With Mobile Social Users
Due to the rapid increases in both the population of mobile social users and the demand for quality of experience (QoE), providing mobile social users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource in media cloud is limited, how to allocate resource among media cloud, brokers, and mobile social users becomes a new challenge. Therefore, in this paper, we propose a game theoretic resource allocation scheme for media cloud to allocate resource to mobile social users though brokers. First, a framework of resource allocation among media cloud, brokers, and mobile social users is presented. Media cloud can dynamically determine the price of the resource and allocate its resource to brokers. A mobile social user can select his broker to connect to the media cloud by adjusting the strategy to achieve the maximum revenue, based on the social features in the community. Next, we formulate the interactions among media cloud, brokers, and mobile social users by a four-stage Stackelberg game. In addition, through the backward induction method, we propose an iterative algorithm to implement the proposed scheme and obtain the Stackelberg equilibrium. Finally, simulation results show that each player in the game can obtain the optimal strategy where the Stackelberg equilibrium exists stably
Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet
Federated edge learning (FEL) is a promising paradigm of distributed machine
learning that can preserve data privacy while training the global model
collaboratively. However, FEL is still facing model confidentiality issues due
to eavesdropping risks of exchanging cryptographic keys through traditional
encryption schemes. Therefore, in this paper, we propose a hierarchical
architecture for quantum-secured FEL systems with ideal security based on the
quantum key distribution (QKD) to facilitate public key and model encryption
against eavesdropping attacks. Specifically, we propose a stochastic resource
allocation model for efficient QKD to encrypt FEL keys and models. In FEL
systems, remote FEL workers are connected to cluster heads via quantum-secured
channels to train an aggregated global model collaboratively. However, due to
the unpredictable number of workers at each location, the demand for secret-key
rates to support secure model transmission to the server is unpredictable. The
proposed systems need to efficiently allocate limited QKD resources (i.e.,
wavelengths) such that the total cost is minimized in the presence of
stochastic demand by formulating the optimization problem for the proposed
architecture as a stochastic programming model. To this end, we propose a
federated reinforcement learning-based resource allocation scheme to solve the
proposed model without complete state information. The proposed scheme enables
QKD managers and controllers to train a global QKD resource allocation policy
while keeping their private experiences local. Numerical results demonstrate
that the proposed schemes can successfully achieve the cost-minimizing
objective under uncertain demand while improving the training efficiency by
about 50\% compared to state-of-the-art schemes
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
In the vehicular mixed reality (MR) Metaverse, the distance between physical
and virtual entities can be overcome by fusing the physical and virtual
environments with multi-dimensional communications in autonomous driving
systems. Assisted by digital twin (DT) technologies, connected autonomous
vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the
vehicular MR Metaverse via digital simulations for sharing data and making
driving decisions collaboratively. However, large-scale traffic and driving
simulation via realistic data collection and fusion from the physical world for
online prediction and offline training in autonomous driving systems are
difficult and costly. In this paper, we propose an autonomous driving
architecture, where generative AI is leveraged to synthesize unlimited
conditioned traffic and driving data in simulations for improving driving
safety and traffic efficiency. First, we propose a multi-task DT offloading
model for the reliable execution of heterogeneous DT tasks with different
requirements at RSUs. Then, based on the preferences of AV's DTs and collected
realistic data, virtual simulators can synthesize unlimited conditioned driving
and traffic datasets to further improve robustness. Finally, we propose a
multi-task enhanced auction-based mechanism to provide fine-grained incentives
for RSUs in providing resources for autonomous driving. The property analysis
and experimental results demonstrate that the proposed mechanism and
architecture are strategy-proof and effective, respectively
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