Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI

• URL: http://arxiv.org/abs/2207.00969v1
• Date: Sun, 3 Jul 2022 06:57:07 GMT
• Title: Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
• Authors: Dingzhu Wen, Peixi Liu, Guangxu Zhu, Yuanming Shi, Jie Xu, Yonina C. Eldar, and Shuguang Cui
• Abstract summary: This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
• Score: 108.08079323459822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge. In this system, multiple ISAC devices perform radar sensing to obtain multi-view data, and then offload the quantized version of extracted features to a centralized edge server, which conducts model inference based on the cascaded feature vectors. Under this setup and by considering classification tasks, we measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain, which is defined as the distance of two classes in the Euclidean feature space under normalized covariance. To maximize the discriminant gain, we first quantify the influence of the sensing, computation, and communication processes on it with a derived closed-form expression. Then, an end-to-end task-oriented resource management approach is developed by integrating the three processes into a joint design. This integrated sensing, computation, and communication (ISCC) design approach, however, leads to a challenging non-convex optimization problem, due to the complicated form of discriminant gain and the device heterogeneity in terms of channel gain, quantization level, and generated feature subsets. Remarkably, the considered non-convex problem can be optimally solved based on the sum-of-ratios method. This gives the optimal ISCC scheme, that jointly determines the transmit power and time allocation at multiple devices for sensing and communication, as well as their quantization bits allocation for computation distortion control. By using human motions recognition as a concrete AI inference task, extensive experiments are conducted to verify the performance of our derived optimal ISCC scheme.

Related papers

• Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy [32.48617448956706]
  A task-oriented over-the-air computation scheme is proposed for a multidevice artificial intelligence system. A novel tractable inference accuracy metric is proposed for classification tasks, which is called minimum pair-wise discriminant gain. This paper jointly optimize the minimum discriminant gain of all feature elements instead of separately maximizing that of each element in the existing designs.
  arXiv  Detail & Related papers  (2024-07-01T04:17:32Z)
• Collaborative Edge AI Inference over Cloud-RAN [37.3710464868215]
  A cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture real-time noise-corrupted sensory data samples and extract the noisy local feature vectors. We allow each RRH receives local feature vectors from all devices over the same resource blocks simultaneously by leveraging an over-the-air computation (AirComp) technique. These aggregated feature vectors are quantized and transmitted to a central processor for further aggregation and downstream inference tasks.
  arXiv  Detail & Related papers  (2024-04-09T04:26:16Z)
• Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks [66.18765335695414]
  We consider a joint device selection and aggregate beamforming design with the objectives of minimizing the aggregate error and maximizing the number of selected devices. To tackle the problems in a cost-effective manner, we propose a random aggregate beamforming-based scheme. We additionally use analysis to study the obtained aggregate error and the number of the selected devices when the number of devices becomes large.
  arXiv  Detail & Related papers  (2024-02-20T23:59:45Z)
• Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization [5.404315085380945]
  Newton-type (NT) methods have been advocated as enablers of robust convergence rates in DO problems. We propose Q-SHED, an original NT algorithm for DO featuring a novel bit-allocation scheme based on incremental Hessian eigenvectors quantization. We show that Q-SHED can reduce by up to 60% the number of communication rounds required for convergence.
  arXiv  Detail & Related papers  (2023-05-18T10:15:03Z)
• Task-Oriented Over-the-Air Computation for Multi-Device Edge AI [57.50247872182593]
  6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system.
  arXiv  Detail & Related papers  (2022-11-02T16:35:14Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Task-Oriented Communication for Multi-Device Cooperative Edge Inference [14.249444124834719]
  cooperative edge inference can overcome the limited sensing capability of a single device, but it substantially increases the communication overhead and may incur excessive latency. We propose a learning-based communication scheme that optimize local feature extraction and distributed feature encoding in a task-oriented manner.
  arXiv  Detail & Related papers  (2021-09-01T03:56:20Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.