Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference

Yang, Kai; Shi, Yuanming; Yu, Wei; Ding, Zhi

doi:10.1109/jiot.2020.2979523

Cited by 42 publications

(31 citation statements)

References 39 publications

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“…Although (15) does not violate the group sparsity structure of v nk , it indicates that the receive beamforming vectors do not contribute to the task selection. Based on the uplink-downlink duality, in the following, we shall propose a virtual downlink formulation to overcome the scaling issue.…”

Section: Block-structured Optimization Approachmentioning

confidence: 96%

“…As it is infeasible to run DNN models on resource-constrained MDs, we in this paper propose to perform inference tasks for the MDs at the BSs. We assume that all the BSs have downloaded the pre-trained DNN models from cloud servers in advance [15].…”

Section: A System Modelmentioning

confidence: 99%

“…However, the communication power consumption is not considered in [13]- [14]. The authors in [15] proposed to minimize the sum of computation power and downlink transmit power consumption (i.e., power required for the BSs to deliver the tasks results to the MDs), while the uplink transmit power (i.e., power required for the MDs to upload data to the BSs) is neglected. However, the uplink traffic load (e.g., raw images for an object recognition task) is usually comparable to the downlink one (e.g., labeled images) in edge inference systems, resulting in high uplink transmit power consumption.…”

Section: Introductionmentioning

confidence: 99%

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Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning

Song

Shi

2019

2019 IEEE Globecom Workshops (GC Wkshps)

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Reconfigurable intelligent surface (RIS) as an emerging cost-effective technology can enhance the spectrum-and energy-efficiency of wireless networks. In this paper, we consider an RIS-aided green edge inference system, where the inference tasks generated from resource-limited mobile devices (MDs) are uploaded to and cooperatively performed at multiple resourceenhanced base stations (BSs). Taking into account both the computation and uplink/downlink transmit power consumption, we formulate an overall network power consumption minimization problem, which calls for the joint design of the set of tasks performed by each BS, transmit and receive beamforming vectors of the BSs, transmit power of the MDs, and uplink/downlink phase-shift matrices at the RIS. Such a problem is a mixed combinatorial optimization problem with nonconvex constraints and is highly intractable. To address the challenge of the combinatorial objective, a group sparse reformulation is proposed by exploiting the group sparsity structure of the beamforming vectors, while a block-structured optimization (BSO) approach is proposed to decouple the optimization variables. Finally, we propose a BSO with mixed 1,2-norm and difference-of-convex-functions (DC) based three-stage framework to solve the problem, where the mixed 1,2-norm is adopted to induce the group sparsity of beamforming vectors and DC is adopted to effectively handle the nonconvex rank-one constraint after matrix lifting. Numerical results demonstrate the supreme gain of deploying an RIS and confirm the effectiveness of the proposed algorithm over the baseline algorithms.Index Terms-Reconfigurable intelligent surface, joint uplink and downlink, green edge inference, block-structured optimization, mixed 1,2-norm,

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Section: Block-structured Optimization Approachmentioning

confidence: 96%

Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning

Song

Shi

2019

2019 IEEE Globecom Workshops (GC Wkshps)

Self Cite

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“…Another promising direction is to offload the training and inference processes to the edge (i.e., edge intelligence in the literature), i.e., AI techniques are employed at the network edge to intelligently process radio signals. For example [184], the joint task allocation and downlink beamforming problem was optimized to minimize the total energy consumption. Applications of a 6G technology, namely reconfigurable intelligent surface, for edge inference, was reported [185].…”

Section: Model Compression and Accelerationmentioning

confidence: 99%

Intelligent Radio Signal Processing: A Survey

et al. 2021

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Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

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“…Offloading can be used to optimize network performance, for example by minimizing communication power consumption in wireless networks [128]. Unlike most similar approaches, this framework uses statistical learning, specifically iteratively reweighted L1 minimization with difference-ofconvex functions regularization.…”

Section: E Networkingmentioning

confidence: 99%

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Goethals¹,

Volckaert

Turck

2021

IEEE Open J. Commun. Soc.

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The use of AI in Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with "Enabling Technology" for Edge Intelligence, "Organization" of the edge using AI, and AI "Applications" in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The "Organization" aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work.

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Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference

Cited by 42 publications

References 39 publications

Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning

Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning

Intelligent Radio Signal Processing: A Survey

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

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