Coded Computing and Cooperative Transmission for Wireless Distributed Matrix Multiplication

Consider a multi-cell mobile edge computing network, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix. This is done through task offloading by means of input uploading, distributed computing at edge nodes (ENs), and output downloading. Tas...

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Bibliographic Details
Published inIEEE transactions on communications Vol. 69; no. 4; pp. 2224 - 2239
Main Authors Li, Kuikui, Tao, Meixia, Zhang, Jingjing, Simeone, Osvaldo
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
coded computing
Computation offloading
Computer networks
Computing time
Distributed processing
Downlink
Downloading
Edge computing
Encoding
Information theory
Interference
Matrix multiplication
Mobile computing
Multiplication
Redundancy
Servers
Signal processing
Signal to noise ratio
straggler
Task analysis
transmission cooperation
Uplink
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Summary:Consider a multi-cell mobile edge computing network, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix. This is done through task offloading by means of input uploading, distributed computing at edge nodes (ENs), and output downloading. Task offloading may suffer long delay since servers at some ENs may be straggling due to random computation time, and wireless channels may experience severe fading and interference. This paper aims to investigate the interplay among upload, computation, and download latencies during the offloading process in the high signal-to-noise ratio regime from an information-theoretic perspective. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for a sufficiently large upload time. By investing more time in uplink transmission, the policy creates data redundancy at the ENs, which can reduce the computation time, by enabling the use of coded computing, as well as the download time via transmitter cooperation. Moreover, the policy allows computation time to be traded for download time. Numerical examples demonstrate that the proposed policy can improve over existing schemes by significantly reducing the end-to-end execution time.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2021.3053018