Machine Intelligence at the Edge with Learning Centric Power Allocation

Shuai Wang, Yik Chung Wu, Minghua Xia, Rui Wang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.

Original languageEnglish (US)
Article number9151375
Pages (from-to)7293-7308
Number of pages16
JournalIEEE Transactions on Wireless Communications
Issue number11
StatePublished - Nov 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Empirical classification error model
  • edge machine learning
  • learning centric communication
  • multiple-input multiple-output
  • resource allocation


Dive into the research topics of 'Machine Intelligence at the Edge with Learning Centric Power Allocation'. Together they form a unique fingerprint.

Cite this