Large-scale convex optimization for dense wireless cooperative networks

Yuanming Shi, Jun Zhang, Brendan O'Donoghue, Khaled Letaief

Research output: Contribution to journalArticle

53 Citations (Scopus)

Abstract

Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

Original languageEnglish
Article number2443731
Pages (from-to)4729-4743
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume63
Issue number18
DOIs
Publication statusPublished - 15 Sep 2015
Externally publishedYes

Fingerprint

Convex optimization
Channel state information
Parallel processing systems
Resource allocation
Scalability
Cones
Wireless networks
Quality of service
Signal processing

Keywords

  • Alternating direction method of multipliers (ADMM)
  • dense wireless networking
  • homogeneous self-dual embedding
  • large-scale optimization
  • matrix stuffing
  • operator splitting method

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Large-scale convex optimization for dense wireless cooperative networks. / Shi, Yuanming; Zhang, Jun; O'Donoghue, Brendan; Letaief, Khaled.

In: IEEE Transactions on Signal Processing, Vol. 63, No. 18, 2443731, 15.09.2015, p. 4729-4743.

Research output: Contribution to journalArticle

Shi, Yuanming ; Zhang, Jun ; O'Donoghue, Brendan ; Letaief, Khaled. / Large-scale convex optimization for dense wireless cooperative networks. In: IEEE Transactions on Signal Processing. 2015 ; Vol. 63, No. 18. pp. 4729-4743.
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