Optimization algorithms for big data with application in wireless networks

doi:10.1017/CBO9781316162750.004

3 - Optimization algorithms for big data with application in wireless networks

from Part I - Mathematical foundations

Published online by Cambridge University Press: 18 December 2015

Ruoyu Sun and

Edited by

Alfred O. Hero, III ,

Zhi-Quan Luo and

José M. F. Moura

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Mingyi Hong: Affiliation:
Iowa State University, USA
Wei-Cheng Liao: Affiliation:
University of Minnesota, USA
Ruoyu Sun: Affiliation:
University of Minnesota, USA
Zhi-Quan Luo: Affiliation:
University of Minnesota, USA
Shuguang Cui: Affiliation:
Texas A & M University
Alfred O. Hero, III: Affiliation:
University of Michigan, Ann Arbor
Zhi-Quan Luo: Affiliation:
University of Minnesota
José M. F. Moura: Affiliation:
Carnegie Mellon University, Pennsylvania

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Summary

This chapter proposes the use of modern first-order large-scale optimization techniques to manage a cloud-based densely deployed next-generation wireless network. In the first part of the chapter we survey a few popular first-order methods for large-scale optimization, including the block coordinate descent (BCD) method, the block successive upper-bound minimization (BSUM) method and the alternating direction method of multipliers (ADMM). In the second part of the chapter, we show that many difficult problems in managing large wireless networks can be solved efficiently and in a parallel manner, by modern first-order optimization methods. Extensive numerical results are provided to demonstrate the benefit of the proposed approach.

Introduction

Motivation

The ever-increasing demand for rapid access to large amounts of data anywhere anytime has been the driving force in the current development of next-generation wireless network infrastructure. It is projected that within 10 years, the wireless cellular network will offer up to 1000× throughput performance over the current 4G technology [1]. By that time the network should also be able to deliver a fiber-like user experience, boasting 10 Gb/s individual transmission rate for data-intensive cloud-based applications.

Achieving this lofty goal requires revolutionary infrastructure and highly sophisticated resource management solutions. A promising network architecture to meet this requirement is the so-called cloud-based radio access network (RAN), where a large number of networked base stations (BSs) are deployed for wireless access, while powerful cloud centers are used at the back end to perform centralized network management [1–4]. Intuitively, a large number of networked access nodes, when intelligently provisioned, will offer significantly improved spectrum efficiency, real-time load balancing and hotspot coverage. In practice, the optimal network provisioning is extremely challenging, and its success depends on smart joint backhaul provisioning, physical layer transmit/receive schemes, BS/user cooperation and so on.

This chapter proposes the use of modern first-order large-scale optimization techniques to manage a cloud-based densely deployed next-generation wireless network. We show that many difficult problems in this domain can be solved efficiently and in a parallel manner, by advanced optimization algorithms such as the block successive upper-bound minimization (BSUM) method and the alternating direction methods of multipliers (ADMM) method.

The organization of the chapter

To begin with, we introduce a few well-known first-order optimization algorithms. Our focus is on algorithms suitable for solving problems with certain block-structure, where the optimization variables can be divided into (possibly overlapping) blocks.

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Type: Chapter
Information: Big Data over Networks , pp. 66 - 100

DOI: https://doi.org/10.1017/CBO9781316162750.004 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2016

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