Optimal stochastic coordinated beamforming for wireless cooperative networks with CSI uncertainty

Yuanming Shi, Jun Zhang, Khaled Letaief

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Transmit optimization and resource allocation for wireless cooperative networks with channel state information (CSI) uncertainty are important but challenging problems in terms of both the uncertainty modeling and performance optimization. In this paper, we establish a generic stochastic coordinated beamforming (SCB) framework that provides flexibility in the channel uncertainty modeling, while guaranteeing optimality in the transmission strategies. We adopt a general stochastic model for the CSI uncertainty, which is applicable for various practical scenarios. The SCB problem turns out to be a joint chance constrained program (JCCP) and is known to be highly intractable. In contrast to all of the previous algorithms for JCCP that can only find feasible but sub-optimal solutions, we propose a novel stochastic DC (difference-of-convex) programming algorithm with optimality guarantee, which can serve as the benchmark for evaluating heuristic and sub-optimal algorithms. The key observation is that the highly intractable probability constraint can be equivalently reformulated as a dc constraint. This further enables efficient algorithms to achieve optimality. Simulation results will illustrate the convergence, conservativeness, stability and performance gains of the proposed algorithm.

Original languageEnglish
Article number6996028
Pages (from-to)960-973
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume63
Issue number4
DOIs
Publication statusPublished - 15 Feb 2015
Externally publishedYes

Fingerprint

Channel state information
Beamforming
Convex optimization
Stochastic models
Resource allocation
Uncertainty

Keywords

  • Coordinated beamforming
  • joint chance constrained programming
  • Monte Carlo simulation
  • performance benchmarking
  • stochastic DC programming
  • wireless cooperative networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Optimal stochastic coordinated beamforming for wireless cooperative networks with CSI uncertainty. / Shi, Yuanming; Zhang, Jun; Letaief, Khaled.

In: IEEE Transactions on Signal Processing, Vol. 63, No. 4, 6996028, 15.02.2015, p. 960-973.

Research output: Contribution to journalArticle

@article{abd29c6642c942edabf237a145c86b09,
title = "Optimal stochastic coordinated beamforming for wireless cooperative networks with CSI uncertainty",
abstract = "Transmit optimization and resource allocation for wireless cooperative networks with channel state information (CSI) uncertainty are important but challenging problems in terms of both the uncertainty modeling and performance optimization. In this paper, we establish a generic stochastic coordinated beamforming (SCB) framework that provides flexibility in the channel uncertainty modeling, while guaranteeing optimality in the transmission strategies. We adopt a general stochastic model for the CSI uncertainty, which is applicable for various practical scenarios. The SCB problem turns out to be a joint chance constrained program (JCCP) and is known to be highly intractable. In contrast to all of the previous algorithms for JCCP that can only find feasible but sub-optimal solutions, we propose a novel stochastic DC (difference-of-convex) programming algorithm with optimality guarantee, which can serve as the benchmark for evaluating heuristic and sub-optimal algorithms. The key observation is that the highly intractable probability constraint can be equivalently reformulated as a dc constraint. This further enables efficient algorithms to achieve optimality. Simulation results will illustrate the convergence, conservativeness, stability and performance gains of the proposed algorithm.",
keywords = "Coordinated beamforming, joint chance constrained programming, Monte Carlo simulation, performance benchmarking, stochastic DC programming, wireless cooperative networks",
author = "Yuanming Shi and Jun Zhang and Khaled Letaief",
year = "2015",
month = "2",
day = "15",
doi = "10.1109/TSP.2014.2385669",
language = "English",
volume = "63",
pages = "960--973",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Optimal stochastic coordinated beamforming for wireless cooperative networks with CSI uncertainty

AU - Shi, Yuanming

AU - Zhang, Jun

AU - Letaief, Khaled

PY - 2015/2/15

Y1 - 2015/2/15

N2 - Transmit optimization and resource allocation for wireless cooperative networks with channel state information (CSI) uncertainty are important but challenging problems in terms of both the uncertainty modeling and performance optimization. In this paper, we establish a generic stochastic coordinated beamforming (SCB) framework that provides flexibility in the channel uncertainty modeling, while guaranteeing optimality in the transmission strategies. We adopt a general stochastic model for the CSI uncertainty, which is applicable for various practical scenarios. The SCB problem turns out to be a joint chance constrained program (JCCP) and is known to be highly intractable. In contrast to all of the previous algorithms for JCCP that can only find feasible but sub-optimal solutions, we propose a novel stochastic DC (difference-of-convex) programming algorithm with optimality guarantee, which can serve as the benchmark for evaluating heuristic and sub-optimal algorithms. The key observation is that the highly intractable probability constraint can be equivalently reformulated as a dc constraint. This further enables efficient algorithms to achieve optimality. Simulation results will illustrate the convergence, conservativeness, stability and performance gains of the proposed algorithm.

AB - Transmit optimization and resource allocation for wireless cooperative networks with channel state information (CSI) uncertainty are important but challenging problems in terms of both the uncertainty modeling and performance optimization. In this paper, we establish a generic stochastic coordinated beamforming (SCB) framework that provides flexibility in the channel uncertainty modeling, while guaranteeing optimality in the transmission strategies. We adopt a general stochastic model for the CSI uncertainty, which is applicable for various practical scenarios. The SCB problem turns out to be a joint chance constrained program (JCCP) and is known to be highly intractable. In contrast to all of the previous algorithms for JCCP that can only find feasible but sub-optimal solutions, we propose a novel stochastic DC (difference-of-convex) programming algorithm with optimality guarantee, which can serve as the benchmark for evaluating heuristic and sub-optimal algorithms. The key observation is that the highly intractable probability constraint can be equivalently reformulated as a dc constraint. This further enables efficient algorithms to achieve optimality. Simulation results will illustrate the convergence, conservativeness, stability and performance gains of the proposed algorithm.

KW - Coordinated beamforming

KW - joint chance constrained programming

KW - Monte Carlo simulation

KW - performance benchmarking

KW - stochastic DC programming

KW - wireless cooperative networks

UR - http://www.scopus.com/inward/record.url?scp=84921873345&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84921873345&partnerID=8YFLogxK

U2 - 10.1109/TSP.2014.2385669

DO - 10.1109/TSP.2014.2385669

M3 - Article

AN - SCOPUS:84921873345

VL - 63

SP - 960

EP - 973

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 4

M1 - 6996028

ER -