Min Flow Rate Maximization for Software Defined Radio Access Networks

Liao, Wei Cheng; Mei, Hong; Farmanbar, Hamid; Li, Xu; Luo, Ze; Zhang, Hang

doi:10.1109/jsac.2014.2328171

Cited by 64 publications

(39 citation statements)

References 41 publications

(104 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently it has become widely popular in modern big data related problems arising in machine learning, computer vision, signal processing, networking and so on; see [8][9][10][11][12][13][14][15] and the references therein. In practice, the algorithm often exhibits faster convergence than traditional primal-dual type algorithms such as the dual ascent algorithm [16][17][18] or the method of multipliers [19].…”

mentioning

confidence: 99%

Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems

Luo²,

2016

Self Cite

View full text Add to dashboard Cite

Abstract. The alternating direction method of multipliers (ADMM) is widely used to solve large-scale linearly constrained optimization problems, convex or nonconvex, in many engineering fields. However there is a general lack of theoretical understanding of the algorithm when the objective function is nonconvex. In this paper we analyze the convergence of the ADMM for solving certain nonconvex consensus and sharing problems. We show that the classical ADMM converges to the set of stationary solutions, provided that the penalty parameter in the augmented Lagrangian is chosen to be sufficiently large. For the sharing problems, we show that the ADMM is convergent regardless of the number of variable blocks. Our analysis does not impose any assumptions on the iterates generated by the algorithm, and is broadly applicable to many ADMM variants involving proximal update rules and various flexible block selection rules.

show abstract

mentioning

confidence: 99%

Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems

Luo²,

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…We observe that parallel implementation of the ADMM approach leads to more than five-fold improvement in computation time. 2 We also note that when the problem size increases, the performance of Gurobi becomes worse than that achieved by the parallel implementation of Algorithm I . Thus, the proposed algorithm (implemented in parallel) appears to scale nicely to large problem sizes.…”

Section: Scenario 3: Multi-commodity Routing Problem With Parallel Immentioning

confidence: 92%

“…The resulting algorithm, termed the network max-min WMMSE (N-MaxMin) algorithm, is given in the following box. Again one can show that this algorithm converges to the set of stationary solutions of the network provisioning problem (3.40); see [2] for detailed analysis.…”

Section: The N-maxmin Algorithmmentioning

confidence: 94%

“…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][2][3][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.…”

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Optimization algorithms for big data with application in wireless networks

Mei

Liao

Sun

et al. 2016

Big Data Over Networks

Self Cite

View full text Add to dashboard Cite

This chapter proposes the use of modern first-order large-scale optimization techniques to manage a cloudbased 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. 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. Disciplines3.1

show abstract

“…In this last few years, new concepts such as heterogeneous network (HetNet) [203,204] have been introduced and the radio access networks (RAN) went through a major structural change. The traditional single high powered base station serving all users in its cell is gradually replaced by a the mass deployment of low power access points in a network mesh connected through high speed backhaul links as well as network routers [205].…”

Section: Open Problems and Future Research Directionsmentioning

confidence: 99%

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

Douik

Sorour

Al-Naffouri

et al. 2017

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications Ahmed DouikFrom its introduction to its quindecennial, network coding has built a strong reputation in enhancing the packet recovery process and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the network by proposing complex schemes that achieve optimal delay. With the shift toward distributed computing on mobile devices, throughput and complexity become both critical factors that affect the efficiency of a coding scheme. Instantly decodable network coding imposed itself as a new paradigm in network coding that trades off these two aspects. This paper presents a survey of instantly decodable network coding schemes that are proposed in the literature. The various schemes are identified, categorized and evaluated. Two categories can be distinguished namely the conventional centralized schemes and the distributed or cooperative schemes. For each scheme, the comparison is carried out in terms of reliability, performance, complexity and packet selection methodology. Although the performance is generally inversely proportional to the computation complexity, numerous successful schemes from both the performance and complexity viewpoint are identified.

show abstract

Min Flow Rate Maximization for Software Defined Radio Access Networks

Cited by 64 publications

References 41 publications

Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems

Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems

Optimization algorithms for big data with application in wireless networks

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

Contact Info

Product

Resources

About