Utility-proportional fairness in wireless networks

Current communication networks support a variety of applications with different quality of service (QoS) requirements which compete for its resources. This continuously increasing competition highlights the necessity for more efficient and fair resource allocation. Current Network Utility Maximization (NUM) framework fails to achieve this target and alternative approaches cannot operate in networks that consist of wireless links. This paper presents a NUM framework for wireless networks that shares resources according to the utility proportional fairness policy. This policy is shown to prevent rate oscillations in the resource allocation process, allocate resources in a more fair manner among different types of applications and lead to the calculation of closed form solutions for the optimal rate allocation function. Based on this policy, a distributed rate and power allocation algorithm is proposed that gives priority to applications with greater need of resources. Finally, numerical results on the performance of the proposed algorithm are presented and compared against other approaches in the literature.

[1]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[2]  Bin Liu,et al.  Utility-Based Bandwidth Allocation for Triple-Play Services , 2007, Fourth European Conference on Universal Multiservice Networks (ECUMN'07).

[3]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[4]  William Stallings.  Data and Computer "Communications, 7th ed , 2004 .

[5]  Ness B. Shroff,et al.  Non-convex optimization and rate control for multi-class services in the Internet , 2005, IEEE/ACM Transactions on Networking.

[6]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[7]  P. Hande,et al.  Distributed Rate Allocation for Inelastic Flows , 2007, IEEE/ACM Transactions on Networking.

[8]  Kin K. Leung,et al.  A new distributed optimization framework for hybrid ad-hoc networks , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[9]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[10]  M. Palaniswami,et al.  Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness , 2006, IEEE/ACM Transactions on Networking.

[11]  Mung Chiang,et al.  To layer or not to layer: balancing transport and physical layers in wireless multihop networks , 2004, IEEE INFOCOM 2004.

[12]  Daniel Pérez Palomar,et al.  Alternative Distributed Algorithms for Network Utility Maximization: Framework and Applications , 2007, IEEE Transactions on Automatic Control.

[13]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[14]  William Stallings,et al.  Data and Computer Communications , 1985 .