Joint Resource Allocation for Parallel Multi-Radio Access in Heterogeneous Wireless Networks

Heterogeneous wireless networks where several systems with different bands coexist for multimedia service are currently in service and will be widely adopted to support various traffic demand. Under heterogeneous networks, a mobile station can transmit over multiple and simultaneous radio access technologies (RATs) such as WLAN, HSPA, and WCDMA LTE. Also, cognitive radio for the efficient use of underutilized/unused frequency band is successfully implemented in some networks. In this letter, we address such operational issues as air interface and band selection for a mobile and power allocation to the chosen links. An optimal solution is sought and analyzed and a distributed joint allocation algorithm is proposed to maximize total system capacity. We investigate the benefit of multiple transmissions by multiple RATs over a single transmission by a single RAT at a time, which can be interpreted as network diversity. Numerical results validate the performance enhancement of our proposed algorithm.

[1]  Haitao Tang,et al.  Multi-radio resource management for communication networks beyond 3G , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[2]  Ramón Agüero,et al.  Feasibility Studies and Architecture for Multi-Radio Access in Ambient networks , 2005 .

[3]  J. Sachs,et al.  Generic link layer: a solution for multi-radio transmission diversity in communication networks beyond 3G , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[4]  Nancy Alonistioti,et al.  Software Defined Radio: Architectures, Systems and Functions , 2003 .

[5]  Ekram Hossain,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks: Introduction , 2009 .

[6]  Anders Furuskar Allocation of multiple services in multi-access wireless systems , 2002, 4th International Workshop on Mobile and Wireless Communications Network.

[7]  Ekram Hossain,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks , 2009 .

[8]  Antti Toskala,et al.  HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications , 2006 .

[9]  J. Stoer,et al.  Introduction to Numerical Analysis , 2002 .

[10]  Reuven Cohen,et al.  Computational Analysis and Efficient Algorithms for Micro and Macro OFDMA Scheduling , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[11]  Roy D. Yates,et al.  Dynamic spectrum allocation for uplink users with heterogeneous utilities , 2009, IEEE Transactions on Wireless Communications.

[12]  M. Salazar-Palma,et al.  A survey of various propagation models for mobile communication , 2003 .

[13]  E. Gustafsson,et al.  Always best connected , 2003, IEEE Wirel. Commun..

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

[15]  Nitin H. Vaidya,et al.  Resource Allocation in Multi-Radio Multi-Channel Multi-Hop Wireless Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[16]  Wei Yu,et al.  FDMA capacity of Gaussian multiple-access channels with ISI , 2002, IEEE Trans. Commun..