Radio resource management in beyond 3G systems

Beyond 3G systems is usually the term used to refer to the new scenarios in the wireless arena where different radio access technologies (RATs) coexist and operate in a coordinated way. This cooperation must indeed be regarded as a new challenge to offer services to the users over an efficient and ubiquitous radio access. In this way, the user can be served through the RAT that fits better to the terminal capabilities and service requirements, and also a more efficient use of the radio resources can be achieved. This challenge calls for the introduction of new radio resource management (RRM) algorithms operating from a common perspective that take into account the overall amount of resources offered by the available RATs. In this context, this paper presents the framework for developing RRM algorithms in the B3G scenarios, including some possible approaches

[1]  Oriol Sallent,et al.  A fuzzy-neural based approach for joint radio resource management in a beyond 3G framework , 2004, First International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks.

[2]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[3]  Antti Tölli,et al.  Performance evaluation of common radio resource management (CRRM) , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[4]  Oriol Sallent,et al.  Radio Resource Management Strategies in UMTS: Perez-Romero/Radio Resource Management Strategies in UMTS , 2005 .

[5]  Matthias Siebert,et al.  Enhanced measurement procedures for vertical handover in heterogeneous wireless systems , 2003, 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003..

[6]  Oriol Sallent,et al.  Common radio resource management: functional models and implementation requirements , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[7]  Oriol Sallent,et al.  On the capacity degradation in W-CDMA uplink/downlink due to indoor traffic , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[8]  Chung-Ju Chang,et al.  A neural fuzzy resource manager for hierarchical cellular systems supporting multimedia services , 2003, IEEE Trans. Veh. Technol..

[9]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[10]  Magnus Almgren,et al.  Radio Resource Management for Wireless Networks , 2001 .

[11]  George Pavlou,et al.  A policy-based quality of service management system for IP DiffServ networks , 2002, IEEE Netw..

[12]  Oriol Sallent,et al.  Radio Resource Management Strategies in UMTS , 2005 .

[13]  Oriol Sallent,et al.  Joint radio resource management algorithm for multi-RAT networks , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[14]  S. Lincke-Salecker The benefits of load sharing when dimensioning networks , 2004, 37th Annual Simulation Symposium, 2004. Proceedings..

[15]  Anders Eriksson,et al.  Providing quality of service in always best connected networks , 2003, IEEE Commun. Mag..

[16]  S. Lincke-Salecker Performance and service issues in selecting adaptive placement as a load distribution technique , 2004, 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514).

[17]  Ronald R. Yager,et al.  Multiple objective decision-making using fuzzy sets , 1977 .

[18]  George T. Karetsos,et al.  Practical Radio Resource Management in Wireless Systems (Artech House Universal Personal Communications Series) , 2004 .