QoS constrained wireless LAN optimization within a multiobjective framework

Wireless LANs have experienced great success in the past five years. This technology has been quickly adopted in private and public areas to provide convenient network access. The fast pace of development has often induced an uncoordinated deployment strategy where WLAN planning tools have barely been used. This article highlights the difficulty of planning such wireless networks in indoor environments. The first issue that must be faced in WLAN planning is accurate description of the quality of a network based on realistic propagation predictions. The second issue is to implement a search strategy that provides several alternative solutions. Thus, the radio engineer can choose the most promising one among them based on his/her experience and maybe some additional constraints. A description of already proposed planning strategies is given and opens out onto a new multiobjective planning formulation. This formulation evaluates coverage, interference level, and quality of service (in terms of data throughput per user) to measure the quality of a planning solution. A Tabu multiobjective algorithm is then implemented to search for the optimal set of non-dominated planning solutions, and a final selection process extracts the most significant solutions for the end user. This multiobjective QoS-oriented method is illustrated with a practical example that shows the performance of looking for several solutions, each expressing different trade-offs between the planning objectives

[1]  Alexandre Caminada,et al.  Multicriteria Design Model for Cellular Network , 2001, Ann. Oper. Res..

[2]  Clifford A. Shaffer,et al.  Globally optimal transmitter placement for indoor wireless communication systems , 2004, IEEE Transactions on Wireless Communications.

[3]  Matthias Unbehaun,et al.  On the deployment of picocellular wireless infrastructure , 2003, IEEE Wireless Communications.

[4]  Fabrice Valois,et al.  Performance evaluation of 802.11 WLAN in a real indoor environment , 2006, 2006 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications.

[5]  P. Siarry,et al.  Multiobjective Optimization: Principles and Case Studies , 2004 .

[6]  J. Gorce,et al.  Assessment of a new indoor propagation prediction method based on a multi-resolution algorithm , 2005, 2005 IEEE 61st Vehicular Technology Conference.

[7]  Gerd Finke,et al.  2-Objective Optimization of Cells Overlap and Geometry with Evolutionary Algorithms , 2004, EvoWorkshops.

[8]  Hanif D. Sherali,et al.  Optimal location of transmitters for micro-cellular radio communication system design , 1996, IEEE J. Sel. Areas Commun..

[9]  Jean-Marie Gorce,et al.  The Adaptive Multi-Resolution Frequency-Domain ParFlow (MR-FDPF) Method for Indoor Radio Wave Propagation Simulation. Part I : Theory and Algorithms , 2005 .

[10]  Yanghee Choi,et al.  Optimization of AP placement and channel assignment in wireless LANs , 2002, 27th Annual IEEE Conference on Local Computer Networks, 2002. Proceedings. LCN 2002..

[11]  Konstantina Papagiannaki,et al.  Self Organization of Interfering 802.11 Wireless Access Networks , 2005 .

[12]  J.-M. Gorce,et al.  Deterministic Approach for Fast Simulations of Indoor Radio Wave Propagation , 2007, IEEE Transactions on Antennas and Propagation.

[13]  Luigi Fratta,et al.  Algorithms for WLAN Coverage Planning , 2004, EuroNGI Workshop.

[14]  Oscar Molina Lopez,et al.  Automatic planning optimal quality-cost wireless networks, the indoor Pareto oriented ABSPAD approach , 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

[15]  P. Wertz,et al.  Automatic optimization algorithms for the planning of wireless local area networks , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.