QoS constrained wireless LAN optimization within a multiobjective framework

• Published in: IEEE wireless communications Vol. 13; no. 6; pp. 26 - 33
• Main Authors: Jaffres-Runser, K., Gorce, J.-M., Ubeda, S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Constraint optimization; Data mining; Formulations; Indoor environments; Interference constraints; Local area networks; Networks; Optimization; Process planning; Quality of service; Searching; Strategic planning; Strategy; Studies; Throughput; Tradeoffs; Wireless communication; Wireless LAN; Wireless networks
• ISSN: 1536-1284; 1558-0687
• DOI: 10.1109/MWC.2006.275195

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