An integrated optimization model for wireless access point deployment construction, and enhancement

Appropriate wireless LAN design is essential for ensuring a good quality of telecommunication service. Optimal access point deployment (APD) presents a typical NP-complex problem that resolves wireless networking infrastructure with the involvement of multiple objectives (MO-APD). MO-APD can be divided into the APD construction (APD-C) problem and the APD enhancement (APD-E) problem. For APD-E problem resolving, the present APD must be taken into consideration for subsequent extensions. This paper proposes a goal-programming-driven model (GM) integrated with a genetic algorithm and an embedded mask mechanism to optimally resolve both the MO-APD-C and MO-APD-E problems in the same model. The GM formulates the target deployment subject to four constraints: budget, coverage rate, capacity requirement and signal interference. In addition, to replicate real wireless communication, the GM addresses the dynamic capacity requirement of the user. Two experiments are designed to validate the feasibility of the GM and compare the experiment results with the Tabu search algorithm. The experimental results of experiment 1 demonstrate that GM can increase 5% networking capacity and decrease 10% interference rate by using less APs and therefore lowering cost. The experimental results of experiment 2 further validate the usefulness of the proposed method to resolve both the APD-C and APD-E problems steadily.