Abstract
Appropriate wireless access point deployment (APD) is essential for ensuring seamless user communication. Optimal APD enables good telecommunication quality, balanced capacity loading, and optimal deployment costs. APD is a typical NP‐complex problem because improving wireless networking infrastructure has multiple objectives (MOs). This paper proposes a method that integrates a goal‐programming‐driven model (PM) and a genetic algorithm (GA) to resolve the MO‐APD problem. The PM identifies the target deployment subject of four constraints: budget, coverage, capacity, and interference. The PM also calculates dynamic capacity requirements to replicate real wireless communication. Three experiments validate the feasibility of the PM. The results demonstrate the utility and stability of the proposed method. Decision makers can easily refer to the PM‐identified target deployment before allocating APs.
Highlights
Appropriate wireless access point deployment APD is essential for ensuring seamless user communication
Experiment 2 included two subtests to confirm the ability of the PM to solve dynamic capacity problems
The experiment results show that the PM resolves many APD problems and achieves dynamic capacity replication
Summary
Appropriate wireless access point deployment APD is essential for ensuring seamless user communication. APD is a typical NP-complex problem 1 because it involves multiple decision objectives, such as budget 2–4 , coverage 2, 5–8 , interference 3, 4, 7 , and dynamic capacity 1, 4, 6–9. These objectives usually contradict each other 7. The capacity requirements of wireless networks compared to wired networks are difficult to evaluate because users are dynamic and can move from place to place This makes APD a dynamic and complex problem. It uses goal programming GP to infer and model the PM and a genetic algorithm GA to search for near optimal solutions These methods are applied to MO-APD problems to reflect real situations. The remainder of this paper is organized as follows: Section 2 defines the problem; Section 3 details the PM; Section 4 presents a discussion on the PM solution process using a GA; Section 5 provides the results of numerical experiments which are given ; lastly, Section 6 offers a conclusion and suggestions for future research
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