Wireless indoor network planning for advanced exposure and installation cost minimization

Abstract

The possibility of having information access anytime and anywhere has caused a huge increase of the popularity of wireless networks. Requirements of users and owners have been ever-increasing. However, concerns about the potential health impact of exposure to radio frequency (RF) sources have arisen and are getting accounted for in wireless network planning. In addition to adequate coverage and reduced human exposure, the installation cost of the wireless network is also an important criterion in the planning process. In this paper, a hybrid algorithm is used to optimize indoor wireless network planning while satisfying three demands: maximum coverage, minimal full installation cost (cabling, cable gutters, drilling holes, labor, etc.), and minimal human exposure. For the first time, wireless indoor networks are being optimized based on these advanced and realistic conditions. The algorithm is investigated for three scenarios and for different configurations. The impact of different exposure requirements and cost scenarios is assessed.

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

“…If the required coverage rate is not yet achieved (line 2), this random TPC solution will be corrected (lines [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The idea of repair is to iteratively cover the uncovered GPs using the potential APs (whose current transmit power is below the maximal level) while maintaining the increase in transmit power as slight as possible (to comply with interference minimization).…”

“…At the first step of correction (lines 3-9), uncovered GPs are all found and their GP-AP links are all established. The second step of correction (lines [10][11][12][13][14][15][16][17][18][19][20][21][22] is an iteration. In each iteration, a GP is randomly selected from uncovered GPs and its nearest potential AP is found (line 11).…”

“…While large-scale optimization is increasingly desired [18,19], most research on coverage optimization problems neglects the scalability of an optimization algorithm [14,[20][21][22][23]. Large-scale problems are characterized in at least one of the following dimensions [24].…”

An efficient genetic algorithm for large-scale planning of dense and robust industrial wireless networks

“…If the grid size is one meter, there are then 83,000 candidate locations for placing an AP. Most of the wireless network planning research is only limited in a small or medium scale environment, varying from several hundreds of square meters to several thousands of square meters (N. Liu, Plets, Vanhecke, Martens, & Joseph, 2015). A large building floor of 12,600 m 2 was considered in (Jaffres-Runser, Gorce, & Ubeda, 2006) for WLAN planning.…”

A mobile app for real-time testing of path-loss models and optimization of network planning

“…9 (b) consists of 3 APs instead of 5, indicating the value of the application. As the cost of the AP itself is only around one third of the total installation cost [12], an accurate network planning could -in this casesave a significant amount of money for the company or person installing the network. Moreover, using less APs also reduces interference.…”