Indoor propagation has been the subject of many research studies. These studies describe either ray models [1–5], numerical solver models [6–9], heuristic predictions [10–13], statistical (site-specific) models [14–22], or specific propagation aspects [23–25]. Our algorithm can be classified as heuristic. Heuristic predictions are based on one or more rules of thumb in order to make an accurate yet fast prediction for the path loss. Ray-tracing and ray-launching model techniques usually require a vector based description of the environment to identify the reflected and diffracted rays from surface and edges [26]. Statistical (site-specific) models predict path loss based on measurements for a specific site or for a specific environment, limiting the validity of the prediction to the propagation environment it represents. Numerical solver models consist of screen or integral methods, Finite-difference time-domain (FDTD), ... [26].

In [1], ray-tracing is used for indoor path loss prediction, with a distinction between LoS and NLoS. Procentual prediction errors range from 5% to 10%, which is higher than for our algorithm. Different ray-tracing approaches (field-sum and power-sum) have been investigated in [4]. Field-sum appeared to be most accurate. In [2, 3], efficient two-dimensional ray-tracing algorithms for an indoor environment have been presented, resulting in a significant reduction in the computational time, without losing prediction accuracy.

A theoretical waveguide model permitting a rigorous modal solution is proposed for predicting path loss inside buildings in [6].

Heuristic approaches have been proposed in [11–13]. An indoor propagation model making use of the estimation of the transmitted field at the corners of each room is presented in [11]. The performance is comparable to that of our algorithm (mean absolute prediction error of 2.17 dB), but the model is only tested for simple configurations, with (ideally) at most one wall between transmitter and receiver. Only the direct ray is considered, which makes the model less suitable for environments where diffraction is the dominant mechanism. Moreover, only one path loss value is obtained for the whole room, based on the values in the corners of the room. This makes the predictions less accurate for concave rooms or rooms with a non-rectangular shape. A more complex version of the dominant path loss model (using more model parameters) is studied in [12]. In the study, the model parameters are calibrated in order to minimize the prediction errors in a certain building. Good results are obtained, but no validation measurements have been performed in other buildings, limiting the validity of the model to the investigated building.

Different statistical models for specific environments have been proposed. In [14], indoor path losses have been statistically investigated for different room categories (adjacent to transmitter room, non-adjacent, ...) in 14 houses. Path losses in five office environments have been determined and the importance of taking wall attenuations into account in the prediction model is indicated in [15]. In [17], low prediction errors are obtained, but the analysis was performed for a site-specific validation of the ITU indoor path loss model (only indoor office environments). In [19], different propagation models were tuned to a measurement set, but no validation measurements were performed. One-slope models and different multi-wall based models were analyzed and results have been provided for a typical office environment in [27]. The standard deviation of the model error was around 6 dB for the best model. In [20], a simple one-slope model was constructed for a mostly-LoS environment. A value of 2 for the parameter *n* (see Equation (1)) was obtained. LoS and NLoS measurements have been fitted to a one-slope model in [22], where the path loss exponent accounted also for the wall losses for the NLoS measurements. However, no model validations in other rooms or buildings were executed. In [21], a statistical path loss model is proposed for different propagation conditions. The use of statistical models is however restricted to the category of buildings the model was constructed for, limiting the general applicability of the model. Moreover, no validation measurements have been performed to test the model.

In [23], upper and lower limits for LoS transmission at 1.8 GHz were investigated. It was found that these were influenced by ceiling height and antenna height.

Concerning network optimization algorithms, a stochastic binary particle swarm optimization (PSO) algorithm is used in [9], to meet the following requirements: minimization of the interference, maximization of the signal-to-interference ratio (SIR), and activation of as few access points as possible to maximize the coverage area and reduce interferences. In [13], a WLAN planning tool was developed to optimize the position and number of access points, as well as the total cost of the required equipment, according to different WLAN suppliers, in indoor and outdoor environments. The indoor propagation algorithm used in [13] uses the indoor dominant path model (IDP) [10]. The presented prediction results (at one test site) have a variable accuracy.