For this alternative, there are use-cases where it is  
Figure 1: Focus on higher complexity and cost in (**a**) positioning system infrastructure, (**b**) positioning device, and (**c**) positioning device with support of edge computing. justified to move the complexity and cost from the core infrastructure to the positioning device. This transition of the complexity and cost is depicted by the arrow pointing from the case in Figure 1a to the case in Figure 1b. With the arrival of the edge computing paradigm [19; 20], an alternative solution to the two presented above consists of transferring part of the complexity of the positioning device to an edge computing platform; see Figure 1c. It is outside of our scope to discuss whether an edge computing platform is considered to be part of the positioning infrastructure or not. An example of its use would be to decrease the complexity and cost of the core infrastructure by avoiding densification of the positioning nodes, increasing the hardware complexity at the positioning device, and complementing it with computing resources from edge computing platforms. This third option is presented to put our work into context and to pave the way for future related work. For example, the case depicted in Figure 1c can be thought of as an extension of the case depicted in Figure 1b. In the context of this work, we focus solely on the case depicted in Figure 1b. In this paper, we propose minimizing the positioning error in fingerprinting-based positioning by increasing the complexity on the positioning device side, as depicted in Figure 1b, exploiting fingerprints in two spatial dimensions. We work with received signal strength (RSS) fingerprints in a radio network scenario operating in downlink. We propose using a two-dimensional (2D) array arrangement of sensors (e.g., antennas and receivers), referred to as a _2D sensor array_, that allows us to measure physically adjacent fingerprints. This arrangement provides 2D spatial side information on the fingerprints at the positioning device. Our primary goal is to learn whether using spatial side information on RSS fingerprints, by means of an ideal 2D sensor array, could lead to a justifiable gain in terms of minimizing the positioning error. Spatial side information is introduced in Section 2.5. An example of the proposed 2D sensor array is depicted in Figure 2. The fingerprints measured by the 2D sensor array will be associated with small discretized areas of the same size as the 2D sensor array. The idea is to scan during a training phase the whole main area where the positioning takes place, as takes place in traditional fingerprinting-based positioning, ideally covering the entire positioning area in pieces equivalent to the size of the 2D sensor array. The positioning area is defined, in general terms, as the area where the positioning is intended. Specifically in the context of the scenario and simulator used in this work, the positioning area is defined and explained in Section 5.1. We carry out a feasibility study through simulations using a system simulator to learn whether the proposed approach could lead to a justifiable gain. This study is performed using simulations based on Monte Carlo methods, testing the performance of the proposed approach at random positions in terms of measuring the positioning error. The system simulator creates synthetic fingerprints in the form of RSS signals, with a radio channel model published by a standardization body for a frequency of 2.6 GHz (other frequencies can be considered, see Section 5.9). We investigate four pattern-matching methods for processing the fingerprints based on machine learning algorithms for supervised learning, implemented with artificial neural networks (NNs). The first method is the traditional fingerprinting method, which associates a single vector of fingerprints with the position to be estimated, and thus has no spatial side information.