The second method is based on regression; it exploits the spatial side information by aggregating the position estimates of the first method for all of the points associated with the 2D sensor array. The third and fourth methods exploit the spatial side information from the fingerprint data from the 2D sensor array in the method itself, that is, before mapping these to a position estimate. The third method is based on regression, implementing a feedforward neural network (FFNN). The fourth method is based on classification of the discretized areas associated with the 2D sensor array, implementing a convolutional neural network (CNN). The fourth pattern-matching method was implemented conjecturing that a pattern-matching method based on the classification of the discretized areas could be applied to estimating the position. We verify through the simulations whether this conjecture holds and whether it allows us to enhance the position estimates when compared with those of the other methods. The performance of the last three methods, using spatial side information, is benchmarked against that of the first method, which is regarded as the common state-of-the-art for estimating a position in fingerprinting-based positioning. Through the use of the proposed pattern-matching methods, we aim as our primary goal to learn whether the use of spatial side information brings some gain. Our secondary goal is to learn how the different pattern-matching methods that process the side information perform against each other. As an additional goal, we are interested in the exploitation of the spatial side information before the fingerprints are mapped to an estimated position using a pattern-matching method, in a so-called fingerprint domain. In this context, two of the pattern-matching methods proposed are designed to exploit the fingerprints from the target and adjacent spatial positions in the fingerprint domain. To the best of our knowledge, there are no previous works addressing the use of spatial side information as is proposed in this article. Our main contributions are the following:  
1. The proposal to transfer the complexity and cost from the infrastructure side to a positioning device. 2. Differentiation between uplink and downlink cases for fingerprinting-based positioning, relating Multiple-Input Multiple-Output (MIMO), massive MIMO, and intelligent surfaces. 3. Differentiation between positioning data aggregation in the fingerprint and position domains. 4. The proposal to collect the fingerprints at adjacent positions relative to a target position by means of a 2D sensor array located at the positioning device. 5. The proposal to use 2D spatial side information from the fingerprints collected by the 2D sensor array to minimize the positioning error. 6. The aggregation of the fingerprinting data from adjacent spatial positions prior to the mapping from the fingerprints to a position estimate. Figure 2: Representative view of a mobile object requiring positioning and implementing a 2D sensor array for fingerprinting-based positioning. The conjecture that a pattern-matching method based on the classification of the discretized areas can be applied to estimating the position. * Three pattern-matching methods for estimating the position by processing the fingerprints with spatial side information. Two methods are based on regression, implemented using FFNNs, and one method is based on the classification of fractions of the positioning area, implemented using a CNN. In turn, one method operates with data in the so-called position domain, and the other two methods operate with data in the so-called fingerprint domain. * A feasibility study, for a given scenario and assumptions, using system simulations based on Monte Carlo methods. * Benchmarking of cases without and with spatial side information. * Benchmarking of the CNN-based method to determine whether it allows us to enhance the position estimates when compared with those from other methods. The structure of this article is as follows.