Hybrid received signal strength (RSS)-angle of arrival (AoA)-based positioning offers low-cost distance estimation and high-resolution angular measurements. Still, it comes at a cost of inherent nonlinearities, geometry-dependent noise, and suboptimal weighting in conventional linear estimators that might limit accuracy. In this paper, we propose a neural network-based approach using a multilayer perceptron (MLP) to directly map RSS-AoA measurements to 3D positions, capturing nonlinear relationships that are difficult to model with traditional methods. We evaluate the impact of input representation by comparing networks trained on raw measurements versus preprocessed features derived from a linearization method. Simulation results show that the learning-based approach consistently outperforms existing linear methods under RSS noise across all noise levels, and matches or surpasses state-of-the-art performance under increasing AoA noise. Furthermore, preprocessing measurements using the linearization method provides a clear advantage over raw data, demonstrating the benefit of geometry-aware feature extraction.
Fifth-generation wireless system (5G) wireless systems are designed to support a diverse set of use cases, including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). Beyond data transmission, these use cases increasingly rely on accurate situational awareness to enable higher-layer applications such as Internet of Things (IoT) services, vehicle-to-everything communication (V2X) communication, unmanned aerial vehicle (UAV)s, and autonomous vehicular systems. In such applications, awareness of the position of communicating devices or targets, such as vehicles or drones, is essential for safe and efficient operation. Consequently, wireless positioning has emerged as a key enabling technology that must be addressed alongside communication performance. Looking ahead, it is widely anticipated that in sixth-generation wireless system (6G) systems, positioning and communication will be even more tightly integrated, with location information actively supporting communication, sensing, and control functions [1], [2].
One common approach for positioning is to use received signal strength (RSS) and angle of arrival (AoA) measurements collected from sensors. RSS information is attractive due to its low cost, while angular measurements can be obtained efficiently in multi-antenna multiple-input multipleoutput (MIMO) systems. A well-studied research direction is to transform the inherently nonlinear positioning problem based on RSS and AoA into a linear form to enable closedform solutions. For example, in [3], the authors approximate the nonlinear model using a Taylor series expansion and a Cartesian-to-spherical transformation, resulting in a linearized system that can be solved via weighted least squares (WLS). Similarly, the approach in [4] applies an alternative linearization method and uses a standard least square (LS) estimator to solve the resulting system. Subsequent work in [5] extends this approach by adding constraints to the WLS cost function, reformulating it as a generalized trust region subproblem (GTRS), and solving it efficiently using a bisection procedure. Despite their computational efficiency, these methods remain limited in practice. RSS-and AoAbased positioning suffers from model nonlinearities, multipath propagation, and noise interference, which reduce accuracy in real-world deployments. Moreover, linearization based on Taylor approximations introduces errors, and the resulting WLS solution is generally suboptimal under realistic noise conditions [6], [7]. In addition, the weighting matrices used in WLS are typically heuristic or approximate, and non-optimal weighting can further degrade performance, especially when measurement noise is heterogeneous across anchors [7].
The use of aritifical intelegence (AI) techniques is widespread in positioning problems. In particular, deep learning (DL)-based models are well suited for capturing nonlinear relationships and can effectively model the nonlinearities inherent in hybrid RSS/AoA positioning [8]. Moreover, the positioning problem is inherently geometry-dependent, and preprocessing has a significant impact on the performance of DL-based systems [9]. Therefore, the system structure and input representation should be explicitly considered rather than relying solely on raw measurements. Consequently, analyzing whether to use preprocessed geometric features or raw measurements is essential to determine which approach enables more effective learning.
In this paper, we propose a neural network-based approach to address the nonlinearities inherent in hybrid RSS/AoA positioning, as studied in [3], [5]. Specifically, we design and train a multilayer perceptron (MLP) network to learn the mapping from measurements to 3D positions, capturing nonlinear relationships that are difficult to model with conventional linear estimators. In addition, we evaluate the impact of input representation by comparing networks trained on raw measurements versus preprocessed features derived from the linearization method in [3], allowing us to quantify how geometry-aware preprocessing can enhance learning performance.
Although WLS approach provides a computationally efficient estimator, its performance is limited in practice due to linearization errors, noise propagation, and potentially suboptimal weighting. By leveraging MLP, our approach can implicitly compensate for these limitations, learning a mapping that mitigates nonlinearities, weighting biases, and geometrydependent errors, resulting in more robust positioning. To validate our method, we plot the root mean square error (RMSE) versus noise variance for both RSS and AoA measurements. The results show that the MLP-based approach consistently outperforms the methods in [3]- [5] in the presence of RSS noise across all noise levels. For increasing AoA noise, the proposed method yields lower RMSE than [4], [5], while achieving RMSE performan
This content is AI-processed based on open access ArXiv data.