# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 1 Introduction  
A Bluetooth Low Energy (BLE) beacon is a small device designed to broadcast Bluetooth packets of information without requiring an active connection with a receiver [1]. The Received Signal Strength Indicator (RSSI) is a metric used to quantify the power level of a received signal. It is essential for evaluating the signal strength between the BLE and a receiver, also gateway receivers, which act as the intermediate receiver for data transmission between BLE devices and a broader network [2]. As the distance between a BLE beacon and the receiving device increases, the RSSI value decreases. Conversely, as the devices move closer, the RSSI value increases. This principle underpins the development of signal fingerprint indoor localization methods [3, 4, 5]. The mapping approach constructs an RSSI map through pre-measured data from multiple reference nodes at known locations [6]. During the offline phase, RSSI measurements are collected at various reference points, creating a unique map for each location. In the online phase, real-time RSSI data from an unknown target is compared to the radio map [7]. [PERSON] and [PERSON] proposed an asset localization method that integrates RSSI data with a Kalman filter and machine learning techniques [8]. In another study [9], the focus was on developing an indoor navigation system that utilizes low-cost BLE beacons to deliver accurate location predictions while users are in motion. The study conducted experiments in a large academic building with 35 beacons placed approximately 3 m apart [9]. The study utilized RSSI values, proximity data, and multiple smoothing algorithms (Kalman filter, Savitzky-Golay filter, feedback filter, moving average filter, and proximity filter) to enhance accuracy. A dynamic micro-location algorithm was implemented within a native iOS application. Testing results revealed that the Feedback filter, Moving average filter, and Proximity filter provided the most accurate outcomes, with an average accuracy of 2.23 m during user movement. [PERSON] et al. examined the impact of signal string measurement window size on the accuracy of BLE fingerprint-based positioning [10]. They proposed an enhanced Cramer-Rao Lower Bound (CRLB) model that incorporates window size as a parameter. To mitigate the effects of reduced window size and RSSI fluctuations, they introduced a Weighted Fingerprint Construction (WFC) method. Experimental results demonstrated that the WFC-KNN algorithm outperformed the Mean-KNN algorithm, particularly with smaller window sizes, in terms of accuracy. [PERSON] et al. conducted a performance comparison of BLE databases, evaluating training time, parameter optimization, computational time, and positioning accuracy [11]. The results indicated that weighted k-nearest neighbors algorithm-based methods outperform others in large-scale deployments, as they require no prior training and minimize optimization time. In contrast, Support Vector Machines provide higher accuracy with reduced computational and training time in smaller environments. This study addresses the limitations of Wi-Fi fingerprinting and highlights BLE as a promising alternative for indoor positioning applications. [PERSON] and [PERSON] reviewed various indoor localization techniques [12], including ranging methods such as Angle of Arrival, Time of Arrival, Time Difference of Arrival, and RSSI, as well as range-combining methods like Triangulation, Trilateration, Proximity, and Fingerprinting. To address the issue of erroneous proximity reference points (RP) and extended data collection times, [PERSON] et al. [6] proposed a strategic selection of reference points, focusing on high-traffic areas, uniform distribution, and less complex indoor architectures. Rather than storing multiple data samples, their method saves only the average value for each RP, effectively correcting errors in Pedestrian Dead Reckoning (PDR). The matching algorithm is distance-based, where the position class returned corresponds to the RP with the shortest distance to the new data point.