You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: An in-Depth Survey of Visible Light Communication Based Positioning Systems 
Chapter Title:  Fingerprinting 
1. Abstract of Cited Paper (BIB001):  Visible Light Communications (VLC) is an innovative technology that uses the visible spectrum for high-speed data communications. In this paper we investigate the use of MIMO in the design of luminaires supporting VLC and, specifically, in enabling localization and high data rate services. Localization and transmission appear as two different problems in the communication system; however, the former is helpful to understand which is the diversity degree that the system can adopt, in order to achieve the required performance in terms of BER and data rate. Finally, we consider also how different users can access the Internet by means of a TDMA-like access scheme. 
2. Abstract of Cited Paper (BIB002):  Nowadays, the use of Visible Light Communications is growing towards several applications, due to the many benefits of this technology. By efficiently modulating visible light, it is possible to reach very high data rates, while at the same time providing illumination. As a result, traditional indoor systems, like open offices and homes, become smarter, providing high efficient services i.e., localization, positioning and navigation. In this paper, we propose a simple method for indoor localization service provided by infrared LED devices. The positioning estimation can be calculated by exploiting the uniform deployment of LED-based transmitters, and the information of the impulse responses. The comparison to the impulse responses time samples of a room map, opportunely built according to the transmitters' deployment, allows to estimate the position of a mobile device inside the room. The estimation error can be then reduced on the basis of the number of LED devices used. 
3. Abstract of Cited Paper (BIB003):  We propose a low-cost system for indoor self-localization of mobile devices using modulated LED ceiling lamps that are fully autonomous and broadcast their identifiers without any synchronization. The proposed self-localization method is designed to handle this lack of synchronization as well as the possibility of blocked line-of-sight connections or severe attenuation in real-world environments. This robustness is achieved by applying a suitable Bayesian signal model and by taking into account the inherent sparsity in detecting the concurrently visible lamps. The proposed estimator of the location approximates optimal Bayesian estimation while maintaining low complexity. Simulation results confirm a significant gain in performance compared to a classical matched-filter approach.

Literature Review:
Fingerprinting, or scene analysis, consists of positioning techniques estimating the relative position by matching online measured data with pre-measured location-related data. Given the irregularities in the distributions of base stations, the indeterminism in the presence of barriers in the environment, and even the inherent variance of each base station, the features (fingerprints) of measured data vary at different locations. For example, due to the uneven distribution of LEDs, the reflections and scatter of light by the wall and by appliances in the room, and even due to the variance in the transmitted power of each LED, the received power varies at different positions in the room. Fingerprinting relies on these differences to estimate the position. There are two phases in fingerprinting algorithm: online phase and offline phase. In the offline phase, the location related data (e.g., the received signal strength) at each place in the environment is collected. In the online phase, the currently measured data is matched to the previously collected dataset to estimate the relative position. Pattern recognition techniques such as probabilistic, k-nearest-neighbor (kNN), and correlation can be used for matching the current data and the prior data set  . .., L n are n location candidates and s is the signal strength vector measured on the online phase. The location estimation now becomes the problem of choosing the most appropriate location L i among n candidates based on the posteriori probability. Let P(s|L i ) denote the probability that the received signal strength vector is s given the mobile device locates at L i , P(L i ) denote the probability that the mobile device locates at L i regardless any other information, and P(s) is the probability that the received signal strength vector is s regardless any other information. Then the probability that the mobile device locates at L i given the received signal strength vector is s can be calculated using Bayes‚ formula  : For every location candidate L i , the posteriori probability P(L i |s) is calculated. The location L i 0 is determined as the location of the mobile device if the value of P(L i 0 |s) is highest. When all location candidates are equidistant (i.e., all location candidates are uniformly distributed in the environment), one can assume that the values of P(L i ) are the same for all L i . Obviously, the value of P(s) is the same for all L i . Therefore, in this case the decision rule can be simplified to the comparison between the likelihood of each location candidate. Usually, the likelihood P(s|L i ) is calculated based on data collected at this location during the offline phase. Assuming that at each location, k samples of received signal strength were measured during the offline phase, then the likelihood of each location candidate has a Gaussian distribution with the mean and standard deviation calculated from the k sample value of the received signal strength. To achieve better classification among location candidates, the measured data should not be a scalar but a vector of multiple units. For example, the measured data could be a vector s = (s 1 , s 2 , s 3 , ..., s m ) of received signal strengths from m base stations. In this case, a mechanism for extracting the strength of the signal from each base station is required. Supposing that the base stations are independent from each other, the overall likelihood of each location candidate can be calculated as the product of the likelihood of the received signal strength of all base stations: where P(s j |L i ) denotes the probability that the received signal strength from the j th base station is s j given that the mobile device locates at L i . In practice, the mobile device can be located at anywhere other than discrete locations L 1 , L 2 , ..., L n . The more accurate positioning coordinates can be interpolated as the weighted average of the coordinates of all location candidates: Similar to the decision rule, if all location candidates are equidistant, Equation (3) can be simplified to: One of the main challenges to the fingerprinting techniques is that the received signal strength can be affected by unpredictable obstructions. The VLC based fingerprinting positioning technique proposed in BIB004 addressed this problem by using a Bayesian model in which the possibility of disturbances is taken into account. By using such model, the proposed algorithm is robust to obstacles that block the line of sight between LEDs and the mobile device. The simulation result showed that an error distance of 0.81 m in an area measuring 30 × 30 m 2 can be achieved with this method. k-NN is a simple algorithm that can be used for estimating the positions of mobile device with fingerprinting. Assume that S ij is the received signal strength from the ith base station measured at the jth location candidate during the offline phase, and s i is the online measured signal strength from the ith base station, where i = 1, 2, ..., m and j = 1, 2, ..., n. The distance d j between the online measured signal strength vector s = (s 1 , s 2 , ..., s m ) and the offline signal strength vector S j = (S 1j , S 2j , ..., S mj ) measured at the jth location candidate is calculated as  : After calculating the distances with respect to all location candidates, k location candidates having smallest distances are chosen and the coordinate (x,ŷ) of the mobile device can be calculated as  : where (x L j , y L j ) are the coordinates of the jth location candidate. For simplicity, k can be set to 1 as in BIB001 . In these cases, the estimated position of the mobile device cannot be an arbitrary location in the environment but one of the location candidates. Although the mechanism of kNN is simpler than the probabilistic method, one can achieve high positioning accuracy with kNN by increasing the number of location candidates. An experiment was conducted with the location candidate inter-distance set to 10 cm in a room measuring 180 × 120 cm 2 that achieved an average distance error ranging from 15 to 20 cm BIB001 . Note that in a wider room, the location candidate inter-distance of 10 cm might lead to higher costs for data collection in the offline phase, and longer times for calculating the distances to all location candidates in the online phase. Since signals from different LEDs need to be distinguished for the data collection in both offline and online phases, a mechanism for extracting the signal from different LEDs is required. The authors of BIB001 used frequency division multiplex to separate signals from different LEDs. Sinusoidal waves were generated to supply the LEDs so that the light emitted from LED had the intensity varying according to these waves. In this way LEDs can transmit signals at different frequencies. Then the power of each modulating frequency will be extracted from the received power spectral density. Another VLC based fingerprinting positioning system using k-nearest neighbors was conceptually proposed in BIB002 and then detailed in BIB003 . In this system, the position was determined as the middle between two nearest location candidates (i.e., k = 2). The simulation was conducted with four LED base stations in a room measuring 10 m ×9 m × 3.1 m. Space-time block coding was used for multiplexing. In the simulation, walls, windows and many obstacles such as chairs, wooden tables, and a bookcase with different reflectivity were assumed. The mobile device was assumed to move according to a 32-step path. In each step, the peaks of the impulse response time from different LEDs were observed and compared to the fingerprinting map of the impulse response time collected in the offline phase. The results showed that the estimation errors ranged from 0.2 m to 0.8 m.