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:  Type of Signal Features Used for Fingerprinting 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  We proposed an optical wireless position sensing system using light emitting diodes (LEDs), demonstrating it via experiment.During experiment, we used four LED lamps composed of 16 LED chips with a view angle of 65° at half power. Further, we assigned a unique address for each LED lamp, being transmitted using LED properties such as lighting and switching. Afterward, we used correlation to locate the target. The proposed positioning system displayed 4.38 cm of mean distance error among 36 points with dimensions of 1 × 1 × 1.2 m3. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:1622–1626, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26880 
3. Abstract of Cited Paper (BIB003):  In this paper, location estimation algorithm was researched for location based service. In order to mitigate interference, location code was sent by using time division multiplexing. Surface equation can be made by using signal feature distribution. We present experimental results that demonstrate the feasibility of proposed system to estimate local position with a high accuracy. 
4. Abstract of Cited Paper (BIB004):  In this article, we propose an indoor location estimation algorithm that uses the signal extinction ratio distribution of LED-based visible light communication.To improve the positioning accuracy, we use the received extinction ratio from different LED transmitters. In order to mitigate interference between transmitters, a location code was sent using time division multiplexing (TDM). We verified the feasibility of the proposed position estimation algorithm with an experimental demonstration. The proposed algorithm provided positioning accuracy with a 1.5787-cm mean distance error for a given equilateral triangle cell with three 60-cm sides. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1385–1389, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27575 
5. Abstract of Cited Paper (BIB005):  Novel concept for integrating visible light produced by LEDs with indoor positioning using tilted multiple optical receivers is presented in this paper. Generally, positioning system based on visible light communication (VLC) estimates transmission distance through received signal strength (RSS), which is based on intensity modulation and direct detection (IM/DD) method. In order to estimate position using trilateration through transmission distances, receiver needs to know the positions of three or more reference nodes. Thus, the system has limitation that optical receiver should receive signals from at least three different transmitters without interference. For this reason, a carrier allocation or TDM technique have been utilized and positioning area is limited by overlapped area of different transmitters. Furthermore, additional information on characteristics of transmitter, which have a decisive effect on signal strength, is required even though the inter-cell interference problem is solved because the characteristics of transmitters can be varied for different indoor environments. We propose an artificially tilted multiple optical receivers structure to control incidence angle gain and positioning algorithm using the received power difference. PDs are densely placed, thus, transmission distances between LED and PDs and radiation angles are the same. Inter-cell interference is fundamentally removed and positioning area is extended when compared with previous researches. In order to estimate the position, the transmitter’s location code from single LED was received using multiple optical receivers. The validity of the proposed scheme was experimentally demonstrated by determining the local position of single transmitter and angle gain profile of receivers. 
6. Abstract of Cited Paper (BIB006):  An indoor optical wireless positioning system using a differential of power received by tilted multiple optical receivers is proposed. The proposed system is based on gain difference by angle of arrival with a fixed single optical transmitter that is a white light-emitting diode. A demonstration shows that the proposed structure and algorithm estimates position successfully without requiring the characteristic information of the transmitter. 
7. Abstract of Cited Paper (BIB007):  A novel concept is proposed for integrating optical wireless visible light communications with 3-D indoor positioning using a single transmitter and multiple tilted optical receivers. We modeled a channel link, which was based on the transmitter and receiver characteristic data obtained in this experiment. The proposed 3-D positioning algorithm is based on gain difference, which is a function of the angle of arrival and the received signal strength. Our demonstration shows that the proposed algorithm can determine accurate positions, including height, without intercell interference. 
8. Abstract of Cited Paper (BIB008):  Indoor localization is the fundamental capability for indoor service robots and indoor applications on mobile devices. To realize that, the cost of sensors is of great concern. In order to decode the signal carried out by the LED beacons, we propose two reliable solutions using common sensors available on consumer electronic devices. Firstly, we introduce a dedicated analog sensor, which can be directly connected to the microphone input of a computer or a smart phone. It decodes both the signal pattern and signal strength of a beacon. Secondly, we utilize rolling-shutter cameras to decode the signal pattern, providing potential solutions to the localization of hand-held devices with cameras. In contrast to existing widely-applied indoor localization approaches, like vision-based and laser-based methods, our approach reveals its advantages as low-cost, globally consistent and it retains the potential applications using Visible Light Communication(VLC). We also study the characteristics of the proposed solutions under typical indoor conditions by experiments.

Literature Review:
Various features of the signal can be chosen for fingerprinting. The accuracy of a fingerprinting algorithm greatly depends on the type of features used. Therefore, fingerprinting algorithms can also be classified based on the type of signal features used for fingerprinting. The most basic type is signal strength, or the signal strength vector type which consists of signal strengths from different base stations. To enhance the accuracy, multiple received signal strengths can be used as fingerprinting. However, there are other types of fingerprinting. In BIB002 , the fingerprinting was the set of impulse response time from multiple base stations. In BIB001 BIB003 , the fingerprinting was based on a series of received signals in a period of time. In BIB004 BIB005 , the fingerprinting was chosen as the so called extinction ratio, which is the ratio between received powers when bit 1 and bit 0 are transmitted. The simulation result showed that an average distance error of 1.58 cm was achieved in a triangular cell measuring 60 cm at each sides. An array of multiple PDs tilted at different angles to receive the light radiated from a unique LED base station in the room was used in the studies BIB006 BIB007 . Since the light impinges at different angles incidence, the received power varies at different PDs. In addition, at different position in the room, the distance from the PD array to the LED will be different, and the angle of incidence at each PD will also be different. Therefore, the received power gain caused by the angle can be measured, to be used as fingerprinting for positioning. The simulation result showed that the achieved mean and maximum errors were 4 cm and 13 cm, respectively. In BIB008 , the fingerprinting was the combination of received signal strength and angle of arrival of the signal. To acquire the angular information, multiple PDs placed at different angles were used to receive the light signal radiated from a unique LED in the room. The received signal strengths at different PDs were compared to estimate the angular information. The positioning accuracy of 6 cm in a space measure 2 × 2 × 2 m 3 was demonstrated through an experiment. In BIB009 , the fingerprinting was the combination of the light intensity and more importantly the signal pattern. The light intensity was received by a PD connected to either a smart-phone or a computer. The signal pattern was decoded as a pattern of stripes on the image. LEDs transmitted these signal patterns by blinking at high frequency. Then the camera on a smart phone used the rolling shutter mechanism to receive the transmitted signal. In the offline phase, a map of light intensity and signal pattern was created. In the online phase, a probabilistic approach was used to determine the position of the smart phone.