With the increasing demand of location-based services, indoor ranging method based on Received Signal Strength Indicator (RSSI) or Channel State Information (CSI) has become an increasingly important technique due to its low hardware requirement and high accuracy. Due to robustness against the multipath e ect, frequency domain Channel State Information (CSI) of Orthogonal Frequency Division Multiplexing (OFDM) systems is supposed to provide an excellent ranging measurement for indoor localization. In this paper, we propose a novel signal attenuation model for indoor ranging with RSSI and CSI. The proposed attenuation model scheme is implemented and validated with experiments in a typical indoor environment. Experimental results are presented to con rm that the proposed model can e ectively reduce ranging error, compared with two existing methods in a typical indoor environment.
1 Kyungpook National University,Electronics Engineering,Daegu, South Korea
wjj0219@naver.com
2 Kyungpook National University,Electronics Engineering,Daegu, South Korea
cjstk891015@naver.com
3 Kyungpook National University,Electronics Engineering,Daegu, South Korea
jgpark@knu.ac.kr
In recent years, the demand for location-based services (LBS)[
Due to the complex indoor environment, RSSI is often a ected by
multipath e ects and noise signals, and the positioning performance is not stable.
With the availability of channel state information from the physical layer,
WiFi-based indoor positioning schemes have gradually shifted from adopting RSSI
indicators to higher resolution CSI[
The rest of this paper is organized as follows. In section 2, we introduce related works. We illustrate the methodology of RSSI and CSI-based propagation model, respectively. Next is the proposed novel ranging model based on RSSI and CSI in Section 3 . The implementation of the novel model and experimental evaluations are presented in Section 4 . Finally, conclusions are presented and suggestions are made for future research in Section 5 . 2 2.1
RSSI is a ected by multiple paths, so we hope to combine a new physical
property CSI [
(1) It has excellent resistance to interference in the 2.4 GHz band signal and has less uctuation in a stable environment, and can re ect the changes in the environment;
(2) Using Orthogonal Frequency Division Multiplexing (OFDM) technology, signals of di erent paths can be distinguished as nely as possible.
CSI is a ne-grained attribute value of the physical layer that describes the amplitude and phase of the frequency domain corresponding to each subcarrier. The CSI can re ect the attenuation of the wireless signal as it travels between the transmitter and receiver. Table 1 demonstrates the di erence between RSSI and CSI.
In this paper, RSSI values and CSI values are collected at xed locations and the results of their e ects on multipath e ects were compared. The distance between these xed locations and access points is 1 meter, 4 meters and 7 meters, respectively. Figure 1 shows the variation of the sampled RSSI value. Figure 2 shows the variation of the amplitude of the CSI sampled on channel 2.
Although the CSI value collected on a certain channel will change, the CSI value collected compared with the collected RSSI value varies little with time and remains basically stable. 2.2
In a free-space model, the average received signal is in a logarithmic relationship
with the distance d between the transmitter and receiver in all environments.The
relationship can be expressed using the famous Friis formula[
RSSI = A 10n lg( d d0 ) + X0 (1)
Where, RSSI indicates the received signal strength indication value and the
unit is dBm. A is the signal strength at 1 m from the source. d represents the
distance from the transmitting node to the receiving node, and the unit is m.
d0 is the unit distance and usually takes 1m. X(0) is the error correction term,
subject to a normal distribution with 0 as the mean.. When the n value is smaller,
the signal attenuation in the transmission process is smaller, and the signal can
spread farther away. The range is generally between 2 and 4.
Currently, WLAN protocols such as 802.11n use Orthogonal Frequency Division
Multiplexing (OFDM) technology and Multiple Input Multiple Output (MIMO)
technology as their standard technologies. MIMO technology enables the
diversity transmission and reception of signals. These two technologies play an
important role in the formation of CSI data.Wu et al.[
CSIeff = 1 K
X fk k fc jjAjjk where CSIeff is the e ective CSI for distance estimation. K is the number of subcarriers. fc is the calculated center frequency, and jjAjjk is the amplitude of the ltered CSI on the kth subcarrier. The propagation distance between the (2) (3) transmitting end and the receiving end can be represented by e ective channel state information.
d =
Where d is the distance between the transmitter and receiver in indoor environments c is the radio velocity, f0 is the central frequency of CSI. n is the path loss attenuation factor, and is the environmental factor. 3
For accurate ranging, this paper proposes a propagation model based on RSSI and CSI. Through analysis, it is found that even in the same indoor environment, the degree of attenuation of each signal transmission is di erent. Based on this, this paper de nes a new indoor ranging model with excellent stability and can re ect environmental changes, based on which distance calculation is performed.
Compared with RSSI, CSI describes multipath propagation to a certain extent, and does not represent the superimposed amplitude response of all subcarriers like RSSI. The channel response of CSI not only re ects the amplitude of each subcarrier but also the phase information of the subcarrier. In this way, CSI expands the single-valued RSSI into a matrix composed of multiple information of multiple channels, so CSI provides richer and ner channel state information for wireless sensing. CSI bene ts from multipath e ects. In di erent propagation environments, subcarriers exhibit di erent amplitude and phase characteristics. In the same indoor environment, the multipath e ect the RSSI and the characteristics of subcarriers remain relatively stable. Compared with the signi cant features, CSI maintains a steady trend.
The origal amplitude information is extracted from multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card by accessing the modi ed device driver of the Intel Wi-Fi Wireless Link 5300. The indoor RSSI value and the CSI value and their corresponding distance measured data are collected, and then the average amplitude of the RSSI and CSI calibrated using the mean value lter is used for indoor ranging. A signal attenuation ranging model based on RSSI and CSI is established by using the attenuation factor propagation model, and the measured values of RSSI and CSI are converted into distance values by using the ranging model. According to Equation 2 and Equation 4, we propose a novel RSSI and CSI based ranging model: d = a 10 A 10RSnSI + (1 a) Where, a 2 [0; 1]
Where c is the radio velocity, f0 is the central frequency of CSI. a is the coe cient of the attenuation model, which varies according to the complex situation of the indoor environment. CSIeff is the e ective CSI, n is the path loss attenuation factor, and is the environmental factor. In fact, the parameters n and pertain for each indoor scenario. We can t the attenuation equation to nd these two parameters. The parameters A is -23.84 and n is 2.206 in the proposed model. The value of a can be adjusted according to changes in the experimental environment. 4
The experiments were carried out in the third- oor corridor of Kyungpook National University IT1 Building, as shown in Figure 4.
Since the Intel 5300 is open source for the solution to obtain CSI data [
In experiments, we use ipTIME N3004 as a node and place it at the height of 0.2m on the ground. The reference node place on 0.2m high to reduce the impact of ground re ection on RSSI value and CSI value. An Intel 5300 wireless network card built-in notebook computer is used as a mobile node and is located 1m away from a xed node for measurement start. Considered reference points are 2m apart sequentially to each other. At each reference point, 1000 times measurement process are executed. We calculate ltered mean RSSI values and ltered mean CSI values at reference points. After that, we obtain a proposed attenuation log model, which valid in range of 1m to 10m, as shown in Figure 5.
We compare the performance of the proposed attenuation model with that of an existing attenuation log model. We collected 20 data at each reference point for performance veri cation.Table2 shows the comparison of the distance measurement errors of the proposed method and exciting method.
From Table 2, we can nd that proposed RSSI attenuation log model provides more accurate distance information compared with an existing RSSI attenuation model. Experimental results show that range error decreases more evidently beyond the range of 9m. At the same time, the proposed method reduces the distance error at 1 meter reference point by 0.025m. At the same time, because the CSI is more stable in the indoor environment, the proposed ranging algorithm can improve the ranging stability. This method can ensure e ective indoor ranging with a high probability. 5
In this paper, we propose a novel ranging model based on RSSI and CSI. The proposed method produces more accurate distance information and is applicable for more extended ranging case, compared with an existing RSSI attenuation log model method. In summary, the main contributions of this article are as follows. First, a novel indoor ranging model combining channel state information with an attenuation factor model is obtained. Secondly, the use of ordinary notebooks and Intel 5300 network card does not require other specialized equipment to implement the proposed indoor positioning algorithm. And experiments have been carried out in a typical indoor environment, which has more signi cant advantages in accuracy and stability than traditional methods, and the system realizes lower cost and is accessible for popularization. In the future, we intend to study indoor ngerprint localization algorithms based on RSSI and CSI.