WIRELESS CHANNEL CHARACTERIZATION: ON THE VALIDATION ISSUES OF INDOOR RF MODELS AT 2.4 GHZ Theofilos Chrysikos (1), Giannis Georgopoulos (1), Konstantinos Birkos (1), Stavros Kotsopoulos (1) (1)
Wireless Telecommunication Laboratory Department of Electrical & Computer Engineering University of Patras – 26500 Greece
[email protected],
[email protected],
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Abstract - This paper presents a case study of a Wi-Fi system operating in the frequency of 2.4 GHz. Two certain types of WLAN environments are examined: a home environment (typical residence) and a complex office environment. Experimental acquired data, for both single floor and multiple floor scenarios, are compared with the theoretical values provided by an existing plethora of indoor RF models, which did not, up to now, emphasize on 2.4 GHz (which is of major importance as it is the operating frequency of wireless local area networks), and provided values for their parameters based on wider ranges of frequencies. The work presented in this paper enables researchers to calculate and predict certain intrinsic characteristics of the buildings and floors (namely, of the overall indoor propagation environment) in order to provide a proper evaluation of the RF models (and therefore, optimize the network design) for the specific frequency of 2.4 GHz.
I.
INTRODUCTION
In modern wireless communications, a wide range of RF models are used to provide the median (average) value of the signal strength at a given distance from the transmitter and for a given frequency spectrum. In this procedure, certain geographical (ground, humidity, terrain irregularities), topological (heavy or scattered population, type of obstacles, density of the buildings) characteristics of the area, as well as certain specifications of the transmitter and receiver antennas (most notably antenna height and antenna gain) have to be taken into consideration. In most cases, the mean value of the path loss is expressed in dB in dependence with the frequency of the operating system and the distance between the transmitter and the receiver (for given antenna characteristics and a certain type of environment where the system operates). Thus, a deterministic knowledge of the average path loss (which along with the average rain loss and diffraction loss provides the overall propagation loss in dB) is available. However, distance and frequency limitations have led research to a further study and
expanding of the existing empirical and semi-empirical models [1], for both outdoor and indoor scenarios. A fundamental parameter-based study of the path loss models is based on the concept that the second power law that is predicted by the Friis equation does not apply in real-life scenarios except for standard LOS paths. The modified power law research allows engineers and scholars to calculate the mean received power of a signal transmitted over a wireless link in a more realistic approach. It has been suggested [2] that the third-power law is more suitable for a plethora of applications based on wireless communications for an outdoor environment. The indoor propagation channel, in particular, demands a lot more than a deterministic formula of calculating the average signal strength as a function of distance and frequency. The increased impact of multipath and other propagation phenomena such as reflection and scattering, as well as the existence of many objects whose proportions are comparatively close to the wavelength of the operating wireless systems, render the propagation of a signal and its arrival at a receiver (mobile or fixed) a rather complex procedure. The precision of the path loss models depends heavily on their ability to demonstrate and reflect, in the calculations, these complex phenomena to the best possible way [3]. II.
SITE DESCRIPTION
A. The indoor topologies Our measurements took place in two different indoor environments: a residence and an office. These two settings provide the fundamental scenarios for a WLAN operating at 2.4 GHz, where a fixed router (Access Point – AP) transmits electromagnetic radiation in an indoor environment in omni-directional fashion. Both topologies are presented in the Appendix area at the end of the paper. Appendix A features the home environment, which consists of two apartments in the third floor of a fifth-floor building in the centre of Patras, Greece. The router,
marked with R, is in the first apartment, while all measurements are marked with Greek letters. Greek letters marked with a tone symbol (i.e., A’) stand for additional measurements that were taken for increased precision to the experiment. Measurements were taken in both apartments of the same floor, whereas in the multiple floor scenario, measurements were taken in one floor below the router’ floor (one-floor difference) and two floors below (two-floor difference). Due to legal issues, we were only able to measure the received signal strength in public areas for the other two floors (close to the elevator and outside the entrances of the other apartments). For the “basic” floor where the router is installed, measurements were taken in both the router-apartment and the second apartment, since access was granted by the flat owners which also reside in these apartments. The topologies of the other two floors are almost identical to the one presented in Appendix A and are therefore omitted from inclusion in the paper. Appendix B demonstrates the office environment, which consists of the Wireless Telecommunications Laboratory, located on the second floor of a building belonging to the Department of Electrical and Computer Engineering, in the University of Patras. Once again, the fixed router is marked with an R, whereas the Greek letters mark the exact locations of the measurements with toned letters standing for extra measurements for precision purposes. As it can be easily observed, the topology in question goes beyond the typical notion of an “office” scenario, with an increased degree of complexity which is, however, common for university laboratories: a large room where the router is located (in an external wall towards the south), and several smaller offices scattered around the main room and across the main hall, side by side. The advantage of taking measurements in such a setting is double: not only is the office scenario thoroughly examined for the frequency at hand (2.4 GHz) but it also escapes the concrete definition of an “office”, providing the foundation for a dynamic evaluation of an office environment and the increased complexity that it bears, adding more to the importance of our measurements. measurements were also taken at the third floor of the same building, belonging to another laboratory of the Department of Electrical and Computer Engineering, (one-floor difference between the router/transmitter and the laptop/receiver). Finally, our measurements also include the ground floor of the same building that hosts three auditoriums and a main hall in front of the building’s entrance, where measurements were also taken (two-floor difference between the transmitter and the receiver). B. Measurements setup In both the residential and the office scenario, a fixed router (Access Point of the WLAN) was the transmitter, with an omni-directional antenna and a transmitting power level of 20 dBm for the home topology and 17 dBm for
the office environment. In both cases, a properly equipped laptop functioned as the receiver, and the NETSTUMBLER 0.40 software was installed and used for measuring the received signal strength. The NETSTUMBLER software allows the operator of the laptop to know the exact received power coming from the specific router of the WLAN, excluding all other signals even if they belong to the same frequency of 2.4 GHz. Thus, the actual received power of the signal being transmitted from the router is measured and stored for further analysis. A number of measurements were taken in a given location for a specific time period of a few minutes, and the best value was finally accepted as the mean signal strength for the specific location. It is, therefore, an experiment conducted in a “best-case” scenario. The measured values are then compared with the theoretical ones which are calculated via indoor RF (path loss) models. C. Path loss models A number of indoor RF (path loss) models exist in modern-day literature, applied for various indoor environments for a wide range of frequencies and different types of obstructed LOS paths (OLOS) or even NLOS scenarios. As opposed to the outdoor scenario, the indoor propagation channel is much more complex, and therefore the proper evaluation of the existing path loss models and, in a next step, the numerical correction and adjustment of the models to the intrinsic indoor characteristics of the topologies in question, is of major importance for an optimal network design and an appropriate link budget of the specific network. Even more so, the lack of any experimental research in the important frequency of 2.4 GHz leaves a daringly open field for researchers and engineers that wish to improve the channel estimation for the indoor WLAN channel. The path loss models can be divided in two categories: the semi-empirical/deterministic ones (products of theoretical work) and the empirical ones (products of experimental data properly acquired and processed later on). Deterministic models are more dominantly described as “semi-empirical” due to the fact that they are not exclusively products of theoretical work but a combination of theory and experimental data. The “fundamental” indoor model is the Free Space Model [4]: 10 log 20 log 20 log 4 20 log (1) For the given frequency and 1 (due to the fact that both the transmitting router and the receiving laptop are equipped with omni-directional antennas), the mean path loss is calculated as a function of distance (in meters): 39.9 20 log (2) The ITU Indoor RF model is given by [5]: Nlog d Lf n 28 dB (3) PL 20log f
Where N is the distance power decay index, f is the frequency in MHz, d is the distance in meters (d > 1m), Lf(n) is the floor penetration loss factor and n is the number of floors between the transmitter and the receiver. The power decay index and the floor penetration loss factor are specified for a number of frequencies and types of indoor environment. However, no specifications exist so far for 2.4 GHz. The Log-Distance indoor model is given by [5]: L PL d Xσ (4) Nlog Where PL(d0) is the path loss at the reference distance, usually taken as (theoretical) free-space loss at 1m, N is the path loss distance exponent and Xs is a Gaussian random variable with zero mean and standard deviation of σ dB. N and σ are derived from experimental data, however no specifications exist for 2.4 GHz. During our work we assumed 95% coverage and thus Xs=1.645σ. The One-Slope Model is a modified power law path loss model that adjusts the value of the power decay index (slope factor) to the experimental data (semi-empirical model) [4]: 10 log (5) K is a constant which depends on the antenna characteristics and the average channel attenuation, d0 is a reference distance for the antenna far-field, and n is actually the power decay index. K in our case (2.4 GHz & omni) equals to -39.2 dB. The value of n depends on the propagation environment: for complex environments it can be obtained via a minimum mean square error (MMSE) fit to empirical measurements. The Motley-Keenan indoor model is a multiple walls model that provides a way to obtain the loss in the environment when the signal impinges several types and amounts of walls. In this equation, PLr is the reference loss [dB] taken at 1 (one) meter of distance among transmitter and receiver, n is the decay index, N is the number of walls between the transmitter and the receiver, ki (kj for floors) is the number of type i (j for floors) walls and Lwi (Lfj for floors) is the penetration loss in the type i walls. The walls and floors that have to be considered are determined by the OLOS path [6]: ∑ ∑ 10 (6) The Multi Wall & Floor (MWF) model takes into account the decreasing penetration loss of walls and floors of the same material as their number increases (as opposed to the M-K model). [7]: 10 Kf
Lf
∑K
L (7)
is the attenuation due to kth traversed wall type i I,J number of wall and floor types
is the attenuation due to kth traversed floor type j number of walls type i number of walls type i. III.
HOME ENVIRONMENT
A. Single floor measurements A total of 27 measurements were taken in the two apartments in the same floor where the router is installed. Table I – Home Measurements (single floor)
Location A B Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Υ’ Φ Φ’ Χ Ψ Ω R’
Distance T-R (m) 3.7 1.5 1.5 3.5 4.5 3.5 2.7 6 3 1.7 5.5 4.3 3.4 5.5 10 9 7.5 15.5 12 19.5 19 16 13.5 15 13 5.5 1m (L0)
Pr (dBm) -40 -30 -24 -33 -44 -36 -32 -44 -49 -44 -54 -52 -41 -52 -55 -54 -53 -68 -68 -80 -80 -79 -77 -76 -64 -46 -20
At 2.4 GHz and for transmitting power of 20 dBm, the received power calculated by the Free Space Model is: 20 20 log (8) For the Log-Distance Model, the appropriate power decay index is assumed to be 30. The standard deviation σ ranges between 6 and 12 dB. For a desired
95% coverage, the fade depth Χs ranges between 9.8 and 19.7 dB, whereas the received power is: 20 30 log (9) The ITU indoor path loss model provides the following equation for the calculation of the mean received power (N=28): 20 28 log (10) This, however, applies to distances up to 6 m (inside the first apartment). For greater distances the power decay index obtains the value N=40: 20 40 log (11)
The slope factor value for the One-Slope Model can be obtained via a minimum mean square error fit to empirical measurement and is equal to 45.8: 20 45.8 log (12) The received power as calculated from the Motley-Keenan model is: ∑ 1 ∑ 1 20 20 log10 (13) The MWF model calculates the received power with the following equation: 20
∑
1
∑
20 log10
1
Figure 1. Semi Empirical PL models (single floor home scenario)
Figure 2 Empirical PL models (single floor home scenario)
∑
1
∑
1
(14)
It is obvious that from the semi-empirical models, the most reliable is the ITU indoor Path Loss model. The Log-Distance path loss model, using a “scalar” adjustment of the shadowing deviation versus distance, provides a reliable prediction of the received power which yet fails to function dynamically. As far as the empirical models are concerned, the One-slope model provides some reliable calculations, yet at certain locations it is rather pessimistic, compared to the actual measurements, due to the fact that the slope factor is a product of an error minimization function. This, at times, underestimates the wireless channel. The Motley-Keenan is rather pessimistic for large distances (larger than 6 m) due to the fact that it calculates a steady penetration of i-type walls as their number increases, while measurements have shown the decreasing losses of i-type walls (and floors) as their number increases. The Multi-Wall-Floor (MWF) model takes this into account, and it provides the most reliable predictions, as it can be seen from Fig.2. B. Multiple floor measurements For the multiple floor measurements we shall omit both the Free Space and the Motley-Keenan models, due to their increased deviation from the actual measured values. The standard deviation σ (for the Log-Distance model) ranges between 11 and 19 dB, for 1 or 2 floors difference respectively. The One slope model obtains a 5.1 and 6.06 power decay index for 1 or 2 floors difference.
Figure 4. The One-slope PL model
The ITU Indoor PL model utilizes an Lf(n) floor penetration loss factor. For a domestic environment this factor receives the value of 4n (where n is the number of floors penetrated).The ITU predictions are very unreliable, especially for the 2-floor difference (Fig. 5). The Multi Wall & Floor model provides the best predictions as shown in Fig 6.
Figure 5. The ITU Model
Figure 3. The Log Distance PL Model Figure 6. The Multi Wall Floor Model
IV.
HOME ENVIRONMENT
A. Single floor measurements Table II – Office Measurements (single floor)
Location Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ
Distance T-R (m) 8 11.5 13 15 5 13 20 21 18 16 20 22 20 10.5 10.5 10 10 16.5 17 11
Pr (dBm) -48 -55 -53 -54 -45 -51 -66 -71 -71 -59 -65 -75 -74 -55 -41 -40 -40 -63 -68 -49
Ψ Ω
15 16.5
-54 -59
The office environment is quite complex since the AP is located in a room of about 100 ! Additionally the building itself is very particular since the floors differ from each other, adding complexity to our calculations. The most noticeable effect of the room itself is that the power decay index, even for free space propagation is lower than 20(!) since the large room where the AP is located in fact “echoes” the signal strength throughout the room. This results in a base power decay index of 1.73(via mean square error techniques) for the measurements in the AP room and 18 for every OLOS calculation path (since the single is strengthened when leaving the room). Other noticeable effects also occur. For instance the Xs Gaussian random variable for the log-distance model does not seem to increase according to distance. There is no shadow fading when calculating LOS paths within the AP room. Thus for a spot located 6 m from the AP, but in a different room, we have a non-zero Xs, while for a distance of 11 m in the AP room we have 0 Xs. In result the power decay index is 18 for the FSM, 26 for the Logdistance model, 28 for the ITU model and finally 30.6 for the one slope model. The standard deviation σ (for the Log-Distance model) ranges between 3 and 7.9 dB, which for a desired 95% coverage leads to a fade depth Χs of 4.935 and 12.99 dB accordingly.
Figure 7. Semi-empirical PL models
Figure 8. Empirical PL models
The three locations where in our measurements we had indications of around -40 dBm are locations inside the AP room. The only models that can actually describe them are the FSM and – with some deviation – the MultiWall-Floor (MWF) model. Their existence has severe negative impact on the One-Slope model curve as it is shown in Figure 8. The MK model would fail in predicting long distances as shown already in figure 2 and thus is discarded. B. Multiple floor measurements When the signal is traversing floors, the standard deviation σ (for the Log-Distance model) ranges between 7.7 and 11.9 dB, for 1 or 2 floors difference respectively. The One slope model gives us a 4.12 and 4.58 power decay index for 1 or 2 floors difference. Figures 9 and 10 show the model predictions compared with the actual measurements. As before the free space Model cannot be applied in a multiple walls scenario.
Figure 9. The Log-Distance PL model
Figure 10. The One-slope PL model
The ITU Indoor PL model utilizes an Lf(n) floor penetration loss factor. For an office environment this factor receives the value of 15 + 4(n - 1) (where n is the number of floors penetrated). Experimental data show that this factor is not applicable in this case. The notion of the standard “office” environment does not stand. The prediction of the ITU model is rather pessimistic. Instead when using the Lf(n) for the home environment we manage to fit the model curve better to the experimental data. The ITU predictions are presented in figure 11.
Figure 11. ITU(adj) PL model
The Multi Wall & Floor model for multiple floors utilizes a power decay index of 18 as in the FSM. The OLOS path that the model considers provides predictions as in figure 12.
Figure 12. The MWF model
V.
CONCLUSIONS
As it can be easily deduced from the above measurements, the Multi-Wall-Floor provides the most reliable and accurate – to an amazing degree – predictions for the average received power, for both single floor and multiple floor scenarios and in both environments. The One-Slope model provides satisfactory predictions as well, based on the fact that it derives its slope factor value out of an error minimization function. This, however, leads, at certain cases, in an underestimation of the wireless channel. The other empirical model, the Motley-Keenan model, provides reliable results only for distances smaller than 6 m. For greater distances it fails to predict accurately and falls “way off” due to the fact that it does not take into account the decreasing loss of walls and floors of a certain type as their number increases.
As far as the deterministic/semi-empirical models are concerned, the Free Space model provides accurate predictions only when a dominant LOS path is present. In all other cases there is a deviation that renders the model inappropriate. The ITU model provides some reliable predictions for the single floor measurements, yet fails completely to work in the multiple floors scenario, especially when there is 2-floor difference between transmitter and receiver. Another interesting observation is that the complex nature of the office environment forced us to choose the “home” formula of the ITU model instead of the “office” formula that fails to match suitably with the measured values. The Log-Distance path loss model cannot go beyond a generic satisfactory level as long as there is a pre-chosen, “statically” defined relation of the shadowing deviation versus distance. VI.
FUTURE WORK
Immediate future work consists of adjusting with the use of numerical factors the ITU indoor RF model so that it fits smoothly the measured data acquired via this experiment. Secondly, the Log-Distance model must be fitted to the measured values dynamically, so that the shadowing deviation is not defined single-handedly as a function of distance, but also taking into consideration the effect of walls and other materials that create a shadowing effect between the transmitting router and the receiving laptop. Thirdly, the “best-case” scenario that we adopted must be re-examined thoroughly, allowing us to provide and process the instantaneous envelope of the received power (and not just its mean value) so that the appropriate fading distribution that describes the indoor WLAN channel at 2.4 GHz for both the home and office environment (for single-floor and multiple-floors scenarios) is derived. VII.
REFERENCES
[1] J. D. Parsons, The Mobile Radio Channel, Wiley Interscience, 2000. [2] A.Aguiar ,J.Gross ,”Wireless Channel Models”, Technical Report,TKN, 2003. [3] S .Kotsopoulos and G.Karagiannidis, Mobile Communication, Papasotiriou SA Publication, 1997. [4] A.Goldsmith, Wireless Communications, Stanford University, 2005. [5] J. Seybold, Introduction to RF Propagation, Wiley Interscience, 2005. [6] A.Lima , L.Menezes , “Motley-Keenan Model Adjusted to the Thickness of the wall” , 2005 SBMO/IEEE MTT-S International Conference, July 2005. [7] M.Lott, I.Forkel , “A Multi Wall and Floor Model for Indoor Radio Propagation” , In Proceedings of VTC 2001 - Vehicular Technology Spring Conference, Rhode Island, Greece, 2001.
Appendix A – Home Environment
Appendix B – Office Environment
