Modeling the Dynamical Human Blockage for 60 GHz WLAN Channel Models

1. Modeling the Dynamical Human Blockage for 60 GHz WLAN Channel Models Date: 2010-01-19 Authors:

2. Abstract • This presentation proposes an approach to develop a dynamical human blockage model for the 60 GHz channel model of [3]. • It contains modeling approaches for PHY layer evaluation as well as MAC layer evaluation

3. PHY layer evaluation model • In an environment with stationary devices, nonstationarity of the 60 GHz channel mainly appears from moving people that may attenuate (or block) the communication link. • Based on methods presented in [1-2] the dynamical channel model for PHY layer evaluation may be developed. • This presentation describes an approach to develop a model of dynamical human blockage for the 60 GHz channel model [3] based on ray tracing in combination with a random walk model and a diffraction model, as presented in [1].

4. Proposed Methodology for Modeling of Dynamical Blockage • It is proposed to include dynamics in the channel model [3] by introducing the ray (cluster) blockage random events. Each cluster blockage event corresponds to substantial decreasing the given ray (cluster) power during some time period due to obstruction by one moving human body [1,2]. • The attenuation coefficient has a specific profile in time domain which was investigated experimentally in details in [1]. The probabilities of blockage events for different number of clusters were estimated from ray-tracing/random walk simulations in [2].

5. Main Assumptions for Modeling of Dynamical Channel Model • For PHY layer performance evaluation we suggest to use the following assumptions/simplifications, because here no correlation between succesive generated impulse responses is needed: • Channel realizations are generated independently for some random time instances. • Blockage random events are independent for different channel clusters. • Under these assumptions for complete development of the proposed dynamical model will require: • Defining the probability of cluster blockage event. • Defining the amplitude (or power) attenuation distribution function for cluster blockage event. • These parameters of the dynamical channel model may be derived from the measurement and simulation data presented in [1-2].

6. Probabilities of Clusters Blockage (STA-AP) Presentation [1] provides results for probabilities of clusters blockage obtained from ray-tracing simulations for STA-AP sub-scenario for Conference Room environment [3], [4]. (Please note that for the approach presented in this document more realistic person dimensions are assumed (0.45m x 0.4m x 1.70m)) In 99.99 % of channel realizations the LOS link was not blocked. In case of 1st order reflections from walls the simulation results show that the probability of multiple simultaneously blocked clusters is 0 %. The probability that no cluster is blocked is 87.4% and hence that one cluster is blocked is 12.6%. We propose with a probability of 12.6 % to block one cluster among the four available, whereas all clusters have equal probabilities to be chosen.

7. Probabilities of Clusters Blockage (STA-AP) (Cont’d) Probabilities of clusters blockage for the 2nd order reflections from walls obtained by simulations and probabilities obtained from binomial distribution are summarized in table 2 below. The parameters of the binomial distribution are suggested to be p = 0.070. Table 2: Second Order Reflections from Walls

8. Probabilities of Clusters Blockage (STA-STA) For the STA-STA sub-scenario for Conference Room environment [3], [4] the same methodology has been used to derive the probabilities of Cluster Blockage than for the STA-AP scenario . Due to the fact that both devices are positioned on the table the 1st order reflections from the ceiling and the LOS link are not influenced by human movement and hence have a blockage probability of 0%. Like for the STA-AP scenario, in case of 1st order reflections from walls the simulation results show that the probability of multiple simultaneously blocked clusters is 0 %. The probability that no cluster is blocked is 76.0% and hence that one cluster is blocked is 24.0 %. In case of 2nd order reflections from walls and ceiling the simulation results also show that the probability of multiple simultaneously blocked clusters is 0 %. Here the probability that no cluster is blocked is 96.3% and hence that one cluster is blocked is 3.7 %. For both cases we propose with a probability of 24.0% and 3.7% to block one cluster among the 4 available, whereas all clusters have equal probabilities to be chosen.

9. Probabilities of Clusters Blockage (STA-STA) (Cont’d) Probabilities of clusters blockage for the 2nd order reflections from walls obtained from ray tracing and probabilities obtained from binomial distribution are summarized in table 5 below. The parameters of the binomial distribution are suggested to be p = 0.175. Table 5: Second Order Reflections from Walls

10. Attenuation Distribution Functions for Cluster Blockage Effect Regarding the attenuation characteristics the ray tracing/ random walk simulations have shown that two types of clusters have to be distinguished: clusters without a reflection at the ceiling and clusters with a reflection at the ceiling The simulations have also shown that the attenuation distribution functions are equal for both sub-scenarios (STA-STA and STA-AP) Please note that 1st order reflections from the ceiling have a blockage probability of 0% and thus an attenuation distribution function is not needed in this case

11. Attenuation Distribution Functions for Cluster Blockage Effect (Cont’d) In case of clusters without a reflection at the ceiling we propose to use a truncated 2nd order Gaussian mixture model (GMM) in log-scale. The parameters of this distribution are suggested to be m1 = -18.2 dB, s1 = 8.3 dB, r1 = 0.83 , m2 = -47.2 dB, s2 = 10.0 dB, r2 = 0.17 In case of clusters without a reflection at the ceiling we propose to use a truncated Gaussian distribution function in log-scale to approximate the attenuation distribution function. The parameters of the Gaussian distribution are suggested to be m = -18.4 dB and s = 8.8 dB In both cases the truncation level is proposed to be equal to 0 dB.

12. MAC layer evaluation model • The dynamical human blockage model so far uses the assumption that channel realizations are generated independently for random time instances, meaning the temporal characteristics of shadowing events are not included. • In case of PHY level simulations these assumptions are valid, because no correlation between successive generated impulse responses is required, since PHY level simulations assume averaging over a large number of channel realizations. • In order to cover the influence of human induced channel dynamics, we propose to consider both temporal characteristics and signal level/SNR degradation in system level simulations that include MAC protocols.

13. MAC layer evaluation model (Cont’d) • This part of the presentation describes an approach to develop a model of dynamical human blockage for the 60 GHz channel based on measurement campaigns investigating the influence of moving humans on the 60 GHz channel [1]. This will be the base for guidelines how to include channel dynamics due to human movement in system level simulations that include MAC protocols. These guidelines could be included in the channel model document [3]. • Statistics about important parameters to model the temporal characteristics of such shadowing events (duration, decay time, rising time, fading depth) will be summarized in the form approximated probability distributions, validated by Kolmogorov–Smirnov test.

14. MAC layer evaluation model (Cont’d) • Definitions • Decay time tdecay (for a given threshold ) • Rising time trise(for a given threshold ) • Duration tD • Mean Attenuation Ameanfor the window (td/3<t<2/3 td) • The figure illustrates modelling approach for a shadowing event. In the figure, exemplary experimental data (solid line) as well as the corresponding shape of the model (dashed line) are shown. Four parameters have been chosen to describe the event. • The shadowing event is modelled by a series consisting of a linearly decaying period, a period with a constant signal level and a period with a linearly increasing signal level. The decay rate as well as the rate of increase can be calculated from the parameters tdecay and trise.

15. Model Parameters • Decay time • Duration • MeanAttenuation • Rising time [s]

16. Conclusion • This presentation proposes an dynamics approach for the 60 GHz channel model of [3] using the data from [1-2]. • For PHY layer evaluation the interception of the channel clusters by moving people is proposed to be modeled by cluster blockage events. Appearance of a cluster blockage event is simulated for different clusters of the channel realization with independent probabilities. • For a given cluster blockage event, attenuation distribution functions were proposed. • In addition a MAC layer evaluation model is proposed, temporal characteristics and signal level/SNR degradation due to human movement are estimated.

17. References • IEEE doc. 802.11-09/1169r0. Human Body Blockage - Guidelines for TGad MAC development. M. Jacob, C. Mbianke, and T. Kurner, Nov. 2009. • IEEE 802.11-09/1170r0. Modeling the human induced 60 GHz channel dynamics. M. Jacob, S. Priebe, and T. Kurner, Nov. 2009. • IEEE doc. 802.11-09/0334r4. Channel models for 60 GHz WLAN systems, A. Maltsev et al, Nov. 2009. • IEEE doc. 802.11-09/0296r12. TGad Evaluation Methodology, Eldad Perahia, Nov. 2009.