Hala Esawi Hana Masri Shorouq Abu Assab Supervised by: Dr.Yousef Dama 5G Propagation Models Prepared by: Hala Esawi Hana Masri Shorouq Abu Assab   Supervised by: Dr.Yousef Dama Are empirical mathematical formulations to charechterize how radio waves behaves as a function of frequency ,surrounding environment and distance It is a mathematical formulation for the characterization of radio wave propagation as a function of frequency, distance and other conditions.

Outlines Introduction Objective Model Description Methodology, Results and Discussion Conclusion Recommendation

Objective To build up an indoor propagation model for the 5G communication with a mathematical representation.

Introduction Indoor radio channel differs from traditional mobile radio channel in two aspects: distances covered are much smaller variability of the environment is greater for a much smaller range of T-R separation distances Propagation within building is strongly affected by: layout of the building construction materials building type The radio propagation mechanism are the same But the codituion are much more variable Higher environmental variability for smaller T-R separation It is strongly influenced by specific features :

Model Description The analysis for this study was carried out on a 107.257m * 43.478m indoor floor at Faculty of Engineering and Geology, An-Najah National University, Nablus. The objects defined to represent the buildings include concrete walls, floors and ceilings, with height approximately 3m. The model was built up using Wireless InSite. The red square lines represent the receiver’s rout with each point showing the specific location of the receiver at a given time The green rectangular points represent the transmitting base stations (BTS). A site specific model uses a 3D presentation for the environment whether it is indoor or outdoor plans with ray-tracing based on the propagation model, and it is accurate for the modelled situation. Deterministic model. Is a graphical method to calculate the path taken by the radio waves through a given environment .it involves taking into account the rays arriving at the receiver after reflecting from all possible reflecting surfaces

3D Indoor Environment Model with Txs and Rxs locations

Parameters Specifications Value Half Wave Dipole Antenna 2 dBi Transmitted Power 0 dBm Receiver Sensitivity 250 dBm Transmitter Height 2 m Receiver Height 1.5 m

Materials specifications No. dielectric layer Material Permittivity Conductivity Thickness 1 Concrete-in 7.000 0.01500 0.220 m Concrete-out 15.000 0.350 m Brick 4.440 0.001000 0.125 m   3 Drywall (front) 2.800 0.0130 m Air 0.0000 0.089 m Drywall (back)

Methodology, Results and Discussion Path loss VS distance Wall factor and materials calculation Simulated data and rule equation Grid Model Validating our results Clustering We started our work by finding the distance between each transmitter and receivers, then we plotted the simulation results as path loss against distance (log), we choose the following samples Tx1-Rx1, Tx2-Rx1, Tx5-Rx1, we apply linear regression to get a best fit for the data points as illustrated in the following figures:

Where: L(v) = clutter loss (dB) d = Tx/Rx separation (m)   Where: L(v) = clutter loss (dB) d = Tx/Rx separation (m) n = signal decay rate f = attenuation per floor k = number of floors traversed w = attenuation per wall p = number of walls traversed Clutter loss : is partly made up of the signal attenuation due to the surrounding environment. 1728 : 38+20log(d) 864: 32+20log d)

Path loss VS distance Path loss strength for Tx1 and Rx5

Wall factor and materials calculation To get the best fitting to reality, we create a simple study model containing only a transmitter and a receiver, 1 meter away from each other surrounded with concrete walls Then we make different trials by adding a wall between them each time with specific type of material as shown below:

 

Simulated data and rule equation By applying a lot of trials through changing n and clutter value in order to get the final equation Path loss strength for Tx1 and Rx1

Path loss strength for Tx2 and Rx1

Path loss strength for Tx5 and Rx1

 

Grid model

Path Loss Strength for Tx2 and Rx1

Validating our results Path Loss Strength for Tx1 and Rx1 at 900MHz

Path Loss Strength for Tx1 and Rx1 at 50GHz

Clustering clustering representation (time, power and angle of arrival) Representation for Tx5 and Rx1_number88

Conclusion  

Recommendation In future, we hope to cover the other two scenarios: Indoor-Outdoor Outdoor-Indoor