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The explosive growth of mobile devices is the main engine to continue evolution in the communications field. The amount of traffic generated by today’s users in applications such as high definition videos, cloud computing, and wearable devices, require a drastic change in mobile telecommunications. 5G Ultra Dense Network (UDN) is one of the key components leading in achieving the high capacity for all users. In UDN, the number of base stations or access nodes equals or exceeds the number of active users by unit area. In this paper, different modeling techniques of UDN are studied. Moreover, a heterogeneous framework modeling was proposed. This framework illustrated a system model for UDN based on Urban Macro (UMa) Scenario. The distance dependent path loss model for UMa was presented and analyzed. The Simulation results of path loss model indicated an increase in the path loss with increasing the distance range from 10m to 500m. The received power simulation results of User Terminal (UT) displayed the power is approaching zero when the distance between the BS and UT goes beyond 250m. Therefore, it is assumed that UTs located 250m away from the BS can reuse the subchannel of AN in another sector with negligible interference.

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Pulse compression is a significant aspect for improving the radar detection and range resolution. To improve the range detection, the pulse width is increased to overcome the transmitter maximum peak power limitations. However, pulse compression is accompanied with time sidelobes that can mask the small targets. The Wavelet Neural Network (WNN) is a new technique used for pulse compression sidelobe reduction. In this paper, Morlet function is applied as an activation function for WNN and the backpropagation (BP) is implemented for training the networks. The WNN is applied based on PAT and Px polyphase codes. The performance of WNN is evaluated in terms of Signal to Noise Ratio (SNR) and the computational complexity. The simulation results indicate that the WNN has higher Peak Sidelobe Level (PSL) than the Auto Correlation Function (ACF) with more than 100 dB and higher PSL than the Neural Network (NN) with more than 100 dB.

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