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Deployment of a small unmanned aerial vehicle (UAV) mounted 5G base station is a promising solution for providing seamless network connectivity to users in a modern, data-centric thrust areas. The key challenge is to find the location, the height and the optimum number of mounts. A machine programming based approach is proposed here for optimal placement of UAV-mounted base station. The location of the deployment is determined using three clustering algorithms such as K-means, K-medoids, and fuzzy cluster means. Different sets of UAV-mounted base stations have been deployed with variable user density at different heights. The impact on the network performance has been quantified through measurements of received power, signal to interference plus noise ratio (SINR), and path loss per active user equipment (UEs). To gain further insights, a scenario where UEs are connected only to the terrestrial base station, that is, the network is devoid of any sort of UAV mounted base station is evaluated. Numerical computations reaffirm that the proposed technique reduces the average path loss of the active UEs. Moreover, the use of height-mounted base station also alleviates the issues arising due to low SINR values. The said technique shows immense potential in terms of seamless connectivity to end users in events of emergency and remote deployment scenarios, where ground-based base station is not possible. The big transition of on-demand connectivity for 5G networks shall be benefitted from purpose-built UAV infrastructure with specific locations or areas in mind.

Due to its flexibility, cost-effectiveness, and quick deployment abilities, Unmanned Aerial Vehicle-mounted base station (UmBS) deployment is a promising approach for restoring wireless services in areas devastated by natural disasters such as floods, thunderstorms, and tsunami strikes. However, the biggest challenges in the deployment process of UmBSs are: ground UEs position information, UmBS transmit power optimization, and UE-UmBS association. In this article, we proposed Localization of ground UEs and their Association with the UmBS (LUAU), an approach that ensures localization of ground UEs and energy-efficient deployment of UmBSs. Unlike existing studies that proposed their work based on the known UE positional information, we first proposed a 3D range-based simultaneous localization and mapping approach (3D-RB-SLAM) to estimate the position information of the ground UEs. Subsequently, an optimization problem is formulated to maximize the UE mean data rate by optimizing the UmBS transmit power and deployment location while taking the interference from the surrounding UmBS into consideration. To achieve the goal of the optimization problem, we utilize the exploration and exploitation abilities of the Q-learning framework. Simulation results demonstrate that the proposed approach outperforms two benchmark schemes in terms of the UE mean data rate and outage percentage.

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In this paper we evaluate the performance of ½ rate convolution coding with different modulation techniques such as Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK) and Quadrature amplitude modulation (QAM-16) for direct sequence code division multiple access(DS-CDMA) system using maximal ratio combining (MRC) and equal gain combining (EGC) diversity techniques over Rician fading channel. The performance of ½ rate convolution coding with different modulation techniques are analyzed in terms of Bit error rate (BER) and Signal to noise ratio (SNR). Based on simulation results we have concluded that we obtain better gain in SNR performance when ½ rate convolution coding is used with different modulation techniques.