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uav-mec-drl

uav-trajectory-planning

The first unofficial implementation of a paper with the title of "UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices" (IEEE Transactions on Wireless Communications 2019)

uav3dbeamforming

secure transmission for UAV communication systems based on deep learning

uav_bs_ctrl

Code implementation of "Cooperative Trajectory Design of Multiple UAV Base Stations with Heterogeneous Graph Neural Networks".

uav_multihop_adhoc

Deployment of UAVs for Optimal Multihop Ad-hoc Networks Using Particle Swarm Optimization and Behavior-based Control

uav_obstacle_avoidance_controller

UAV Obstacle Avoidance using Deep Recurrent Reinforcement Learning with Temporal Attention

uplink-noma-uav-mobility

Adaptive Decoding Mechanisms for UAV-enabled Double-Uplink Coordinated NOMA, IEEE Transactions on Vehicular Technology, Mar. 2023

urllc_massive_mimo

Simulation routines for "URLLC with Massive MIMO: Analysis and Design at Finite Blocklength", Johan Östman, Alejandro Lancho, Giuseppe Durisi and Luca Sanguinetti, IEEE Transactions on Wireless Communications

whale-optimizer-for-partitoned-hwsn

The problem of network partition in ad-hoc networks received attention in the recent years. Many solutions have been proposed such as algorithms based, heuristics based, approximations based and meta-heuristic based to place additional relay nodes in partitioned heterogeneous wireless sensor networks to resume its operation. However, placing additional relay nodes in the partitioned network is shown an NP-Hard problem, because locations for relay node placements are not known in advance. Meta-heuristics are proven best-suited solutions to solve such kind of NP-Hard problem as well as optimization problem due to their problem independent and stochastic nature. In this research paper, we have introduced a network partition problem and developed a new nature inspired solution called Whale Optimizer to Repair Partitioned Heterogeneous wireless sensor networks (WORPH) based on the social behaviour of whales in the nature. In the proposed solution, a whale tries to find the optimal locations for attacking its prey. We have mimicked the said behaviour of whales in our proposed solution while considering the initial locations of deployed RNs inside disjoint partitions. The observed optimal positions are being used to find the optimal locations for deploying new RNs in such a way that partitioned network is restored in an optimal way. The simulation results are observed and compared with state-of-the-art approaches to prove the effectiveness of our proposed solution.

woa-to-train-neural-network

Whale Optimization Algorithm used to train Neural Network

woa_5g_communications

This repository includes the same codes for my paper "Whale Optimization Algorithm with Applications to Resource Allocation in Wireless Networks"

wsr_maximization_for_ris_system

This repository contains the source codes for the paper ``Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks'' in IEEE Transactions on Wireless Communications.