Performance analysis of Energy Efficient UAV Enabled MEC Networks

Title :- Energy-Efficient UAV-Enabled MEC with RIS-Assisted NOMA for IoT Systems
-----------------------------------------------------------------------------------

Implementation plan:
--------------------------

Scenario 1:[with RIS]:
-------------------------

Step 1: Initially, we will create an Environment with 100 = IoT Devices, 2 = UAV, 1 = RIS and 1 = Base station.

Step 2: Then, we estimate the channel State Information communication between UAV, RIS, IoT Devices.

Step 3: Next, we implement the Non-Orthogonal Multiple Access (NOMA) protocol for efficient resource allocation between multiple IoT devices.

Step 4: Next, we incorporate the Energy Efficiency Objective function to maximize the ratio of completed task bits to energy consumption.

Step 5: Next, we optimize the UAV trajectory, RIS phase shift, bit allocation and transmit power for IoT devices by using Dinkelbach’s method and Block Coordinate Descent (BCD) Algorithm.

Step 6: Next, we allocate the resource to IoT devices using the optimal bit allocation, power control and adjust the transmission power.

Step 7: Finally, we plot performance for the following metrics:

7.1: Number of Iteration vs. Energy efficiency (bits\J)

7.2: UAV Trajectories with RIS

7.3: Average Energy Efficiency with RIS

7.4: Number of RIS Element vs. Energy efficiency (bits\J)

Scenario 2:[without RIS]:
------------------------------

Step 1: Initially, we will create an Environment with 100 = IoT Devices, 2 =UAV, and 1 = Base station.

Step 2: Then, we estimate the channel State Information communication between UAV, IoT Devices.

Step 3: Next, we implement the Non-Orthogonal Multiple Access (NOMA) protocol for efficient resource allocation between multiple IoT devices.

Step 4: Next, we incorporate the Energy Efficiency Objective function to maximize the ratio of completed task bits to energy consumption.

Step 5: Next, we optimize the UAV trajectory, bit allocation and transmit power for IoT devices by using Dinkelbach’s method and Block Coordinate Descent (BCD) Algorithm.

Step 6: Next, we allocate the resource to IoT devices using the optimal bit allocation, power control and adjust the transmission power.

Step 7: Finally, we plot performance for the following metrics:

7.1: Number of Iteration vs. Energy efficiency (bits\J)

7.2: UAV Trajectories without RIS

7.3: Average Energy Efficiency without RIS


Software Requirements:
----------------------------

1. Development Tool: Matlab-R2023a or above

2. Operating System: Windows-10 (64-bit) or above

Note:
------

[1] If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

[2] Please note that this implementation plan does not include any further steps after it is put into implementation.

[3] Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.

[4] If the above plan satisfies your requirement please confirm with us.
------------------------------------------------------------------------------------------------------
#EnergyEfficient
#UAVTechnology
#MECNetworks
#performanceanalysis
#droneinnovation
#SmartNetworks
#SustainableTech
#WirelessCommunication
#TechForGood
#FutureOfEnergy
-------------------------------------------------------------
We are pleased to offer guidance and support for your MATLAB and SIMULINK results or
solutions.
Our team of experts is equipped to provide comprehensive assistance to ensure optimal outcomes.
We support Simulation source for all your MATLAB requirements .

Contact us at:

https://matlabprojects.org/

[email protected]

(+91) – 9790238391