Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network

Area of Science:

• Computer Science
• Electrical Engineering
• Telecommunications

Background:

• Mobile Edge Computing (MEC) advances wireless networks, but faces challenges in resource-limited environments.
• Energy Harvesting (EH) integration enhances MEC sustainability by optimizing mobile device power consumption.
• Ultra-Dense Networks (UDN) combined with MEC are vital for 5G ultra-low-latency applications.

Purpose of the Study:

• To enhance system performance in resource-limited MEC environments through fairness and energy efficiency.
• To address computation offloading and resource allocation challenges in UDNs for 5G networks.
• To optimize task execution efficiency and user satisfaction in wireless networks.

Main Methods:

• Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique for computation offloading and resource allocation.
• Lyapunov drift-plus-penalty method to solve the optimization problem.
• MILP approach for optimal offloading selection and fairness-oriented resource allocation.

Main Results:

• The LYMOC algorithm significantly decreases overall system execution costs.
• Fairness-oriented resource allocation prevents service degradation in dense networks.
• Improved overall system performance and user satisfaction compared to conventional methods.

Conclusions:

• The proposed LYMOC technique effectively manages resources in MEC systems, prioritizing fairness and energy efficiency.
• This approach enhances network efficiency and user experience, particularly in UDNs.
• LYMOC offers a superior alternative to existing methods for computation offloading and resource allocation.