# Joint Computation Offloading and Resource Allocation in Cloud Based Wireless HetNets

## TL;DR

This paper presents a novel joint computation offloading and resource allocation framework for cloud-based wireless HetNets, optimizing energy consumption and latency with scalable algorithms.

## Contribution

It introduces a new approach to solve the complex MINLP problem in HetNets, including a bisection method and a low-complexity algorithm for scalable optimization.

## Key findings

- Achieves about 55% energy savings compared to local computation.
- Effectively minimizes maximum weighted energy consumption under latency constraints.
- Provides scalable solutions suitable for practical wireless HetNets.

## Abstract

In this paper, we study the joint computation offloading and resource allocation problem in the two-tier wireless heterogeneous network (HetNet). Our design aims to optimize the computation offloading to the cloud jointly with the subchannel allocation to minimize the maximum (min-max) weighted energy consumption subject to practical constraints on bandwidth, computing resource and allowable latency for the multi-user multitask computation system. To tackle this non-convex mixed integer non-linear problem (MINLP), we employ the bisection search method to solve it where we propose a novel approach to transform and verify the feasibility of the underlying problem in each iteration. In addition, we propose a low-complexity algorithm, which can decrease the number of binary optimization variables and enable more scalable computation offloading optimization in the practical wireless HetNets. Numerical studies confirm that the proposed design achieves the energy saving gains about 55% in comparison with the local computation scheme under the strict required latency of 0.1s.