Computation Offloading for IoT in C-RAN: Optimization and Deep Learning

TL;DR

This paper addresses computation offloading in IoT over MIMO C-RAN, formulating an optimization problem to minimize power while meeting latency, and introduces a deep learning approach for efficient solutions.

Contribution

It proposes a joint optimization framework for offloading in MIMO C-RAN and develops a supervised deep learning method to approximate the optimization solutions.

Findings

The optimization problem is non-convex but can be effectively solved with the proposed method.

The deep learning approach achieves near-optimal solutions with lower complexity.

Numerical results demonstrate the effectiveness of the joint optimization and deep learning methods.

Abstract

We consider computation offloading for Internet-of-things (IoT) applications in multiple-input-multiple-output (MIMO) cloud-radio-access-network (C-RAN). Due to the limited battery life and computational capability in the IoT devices (IoTDs), the computational tasks of the IoTDs are offloaded to a MIMO C-RAN, where a MIMO radio resource head (RRH) is connected to a baseband unit (BBU) through a capacity-limited fronthaul link, facilitated by the spatial filtering and uniform scalar quantization. We formulate a computation offloading optimization problem to minimize the total transmit power of the IoTDs while satisfying the latency requirement of the computational tasks, and find that the problem is non-convex. To obtain a feasible solution, firstly the spatial filtering matrix is locally optimized at the MIMO RRH. Subsequently, we leverage the alternating optimization framework for joint optimization on the residual variables at the BBU, where the baseband combiner is obtained in a closed-form, the resource allocation sub-problem is solved through successive inner convexification, and the number of quantization bits is obtained by a line-search method. As a low-complexity approach, we deploy a supervised deep learning method, which is trained with the solutions to our optimization algorithm. Numerical results validate the effectiveness of the proposed algorithm and the deep learning method.