# Low-Latency Oriented Joint Data Compression and Resource Allocation in NOMA-MEC Networks: A Deep Reinforcement Learning Approach

## TL;DR

This paper introduces a deep reinforcement learning method to optimize data compression and resource allocation in MEC networks, reducing task processing latency.

## Contribution

A novel deep reinforcement learning algorithm is proposed to jointly optimize data compression, resource allocation, and task offloading in NOMA-MEC networks.

## Key findings

- The proposed algorithm achieves faster convergence and lower task processing latency compared to benchmark methods.
- The method effectively handles non-convex optimization challenges and dynamic network conditions.

## Abstract

To alleviate communication pressure and terminal resource constraints in mobile edge computing (MEC) networks, this paper proposes a resource allocation optimization method for MEC systems that integrates data compression technology and non-orthogonal multiple access technology. This method considers practical constraints such as terminal device battery capacity and computational resource limitations. By jointly optimizing computational resource allocation, task offloading strategies, and data compression ratios, it constructs an optimization model aimed at minimizing the total task processing latency. Addressing the challenges stemming from the non-convex nature of the problem and the dynamic variations in channel conditions and task requirements, this paper proposes a softmax deep double deterministic policy gradient algorithm, where softmax operator function mitigates both overestimation and underestimation biases inherent in traditional reinforcement learning frameworks, enhancing convergence performance. Utilizing a deep reinforcement learning framework, the algorithm achieves joint decision-making optimization for computational resources, task offloading, and compression ratios, thereby minimizing the total task processing latency while satisfying transmit power and computational resource constraints. Simulation results demonstrate that the proposed scheme exhibits significant advantages over benchmark algorithms in terms of convergence speed and task processing latency.