# Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks

arXiv: https://arxiv.org/abs/2312.08016 · HTML: https://mlsanity.com/paper/2312.08016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning](https://mlsanity.com/paper/1802.06480/) (2018)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019)
- [Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin](https://mlsanity.com/paper/1907.01523/) (2019)
- [Blockchain-aided Secure Semantic Communication for AI-Generated Content in Metaverse](https://mlsanity.com/paper/2301.11289/) (2023)
- [Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory](https://mlsanity.com/paper/1809.08387/) (2018)
- [TrustChain: Trust Management in Blockchain and IoT supported Supply Chains](https://mlsanity.com/paper/1906.01831/) (2019)
- [Practical Modeling and Analysis of Blockchain Radio Access Network](https://mlsanity.com/paper/1911.12537/) (2019)

## Built on by (most cited first, 2 of 2)

- [BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination](https://mlsanity.com/paper/2408.07877/) (2024)
- [Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation](https://mlsanity.com/paper/2401.10253/) (2023)

## Abstract (arXiv)

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks.} {We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability}. {We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to} validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and {MEC} resource allocation algorithms.