A Cross-Layer Routing Protocol Based on Quasi-Cooperative Multi-Agent Learning for Multi-Hop Cognitive Radio Networks

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

In the case of new technology application, the cognitive radio network (CRN) addresses the bandwidth shortfall and the fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity of resources and mobility. In this paper, we present a reconfigurable CRN-based cross-layer routing protocol with the purpose of increasing routing performance and optimizing data transfer in reconfigurable networks. Recently developed spotted hyena optimizer (SHO) is used for tuning the hyperparameters of machine-learning models. The system produces a distributor built with a number of tasks, such as load balance, quarter sensing and the development path of machine learning. The proposed technique is sensitive to traffic and charges, as well as a series of other network metrics and interference (2bps/Hz/W average). The tests are performed with classic models that demonstrate the residual energy and strength of the resistant scalability and resource.

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).