Intelligent Resource Management in Symbiotic Radio under a Trusted Coevolution

To accommodate the growing number of heterogeneous radios with limited wireless resources, symbiotic communication (SC) inspired by biology has been recently proposed to establish a symbiotic radio (SR) ecosystem. In this SR ecosystem, through collaboratively optimizing service/resource exchange policies, radios can coevolve like organisms, thus enabling various radio resources (such as spectrum, energy, and computing power) to complement each other. However, one critical challenge is securing a trusted coevolution environment in an SR ecosystem since the SRs with different network operators should coevolve under unreliable wireless links with complex electromagnetic interference. Moreover, multi-dimensional resources participated and a wide array of service requirements pose additional challenges to service/resource exchange decision-making across massive SRs. In this paper, we propose a Blockchain-empowered Intelligent cOevolution scheme for SRs, named BIO-SR. Specifically, BIO-SR exploits the digital acyclic graph (DAG) blockchain consensus in securing a trusted environment of SRs and applies deep reinforcement learning (DRL) in service exchange decision-making. The simulation results show that the BIO-SR scheme outperforms conventional solutions in terms of transmission rate and latency under both non-attack and malicious attack scenarios.