Signaling Design for Cooperative Resource Allocation and its Impact to Reliability

Decentralized cooperative resource allocation schemes for robotic swarms are essential to enable high reliability in high throughput data exchanges. These cooperative schemes require control signaling with the aim to avoid half-duplex problems at the receiver and mitigate interference. We propose two cooperative resource allocation schemes, device sequential and group scheduling, and introduce a control signaling design. We observe that failure in the reception of these control signals leads to non-cooperative behavior and to significant performance degradation. The cause of these failures are identified and specific countermeasures are proposed and evaluated. We compare the proposed resource allocation schemes against the NR sidelink mode 2 resource allocation and show that even though signaling has an important impact on the resource allocation performance, our proposed device sequential and group scheduling resource allocation schemes improve reliability by an order of magnitude compared to sidelink mode 2

Distributed deep reinforcement learning resource allocation scheme for industry 4.0 Device-To-Device scenarios

This paper proposes a distributed deep reinforcement learning (DRL) methodology for autonomous mobile robots (AMRs) to manage radio resources in an indoor factory with no network infrastructure. Hence, deep neural networks (DNN) are used to optimize the decision policy of the robots, which will make decisions in a distributed manner without signalling exchange. To speed up the learning phase, a centralized training is adopted in which a single DNN is trained using the experience from all robots. Once completed, the pre-trained DNN is deployed at all robots for distributed selection of resources. The performance of this approach is evaluated and compared to 5G NR sidelink mode 2 via simulations. The results show that the proposed method achieves up to 5% higher probability of successful reception when the density of robots in the scenario is high.This work has been partially funded by Junta de Andalucía (projects EDEL4.0:UMA18-FEDERJA-172 and PENTA:PY18-4647) and Universidad de Málaga (I Plan Propio de Investigación, Transferencia y Divulgación Científica). Ramoni Adeogun is supported by the Danish Council for Independent Research, grant no. DFF 9041-00146B. The authors would like to express their profound gratitude to Nokia Standardization Aalborg and Aalborg University for funding the first author’s research stay. The authors thank Assoc. Prof. Gilberto Beradinelli for his comments on the manuscript