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

Effectiveness of a universal personalized intervention for the prevention of anxiety disorders: Protocol of a randomized controlled trial (the prevANS project)

Background: To date, all preventive anxiety disorders interventions are one-fit-all and none of them are based on individual level and risk profile. The aim of this project is to design, develop and evaluate an online personalized intervention based on a risk algorithm for the universal prevention of anxiety disorders in the general population. Methods: A randomized controlled trial (RCT) with two parallel arms (prevANS vs usual care) and 1-year follow- up including 2000 participants without anxiety disorders from Spain and Portugal will be conducted. The prevANS intervention will be self-guided and can be implemented from the prevANS web or from the participants' Smartphone (through an App). The prevANS intervention will have different intensities depending on the risk level of the population, evaluated from the risk algorithm for anxiety: predictA. Both low and moderate-high risk participants will receive information on their level and profile (risk factors) of anxiety disorders, will have access to stress management tools and psychoeducational information periodically. In addition, participants with a moderate-high risk of anxiety disorders will also have access to cognitive-behavioral training (problem-solving, decision-making, communication skills, and working with thoughts). The control group will not receive any intervention, but they will fill out the same questionnaires as the intervention group. Assessments will be completed at baseline, 6 and 12-month follow-up. The primary outcome is the cumulative incidence of anxiety disorders. Secondary outcomes include depressive and anxiety symptoms, risk probability of anxiety disorders (predictA algorithm) and depression (predictD algorithm), improvement in physical and mental quality of life, and acceptability and satisfaction with the intervention. In addition, cost-effectiveness and cost-utility analyses will also be carried out from two perspectives, societal and health system, and analyses of mediators and moderators will also be performedSpanish Ministry of Health, the Institute of Health Carlos III, co-funded by the European Social Fund “Investing in your future” (grant references: CP19/00056), and the Chronicity, Primary Care and Health Promotion Research Network ‘RICAPPS’ (RD21/0016/0012); and Spanish Ministry of Science and Innovation, the State Investigation Agency (PID2020-119652RA-l00). These funding sources had no role in the design of this study and will not have any role during its execution, analyses, interpretation of the data, or decision to submit resultsS