Federated Multi-Agent DRL for Radio Resource Management in Industrial 6G in-X subnetworks

Recently, 6G in-X subnetworks have been proposed as low-power short-range radio cells to support localized extreme wireless connectivity inside entities such as industrial robots, vehicles, and the human body. Deployment of in-X subnetworks within these entities may result in rapid changes in interference levels and thus, varying link quality. This paper investigates distributed dynamic channel allocation to mitigate inter-subnetwork interference in dense in-factory deployments of 6G in-X subnetworks. This paper introduces two new techniques, Federated Multi-Agent Double Deep Q-Network (F-MADDQN) and Federated Multi-Agent Deep Proximal Policy Optimization (F-MADPPO), for channel allocation in 6G in-X subnetworks. These techniques are based on a client-to-server horizontal federated reinforcement learning framework. The methods require sharing only local model weights with a centralized gNB for federated aggregation thereby preserving local data privacy and security. Simulations were conducted using a practical indoor factory environment proposed by 5G-ACIA and 3GPP models for in-factory environments. The results showed that the proposed methods achieved slightly better performance than baseline schemes with significantly reduced signaling overhead compared to the baseline solutions. The schemes also showed better robustness and generalization ability to changes in deployment densities and propagation parameters.Comment: Accepted for Workshop on Industrial Wireless Networks - IEEE International Symposium on Personal, Indoor and Mobile Radio Communication

Federated Multi-Agent DRL for Radio Resource Management in Industrial 6G in-X subnetworks

Recently, 6G in-X subnetworks have been proposed as low-power short-range radio cells to support localized extreme wireless connectivity inside entities such as industrial robots, vehicles, and the human body. Deployment of in-X subnetworks within these entities may result in rapid changes in interference levels and thus, varying link quality. This paper investigates distributed dynamic channel allocation to mitigate inter-subnetwork interference in dense in-factory deployments of 6G in-X subnetworks. This paper introduces two new techniques, Federated Multi-Agent Double Deep Q-Network (F-MADDQN) and Federated Multi-Agent Deep Proximal Policy Optimization (F-MADPPO), for channel allocation in 6G in-X subnetworks. These techniques are based on a client-to-server horizontal federated reinforcement learning framework. The methods require sharing only local model weights with a centralized gNB for federated aggregation thereby preserving local data privacy and security. Simulations were conducted using a practical indoor factory environment proposed by 5G-ACIA and 3GPP models for in-factory environments. The results showed that the proposed methods achieved slightly better performance than baseline schemes with significantly reduced signaling overhead compared to the baseline solutions. The schemes also showed better robustness and generalization ability to changes in deployment densities and propagation parameters

On the Number of Maximal Bipartite Subgraphs of a Graph

We show new lower and upper bounds on the number of maximal induced bipartite subgraphs of graphs with n vertices. We present an infinite family of graphs having 105^{n/10} ~= 1.5926^n such subgraphs, which improves an earlier lower bound by Schiermeyer (1996). We show an upper bound of n . 12^{n/4} ~= n . 1.8613^n and give an algorithm that lists all maximal induced bipartite subgraphs in time proportional to this bound. This is used in an algorithm for checking 4-colourability of a graph running within the same time bound

Angiogenesis PET Tracer Uptake (⁶⁸Ga-NODAGA-E[(cRGDyK)]₂) in Induced Myocardial Infarction and Stromal Cell Treatment in Minipigs

Angiogenesis is considered integral to the reparative process after ischemic injury. The αvβ3 integrin is a critical modulator of angiogenesis and highly expressed in activated endothelial cells. 68Ga-NODAGA-E[(cRGDyK)]2 (RGD) is a positron-emission-tomography (PET) ligand targeted towards αvβ3 integrin. The aim was to present data for the uptake of RGD and correlate it with histology and to further illustrate the differences in angiogenesis due to porcine adipose-derived mesenchymal stromal cell (pASC) or saline treatment in minipigs after induction of myocardial infarction (MI). Three minipigs were treated with direct intra-myocardial injection of pASCs and two minipigs with saline. MI was confirmed by 82Rubidium (82Rb) dipyridamole stress PET. Mean Standardized Uptake Values (SUVmean) of RGD were higher in the infarct compared to non-infarct area one week and one month after MI in both pASC-treated (SUVmean: 1.23 vs. 0.88 and 1.02 vs. 0.86, p < 0.05 for both) and non-pASC-treated minipigs (SUVmean: 1.44 vs. 1.07 and 1.26 vs. 1.04, p < 0.05 for both). However, there was no difference in RGD uptake, ejection fractions, coronary flow reserves or capillary density in histology between the two groups. In summary, indications of angiogenesis were present in the infarcted myocardium. However, no differences between pASC-treated and non-pASC-treated minipigs could be demonstrated

Diagnostic performance of an acoustic-based system for coronary artery disease risk stratification

ObjectiveDiagnosing coronary artery disease (CAD) continues to require substantial healthcare resources. Acoustic analysis of transcutaneous heart sounds of cardiac movement and intracoronary turbulence due to obstructive coronary disease could potentially change this. The aim of this study was thus to test the diagnostic accuracy of a new portable acoustic device for detection of CAD.MethodsWe included 1675 patients consecutively with low to intermediate likelihood of CAD who had been referred for cardiac CT angiography. If significant obstruction was suspected in any coronary segment, patients were referred to invasive angiography and fractional flow reserve (FFR) assessment. Heart sound analysis was performed in all patients. A predefined acoustic CAD-score algorithm was evaluated; subsequently, we developed and validated an updated CAD-score algorithm that included both acoustic features and clinical risk factors. Low risk is indicated by a CAD-score value ≤20.ResultsHaemodynamically significant CAD assessed from FFR was present in 145 (10.0%) patients. In the entire cohort, the predefined CAD-score had a sensitivity of 63% and a specificity of 44%. In total, 50% had an updated CAD-score value ≤20. At this cut-off, sensitivity was 81% (95% CI 73% to 87%), specificity 53% (95% CI 50% to 56%), positive predictive value 16% (95% CI 13% to 18%) and negative predictive value 96% (95% CI 95% to 98%) for diagnosing haemodynamically significant CAD.ConclusionSound-based detection of CAD enables risk stratification superior to clinical risk scores. With a negative predictive value of 96%, this new acoustic rule-out system could potentially supplement clinical assessment to guide decisions on the need for further diagnostic investigation.Trial registration numberClinicalTrials.gov identifier NCT02264717; Results.</jats:sec