Uncertainty Propagation in Neural Network Enabled Multi-Channel Optimisation

Li, Chen; Sun, Schyler C.; Al‐Rubaye, Saba; Tsourdous, Antonios; Guo, Weisi

doi:10.1109/vtc2020-spring48590.2020.9128702

Cited by 4 publications

(4 citation statements)

References 17 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This scenario increases the likelihood of interference as compared to ground-based users, posing a significant challenge for interference management in cellular networks that include UAVs. Several techniques have been proposed in extant literature to mitigate interference in terrestrial networks, including inter-cell interference coordination (ICIC) [13,14] and coordinated multi-point (CoMP) transmission [15,16]. However, these techniques prove inadequate in handling new interference challenges arising from UAVs and the emergence of LoS-dominated air-ground links at high altitudes.…”

Section: Background and Related Workmentioning

confidence: 99%

AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks

Warrier,

Al-Rubaye,

Inalhan

et al. 2023

Aerospace

Self Cite

View full text Add to dashboard Cite

Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks

Warrier,

Al-Rubaye,

Inalhan

et al. 2023

Aerospace

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although various interference mitigation techniques have been studied in the literature some of which were applied to the terrestrial networks such as inter-cell interference coordination (ICIC) [8] [9], coordinated multi-point (CoMP) transmission [10] [11] they may be inadequate to deal with the new and more severe interference issue brought by UAVs, owing to their unique LoS-dominant air-ground channels. The industry standards adopted the method of almost blank sub-frame (ABS) to resolve the co-channel inter-cell interference problem in LTE where two BSs interfere with one another.…”

Section: Background and Related Workmentioning

confidence: 99%

Interference Mitigation for 5G-Connected UAV using Deep Q-Learning Framework

Warrier

Al‐Rubaye

Panagiotakopoulos

et al. 2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

View full text Add to dashboard Cite

To boost large-scale deployment of unmanned aerial vehicles (UAVs) in the future, a new wireless communication paradigm namely cellular-connected UAVs has recently received an upsurge of interest in both academia and industry. Fifth generation (5G) networks are expected to support this largescale deployment with high reliability and low latency. Due to the high mobility, speed, and altitude of the UAVs there are numerous challenges that hinder its integration with the 5G architecture. Interference is one of the major roadblocks to ensuring the efficient co-existence between UAVs and terrestrial users in 5G networks. Conventional interference mitigation schemes for terrestrial networks are insufficient to deal with the more severe air-ground interference, which thus motivates this paper to propose a new algorithm to mitigate interference. A deep Q-learning (DQL) based algorithm is developed to mitigate interference intelligently through power control. The proposed algorithm formulates a non-convex optimization problem to maximize the Signal to Interference and Noise Ratio (SINR) and solves it using DQL. Its performance is measured as effective SINR against the complement cumulative distribution function. Further, it is compared with an adaptive link technique: Fixed Power Allocation (FPA), a standard power control scheme and tabular Q-learning(TQL). It is seen that the FPA has the worst performance while the TQL performs slightly better. This is since power control and interference coordination are introduced but not as effectively in the TQL method. It is observed that DQL algorithm outperforms the TQL implementation. To solve the severe air-ground interference experienced by the UAVs in 5G networks, this paper proposes a DQL algorithm. The algorithm effectively mitigates interference by optimizing SINR of the air-ground link and outperforms the existing methods. This paper therefore, proposes an effective algorithm to resolve the interference challenge in air-ground links for 5G-connected UAVs.

show abstract

“…The trust broker assists with compliance. [14] V. CONCLUSION Those organisations that establish clear principles for risk management and compliance with respect to AI development will become more trusted by the regulator. A clear demonstration of intent together with a practical explanation of the constituent parts of an AI system adopting principles that include intellectual property disclosure that is safe in a high-risk category will garnish some favour with the regulator.…”

Section: Figure 5 Evolution Of Defence Ai Technologiesmentioning

confidence: 99%

UTM Regulatory Concerns with Machine Learning and Artificial Intelligence

Ryan

Al‐Rubaye

Braithwaite

2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

View full text Add to dashboard Cite

Artificial intelligence (AI) and machine learning will have a significant impact on the application of drones and the integration of universal traffic management (UTM) that relate to unmanned operations in urban environments at very low-level airspace. Artificial intelligence will necessitate high levels of automation and act as an enabler with respect to the integration of unmanned and manned aviation and AI will ultimately enable safe operations with respect to high numbers of drones utilising the same airspace, and more specifically with respect to detect and avoid capability. AI is going to be heavily developed and utilised by organisations that certify as U-space service providers (USSP's) when providing a service to UAS (Unmanned Aerial Systems) Operators. The equipment utilised by UAS Operators will to some extent already benefit from AI, but the level of automation is currently constrained by regulation. A legal framework must exist, as AI will not only have a significant impact upon existing laws but will ensure a framework that facilitates safety and the fundamental rights of citizens and businesses with respect to AI. The EU has published a proposed law, namely the Artificial Intelligence Act as permitted under Article 114 of the Treaty on the Functioning of the European Union (TFEU).

show abstract

Uncertainty Propagation in Neural Network Enabled Multi-Channel Optimisation

Cited by 4 publications

References 17 publications

AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks

AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks

Interference Mitigation for 5G-Connected UAV using Deep Q-Learning Framework

UTM Regulatory Concerns with Machine Learning and Artificial Intelligence

Contact Info

Product

Resources

About