Joint Energy and SINR Coverage Probability in UAV Corridor-assisted RF-powered IoT Networks

Kernekoncepter

The joint energy and SINR-based coverage probability is analytically derived for a UAV corridor-assisted RF-powered IoT network, where the UAV-BSs are spatially distributed according to a one-dimensional binomial point process.

Resumé

The paper studies the joint energy and signal-to-interference-plus-noise (SINR)-based coverage probability in Unmanned Aerial Vehicle (UAV)-assisted radio frequency (RF)-powered Internet of Things (IoT) networks. The key highlights are:

1. The UAVs are spatially distributed in an aerial corridor, modeled as a one-dimensional (1D) binomial point process (BPP). This is a novel approach to capture the spatial distribution of UAV-BSs in aerial corridors.

2. An exact form expression for the energy coverage probability is derived by accurately capturing the line-of-sight (LoS) probability of a UAV through large-scale fading.

3. A tight approximation for the overall coverage performance is obtained using the Gamma distribution to model the harvested energy.

4. Numerical results reveal the optimal number of deployed UAV-BSs that maximizes the joint coverage probability, as well as the optimal length of the UAV corridors when designing such UAV-assisted IoT networks.

5. The introduction of UAV corridors is a novel concept that enables safe, efficient, and coordinated use of airspace by UAVs while minimizing the risk of collisions.

Statistik

The average harvested energy from the N-1 interfering UAV-BSs is given by:
¯E(r) = (N-1)pητT ∫_r^√(h^2+R^2) l(v_i)Sh_i f_Sh_i(Sh_i)f_v_i|r(v_i) dv_i dSh_i
The second moment of the harvested energy from the N-1 interfering UAV-BSs is given by:
E[(Eh|r)^2|r] = ∫_0^∞ ... ∫_0^∞ ∫r^√(h^2+R^2) ... ∫r^√(h^2+R^2) Σ(k_2,...,k_N) (2/(k_2,...,k_N)) Π(i=2)^N (pητTSh_i hh_i l(v_i))^k_i f_v_i|r(v_i)f_Sh_i(Sh_i)f_hh_i(hh_i) dv_i dSh_i dhh_i

Citater

"The introduction of UAV corridors is a totally new research idea that leads to a UAV corridor-assisted RF-powered IoT network."
"Numerical results reveal the optimal number of deployed UAV-BSs that maximizes the joint coverage probability, as well as the optimal length of the UAV corridors when designing such UAV-assisted IoT networks."

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Joint Energy and SINR Coverage Probability in UAV Corridor-assisted RF-powered IoT Networks

by Harris K. Ar... kl. arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.07333.pdf

Dybere Forespørgsler

How can the proposed framework be extended to include multiple UAV lanes in the 2D plane?

The proposed framework for UAV corridor-assisted RF-powered IoT networks can be extended to include multiple UAV lanes in the 2D plane by adopting a multi-dimensional stochastic geometry approach. This involves modeling the UAVs as a two-dimensional (2D) point process rather than a one-dimensional (1D) binomial point process (BPP). Each UAV lane can be represented as a parallel line segment in the 2D plane, with specific altitude levels assigned to each lane to prevent vertical overlap and potential collisions.
To achieve this, the following steps can be taken:

Lane Definition: Define multiple UAV lanes, each with a designated altitude and width, ensuring that the lanes are spaced adequately to minimize interference and collision risks.
Spatial Distribution: Model the spatial distribution of UAVs within each lane using a 2D point process, such as a Poisson point process (PPP) or a BPP, to capture the randomness in UAV placement.
Inter-lane Interference: Analyze the inter-lane interference effects on both energy harvesting and communication coverage probabilities. This requires deriving new expressions for the signal-to-interference-plus-noise ratio (SINR) that account for interference from UAVs in adjacent lanes.
Coordination Mechanisms: Implement coordination mechanisms among UAVs in different lanes to optimize resource allocation and minimize interference. This could involve dynamic frequency allocation or time-division multiple access (TDMA) strategies.
Performance Metrics: Extend the performance metrics to evaluate the joint energy and communication coverage probabilities across multiple lanes, considering the unique challenges posed by the increased complexity of the network.

By incorporating these elements, the framework can effectively model and analyze the performance of UAV-assisted IoT networks with multiple UAV lanes, leading to enhanced coverage and efficiency.

What are the potential challenges and trade-offs in implementing the UAV corridor concept in real-world IoT deployments?

Implementing the UAV corridor concept in real-world IoT deployments presents several challenges and trade-offs that must be carefully considered:

Regulatory Compliance: UAV operations are subject to strict regulations imposed by aviation authorities. Ensuring compliance with these regulations while designing UAV corridors can be challenging, particularly in urban environments where airspace is congested.

Collision Avoidance: As UAVs operate in designated corridors, the risk of collisions increases, especially in scenarios involving multiple UAVs. Implementing effective collision avoidance systems and protocols is essential to ensure safe operations.

Interference Management: The presence of multiple UAVs in close proximity can lead to significant interference, affecting both energy harvesting and communication performance. Balancing the number of UAVs deployed with the need to minimize interference is a critical trade-off.

Energy Efficiency: While UAV corridors can enhance energy harvesting capabilities, the energy consumption of UAVs during flight and operation must be managed. Trade-offs between flight duration, energy harvesting efficiency, and communication performance need to be evaluated.

Scalability: As the number of IoT devices and UAVs increases, the scalability of the corridor concept becomes a concern. Ensuring that the system can handle a growing number of devices without degradation in performance is vital.

Environmental Factors: Real-world deployments must account for environmental factors such as weather conditions, which can affect UAV performance and reliability. Adapting the corridor design to mitigate these effects is necessary.

Cost Implications: The implementation of UAV corridors may involve significant costs related to infrastructure, technology, and regulatory compliance. Evaluating the cost-benefit ratio of deploying such systems is essential for stakeholders.

By addressing these challenges and trade-offs, stakeholders can develop effective strategies for implementing UAV corridors in real-world IoT deployments, ultimately enhancing the performance and reliability of UAV-assisted networks.

How can the energy and communication coverage performance be further improved by incorporating advanced interference management and coordination schemes among the UAV-BSs?

To enhance the energy and communication coverage performance in UAV corridor-assisted RF-powered IoT networks, advanced interference management and coordination schemes among UAV base stations (UAV-BSs) can be implemented through the following strategies:

Dynamic Resource Allocation: Implementing dynamic resource allocation techniques allows UAV-BSs to adaptively assign frequency bands, time slots, or power levels based on real-time network conditions. This can help mitigate interference and optimize both energy harvesting and communication performance.

Cooperative Communication: Utilizing cooperative communication strategies, where multiple UAV-BSs work together to serve a single IoT device, can improve signal quality and coverage. This approach can enhance the SINR at the receiver, leading to better communication reliability.

Interference Cancellation Techniques: Advanced interference cancellation techniques, such as successive interference cancellation (SIC) or multi-user detection (MUD), can be employed to reduce the impact of interference from neighboring UAV-BSs. These techniques can enhance the effective SINR and improve communication performance.

Coordination Protocols: Developing coordination protocols among UAV-BSs can facilitate efficient handover processes and resource sharing. For instance, UAV-BSs can coordinate their flight paths and communication schedules to minimize overlap and interference during operation.

Machine Learning Algorithms: Incorporating machine learning algorithms can enable UAV-BSs to predict interference patterns and optimize their operations accordingly. These algorithms can analyze historical data to make informed decisions about resource allocation and coordination.

Adaptive Flight Paths: Allowing UAV-BSs to adjust their flight paths based on real-time feedback from the IoT devices can help optimize coverage and minimize interference. This adaptability can enhance both energy harvesting and communication performance.

Network Slicing: Implementing network slicing techniques can create virtual networks tailored to specific applications or services. This allows for optimized resource allocation and interference management based on the unique requirements of different IoT applications.

By integrating these advanced interference management and coordination schemes, the overall performance of UAV corridor-assisted RF-powered IoT networks can be significantly improved, leading to enhanced energy and communication coverage capabilities.