Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs (1509.01187v2)

Abstract: In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide on the fly wireless communications to a given geographical area is analyzed. In particular, the co-existence between the UAV, that is transmitting data in the downlink, and an underlaid device-todevice (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, optimal values for the UAV altitude exist for which the system sum-rate and the coverage probability are maximized. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, is minimized. Finally, in order to provide a full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.

• The paper derives analytical models for coverage probabilities and sum-rates in integrated UAV and D2D networks.
• It examines both static and mobile UAV scenarios, applying the disk covering problem to determine optimal stop points.
• Results show that optimal UAV altitudes decrease with higher D2D density, balancing coverage, delay, and outage probability.

Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs

The paper introduces a comprehensive analysis of a network model integrating both Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communications. This work is of particular relevance to the emerging field where leveraging UAVs as flying base stations can significantly enhance network coverage and capacity, especially in scenarios where conventional terrestrial networks may not suffice.

Key Contributions

The paper identifies and solves several critical issues surrounding the coexistence of UAVs and D2D communications:

1. Coverage and Rate Analysis: The authors derive analytical expressions for coverage probabilities and average rates for both D2D users and downlink users (DUs) served by a UAV.
2. Static and Mobile UAV Scenarios: The performance is analyzed for both static and mobile UAVs, providing insights into the optimal deployment and movement of the UAVs to maximize system performance.
3. Impact of UAV Altitude: The paper explores optimal UAV altitudes, illustrating how altitude influences coverage probability and system sum-rate.
4. Disk Covering Problem Applied to UAVs: A novel application of the disk covering problem is employed to determine the minimum number of UAV stop points needed for full area coverage, providing a solution that minimizes both coverage delay and UAV transmit power.

Analytical Framework and Assumptions

The paper utilizes tools from stochastic geometry to derive tractable analytical results. Several assumptions are made for the analysis:

• UAVs operate in low altitudes to act as flying base stations.
• LoS (Line-of-Sight) and NLoS (Non-Line-of-Sight) probabilistic models are considered for air-to-ground channels.
• The D2D users are modeled using a homogeneous Poisson Point Process (PPP).
• Downlink users (DUs) are considered to be uniformly distributed within the target area.

These assumptions enable the authors to derive exact and approximate results for network performance metrics under various configurations.

Main Results

Static UAV Scenario

Coverage Probabilities:

• For D2D users: An expression incorporating D2D user density, UAV altitude, and transmit power shows the non-trivial impact of UAV placement on D2D performance.
• For DUs: Upper and lower bounds on coverage probability capture the trade-offs between LoS benefits and increased path loss at higher altitudes.

System Sum-Rate:

• The analysis reveals an optimal density of D2D users that maximizes the sum-rate due to the interplay between interference and the number of transmitting D2D pairs.

Optimal UAV Altitude:

• Results demonstrate that as D2D user density increases, the altitude at which the UAV maximizes the DU coverage probability decreases, highlighting a key parameter tuning aspect.

Mobile UAV Scenario

Disk Covering Application:

• The authors employ the disk covering problem to determine the optimal stop points for the UAV to ensure complete coverage with the minimum required power. This is aligned to minimize the UAV's power consumption and the number of stops to cover the entire area.

Trade-offs and Delay:

• Increasing the number of stop points provides better overall coverage (leading to higher coverage probabilities for DUs) but also increases the delay (defined as the number of UAV stops required to cover the area).

Outage Probability for D2D Users:

• The paper derives the overall outage probability for D2D communications over multiple retransmissions. It shows that with an increase in the number of stop points, there is an increased likelihood of outage due to more frequent interference from the UAV.

Practical Implications and Future Directions

The implications of this research are multifaceted. Practically, it guides network designers on the optimal deployment and movement strategies for UAVs to boost network performance in scenarios without robust terrestrial infrastructure. The engineering trade-offs between coverage, delay, and outage probability offer a foundation for tuning UAV parameters to match specific network requirements.

Theoretically, this research enriches the field by extending the application of the disk covering problem to UAV deployment strategies and offers new avenues for investigating interference dynamics in mixed UAV-D2D environments.

Future research can build on these findings by exploring machine learning techniques to dynamically adapt UAV trajectories based on real-time network conditions or investigating multi-UAV coordination in dense D2D settings.

Overall, the detailed analytical framework and practical deployment insights offered by this paper form a robust foundation for future advancements in UAV-assisted wireless communications.