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Coverage analysis and Efficient placement of drone BS review

· 약 3분
Eunkwang Shin
Eunkwang Shin
Owner

Summary​

  • This study reformulates Drone-BS 3D deployment as a SINR-based coverage probability optimization problem rather than a distance- or area-based one.
  • By jointly considering LOS/NLOS propagation, altitude-dependent SINR behavior, and system constraints, the deployment problem is formulated as an NP-hard MINLP.
  • A meta-heuristic optimizer (GWO) is used as a practical solver, demonstrating that high coverage can be achieved with a minimal number of Drone-BSs.

Introduction​

Background​

  • In 5G networks, Drone-BSs (UAV-BSs) have attracted significant attention as a promising solution to enhance coverage and capacity in dense urban environments.
  • Although the use of the mmWave band enables high data rates, determining the optimal 3D deployment (horizontal location and altitude) of Drone-BSs has emerged as a critical challenge.

Limitations of Existing Studies​

  • Assumption of fixed Drone-BS altitude
  • Reliance on heuristic-based approaches
  • Consideration of limited system constraints

Objective of This Study​

  • This study aims to derive an optimal Drone-BS deployment strategy by combining
  • SINR-based downlink coverage probability analysis and
  • a meta-heuristic optimization algorithm, namely the Grey Wolf Optimizer (GWO).

Methods​

System Model​

  • Users (N) and Drone-BSs (M) are randomly distributed in an urban environment.
  • A mmWave path loss model considering LOS and NLOS propagation conditions is applied.
  • A predefined SINR threshold is used as the Quality-of-Service (QoS) criterion.

Coverage Probability Analysis​

  • The probability that SINR exceeds a predefined threshold is derived based on stochastic geometry.
  • Shadow fading is modeled as a Gaussian random variable.
  • The downlink coverage probability is expressed using a Q-function formulation.

Optimization Problem Formulation​

  • Objective function
    • Maximize the number of covered users.
  • Constraints
    • Minimum number of deployed Drone-BSs
    • Drone-BS altitude limits
    • Total available bandwidth constraint
  • The problem is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem and is NP-hard.

Solution Approach​

  • The Grey Wolf Optimizer (GWO) is employed to search for the optimal 3D locations (x, y, h) of Drone-BSs.

Results​

Simulation Setup​

  • Area size: 2 × 2 km²
  • Number of users: 200
  • Maximum number of Drone-BSs: 10
  • Carrier frequency: 28 GHz

Key Findings​

  • The downlink coverage probability increases as the Drone-BS altitude increases.
  • A coverage probability of approximately 0.76–0.82 can be achieved with only five Drone-BSs.
  • Reducing the number of Drone-BSs leads to uncovered regions.
  • Increasing the number of Drone-BSs may result in higher inter-cell interference.

Discussion​

Performance Analysis​

  • The GWO-based deployment achieves high coverage performance while minimizing the number of Drone-BSs.
  • Increasing altitude improves coverage but introduces a trade-off in terms of reduced energy efficiency.

Limitations and Future Work​

  • The proposed approach does not consider blockage effects or handover and coverage overlap issues.
  • These aspects are identified as important directions for future research.

Terminology​

  • Drone-BS: Drone Base Station
  • GWO: Grey Wolf Optimizer, a nature-inspired optimization algorithm based on the social hierarchy and hunting behavior of grey wolves.
  • LOS: Line of Sight, a direct path between transmitter and receiver without obstructions.
  • MINLP: Mixed Integer Non-Linear Programming, an optimization problem involving both integer and continuous variables with non-linear relationships.
  • NLOS: Non-Line of Sight, a path between transmitter and receiver that is obstructed.
  • SINR: Signal to Interference plus Noise Ratio, a measure of signal quality.
  • UVA-BS: Unmanned Aerial Vehicle Base Station

Ref​

  • Ouamri, M. A., Oteşteanu, M.-E., Barb, G., & Gueguen, C. (2022). Coverage Analysis and Efficient Placement of Drone-BSs in 5G Networks. The 1st International Conference on Computational Engineering and Intelligent Systems, 18. https://doi.org/10.3390/engproc2022014018