Fixed-wing UAV relay optimization for coverage hole recovery

Abstract

Unmanned aerial vehicles (UAVs) fill coverage holes as wireless relays during emergency situations. Fixed-wing UAVs offer longer flight duration and larger coverage in such situations than rotary-wing counterparts. Maximizing the effectiveness of fixed-wing UAV relay systems requires careful tuning of system and flight parameters. This process is challenging because factors including flight trajectory, timeshare, and user scheduling are not easily optimized. In this paper, we propose an optimization for UAV-based wireless relaying networks based on a setup which is applicable to arbitrary spatial user positions. In the setup, a fixed-wing UAV flies over a circular trajectory and relays data from ground users in a coverage hole to a distant base station (BS). Our optimization iteratively maximizes the average achievable spectral efficiency (SE) for the UAV trajectory, user scheduling, and relay timeshare. The simulation results show that our optimization is effective for varying user distributions and that it performs especially well on distributions with a high standard deviation.

Figures (5)

• Figure 1: The system and communication model of the UAV-enabled wireless relay system. The UAV flies on a circular trajectory at a fixed altitude and serves users in a distant coverage dead zone by connecting them with a BS located near the origin. We use a slotted communication model, with multiple users served per time slot. Each time slot is divided between the user-to-UAV link (denoted GV) and UAV-to-BS link (denoted VB).
• Figure 2: Impact of the standard deviation of the user distribution on the achieved SE of the system. The upper bound performs well regardless of standard deviation, because it is optimized per-user. Comparing the optimized model with the static model, which represents a reasonable heuristic, shows that our optimization is more robust to standard deviation increases.
• Figure 3: The effect of UAV transmission power on the SE achieved in the system. As UAV transmission power increases, the throughput bottleneck becomes the transmission power of the users, and the achievable spectral efficiency levels off. The static model cannot adapt to utilize the increase in UAV transmission power and levels very quickly. The optimized model can take advantage of increased transmission power by flying closer to users, mitigating the user-to-UAV limitation.
• Figure 4: Impact of the UAV transmission power on the optimized radius. Optimal radius is positively correlated with transmission power, because greater transmission power gives the UAV more freedom to travel far from the BS and closer to ground users. This freedom is more pronounced in distributions with a larger standard deviation, because a larger radius is required to serve users when their spread is greater.
• Figure 5: SE gain of a system with optimized parameters over a system with static parameters relative to the height of the UAV and the distance of the users from the BS. Flight altitude is highly correlated with SE gain because variance is increasingly impactful at lower altitudes. User distance is less so because both optimized and static models are able to account for it with timesharing.