System Throughput

system-throughput

Throughput and Fairness Trade-off Balancing for UAV-Enabled Wireless Communication Systems — Kejie Ni et al. (2024) (arXiv:2406.04750, 2024)

What this evaluates

This evaluation probes the trade-off between total system throughput and user fairness in UAV-enabled wireless networks. It measures how effectively a resource allocation and trajectory design scheme balances maximizing aggregate data rates against ensuring equitable service across users with varying channel conditions.

Datasets

• UAV Wireless Communication Simulation — total ?; splits: test (-1)

Metrics

• system throughput (primary) — range: bps
  • Average throughput of K users over N time slots, calculated as the sum of per-user per-slot throughputs divided by the total number of user-time slots.
• variance of throughput — range: other
  • Statistical variance of the average throughput across all users, used to quantify the stability and fairness of resource allocation over time.

Input / output format

Input: User locations (K=9 on horizontal plane), UAV altitude (500m), max speed (40m/s), bandwidth (10MHz), noise power (-169 dBm/Hz), max transmit power (0.1W), channel gain at 1m (-50dB), Rician fading parameters, flight period (50s), time slots (50), and fairness factor α.

Output: UAV trajectory, bandwidth and power allocation per user per time slot, and resulting per-user throughput R_k[n] for each time slot.

Scoring recipe

def compute_metrics(R_k_n, K, N):
    # R_k_n is a matrix of shape (K, N) containing throughput per user per slot
    system_throughput = sum(R_k_n[k][n] for k in range(K) for n in range(N)) / (K * N)
    user_avg = [sum(R_k_n[k][n] for n in range(N)) / N for k in range(K)]
    mean_user = sum(user_avg) / K
    variance_throughput = sum((u - mean_user)**2 for u in user_avg) / K
    return system_throughput, variance_throughput

Common pitfalls

• The fairness factor α must be tuned to ensure convexity of the optimization constraints (α R_k[n] ≤ 1), which limits its valid range and affects convergence.
• Benchmark schemes are evaluated under the most stringent QoS constraints, which may overstate their performance compared to unconstrained baselines.
• Throughput is highly sensitive to user density; fewer users yield higher throughput but reduce the dynamic range of α that guarantees convex constraints.

Evidence (verbatim from paper)

In Fig. 4 (a), we analyze system throughput (i.e. average throughput of K users over N time slots) of the developed UAV communication system. ... We use the variance of throughput as a metric to quantify the stability of throughput as depicted in Fig. 5 (b).

Citation

@misc{ni2024throughput,
  title={Throughput and Fairness Trade-off Balancing for UAV-Enabled Wireless Communication Systems},
  author={Kejie Ni et al. (2024)},
  year={2024},
  note={arXiv:2406.04750}
}

• arXiv: 2406.04750