Milan Bs Sleep Eval

milan-bs-sleep-eval

Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control — Wu et al. (2021) (arXiv:2101.08391, 2021)

What this evaluates

Evaluates a deep reinforcement learning framework for dynamic base station sleep control and spatio-temporal traffic forecasting in a real-world cellular network. It probes the model's ability to accurately predict mobile traffic demand across geographical grids and make energy-efficient on/off decisions for base stations while balancing switching costs and quality of service.

Datasets

• Telecom Italia Milan Mobile Traffic Dataset — total ?; splits: train (-1), test (-1)

Metrics

• NMAE (primary) — range: [0, 1]
  • Normalised Mean Absolute Error: NMAE = (1/d_bar) * sum(|d_hat_k - d_k| / N), where d_hat_k is predicted traffic, d_k is ground truth, N is number of grids, and d_bar is mean traffic.
• NRMSE — range: [0, 1]
  • Normalised Root Mean Square Error: NRMSE = (1/d_bar) * sqrt(sum((d_hat_k - d_k)^2 / N)).
• Normalized system cost — range: percent
  • Aggregated cost metric combining energy consumption, base station switching costs, and QoS degradation, normalized against a baseline traffic-oblivious policy.

Input / output format

Input: For forecasting: historical mobile traffic measurements over the previous 48 time slots (30-min intervals) across 100 geographical grids. For sleep control: a 200-dimensional state vector comprising traffic demand in 100 grids and current operation modes of 100 base stations.

Output: For forecasting: predicted mobile traffic demand per grid for the next time step. For sleep control: binary action vector (100 dimensions) indicating active or sleep mode for each base station.

Scoring recipe

import numpy as np
def compute_metrics(d_hat, d):
    N = len(d)
    d_bar = np.mean(d)
    nmae = (1 / d_bar) * np.sum(np.abs(d_hat - d) / N)
    nrmse = (1 / d_bar) * np.sqrt(np.sum((d_hat - d)**2 / N))
    return nmae, nrmse
# Normalized system cost = (Total Cost of Method) / (Total Cost of Traffic-Oblivious Baseline)

Common pitfalls

• Aggregating 10-minute interval data to 30-minute intervals without proper alignment can distort traffic patterns and mislead the model.
• Ignoring semantic correlations between non-adjacent grids with similar traffic patterns leads to suboptimal forecasting compared to purely spatial models like CNN-LSTM.
• High variance in cost estimation due to traffic fluctuations can mislead DRL training unless benchmark transformation is applied.

Evidence (verbatim from paper)

To quantify the performance of the proposed GS-STN method and existing prediction methods, we use the Normalised Mean Absolute Error (NMAE) and Normalised Root Mean Square Error (NRMSE) as given below: NMAE = (1/d_bar) sum(|d_hat_k - d_k|/N), NRMSE = (1/d_bar) sqrt(sum((d_hat_k - d_k)^2/N))... We train our DeepBSC framework on the dataset during the first 20 days and evaluate on the last 10 days.

Citation

@misc{wu2021deepbsc,
  title={Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control},
  author={Wu et al. (2021)},
  year={2021},
  note={arXiv:2101.08391}
}

• arXiv: 2101.08391