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Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN

Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Goutam Das, Di Wang, Mohamed-Slim Alouini, Vaneet Aggarwal

TL;DR

The paper addresses the challenge of obtaining location-specific wireless coverage probabilities, which are expensive to compute with ray tracing or stochastic geometry methods. It proposes a conditional GAN that maps a binary base-station-location matrix to a coverage-manifold within a region-of-evaluation, trained on data from four countries. The key contributions include achieving at least two orders of magnitude lower $L_1$ error than CNN-based autoencoders and SG baselines, and producing coverage maps visually indistinguishable from ground truth, enabling a fast offline simulator for network planning. This approach offers a scalable, accurate tool for rapid network evaluation and planning, with potential extensions to CoMP and 5G/O-RAN scenarios.

Abstract

This paper presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error ($L_1$ difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth.

Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN

TL;DR

The paper addresses the challenge of obtaining location-specific wireless coverage probabilities, which are expensive to compute with ray tracing or stochastic geometry methods. It proposes a conditional GAN that maps a binary base-station-location matrix to a coverage-manifold within a region-of-evaluation, trained on data from four countries. The key contributions include achieving at least two orders of magnitude lower error than CNN-based autoencoders and SG baselines, and producing coverage maps visually indistinguishable from ground truth, enabling a fast offline simulator for network planning. This approach offers a scalable, accurate tool for rapid network evaluation and planning, with potential extensions to CoMP and 5G/O-RAN scenarios.

Abstract

This paper presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error ( difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth.
Paper Structure (8 sections, 5 equations, 3 figures, 1 table)

This paper contains 8 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. cGAN-based Coverage Manifold Prediction
  4. Preliminaries of the cGAN
  5. Proposed Architecture
  6. Training and Testing Procedures
  7. Numerical Results
  8. Conclusion

Figures (3)

  • Figure 1: Working principle of cGAN. Fig. \ref{['fig_1a']} depicts the training process while Fig. \ref{['fig_1b']} describes how to generate output from a trained network. The matrix, $\mathbf{x}$ describes the location of BSs in an RoI whereas the matrix, $\mathbf{y}$ defines the simulated coverage manifold within its RoE.
  • Figure 2: Architectural details of cGAN. Fig. \ref{['subfig_generator']}, \ref{['subfig_discriminator']} describe the structure of the generator and discriminator respectively while Fig. \ref{['subfig_encoderdecoder']} depicts the structure of encoder and decoder blocks.
  • Figure 3: Visual comparison of the coverage manifolds estimated by cGAN and CNN with the ground truth. RoEs of size $128\times 128$ are randomly chosen from the available dataset to generate the manifolds. SINR threshold is $0$ dB.