Base Station Deployment under EMF constrain by Deep Reinforcement learning

As 5G networks rapidly expand and 6G technologies emerge, characterized by dense deployments, millimeter-wave communications, and dynamic beamforming, the need for scalable simulation tools becomes increasingly critical. These tools must support efficient evaluation of key performance metrics such as coverage and radio-frequency electromagnetic field (RF-EMF) exposure, inform network design decisions, and ensure compliance with safety regulations. Moreover, base station (BS) placement is a crucial task in the network design, where satisfying coverage requirements is essential. To address these, based on our previous work, we first propose a conditional generative adversarial network (cGAN) that predicts location specific received signal strength (RSS), and EMF exposure simultaneously from the network topology, as images. As a network designing application, we propose a Deep Q Network (DQN) framework, using the trained cGAN, for optimal base station (BS) deployment in the network. Compared to conventional ray tracing simulations, the proposed cGAN reduces inference and deployment time from several hours to seconds. Unlike a standalone cGAN, which provides static performance maps, the proposed GAN-DQN framework enables sequential decision making under coverage and exposure constraints, learning effective deployment strategies that directly solve the BS placement problem. Thus making it well suited for real time design and adaptation in dynamic scenarios in order to satisfy pre defined network specific heterogeneous performance goals.

Neyman-Pearson Detector for Ambient Backscatter Zero-Energy-Devices Beacons

Recently, a novel ultra-low power indoor wireless positioning system has been proposed. In this system, Zero-Energy-Devices (ZED) beacons are deployed in Indoor environments, and located on a map with unique broadcast identifiers. They harvest ambient energy to power themselves and backscatter ambient waves from cellular networks to send their identifiers. This paper presents a novel detection method for ZEDs in ambient backscatter systems, with an emphasis on performance evaluation through experimental setups and simulations. We introduce a Neyman-Pearson detection framework, which leverages a predefined false alarm probability to determine the optimal detection threshold. This method, applied to the analysis of backscatter signals in a controlled testbed environment, incorporates the use of BC sequences to enhance signal detection accuracy. The experimental setup, conducted on the FIT/CorteXlab testbed, employs a two-node configuration for signal transmission and reception. Key performance metrics, which is the peak-to-lobe ratio, is evaluated, confirming the effectiveness of the proposed detection model. The results demonstrate a detection system that effectively handles varying noise levels and identifies ZEDs with high reliability. The simulation results show the robustness of the model, highlighting its capacity to achieve desired detection performance even with stringent false alarm thresholds. This work paves the way for robust ZED detection in real-world scenarios, contributing to the advancement of wireless communication technologies.

Channel Estimation by Infinite Width Convolutional Networks

In wireless communications, estimation of channels in OFDM systems spans frequency and time, which relies on sparse collections of pilot data, posing an ill-posed inverse problem. Moreover, deep learning estimators require large amounts of training data, computational resources, and true channels to produce accurate channel estimates, which are not realistic. To address this, a convolutional neural tangent kernel (CNTK) is derived from an infinitely wide convolutional network whose training dynamics can be expressed by a closed-form equation. This CNTK is used to impute the target matrix and estimate the missing channel response using only the known values available at pilot locations. This is a promising solution for channel estimation that does not require a large training set. Numerical results on realistic channel datasets demonstrate that our strategy accurately estimates the channels without a large dataset and significantly outperforms deep learning methods in terms of speed, accuracy, and computational resources.

Indoor Localization of Smartphones Thanks to Zero-Energy-Devices Beacons

In this paper, we present a new ultra-low power method of indoor localization of smartphones (SM) based on zero-energy-devices (ZEDs) beacons instead of active wireless beacons. Each ZED is equipped with a unique identification number coded into a bit-sequence, and its precise position on the map is recorded. An SM inside the building is assumed to have access to the map of ZEDs. The ZED backscatters ambient waves from base stations (BSs) of the cellular network. The SM detects the ZED message in the variations of the received ambient signal from the BS. We accurately simulate the ambient waves from a BS of Orange 4G commercial network, inside an existing large building covered with ZED beacons, thanks to a ray-tracing-based propagation simulation tool. Our first performance evaluation study shows that the proposed localization system enables us to determine in which room a SM is located, in a realistic and challenging propagation scenario.