Paulo Victor Rodrigues Ferreira

Cognitive radio-based geostationary satellite communications for Ka-band transmissions

This paper proposes an adaptive modulation scheme using rain fading predictions obtained via Kalman filtering in order to mitigate the effects of rain on cognitive radio-based geostationary (GEO) satellites operating in the Ka-band. In the proposed scheme, the need for adaptation is identified prior to the rain attenuation event, allowing for enough time for the transmitter and receiver to reconfigure, which is a requirement when one of the communicating nodes are moving at a certain relative speed. We show that the bit error rate (BER) performance can be improved by two orders of magnitude for a system that accounts for the overall delay when adapting its modulation scheme based on the proposed predictor outputs.

Multi-objective reinforcement learning-based deep neural networks for cognitive space communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through ‘virtual environment exploration’. Improvements in the multi-objective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located on-board the International Space Station.

Interactive Multiple Model Filter for Land-Mobile Satellite Communications at Ka-Band

In this paper, an Interactive Multiple Model filter design is proposed to improve signal shadowing detection performance on land-mobile channels during rain fading for a downlink operating at Ka-band through a geostationary satellite. A robust solution for on-line determination of the filter measurement error covariance is provided. Analyses are performed for a combination of channels under different atmospheric conditions, clear sky and rain, and different scenarios, suburban and rural. Using International telecommunications union-based synthesized attenuation time-series, the proposed filter achieved a reduction in the incorrect detection duration of 32% for the scenario with a mobile terminal experiencing rain, and by 60% for the scenario with a fixed terminal experiencing rain.

Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications

Previous research on cognitive radios has addressed the performance of various machinelearning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions.

Implementation of a space communications cognitive engine

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource-allocation-management controller was then integrated with the larger space-ground system developed by NASA Glenn Research Center (GRC).

Performance Analysis of UHF Mobile Satellite Communication System Experiencing Ionospheric Scintillation and Terrestrial Multipath Fading

This paper analyzes a mobile satellite communication system with respect to the BER performance between a geostationary satellite and a moving node. Specifically, we study a scenario where the channel causes ionospheric scintillation-based multipath fading within the UHF frequency band. The scenario considers medium and high ionospheric scintillation indexes for Rural Area (RA) and Hilly Terrain (HT) multipath profiles. A BER performance decrease of more than 2 orders of magnitude for an ionospheric scintillation index of 0.3 occurs when the terrestrial multipath fading is added to ionospheric scintillated signals. This effect can be associated with satellite link loss, such as the one observed during Operation Anaconda at the Battle of Takur Ghar [1] in Afghanistan. Consequently, we propose a channel model that accounts for these effects, composed by two Rician channels connected in series and employs a K-factor equation in terms of the terrains reflection coefficient.