Abstract
Software-defined radio (SDR) can have high communication quality with a reconfigurable RF front-end. One of the main challenges of a reconfigurable RF front-end is finding an optimal configuration among all possible configurations. In order to efficiently find an optimal configuration, Environment-Adaptable Fast (EAF) optimization utilizes calculated signal-to-interference-and-noise ratio (SINR) and narrows down the searching space (Jun et al., Environment-adaptable efficient optimization for programming of reconfigurable Radio Frequency (RF) receivers, 2014). However, we found several limitations for applying the EAF optimization to a realistic large-scale Radio Frequency-Field Programmable Gate Array (RF-FPGA) system. In this paper, we first investigated two estimation issues of RF impairments: a saturation bias of nonlinearity estimates and limited resources for RF impairment estimation. Using the estimated results, the SINR formula was calculated and used for the Environment-Adaptable Fast Multi-Resolution (EAF-MR) optimization, which was designed by applying the EAF optimization to multi-resolution optimization. Finally, our simulation set-up demonstrated the efficiency improvement of the EAF-MR optimization for a large-scale RF-FPGA.
Highlights
Wireless communication is ubiquitous in our daily life using an excessive number of communication standards: 3G/4G/LTE cellular service, Wi-Fi, bluetooth, and so on
In order to utilize the large-scale radio frequency (RF)-FPGA for cognitive radio, our research focused on developing an efficient algorithm, called Environment-Adaptable Fast MultiResolution (EAF-MR) method
We investigate a model-based approach to solve this problem in order to use the EAF optimization in large-scale Radio Frequency-Field Programmable Gate Array (RF-FPGA) systems
Summary
Wireless communication is ubiquitous in our daily life using an excessive number of communication standards: 3G/4G/LTE cellular service, Wi-Fi, bluetooth, and so on. This optimization has improved selectivity of optimal configuration, and when the reconfigurable radio is initially programmed, several base configurations (i.e., default configurations) can be pre-programmed into it, based on the desired communication standards It has a limited capability of reducing reconfiguration load for a dynamic communication environment, with blockers and large interferers appearing randomly at different frequencies. (2) environment-adaptable algorithm: when choosing an optimal configuration in a large-scale RF-FPGA, the EAR-MR algorithm reflects communication quality changes due to interference in the field This interference effect is measured using the signal-to-interference-and-noise ratio (SINR) calculation in the algorithm. Using the SINR calculation, we designed the EAF-MR optimization method that hastens an optimization process for finding an operable configuration in a large-scale RF-FPGA system. Component value: the metric that represents the RF impairment of a component
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