Environment-Adaptable Fast Multi-Resolution (EAF-MR) optimization in large-scale RF-FPGA systems

• Main Authors: Minhee Jun, Rohit Negi, Shihui Yin, Mohamed Alawieh, Fa Wang, Megha Sunny, Tamal Mukherjee, Xin Li
• Format: Article
• Language: English
• Published: SpringerOpen 2018-03-01
• Series: EURASIP Journal on Wireless Communications and Networking
• Subjects: Software-defined radio (SDR); Reconfigurable RF front-end; Large-scale RF-FPGA; Estimation of RF impairments; Environment-Adaptable Fast Multi-Resolution (EAF-MR) optimization
• Online Access: http://link.springer.com/article/10.1186/s13638-018-1042-4

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.