ABSTRACT
The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.
ABSTRACT
The posterior interosseous nerve (PIN) is the terminal branch of the radial nerve. The symptoms of PIN palsy vary markedly according to its types. In this report, we present the case of a 61-years-old male patient with an unusual manifestation of non-traumatic novel type of PIN palsy. A complicated course was involved in the diagnosis of this disease. The operation was performed after verification of PIN palsy. Recovery of symptoms was observed in a follow-up conducted three years later. Additionally, the electromyography examination returned to normal.
