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"6G is expected to offer 10 times lower latency compared to 5G" Hussein, A., Saha, D., & Elgala, H. (2021). Mixed-Carrier Communication for Technology Division Multiplexing. Electronics. "6G is expected to have lower latency compared to 5G due to the support of massive MIMO systems and mmWave." Kadir, E., Shubair, R., Rahim, S., Himdi, M., Kamarudin, M., & Rosa, S. (2021). B5G and 6G: Next Generation Wireless Communications Technologies, Demand and Challenges. 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 1-6. "6G wireless communications system is predicted to have lower latency compared to 5G." Swamy, A. (2022). Advance cellular networks (4G, 5G, 6G). International journal of health sciences. "6G network technology will support submicrosecond delays, making communication almost instantaneous and ensuring lower latency." Eren, T., Oktay, Z., Dogan, H., & Savci, H. (2020). 6 GHz Low Noise Amplifier design with 65nm CMOS for 5G/6G Applications. 2020 12th International Conference on Electrical and Electronics Engineering (ELECO), 88-92. "6G networks may use non-orthogonal multiple access (NOMA) to maintain higher data rates, throughput, and lower latency compared to 5G." Ahmed, A., & Al-Raweshidy, H. (2022). Performance evaluation of Serial and Parallel Concatenated Channel Coding Scheme with Non-Orthogonal Multiple Access for 6G Networks. IEEE Access, PP, 1-1.
tldr; New research from Technische Universität Dresden in Germany has developed a breakthrough material design for neuromorphic computing, which could have significant implications for blockchain and AI. The researchers used a technique called "reservoir computing" to create a method for pattern recognition using magnons. They also demonstrated the potential for neuromorphic computing to work on a standard CMOS chip. Unlike classical computers, which use binary transistors, neuromorphic computers use artificial neurons to imitate brain activity, making them well-suited for pattern recognition and machine learning algorithms. This technology could greatly improve the efficiency and speed of blockchain operations and machine learning systems, particularly in real-time applications such as self-driving cars and crypto market analysis. *This summary is auto generated by a bot and not meant to replace reading the original article. As always, DYOR.*