This paper describes NexusV, a small, ultra-low-power, 32-bit RV32I+ZiCSR RISC-V softcore microcontroller optimized for resource-constrained embedded systems. The processor employs a deterministic multi-cycle design that timemultiplexes a single Arithmetic Logic Unit (ALU) to minimize datapath logic. Synthesized on the PYNQ-Z2 FPGA board, the core operates at a maximum frequency of 90 MHz, achieving a performance of 0.18 DMIPS/MHz while utilizing only 1,134 LUTs and 406 Flip-Flops. The entire system consumes a highly competitive dynamic power of 18 mW and features a full software ecosystem capable of running C code via the standard RISC-V GCC toolchain. Operational integrity was verified through physical hardware testing and the Dhrystone benchmark alongside integrated AMBA APB3 UART, SPI, and PWM peripherals. These features make NexusV a viable Intellectual Property (IP) core for low-power control units in battery-powered devices.
Prithiviraj Rajalingam, Sudeshna Shettygari, S. S. et al.· 2026 International Conferenc...· 0 citations
The wildest boom of wireless devices and the shift to 5G/6G ecosystems contributed to the lack of the spectrum, making the old traditional methods of static allocation less and less efficient. cognitive radio networks provide an alternative with dynamic nature, the current solutions tend to fail because of the sophisticated nature of imperfect channel state information, large-dimensional state space and the rapid mobility of users. This model presents an Attention-Augmented Multi-agent Deep Reinforcement Learning model, which is used to maximize autonomous spectrum sharing by using spatial-temporal awareness. The architecture is based on convolutional neural networks in mapping spatial interference and long short-term memory layers in temporal mobility tracking with a multi-head self-attention mechanism to coordinate interference management between secondary users. To obtain accurate resource mapping, layers of Sinkhorn are incorporated to be bi-stochastic. Simulation shows that the spectral efficiency is 22.14% higher and the collision rate is also 6.82 times lower with the 3GPP channel models than with regular deep Q-networks. The system can be 85.36% efficient even in the presence of serious channel state errors, which is a strong solution to ensure trustworthy ultra-dense urban connectivity.
J. P. Dharshini, L. Subi, Lydia D. Isaac et al.· International Conference Com...· 0 citations
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