Logic Design and Power Optimization of Floating-Point Multipliers

Under IEEE-754 standard, for the current situation of excessive time and power consumption of multiplication operations in single-precision floating-point operations, the expanded boothwallace algorithm is used, and the partial product caused by booth coding is rounded and predicted with the symbolic expansion idea, and the partial product caused by single-precision floating-point multiplication and the accumulation of partial products are optimized, and the flowing water is used to improve the throughput. Based on this, a series of verification and synthesis simulations are performed using the SMIC-7 nm standard cell process. It is verified that the new single-precision floating-point multiplier can achieve a smaller power share compared to the conventional single-precision floating-point multiplier.

An uplink power optimization solution for satellite HUBs station based on closed-loop control algorithm

In this paper, we present a power optimization solution based on the automatic power control method using a closed-loop algorithm for the uplink power control device (UPC) of the satellite communication HUB station. The results are evaluated by theoretical analysis and validated by simulation model on Matlab software, finally the model has also been used in the UPC device and tested in practice. The simulation process, and the test is carried out under the impact of varying rainfall on the wave path, the results are compared with the solutions that have been and are being used to demonstrate the effectiveness of the solution. This paper is the research product of the project "Researching, designing and manufacturing the uplink power controller military satellite communication system".

NOMA resource allocation method in IoV based on prioritized DQN-DDPG network

To meet the demands of massive connections in the Internet-of-vehicle communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning network is utilized for resource optimization in NOMA system, where a uniform sampled experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. To this point, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the system sum rate. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. Although the sum rate is improved by only 2%, the computational complexity is reduced by 43% and 64% compared to the exhaustive search algorithm and the optimal iterative power optimization algorithm, respectively.