Computational Power Evaluation for Energy-Constrained Wireless Communications Systems

Abstract

Estimating the power consumption and computational complexity of various digital signal processing (DSP) algorithms used in wireless communications systems is critical to assess the feasibility of implementing such algorithms in hardware, and for designing energy-constrained communications systems. Therefore, this paper presents a novel approach, based on practical system measurements using field programmable gate array (FPGA) and application-specific integrated circuit (ASIC), to evaluate the power consumption and the associated computational complexity of the most common mathematical operations performed within various DSP algorithms. Using the proposed approach, a new metric is developed for mapping the computational complexity to the computational power consumed by the mathematical operation in wireless transceivers. This allows combining the commonly used computational complexity metrics that are typically computed for each mathematical operation separately. Consequently, a single unified metric can be used to describe the entire algorithm. Therefore, the comparison and trade-offs between different algorithms become easier and more informative. The developed approach is used to evaluate the computational power of several DSP algorithms used in wireless communications systems, and perform thorough computational complexity comparisons. The obtained results reveal that computational complexity comparisons using different mathematical operations can be highly misleading in several scenarios. The power consumption evaluation of the considered DSP algorithms show that some algorithms may require a prohibitively high power, which makes such algorithms unsuitable for power-constrained wireless communications systems. The results also show that the proposed methodology can be adopted for various hardware implementation, however, some calibration might be required based on the adopted platform.

Keywords

• Power
• complexity
• power optimization
• computational power
• computational complexity
• FPGA