Power allocation for distributed compressive sensing with 1-bit quantization over noisy channels
He, Jiguang; Leinonen, Markus; Nguyen, Kien-Giang; Li, Yong; Silvén, Olli; Juntti, Markku (2019-08-30)
J. He, M. Leinonen, K. Nguyen, Y. Li, O. Silvén and M. Juntti, "Power Allocation for Distributed Compressive Sensing with 1-Bit Quantization over Noisy Channels," 2019 16th International Symposium on Wireless Communication Systems (ISWCS), Oulu, Finland, 2019, pp. 587-591. doi: 10.1109/ISWCS.2019.8877262
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https://urn.fi/URN:NBN:fi-fe2019121848671
Tiivistelmä
Abstract
Cost-efficient implementation with a low-complexity analog-to-digital converter is necessary for the sensor nodes in the internet of things. In the paper, we study the distributed compressive sensing (DCS) under the constraint of 1-bit quantization at each node. The entire transmission chain, composed of compressive sensing, 1-bit quantization, and joint source-channel coding (JSCC), is taken into consideration with joint signal reconstruction at the fusion center. A lower bound on the end-to-end mean square error distortion, which is a function of the measurement rate, distortion of 1-bit quantization, and that of JSCC, is derived under the assumption of the oracle reconstruction. The time-varying channel conditions have a major impact on the distortion of JSCC. Therefore, a suboptimal yet efficient power allocation scheme based on the successive convex approximation method is proposed to minimize the lower bound on the end-to-end distortion. Moreover, a practical coding and joint signal reconstruction scheme is provided to show its consistence with the derived theoretical limits.
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