Analog-Digital Hybrid Transceiver Optimization for Data Aggregation in IoT Networks

Heng Liu, Shuai Wang, Xin Zhao, Shiqi Gong, Nan Zhao, Tony Q.S. Quek

研究成果: Article同行評審


Data aggregation is a promising technology in the Internet-of-Things (IoT) network for a wide range of applications, e.g., environmental monitoring, traffic control, and real-time surveillance. In order to meet the high requirement of transmission rate for data aggregation, we investigate the transceiver optimization to improve the spectral efficiency. As a tradeoff between the system complexity and performance, hybrid transceivers are adopted for data aggregation in the IoT network. We first present the optimal structures of digital precoders and unconstrained analog transceivers to maximize the spectral efficiency. Then, we propose two different kinds of iterative algorithms to optimize the analog transceivers under nonconvex unit-modulus constraints. The first algorithm is based on the framework of the alternating direction method of multipliers (ADMM). The second one is the steepest descent (SD) algorithm based on the Riemannian geometry, which has lower computational complexity than the first one. For both algorithms, closed-form solutions are derived in each iteration. Finally, numerical results demonstrate that the performance of the proposed algorithms in the hybrid transceiver design is very close to the fully digital solution but with less hardware complexity and power consumption.

頁(從 - 到)11262-11275
期刊IEEE Internet of Things Journal
出版狀態Published - 2020 十一月

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

指紋 深入研究「Analog-Digital Hybrid Transceiver Optimization for Data Aggregation in IoT Networks」主題。共同形成了獨特的指紋。