Dynamic Metasurface Antennas for MIMO-OFDM Receivers with Bit-Limited ADCs

December 14, 2019 Β· Declared Dead Β· πŸ› IEEE Transactions on Communications

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Authors Hanqing Wang, Nir Shlezinger, Yonina C. Eldar, Shi Jin, Mohammadreza F. Imani, Insang Yoo, David R. Smith arXiv ID 1912.06917 Category cs.IT: Information Theory Cross-listed eess.SP Citations 72 Venue IEEE Transactions on Communications Last Checked 5 months ago
Abstract
The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We present a model for DMAs which accounts for the configurable frequency selective profile of its metamaterial elements, resulting in a spectrally flexible hybrid structure. We then exploit previous results in task-based quantization to show how DMAs can be configured to improve recovery in the presence of constrained ADCs, and propose methods for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that the DMA-based receiver is capable of accurately recovering OFDM signals. In particular, we show that by properly exploiting the spectral diversity of DMAs, notable performance gains are obtained over existing designs of conventional hybrid architectures, demonstrating the potential of DMAs for MIMO-OFDM setups in realizing high performance massive antenna arrays of reduced cost and power consumption.
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