Hybrid Architectures with Few-Bit ADC Receivers: Achievable Rates and Energy-Rate Tradeoffs

Jianhua Mo, Ahmed Alkhateeb, Shadi Abu-Surra, Robert W. Heath

Research output: Contribution to journalArticle

81 Scopus citations

Abstract

Hybrid analog/digital architectures and receivers with low-resolution analog-to-digital converters (ADCs) are two low power solutions for wireless systems with large antenna arrays, such as millimeter wave and massive multiple-input multiple-output systems. Most prior work represents two extreme cases in which either a small number of radio frequency (RF) chains with full-resolution ADCs, or low-resolution ADC with a number of RF chains equal to the number of antennas is assumed. In this paper, a generalized hybrid architecture with a small number of RF chains and a finite number of ADC bits is proposed. For this architecture, achievable rates with channel inversion and singular value decomposition-based transmission methods are derived. Results show that the achievable rate is comparable to that obtained by full-precision ADC receivers at low and medium SNRs. A trade-off between the achievable rate and power consumption for the different numbers of bits and RF chains is devised. This enables us to draw some conclusions on the number of ADC bits needed to maximize the system energy efficiency. Numerical simulations show that coarse ADC quantization is optimal under various system configurations. This means that hybrid combining with coarse quantization achieves better energy-rate trade-off compared with both hybrid combining with full-resolutions ADCs and 1-bit ADC combining.

Original languageEnglish (US)
Article number7876856
Pages (from-to)2274-2287
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume16
Issue number4
DOIs
StatePublished - Apr 2017

Keywords

  • Massive MIMO
  • analog-to-digital converters
  • hybrid analog/digital architecture
  • millimeter wave communication

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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