IEEE Transactions on Signal Processing, vol. 65, no. 23, pp. 6201-6216, Dec. 2017, DOI 10.1109/TSP.2017.2745440.

Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications

Jinseok Choi (1), Brian L. Evans (1) and Alan Gatherer (2)

(1) Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA -

(2) Huawei Technologies, Plano, Texas USA

Paper Draft - Software Release

Related conference papers:
"ADC Bit Optimization for Spectrum- and Energy-Efficient Millimeter Wave Communications" (2017)
"ADC Bit Allocation Under a Power Constraint for mmWave Massive MIMO Communication Receivers" (2017)
"Space-Time Fronthaul Compression of Complex Baseband Uplink LTE Signals" (2016)

Related poster:
"ADC Bit Allocation under a Power Constraint for MmWave Massive MIMO Communication Receivers"

Multiantenna Communications Project


In this paper, we propose a hybrid analog-digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that the optimal number of ADC bits is logarithmically proportional to the RF chain’s signal-to-noise ratio raised to the 1/3 power. Using the solutions, two proposed BA algorithms minimize the mean square quantization error of received analog signals under a total ADC power constraint. Contributions of this paper include
  1. ADC bit-allocation algorithms to improve communication performance of a hybrid MIMO receiver,
  2. approximation of the capacity with the BA algorithm as a function of channels, and
  3. a worst-case analysis of the ergodic rate of the proposed MIMO receiver that quantifies system tradeoffs and serves as the lower bound.
Simulation results demonstrate that the BA algorithms outperform a fixed-ADC approach in both spectral and energy efficiency, and validate the capacity and ergodic rate formula. For a power constraint equivalent to that of fixed 4-bit ADCs, the revised BA algorithm makes the quantization error negligible while achieving 22% better energy efficiency. Having negligible quantization error allows existing state-of-the-art digital beamformers to be readily applied to the proposed system.

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Last Updated 11/15/18.