*Proc. IEEE Global Communications Conference*,
Dec. 4-8, 2017, Singapore.
# ADC Bit Optimization for Spectrum- and Energy-Efficient Millimeter Wave Communications

*Jinseok Choi* (1),
*Junmo Sung* (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

jinseokchoi89@gmail.com -
bevans@ece.utexas.edu

(2) Huawei Technologies, Plano, Texas USA

Paper -
Slides -
Software Release

Related journal paper:

"Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications" (2018)

Related conference papers:

"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 posters:

"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" (2016)

Multiantenna Communications Project

## Abstract

A spectrum- and energy-efficient system is essential for millimeter
wave communication systems that require large antenna arrays with
power-demanding ADCs.
We propose an ADC bit allocation (BA) algorithm that solves a minimum
mean square quantization error problem under a power constraint.
Unlike existing BA methods that only consider an ADC power constraint,
the proposed algorithm regards total receiver power constraint for a
hybrid analog-digital beamforming architecture.
The major challenge is the non-linearities in the minimization problem.
To address this issue, we first convert the problem into a convex
optimization problem through real number relaxation and substitution of
ADC resolution switching power with constant average switching power.
Then, we derive a closed-form solution by fixing the number of
activated radio frequency (RF) chains M.
Leveraging the solution, the binary search finds the optimal M and
its corresponding optimal solution.
We also provide an off-line training and modeling approach to estimate
the average switching power.
Simulation results validate the spectral and energy efficiency of the
proposed algorithm.
In particular, existing state-of-the-art digital beamformers can be
used in the system in conjunction with the BA algorithm as it makes
the quantization error negligible in the low-resolution regime.
## Questions & Answers

Q1) Is there a power limit for each ADC for the case when certain ADCs get too many bits?

A1) Firstly, the proposed algorithm barely assigns too many bits to certain ADCs as it degrades energy efficiency.
Secondly, we can set a resolution limit for each ADC if there is a power limit for each ADC.
Q2) How often does the receiver switch the resolutions of ADCs?

A2) The receiver needs to switch the ADC resolutions at every channel coherence time for the best performance.
If it is not feasible in practice, however, the receiver can switch the resolution at the coherence time of the large scale fading or angle of arrivals that are the slowly changing channel characteristics, which still achieve higher spectral and energy efficiency than fixed ADC and mixed ADC cases.

Q3) How practical is the algorithm?

A3) Flash ADCs can have very high speed conversion rate and resolution switching rate compared to other ADCs.
Thus, the mmWave system with the proposed method can be realized with the flash ADCs.

Q4) For a very small receiver power limitation which is less than (b < 1) in the plot, it seems like that the performance gap between the proposed algorithm and other cases becomes larger.

A4) Yes, the performance gap will increase as the receiver power limitation decreases since the proposed algorithm optimizes the ADC bit allocation subject to the receiver power constraint unlike the other cases.

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Last Updated 11/15/18.