1. ADC Bit Optimization for Spectrum- and
Energy-Efficient Millimeter Wave Communications
Jinseok Choi, Brian L. Evans, and Alan Gatherer
Project Supported
by Huawei
MOTIVATION I
III
POWER CONSTRAINED BIT ALLOCATION
Algorithm: Power Constrained ADC Bit Allocation
Challenge 1: Optimal # of ADC Bits
MmWave Massive MIMO
Large bandwidth to achieve multi-gigabit data rates
Small antenna sizes due to high carrier frequency
Large antenna arrays to compensate large pathloss
Set power constraint p
Sort channel gains to be
Compute Mmax
Set
Binary Search at stage s with
Convex optimization problem
# of activated RF chains
Modeled Psw
KKT condition
Real number relaxation
Millimeter Wave Spectrum [Pi11]
Closed-form optimal solution
: Aggregated channel gain at jth RF chain
For
Goal
Reduce uplink power consumption at base station
Need to reduce power consumption at ADCs
Challenge 2: Non-Linearity in Total Power Consumption
non-linearity
compute bs
compute
Optimal solution
Enforce positivity & append zeros
unknown optimal
Optimal Nact ▷ binary search O(log(Nr))
Approach
Exploit beamspace sparsity in mmWave MIMO channels : Apply analog processing (beamspace projection)
ADC bit allocation subject to a total power constraint : Some ADCs/RF chains will be turned off to save power : Other ADCs will have a variable number of bits
compare total quantization error for
If Ms has minimum total quantization error
# of activated RF chains at search stage s
Non-linear ADC resolution switching power ▷ training switching power model
map of Ms to nearest integer, then
return
else go to smaller half
MODELS & ASSUMPTIONS II IV
VALIDATION & CONCLUSIONS
* ULA: Uniform linear array
Multiuser Massive MIMO Uplink
Ergodic Spectral Efficiency w. MRC
Energy Efficiency
Nu users, each with single antenna
Narrowband channel
Nr ULA* antennas at base station (Nr >> Nu)
Known channel state information at receivers
Received signals after analog combining y yq
Tx power
User symbols
AWGN
Analog combiner
Hybrid receiver with adaptive-resolution ADCs
Millimeter Wave Channel
Quantization Model
L major propagation paths (limited scattering)
Array response vector under ULA
Additive quantization noise model
Variable number of quantization bits bi for each ADC
Angle of Arrival
Complex path gain
Array response vector
Conclusions
Quantization noise
Quantization gain matrix
Beamspace channel
Pathloss
Achieves highest spectral/energy efficiency for low-resolution ADCs
Eliminates most of quantization distortion with min. power consumption
Enables existing state-of-the-art digital combiners to be employed
Allows more power consumption for downlink communication
System parameters
Cell radius
Min dist.
Noise fig. fc BW
200 m
30 m
5 dB
28 GHz
1 GHz
# rx ant.
# RF chains
# users
# paths
Tx Power
256
128 10 13
20 dBm
where
where
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November, 2017