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HAL Id: hal-01731053https://hal.archives-ouvertes.fr/hal-01731053
Submitted on 31 Jul 2018
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Hybrid Analog and Digital Precoding in MillimeterWave Massive MIMO Systems with Realistic Hardware
and Channel ConstraintsMohamed Shehata, Matthieu Crussière, Maryline Hélard, Patrice Pajusco,
Bernard Uguen
To cite this version:Mohamed Shehata, Matthieu Crussière, Maryline Hélard, Patrice Pajusco, Bernard Uguen. HybridAnalog and Digital Precoding in Millimeter Wave Massive MIMO Systems with Realistic Hardwareand Channel Constraints. 2017 IEEE SPS Summer School on Signal Processing for 5G WirelessAccess, May 2017, Gothenburg, Sweden. 2017. �hal-01731053�
INSTITUT DβΓLECTRONIQUE ET DE
TΓLΓCOMMUNICATIONS DE RENNES
1
Hybrid Analog & Digital Precoding in Millimeter
Wave Massive MIMO Systems with Realistic
Hardware & Channel Constraints
M. Shehata1, M. Crussière1, M. Hélard1, P. Pajusco2, B. Uguen3
Signal Processing for 5G Wireless Access 2017, Gothenburg, Sweden
1 INSA, IETR, CNRS UMR 6164, Rennes, France 2 Institut Mines-Telecom, Telecom Bretagne, Brest, France
3 UniversitΓ© Rennes 1, IETR, CNRS UMR 6164, Rennes, France
Authors
Abstract
System & Channel Model
Initial Results
0 100 200 300 400
010
020
030
040
050
0
X(m)
Y(m
)
Tx
Rx K
Rx 1
Outdoor LOS Scenario
User
Broadside axis
ΞΈ
Ξ¦
MH
Mv
Square Planar Array
DH=Ξ»/2
Dv=Ξ»/2
15
m
10 m
30 m
Line of Sight
Ground Reflection
Wall Reflection
Downlink Scenario 1 (Digital Massive MIMO)
Transmitter (eNodeB)
ArrayElements
Nt β€ 64
Power Amplifiers
FrequencyUpconverters
Dig
ita
l Ba
seb
an
d S
ign
al
Antenna WeightsAssignment
(Beamforming)
Receiver 1 (User Equipment)
Antenna Weights Assignment (Combinning)
ArrayElements
Nr β€ 2 Low Noise Amplifiers
FrequencyDownconverters
β
Dig
ital
Bas
eban
d Si
gnal
Propagation Channel
DAC
RF Chain 1
RF Chain 2
DAC
RF Chain Nt
DAC
RF Chain 1
ADC
RF Chain 2
ADC
Receiver k (User Equipment)
Antenna Weights Assignment (Combinning)
ArrayElements
Nr β€ 2 Low Noise Amplifiers
FrequencyDownconverters
β
Dig
ital
Bas
eba
nd
Si
gnal
RF Chain 1
ADC
RF Chain 2
ADC
Beamforming Scenarios
Downlink Scenario 2 (Analog Massive MIMO)
Transmitter (eNodeB)
FrequencyUpconverters
DAC
Dig
ita
l B
ase
ba
nd
Sig
na
l
Antenna WeightsAssignment
(Beamforming)
ArrayElements
Nt β€ 64
Power Amplifiers
Propagation Channel
Receiver 1 (User Equipment)
Antenna Weights Assignment (Combinning)
ArrayElements
Nr β€ 2
β ADC
FrequencyDownconverters
Dig
ita
l B
ase
ba
nd
S
ign
al
Receiver K (User Equipment)
Antenna Weights Assignment (Combinning)
ArrayElements
Nr β€ 2
β ADC
FrequencyDownconverters
Dig
ita
l B
ase
ba
nd
S
ign
al
Downlink Scenario 3 (Hybrid Analog/Digital MIMO)
Transmitter (eNodeB)
RF ChainsNRF β€ 64
Nu
mb
er
of
Str
ea
ms
Ns
Baseband Precoder(FBB)
RF Chain 1
RF Chain NRF
Array Elements (Nt can be > 64)
β
β
β
RF Precoding (FRF) through phase
shifters
Receiver 1 (User Equipment)
RF Chain 1
RF Chain 2
Nu
mb
er
of
Str
ea
ms
Ns
BasebandCombiner
(WBB)
Array Elements (Nr can be > 2) RF Chains NRF β€ 2
RF combiner (WRF) through phase shifters
Receiver k (User Equipment)
RF Chain 1
RF Chain 2
Nu
mb
er
of
Str
ea
ms
Ns
BasebandCombiner
(WBB)
Array Elements (Nr can be > 2) RF Chains NRF β€ 2
RF combiner (WRF) through phase shiftersPropagation Channel
M5HESTIA
2 projects in parallel project Organization Consortium
Digital Beamforming
METIS TC2 (Madrid Grid)
Antenna Array Structure
β’ Multi-User (MU) MIMO system with πΎ User Equipments (UEs) each equipped with
ππ antennas.
β’ The transmitting Base Station (BS) is equipped with ππ antennas & serving each UE
with ππ streams.
β’ The received signal vector π is given as follows (we omit the UE index π for clarity):
π = ππΎπ»π―ππ +πΎπ»n
π― = πΆππβπ2ππππππ (ππ ,π , ππ ,π)ππ
π» (ππ,π , ππ,π)
ππ
π=1
Street Canyon
Analog Beamforming
Hybrid Beamforming
Simulation Chain Architecture
MHESTIA: Millli-Meter-wave Multi-user Massive MIMO Hybrid Equipment for
Sounding, Transmissions and hardware ImplementAtion.
Number of paths
Complex gains
considering pathloss
and reflections
Delays Receive steering
vector, where:
ππ ,π: Elevation angle
ππ ,π: Azimuth angle
Millimeter-Wave (mmWave) systems recently attracted attention as one of the key
enablers for the Fifth Generation (5G) networks. The small wavelength at mmWave
frequencies enables deploying massive Multiple Input Multiple Output (MIMO) antenna
arrays with reasonable form factor. However, Massive MIMO mmWave systems
suffer from a lot of practical limitations, specifically due to channel and hardware
characteristics at such high frequencies. In this poster we propose multiple solutions
to design a realistic massive MIMO cellular system that takes into account the
imposed limitations and achieves considerable gains in terms of spectral efficiency.
Advantages β’ Can serve interference free MU scenarios with
number of UEs = ππ
β’ A lot of existing digital precoding techniques
exist in the literature
Disadvantages β’ RF chain is required for each antenna
β’ This applies limitations on implementing
Massive MIMO mmWave systems, since RF
chains are complex and power hungry in
mmwave regime
Advantages β’ Low hardware complexity and power
consumption (Only 1 RF chain is required)
β’ Can increase the link budget
β’ Can serve MU scenarios through multiplexing
in Time/Frequency
Disadvantages β’ Canβt support MU MIMO scenarios with spatial
multiplexing
β’ Therefore it is not favourable from Spectral
Efficiency (SE) point of view
Advantages β’ Can employ much more transmit antennas than
the number of RF chains
β’ Henceforth, can achieve higher transmit gain
compared to digital systems with the same
number of RF chains (hardware complexity)
Disadvantages β’ Analog impairments due to the power loss
and limited resolution of phase shifters
Simulation Parameters:
π = ππΎπ΅π΅π»π―ππ΅π΅π + πΎπ΅π΅
π»π
Total transmit power constraint: ππ΅π΅2πΉ β€ 1
π = ππΎπ πΉπ»π―ππ πΉπ + πΎπ πΉ
π»π
Phase shifters constraints (constant Amplitude &
quantized angles): ππ πΉ β ππ πΉ ,πΎπ πΉ β π¦π πΉ
π = ππΎπ΅π΅π»πΎπ πΉ
π»π―ππ πΉππ΅π΅π + πΎπ΅π΅π»πΎπ πΉ
π»π
Total transmit power constraint: ππ πΉππ΅π΅2πΉ β€ 1
Phase shifters constraints (constant Amplitude &
quantized angles): ππ πΉ β ππ πΉ ,πΎπ πΉ β π¦π πΉ
β’ Environment: Street Canyon
β’ System: MU-MIMO
β’ Channel Model: PyLayers [1]
β’ Antenna Array height: 10m
β’ Antenna Spacing: Ξ»
2
β’ Scenario: Outdoor LoS
β’ Center Frequency: 60 GHz
β’ Intercarrier spacing: 20 MHz
β’ Transmit Antennas: 16
β’ Transmit Antenna Array: Square
β’ Receive Antennas: 1
β’ UEs interdistance: 1m
β’ Transmit Power: 26 dbm
[1] PyLayers : An Open Source Dynamic Simulator for Indoor Propagation and Localization, N.
Amiot, M.Laaraiedh, B.Uguen, Communications Workshops ICC 2013
Sum Capacity Comparison
Conclusion: β’ ZF overperfoms CB and DBS in sum capacity when the
number of UEs is smaller than the channel rank.
β’ CB and DBS still achieve high sum capacity in mmWave
systems even with exceeding the degrees of freedom
with lower complexity compared to ZF.
β’ The main advantage of the DBS lies on estimating only
2πΎ angles instead of πΎΓππΓππΉπΉπ
ππ΅πΆ parameters in ZF and
CB.
πΎ: the number of UEs ππ: number of transmit antennas, ππΉπΉπ: number of
subcarriers, ππ΅πΆ : number of subcarriers per coherence BW
Transmit power Combiner matrix of
dimensions ππ Γ ππ
Precoder matrix of
dimensions ππ Γππ
Signal vector of
dimensions ππ Γ 1
AWGN
Transmit steering
vector, where:
ππ,π: Elevation angle
ππ,π: Azimuth angle
Research Objectives
Channel Estimation Course Quantization
Spatial sparsity of channel β
Compressive Sensing