MIMO-Hybrid Beamforming in Massive-MIMO mmWav

1. Hybrid Beamforming in Massive-MIMO mmWave
Systems Using LU Decomposition
By:
Fatimah Azeez
Supervised by:
Prof.dr. hikmat N. Abdullah

2. Content 01 MIMO Antenna Concept
02 What is Massive MIMO
03 Overview of Massive MIMO
04 Why Massive MIMO? & Features
05 Opportunities and challenges
07 Applications & Benefits of Beamforming
08 Hybrid Beamforming
06 What is Beamforming?
09
Hybrid Beamforming in Massive-MIMO mmWave
Systems Using LU Decomposition
10 SIMULATION RESULTS & CONCLUSIONS

3. MIMO Antenna Concept
channel
Radio
D
S
P
Bits
TX
Radio
Radio
D
S
P
Bits
RX
Radio

4. What is Massive MIMO

5. Overview of Massive MIMO

6. Massive Antenna based Multi-User MIMO
system

7. Why Massive MIMO?

8. Massive MIMO Features

9. Massive MIMO Features
1.Massive MIMO can increase the capacity 10 times or more:
The capacity increase results from the aggressive spatial multiplexing used in massi
ve MIMO. [1]
2) Massive MIMO increases data rate[1]:
because the more antennas, the more independent data streams can be sent out and
the more terminals can be served simultaneously.
Each terminal can be given the whole bandwidth
3) Massive MIMO can be built with inexpensive, low-power components [1]:
With massive MIMO, expensive, ultra-linear 50 Watt amplifiers used in conventional s
ystems are replaced by hundreds of low-cost amplifiers with output power in the milli-W
att range
Massive MIMO reduces the constraints on accuracy and linearity of each individual a
mplifier and RF chain

10. Massive MIMO Features
4) Improved energy efficiency[1]:
because the base station can focus its emitted energy into the spatial directions where
it knows that the terminals are located
5) Reduce interference:
because the base station can purposely avoid transmitting into directions where spread
ng interference would be harmful.

11. Massive MIMO Features
6) Massive MIMO enables a significant reduction of latency on the air interface[1]:
Massive MIMO relies on the law of large numbers and beamforming in order to avoid fa
ding dips, so that fading no longer limits latency.
7) Massive MIMO simplifies the multiple-access layer[1]:
the channel hardens so that frequency-domain, scheduling no longer pays off.
Each terminal can be given the whole bandwidth, which renders most of the physical-la
yer control signaling redundant.
8) Massive MIMO increases the robustness to intentional jamming[1]:
Massive MIMO offers many excess degrees of freedom that can be used to cancel sign
als from intentional jammers.
If massive MIMO is implemented by using uplink pilots for channel estimation, then sma
rt jammers could cause harmful interference with modest transmission power. However,
more clever implementations using joint channel estimation and decoding should be able
to substantially diminish that problem.
9) These degrees of freedom can be used for hardware-friendly signal shaping[1]:
A massive MIMO system has a large surplus of degrees of freedom.

12. 4G Vs. 5G

13. WSN & IOT
Opportunities and challenges

14.  Beam-forming is a technique that
focuses the transmitted energy into
a specific direction.
 Increase energy to a specific user.
 Reduce interference elsewhere
What is Beamforming?

15. Beamforming Applications

16. Omni directional vs directional (Smart)
Antenna

17. Beam-forming in 5G

18. Benefits of Beamforming
 Power gain or Enhanced energy efficiency (applicable only to transmit
beamforming)
 Array gain : “dynamic high-gain antenna”
 Improved spectral efficiency (bps)
 Interference reduction
 Diversity gain: combats fading effects
 Increased system security
 Multipath mitigation
 Applicability for mm-wave bands

19. Digital Vs Analog Beamforming

20. Hybrid Beamforming

21. Hybrid Beamforming System Model

22. Hybrid Beamforming System Model
Where
sk;f 2 CNs is the transmitted symbol vector for the k-t
h user on the f-th subcarrier
Here FBBk;f 2 CNt RF _Ns is the digital baseband pre
coder (at the transmitter).
WBBk;f 2 CNr RF _Ns is the digital baseband combin
er (at the reciever).
FRF 2 CNt_Nt RF is the analog RF precoder at the tra
nsmitter.
WRFk 2 CNr_Nr RF is the analog RF combiner at the receiver
.
RF precoder at the transmitter. WRFk 2 CNr_Nr RF is
the analog RF combiner at the receiver. Both FRF and
WRF are subcarrier independent operations. nk;f 2 C
Nr is the adaptive noise with i.i.d. Gaussian distributio
n N(0; _2).
k is the number of users. f is the number of subcarrier
s of the OFDM. The other assumptions about these va
lues are that they are subject to the hardware constrai
nts KNs _ NtR F _ Nt and Ns _ NrR F _ Nr for K users
[1].
A. Received Signal

23. Hybrid Beamforming System Model
between the BS and the k-th user.
_il;k is the gain of the l-th ray in the i-th
propagation cluster. ar(_ill;k) is the receiv
e array response vector.
at(ϕil;k) is the transmit array response vec
tor. _il;k is the Angle of Arrival (AoA), and
ϕil;k is the Angle of Departure (AoD).
B. Channel Model
The mmWave MIMO channel between the BS and the
k-th user in the f-th subcarrier, denoted as Hk;f , can be
characterized by the Saleh-Valenzuela model:

24. Hybrid Beamforming System Model
where A represents the unit modulus constraint
(which is not a convex and need to be relaxed o
r changed), and Fopt is the fully digital beamfor
ming matrix and considered as the optimal uppe
r bound we need to reach. Fopt in the transmitte
r and Wopt in the receiver are comprised of the
first Ns columns of V and U resulted from the si
ngular value decomposition (SVD) of the chann
el matrix H respectively
C. The Problem Formulation
The underlying problem is to design the hybrid beamforming
transceivers to achieve the maximum spectral efficiency
(or the rate) for the possible digital and analog components,
which can be expressed as
where the data rate R is given by:

25. Where
K is the number of users,
H is the channel matrix,
Nt is the number of transmitting antennas
Nr is the number of receiving antennas,
Ns is the number of data streams
Wopt is the fully digital matrix in the
combiner side,
WRF is the analog RF matrix in the
combiner side, and
the WBB is the digital baseband matrix
in the combiner side
Hybrid Beamforming in Massive-MIMO mmWave
Systems Using LU Decomposition

26. SIMULATION RESULTS

27. 16X4 MIMO system achievable spectral efficiency comparison for
Single user

28. 64X16 MIMO system achievable spectral efficiency comparison for
single user

29. 144X36 MIMO system achievable spectral efficiency comparison for
single user

30. 16X4 MIMO system achievable spectral efficiency comparison for
5 user

31. 64X16 MIMO system achievable spectral efficiency comparison for
5 user

32. 144X36 MIMO system achievable spectral efficiency comparison for
5 user

33. CONCLUSIONS AND FUTURE WORK
 we have developed a simple yet effective matrix factorization method to implement a
n efficient hybrid beamforming system using the LU.
 Extensive simulation have been conducted to ensure our method effectiveness over
some of the well known systems in this field.
 The resulted matrices from the decomposition were tested to make sure they are sub
jected to the hardware limitations and that they maximize the system achievable
 rate.
 Future work includes working in environments where the channel is not fully availabl
e or partially estimated with no pilot signals, working in a multiple cells system, and
 investigating other factorization techniques like SVD and QR.

34. Missive MIMO Research problem

36. Thank you
^_^