A quick introduction to MIMO with some references to Machine learning for MIMO receiver algos

1. Introduction to Multiple Input Multiple
Output (MIMO) – Technology sharing series
- Ashok
Govindarajan
23-12-2015 Technology sharing series 1

2. Contents
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• What is MIMO – the big picture and the area of focus
• Defining MIMO
• An analogy and the relation to SISO/MIMO
• Machine learning algorithms and associated libraries in Python

3. The Big Picture – What is the context?
Area of focus
LTE Network Architecture
The area of focus
comprises of the following:
• The UE that is the User
equipment
• The eNB that is the
evolved Node-B, the
base station in 4G
• The wireless channel
between the eNB and
the UE
• The UE and the eNB are
network elements. The
others are MME, SGW,
PGW.
23-12-2015 Technology sharing series 3

4. SISO, MISO and MIMO – Conceptually
1
1
2
1
2
2
SISO : 1
Antenna
at the UE and
eNB – Single
Input Single
Output
MISO : 1 Antenna
at the UE and multiple at
eNB; Here 2 antennas are
considered at eNB –
Multiple Input Single
Output
MIMO : Multiple Antennae
at the UE and eNB; Here 2
antennas are considered at
eNB and UE for illustrative
purposes – Multiple Input
Multiple Output; Basically
a multi-antenna
communication system
The numbers on the
lines indicate the
number of antenna in
the respective
network element
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5. An Analogy
• Consider a car travel from Chennai to Pondicherry
• Approximately the distance is 170 kms and consumes around 12 litres of petrol.
• 4 people can travel in the car
• So, we can say that the 12 litres of fuel is needed to transport 4 people
• By extension, it is obvious that 48 litres of petrol will be needed to transport 16 people,
assuming we have 4 cars.
• Please note that the above analogy is not fully accurate in the context of MIMO. It is mainly
meant for motivating a preliminary understanding.
• Is it possible to transport 16 people in 4 cars, with only 12 litres of petrol? This is the question
asked, in the context of MIMO. The answer is YES, under certain conditions of channel
• In the above analogy, 1 car maps to SISO and 4 cars maps to MIMO
• It must be noted that the number of cars map to number of antenna and the fuel maps to
signal power and the number of people in the car maps to the data transfer rate.
Relation to SISO and MIMO
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100 kHz, 25 dB Rx SNR and
SISO (1*1)
0.7Mbps (Reference case)
100 kHz, 25 dB Rx SNR and
MIMO (2*2) and (4*4)
1.4 Mbps and 2.8 Mbps;
A few reference numbers:

6. Machine learning algorithms and associated libraries in
Python
• Research in Machine learning is key in driving the development of MIMO receive
algorithms – especially when the dimensions get bigger ; Antennas in the order of
400.
• Massive MIMO is in vogue – http://www.ni.com/white-paper/52382/en/
 5G massive MIMO testbed Prototype by National Instruments, to further 5G research – Sep 2015
 Demonstration by Tata DoCoMo in Dec 2012 with 16 antennae and 400 MHz bandwidth
• Low complexity, near optimal MIMO detector algorithms from Machine learning
• http://pybrain.org/pages/features
-- Machine learning
• https://pypi.python.org/pypi/algorithms/0.1
-- Data structures, searching, sorting and shuffling
• http://docs.python-guide.org/en/latest/scenarios/scientific/
-- SciPy – covers aspects associated with Linear Algebra and Signal Processing
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7. 23-12-2015 Technology sharing series 7
Thank You

8. 23-12-2015 Technology sharing series 8
Backup slides

9. SISO, MISO and MIMO – Diagrammatically
Ti
represents the i th transmit
antenna on the eNB side
Ri
represents the i th receive antenna
on the UE side
hⁱᴷ
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represents the channel coefficient between Tx and Rx