mimo

1. MIMO Communications
April 2023
Dr. Ali ARSAL

2. Outline
• Overview of MIMO communications
• Single-user MIMO
• Multi-user MIMO
• Massive MIMO

3. MIMO (Multiple-Input Multiple-Output)
• Transmitter/receiver can have multiple antennas
• A modern wireless communication technology:
• Theory: late 1980’s
• Standards and products: after 2000’s
• Now: core feature in WLAN (802.11 Wi-Fi) and cellular (3G, 4G LTE, 5G NR)
• Two benefits, simply put
• Improve link SNR (Signal to Noise Ratio)
• Improve link concurrency

4. MIMO Network Architectures
• Single-user MIMO (in 802.11n-2009, LTE):
• One TX, one RX.
• Either TX or RX or both can have multiple antennas
• Multi-user MIMO (in 802.11ac-2014, LTE-Advanced):
• One Tx, multiple Rx
• Parallel transmissions
• Massive MIMO (in 5G NR):
• Very large number of antennas at Tx
• One Tx, multiple Rx
• Parallel transmissions

5. Single-User MIMO Communication Modes

6. MIMO Benefits: an Audio Metaphor

7. Single-User MIMO Benefits: Capacity Gains
• Diversity Gains:
• Receiver Diversity
• Transmitter Diversity
• Multiplexing gain:
• Spatial Multiplexing

8. Receiver Diversity
• Receiver coherently combines
signals received by multiple antennas
• Asymptotic gain: Increasing SNR proportionally to Nr (#of receive
antennas):
• Intuition: received signal power adds up
• What’s the capacity gain?
• Logarithmically, according to Shannon’s equation: C=B log(1+SNR)
• When SNR is low, ,, so gain is almost linear w.r.t. Nr

9. Implementing Receiver Diversity
• Selection combining improves SNR to:
• Maximum ratio combining improves SNR to:

10. Mitigating Fading with Receiver Diversity
• Multiple receive antennas allow compensation of by non notches
in the other

11. Transmitter Diversity
• Transmitter sends multiple versions
of the same signal, through multiple
antennas.
• Two modes of transmitter diversity:
• Open-loop transmit diversity
• Closed-loop transmit diversity

12. Open-Loop Transmitter Diversity
• Send redundant versions of the same signal (symbol), over multiple
time slots, and through multiple antennas.
• Encode the symbols differently for different time slots and TX
antennas:
• Space-Time Block Code (STBC)

13. Open-Loop Transmitter Diversity
• Example: 2 TX antenna STBC
• Send 2 data symbols such as
• Received Signals:
Time slot 1:
Time slot 2:

14. Open-Loop Transmitter Diversity
• Example: 2 TX antenna STBC
• Diversity combining:
• i.e., signal power is boosted from to
• Open-loop transmit diversity gain:
• In general, open-loop transmit diversity increases SNR linearly with the number of
transmit antennas

15. Closed-Loop Transmitter Diversity
• Send redundant versions of the same signal (symbol), over the same
time slot.
• Encode the symbols differently for different TX antennas
• i.e., weight the symbols on different antennas, following a precoding
algorithm
• Precoding design requires feedback of channel state information (CSI)

16. Closed-Loop Transmitter Diversity
• Why precoding?
• Signals from different antennas need to sync (align) their phases.
• But the different channels (between TX antennas and RX antenna) distort
signals differently, causing phase offset.

17. Closed-Loop Transmitter Diversity
• How does precoding help?
• Precoding: Tx compensates the phase offset, and aligns the phases of signals
going through different channels
• Why CSI feedback is needed for precoding?
• TX must know the phase offset, in order to perform compensation.

18. Closed-Loop Transmitter Diversity
• Asymptotic gain from closed-loop transmit diversity
• Signal level combining, also called transmit beamforming
• Suppose we have 2 transmit antennas, then instead of x, we receive:
x+x=2x, received power becomes , SNR increases to 4 times!
• More generally, with TX antennas, SNR increases to
• What’s the capacity gain?

19. Spatial Multiplexing
• Form multiple independent links (on the same spectrum band)
between TX and RX, and send data in parallel through them.
• Unfortunately, there is cross-talk between antennas.
• Cross-talk must be removed by digital signal processing algorithms.

20. Spatial Multiplexing: Signal Processing
• Example: 2x2 MIMO Spatial Multiplexing
• Data to be sent over two tx antennas:
• Data received on two rx antennas:
• Channel distortions: can be estimated by the receiver
• Only two unknowns: easily obtained by solving the equations!

21. Spatial Multiplexing Gain
• Asymptotic gain:
• In general, capacity gain from spatial multiplexing scales linearly with
• In practice, spatial multiplexing gain also depends on channel “condition”
• If the channels between different antennas are correlated, e.g., are are all the
same, then you can’t solve the equations. Spatial multiplexing becomes infeasible!
• Channel condition can be profiled using “condition number”.
• Practical wireless devices’ multiple antennas are separated sufficiently far (further
than half-wavelength), so the channel is usually uncorrelated

22. Multi-User MIMO (MU-MIMO)
• Simultaneously transmitting different layers in separate beams to different
users using the same time and frequency resource.

23. MU-MIMO: How does it work?
• MU-MIMO differs from traditional MIMO.
• Data to be sent over two TX antennas:
• Data received on two RX nodes:
• Each RX only has one equation, but two variables; no way to solve it directly.
• x2 causes cross-talk interference to x1, and vice versa.

24. MU-MIMO: How does it work?
• How to remove cross talk?
• Send a weighted mix of x1 and x2
• TX antenna1 sends:
• TX antenna2 sends:
• Data received on RX1:
• RX1 only wants x1, so ideally, we should have

25. MU-MIMO: How does it work?
• MU-MIMO precoding:
• Tx can obtain from RXs’ feedback, so it can tune to satisfy
• This cancels the cross-talk interference from x2 to x1
• Similarly, we can cancel that from x1 to x2
• This is called Zero-Forcing Beamforming (ZFBF)
• How does TX obtain channel state information
• Simplest approach in 802.11ac: CSI feedback scheduling

26. MU-MIMO Capacity Gain
• Asymptotic capacity gain:
• If the transmitter has antennas, then it can send streams of data
simultaneously to users, increasing capacity to times compared with
single-antenna transmitter.
• Limitation:
• MU-MIMO is essentially a form of spatial multiplexing.
• So, the channel must be well-conditioned.

27. Massive MIMO
• Also known as “Large-Scale Antenna Systems”, “Very Large MIMO”, “Hyper
MIMO”, “Full-Dimension MIMO”
• A very large antenna array at the base station
• A large number of users are served simultaneously

28. Massive MIMO Potential-1
• Massive MIMO can increase the capacity 10 times or more:
• The capacity increase results from the aggressive spatial multiplexing used in
massive MIMO.

29. Massive MIMO Potential-2
• Massive MIMO increases data rate:
• 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

30. Massive MIMO Potential-3
• Massive MIMO can be built with inexpensive, low-power components:
• With massive MIMO, expensive, ultra-linear 50 Watt amplifiers used in conventional
systems are replaced by hundreds of low-cost amplifiers with output power in the
milli-Watt range.
• Massive MIMO reduces the constraints on accuracy and linearity of each individual
amplifier and RF chain.

31. Massive MIMO Potential-4
• Improved energy efficiency:
• Because the base station can focus its emitted energy into the spatial directions
where it knows that the terminals are located.
• Massive MIMO reduces the constraints on accuracy and linearity of each individual
amplifier and RF chain.
• Reduce interference:
• Because the base station can purposely avoid transmitting into directions where
spreading interference would be harmful.

32. Massive MIMO Potential-5
• Massive MIMO enables a significant reduction of latency on the air
interface:
• Massive MIMO relies on the law of large numbers and beamforming in order to
avoid fading dips, so that fading no longer limits latency.
• Massive MIMO simplifies the multiple-access layer:
• 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-layer control signaling redundant.

33. Massive MIMO Potential-6
• Massive MIMO increases the robustness to intentional jamming:
• Massive MIMO offers many excess degrees of freedom that can be used to cancel
signals from intentional jammers.
• If massive MIMO is implemented by using uplink pilots for channel estimation, then
smart 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.
• These degrees of freedom can be used for hardware-friendly signal
shaping:
• A massive MIMO system has a large surplus of degrees of freedom. For example,
with 200 antennas serving 20 terminals, 180 degrees of freedom are unused.

34. TDD or FDD
• We need a pilot signal for Channel state information (CSI) estimation, but
there are two problems.
• First, optimal downlink pilots should be mutually orthogonal between the antennas.
• Second, the number of channel responses that each terminal must estimate is also
proportional to the number of base station antennas.
• The solution is to operate in TDD (Time Division Duplex) mode, and rely on
reciprocity between the uplink and downlink channels.

35. Massive MIMO Limitations
• Channel Reciprocity:
• The hardware chains in the base station and terminal transceivers may not be
reciprocal between the uplink and the downlink.
• Pilot Contamination:
• Orthogonal pilot sequences have to be reused from cell to cell. Therefore, the
channel estimate obtained in a given cell will be contaminated by pilots transmitted
by users in other cells. This effect, called “pilot contamination”, reduces the system
performance, also the interference between users.

36. Massive MIMO Limitations-1
• Non CSI at TX:
• Before a link has been established with a terminal, the base station has no way of
knowing the channel response to the terminal. This means that no array
beamforming gain can be harnessed. In this case, probably some form of space-
time block coding is optimal.
• Once the terminal has been contacted and sent a pilot, the base station can learn
the channel response and operate in coherent MU-MIMO beamforming mode,
reaping the power gains offered by having a very large array.

37. Massive MIMO Limitations-2
• Distance between users and base station:
• closely located with line of-sight (LOS) conditions: it is implied that the spatial
separation of the users is particularly difficult, The LOS condition may cause high
correlation between the channels to different users.
• co-located with NLOS: we still have the users closely spaced but with NLOS to the
base station antenna arrays. The NLOS condition with rich scattering should
improve the situation by providing more “favorable” propagation and thus allowing
better spatial separation of the users.
• well separated with LOS: the increased separation of users should help to improve
the performance, the spatial fingerprints of the four users are significantly different,
which indicates a favorable user decorrelation situation for the large arrays.