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WELCOME. Chen Chen. Simulation of MIMO Capacity Limits. Professor: Patric Ö sterg å rd Supervisor: Kalle Ruttik Communications Labortory. Agenda. Introduction to Multiple-In Multiple-Out(MIMO) MIMO Multiple Access Channel(MAC) Water-filling algorithm(WF) MIMO Broadcast Channel(BC)

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WELCOME

Chen Chen


Simulation of MIMO Capacity Limits

Professor: Patric Östergård

Supervisor: Kalle Ruttik

Communications Labortory


Agenda

  • Introduction to Multiple-In Multiple-Out(MIMO)

  • MIMO Multiple Access Channel(MAC)

  • Water-filling algorithm(WF)

  • MIMO Broadcast Channel(BC)

  • Zero-forcing method(ZF)

  • Simulation results

  • Conclusion


What is MIMO

Input vector:

Output vector:

Noise vector :

Hij is the channel gain from Txito Rxj with


MIMO MAC (uplink)

MAC is a channel which two (or more) senders send information to a common receiver


Water-filling algorithm

The optimal strategy is to ‘pour energy’ (allocate energy on each channel).

In channels with lower effective noise level, more energy will be allocated.


Iterative water filling algorithm

Initialize Qi = 0, i = 1 …K.

repeat;

for j = 1 to K;

end;

until the desired accuracy is reached


MIMO MAC capacity

Single-user water filling

K-user Water-filling

When we apply the water filling Qi=Q.


MIMO MACcapacity region

The capacity region of the MAC is the closure of the set of achievable rate pairs (R1, R2).


MAC sum capacity region (WF)

The sum rate converges to the sum capacity.

(Q1……. Qk) converges to an optimal set of input covariance matrices.


MIMO BC (downlink)

Single transmitter for all users


Zero-forcing method

To find out the optimal transmit vector, such that all multi-user interference is zero, the optimal solution is to force

HjMj = 0, for i≠ j,

so that user j does not interfere with any other users.


BC capacity region for 2 users

The capacity region of a BC depends only on the

Conditional distributions of


BC sum capacity

1. Use water filling on the diagonal elements of to determine the optimal power loading matrix under power constraint P.

2. Use water-filling on the diagonal elements of to calculate the power loading matrix that satisfies the power constraint Pj corresponding to rate Rj. (power control)

3. Let mj be the number of spatial dimensions used to transmit to user j, The number of sub-channels allocated to each user must be a constant when K = Nt/ mj , (known sub-channel)


Examples of simulation results

Ergodic capacity with different correlations (single user)


Ergodic capacity (single user)

4 different set correlations magnitude coefficient


MIMO MAC sum capacity (2 users)


MIMO MAC sum capacity (2 users)

3

2

1


MIMO MAC sum capacity (2 users)


MIMO MAC sum capacity (3 users)

Tx = Rx= 5

SNR=20


MIMO MAC capacity (3 users)


MIMO MAC capacity (WF)(2 users)


MIMO MAC capacity (WF) (2 users)


MIMO MAC capacity (WF) (3 users)

Tx= Rx =4

SNR=20


MIMO MAC capacity (WF) (3 users)


BC sum capacity

Tx=4;

Rx=2;

SNR=20;


BC sum capacity: with Power Control

Tx=4;

Rx=2;

SNR=20;


BC sum capacity: Coordinated Tx-Rx

Tx=4;

Rx=2;

SNR=20;

mj =2


BC sum capacity


Conclusion

MIMO capacity:

1. It depends on H, the larger rank and eigen values of H, the

more MIMO capacity will be.

2. If we understood better the knowledge of Tx and Rx, we can

get higher channel capacity. With power control, the capacity

will also be increased.

3. When water-filling is applied: the capacity will be incresaing

significantly.


Main references

1. T. M. Cover, “Elements if information theory”, 1991.

2. W. Yu, “Iterative water-filling for Gaussian vector multiple access channels”, 2004.

3. Quentin H.Spencer, “Zero-forcing methods for downlink spatial multiplexing”, 2004.


THANK YOU!

Any questions?


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