Conclusions

In this chapter, we derived the approximation of bit metric for MMSE detector and ZF detector, respectively. We analyze the performance of bit metric calculation with equal and weighted coefficients for the MMSE detector and the ZF detector There are about 3~4dB improvement by using weighted coefficients compared to equal coefficients. But in the lower modulation scheme, the Gaussian approximation of the interference would be loose. Hence, the improvement for BPSK and QPSK is only about 1dB in the MMSE detector. By the way, the ZF detector has noise enhancement so the performance of MMSE detector is better than those of ZF detector about 1~4dB, especially at lower SNR. At high SNR, the performance of the ZF detector is close to those of the MMSE detector.

Chapter 4:

Low-Complexity Iterative Detection

Under the condition that the transmitter architecture is of no change and the receiver only uses available received signals, this chapter utilizes an iterative method to improve the performance of MIMO BICM systems. The receiver joints signal detection and soft decoding with turbo principles to suppress the strong co-antenna interference in MIMO systems. The receiver returns soft information of the MAP decoder back to the multistage detector to enhance the ability of detecting signals. The subchannel, i.e. subcarrier, of MIMO-OFDM system has constant channel gain on the multipath Rayleigh fading channel. The MIMO-OFDM receiver detects signals per subcarrier. It is similar to the receiver of MIMO Single-Carrier system on the flat fading channel. Here, our proposed algorithm can be used for general MIMO systems.

It is more convenient to me to depict our proposed algorithm for MIMO BICM systems. The block diagram of MIMO transmitter structure is shown in Fig. 4-1.

Fig. 4-1: A MIMO transmitter

{ } b

n n^Lb₌₀^-1

{ } ^c

^{n -1L}_n^c₌₀

{ } ^c

^np _n^Lc₌^-1₀

^{{ } s}

^{tp tL=s0-1}

{ } ^c

^{n1 L}_n^c₌^-1₀

{ } ^c

^{nNT L}_n^c₌^-1₀

{ } ^s

^１t _t^L₌^s₀^-1

{ } ^s

^tNT _t^L₌^s₀^-1

where

: the number of transmitter antennas

N

T

i

: the number of information bits

b n

The MIMO channel is shown in Fig. 4-2.

Fig. 4-2: The MIMO channel

The received signal is

=0

The block diagram of MIMO iterative receiver architecture is shown in Fig. 4-3.

Fig. 4-3: A MIMO iterative receiver

where

The index

i and

o denote the log likelihood ratio (LLR) associated with the inner detector and outer decoder, respectively. And the superscripts a and e denote a priori (intrinsic) information and extrinsic information, respectively. ^π

( ) ^•

^{is an}

interleaver function.

This chapter is organized as follows: In the section 4.1, to describe the optimal detector based on MAP algorithm and MAP (BCJR) decoder. In the section 4.2, to provide the suboptimal low-complexity linear detector based on MMSE algorithm, and we propose four approximations to reduce the computation complexity of iterative MMSE receiver. Finally, in the section 4.3, the performances of various

Channel

iterative MMSE receiver schemes proposed in this chapter are examined.

Equation Section 4

4.1 Optimal Receiver Based on MAP Algorithm

Assume MIMO channel is an flat quasi-static Rayleigh fading channel matrix H.

The received signal

r

_t^q at the

q receiver antenna at time

^th

t is

Then the received signal vector

r is defined as

_t

NT 1

How to design an optimal receiver for MIMO system is to maximize a posteriori probability of information bit

b with all received signal vectors.

_n

{ { }

^=0-1

}

Define a posteriori log likelihood ratio of

b as

_n

( ) { }

Detect information bit

b ,

_n

( ) ( )

By the total probability theorem, the a posteriori probability of

b can be shown as

_n

{ } { } { }

Due to information bit

b depending on detected signal vector sequences

_n

{ } s

t t^L₌₀^s-1, then

The channel is a flat fading and discrete memoryless channel so the detected signal vector

s at time t only depends on the received signal vector

_t

r at time t. Then,

_t

Finally, the optimal receiver is able to calculate a posteriori LLR of information bit

b .

_n

( ) { }

But the computation complexity of the optimal receiver is too high. It is impossible to realize an optimal receiver. In order to reduce the computation complexity, we divide the receiver into two parts: inner detector and outer decoder, as Fig. 4-4 .

Fig. 4-4: A inner detector and a outer decoder

4.1.1 MAP Detector

The optimal detector for iterative receiver is an a posteriori probability (APP) detector.

{ }

At the first iteration, there is no soft information about transmitted signal vector

s

_t. It means thatp s

[ ]

t are equal. Then, the MAP detector is a maximum-likelihood (ML) detector.

{ } { }

=arg max =arg max

t t

t

p

t t

p

t t

∈Ψ ⎡⎣ ⎤⎦ ∈Ψ ⎡⎣ ⎤⎦

s s

s s r r s

(4.12)

The computation complexity of MAP detector (ML detector) is order of

M

^NT . MAP detector is not feasible for larger number of transmit antennas or higher modulation schemes. The suboptimal detector is a linear detector based on MMSE criterion.

4.1.2 MAP (BCJR) Decoder

In this section, we describe how to use a MAP decoder as an optimal decoder and how to calculate the soft information pass to inner detector. Because the transmitter uses a bit interleaver after a convolutional encoder to overcome Rayleigh fading channel, the receiver needs to calculate the bit metrics before a bit de-interleaver for soft Viterbi decoding or MAP decoding. The de-interleaved codeword is denoted by

c

_n. It is an encoder output tuple by encoding information bit

b

_n. Assume the code rate of a convolutional encoder is

R = 1/2 .

_c

The a posteriori log likelihood ratio of

c

_{n, j} for MAP decoder is denoted as

The a posteriori probability can be written as

{ ( ) } { ^{( )} }

By [19], the authors tell us,

By the equations(4.14) and(4.15), the a posteriori LLR is

( ) { ^{( )} }

( ) ( ) ( )

Finally, the a posteriori LLR can be shown as

( ) ( ) ( )

= extrinsic information

S' S S S' (S',S)

If want to reduce the computation complexity of a decoder, you can use a suboptimal decoder, SOVA decoder.

4.2 Iterative MMSE Detector

The optimal detector of the iterative receiver, MAP detector, causes a large computational complexity. A suboptimal and low complex detector is using adaptive linear filter techniques. A linear minimum mean squared error (MMSE) detector is a simplified approach compared with an MAP detector. An MMSE detector has higher performance than other linear detector.

The received signal vector

r as (4.2), can be decomposed three part: desired

_t signal, co-antenna interferences and noise, see (4.30).

desired signal interference noise

= + =

^{p p}

+

^{p p}

+

First step, to estimate the co-antenna interference

μ based on soft information

_t^p

^λ

^ia

( ) ^c

^{nj ,}

see(4.31). Assume the channel estimation is perfect.

= (i) The modulator maps the coded bits to complex symbol

s .

_t^j

(

0 log2 -1

)

=map , ,

j j j

t t, t, M

s c c

(4.32)

Calculate

^s

^tj(i)=E

{ } ^s

^tj based on a priori information

{ ^λ

^ia

( ) ^c

^t,mj

}

^log_m=0^2M-1 from a MAP decoder.

Then, to remove the co-antenna interference

(

⁽ⁱ⁾

)

= = + +

p p p p p p p

t t t t

s

t t t t t

x r μ

－

h H s

－

s n

(4.33)

Output signal of adaptive linear detector

y is

_t^p

( )

^H

p= p p

t t t

y g x (4.34)

To calculate the coefficients of adaptive linear detector based on MMSE Criterion,

( )

( )

{ { } }

The coefficients of adaptive linear detector

( ) ^g

^{tp His}

( ) g

^{tp H=E}

{ ^s

^tp

( ) ^s

^{tp *}

} ^{( )} h

^{tp H⎡}⎢⎣

h

^tpE

{ ^s

^tp

^{( ) s}

^{tp *}

} ^{( )} h

^{tp H+}

H V H

^{tp tp}

^{( )}

^{tp H+}

^σ

ⁿ²

I

^NR⎤⎥⎦^-1(4.39) Before bit de-interleaving and MAP decoding, we need to calculate bit metrics with output signal of adaptive linear detector

y

_t^p.

We redefine coded and interleaved bit

c

_n^pto be

c

_t,m^p , as Fig. 4-5,

Interleaved coded bit sequences

Every log M coded bits to map a modulated symbol₂

where =

M^# log M₂

Fig. 4-5: To group log M₂ interleaved-coded bits to map a modulated symbol for MIMO systems

where symbol, and at the

p sub-stream. By the simplified computation of bit metrics of

^th coded bit

c

_t,m^p , it is can be presented as

(1)

The extrinsic information is defined as

( )

⁽¹⁾

And the a priori (intrinsic) information is defined as

( ) ⁼¹

By the turbo principle, the inner detector forwards the extrinsic information

^λ

^ie

( ) ^c

^{t,mp to}

the MAP decoder. We need to ensure that the equation (4.44) being PURE extrinsic information. It means that the conditional probability

p y s

^⎡_{⎣ t}^p _t^p=

ψ

^⎤⎦ should not depend

Fig. 4-6 : The block diagram of the proposed iterative MMSE receiver

The computation complexity of this iterative MMSE detector is proportional to

T s iteration 2

N

⋅ ⋅

L N

⋅

log M

, where

N

_iterativeis the number of iterations.

4.2.1 Approximation I of the proposed iterative MMSE detector

The computation complexity of the proposed iterative MMSE detector is very high. It needs to compute

N

_T⋅ ⋅

L N

_{s iteration}⋅

log M

₂ times the coefficients of iterative MMSE detector (pseudo inverse operations). In order to reduce the computation complexity, let ^E

{ } s

t^{p =0} and^E

{ ^s

^tp

( ) ^s

^{tp *}

}

⁼¹ when the receiver detects the

p

^th spatial stream signal at time t . Then, the coefficients of adaptive linear detector

( ) ^g

^{tp H}is simplified to proposed iterative MMSE detector.

r t

Adaptive MMSE

Fig. 4-7: The block diagram of the approximation I of the proposed iterative MMSE receiver

The computation of

( ) ^g

^{apIp His}

^N

^{T⋅ ⋅}

^{L N}

^{s iterative} pseudo inverse operations. It does not

need to calculate

( ) ^g

^{apIp H}per modulated bit.

Using Gaussian approximation to calculate the weight of bit metrics (chapter 3),

( )

apI apI apI apI

=

The weight of bit metricsW_apI^p is similar as signal-to-interference-and-noise ratio.

4.2.2 Approximation II of the proposed iterative MMSE

detector

From the equation (4.47), the coefficients of adaptive linear detector

( ) ^g

^{apIp His}

depends on the variance of interference

v

_t¹, ,

v

_t^p-1,

v

_t^{p 1+} , ,

v

_t^NT . The iterative receiver needs to compute

( ) ^g

^{apIp H}at each time per transmitter antenna per iteration. The computation of the coefficients of adaptive linear detector is

N

_T⋅ ⋅

L N

_{s iterative}. Because it needs to compute pseudo inverse, the computation complexity is still higher. As the variances of signal within each layer to be similar,

v can be approximated by its

_t^j average.

We use approximation to calculate

( ) ^g

^{apIp H}by averaging the variance of interference, as (4.55)

Average the variance of the signal from j^th transmitter antenna over the transmitted symbols.

Then, assume in quai-static Rayleigh fading channel

( ) ( )

^{apII H}

⁼

^H

( )

^H

⁺ ( )

^H

⁺

² R ^-1

Using Gaussian approximation to calculate the weight of bit metrics,

( ) ( )

( ) ( ) ( )

H H

apII apII

apII H H 2 H

apII apII apII apII

ˆ ˆ

The weight of bit metrics W_apII^p is similar as signal-to-interference-and-noise ratio.

r t

Adaptive MMSE

Fig. 4-8: The block diagram of the approximation II of the proposed iterative MMSE receiver

We only need to compute

( ) ^g

^{apIIp HandWapIIp} per transmitter antenna per iteration.

The

( ) ^g

^{apIIp H}is the same value over all the time. This approximation to reduce

( ) ^g

^{apIIp H}computations from

N

_T⋅ ⋅

L N

_{s iterative}to

N

_T⋅

N

_iterative.

4.2.3 Approximation III of the proposed iterative MMSE detector

interference is close to zero.

( )

>> 0 E

{ ( )

^*

}

E

^{{ } ( )}

E

{ }

^* and 0

Finally, the

( ) ^g

^{tp H}can be approximated to

Fig. 4-9: The block diagram of the approximation III of the proposed iterative MMSE receiver

Using Gaussian approximation to calculate the weight of bit metrics,

( ) ( )

The weight of bit metrics W_apIII^p is similar as signal-to-noise ratio.

We only need to compute

( ) ^g

^{apIIIp HandWapIIIp} per transmitter antenna at the first iteration. The

( ) ^g

^{apIIIp H}is the same value over all the time and at all iterations. This approximation to reduce

( ) ^g

^{apIIIp H}computations to

N .

_T

4.2.4 Approximation IV of the proposed iterative MMSE detector

Compare to the first term of inverse of

( ) ^g

^{apIIp H,}

^{h h}

^p

( )

^{p H, the term}σn²INRis very small at high SNR. Therefore, we can ignore the term

R

2 n N

σ I at high SNR or no information about SNR in the receiver.

Then,

( ) ( )

^{gapIVp H}

⁼

^{hp H}

^⎡ _{⎢ ⎣}

^{h hp}

( )

^{p H}

^⎤ _{⎥ ⎦}

^-1

^=pinv ( )

^{hp (4.62)}

where

^pinv ( ) • is a pseudo inverse function

It is similar as to Maximum Ration Combining (MRC) with normalization.

The computation of

( ) ^g

^{apIVp His}

N . We need to compute

^T

( ) ^g

^{apIVp H}per transmitter antenna at the first iteration. The

( ) ^g

^{apIVp H}is the same value over all the time and at any iteration. And this approximation is suitable to no information about SNR or at high SNR condition.

4.3 Simulation Results

Our simulation platform is based on the proposal of TGn Sync. The signal bandwidth (BW) is 20MHz. The transmitter and receiver use 128-points IFFT and FFT, respectively. The antenna spacing in the transmitter and receiver are equal to 0.5 wavelength. The decoder uses MAP algorithm (BCJR) to decide information bits with trace back length of 42. Assume there are perfect synchronization in the receiver, i.e.

without frequency offset, clock offset, and phase rotation. The channel is well-kwon

in the receiver. And the channel model is IEEE802.11n Channel Model B. There are at least 200 packet errors down to 1% packet error rate (PER) or a total of 3,000 packets in our simulation. The iterative detector design in this section is based on the MMSE criterion. Compare the performance of iterative MMSE detector with proposed algorithm and four approximations. The SNR is defined in chapter 2.

Case1: Observe the performance of proposed iterative MMSE detector

( ) g

^{ˆt,mp H=E}

{ ^s

^tp

( ) ^s

^{tp *}

} ^{( )} h

^{tp H⎡}⎢⎣

h

^tpE

{ ^s

^tp

^{( ) s}

^{tp *}

} ^{( )} h

^{tp H+}

H V H

^{tp tp}

^{( )}

^{tp H+}

^σ

ⁿ²

I

^NR⎤⎥⎦^-1 From the simulation result Fig. 4-11, we find that there is 1dB enhancement at first iteration and about 2dB enhancement at more iteration.

23 26 29 32 35

10^-1 10⁰

proposed iterative MMSE detector (64-QAM,R

c=3/4, 2x2)

SNR_dB

PER

iter 1 iter 2 iter 3 iter 4 iter 5 iter 6

Fig. 4-10: Performance of the proposed iterative MMSE detector (64QAM, Rc=3/4, 2x2)

Case2: Observe the performance of proposed iterative MMSE detector with

approximation I, shown in Fig. 4-11, Fig. 4-12, and Fig. 4-13.

( ) ( )

^{apI H}

⁼

^H

( )

^H

⁺ ( )

^H

⁺

² R ^-1

p p p p p p p

t

^⎡ _⎢

t t t t t σn N

^⎤ _⎥

⎣ ⎦

g h h h H V H I

From the simulation results Fig. 4-10 and Fig. 4-12, the performance of the proposed iterative MMSE detector with approximation I is very close to the performance of the proposed iterative MMSE detector.

3 5 7 9

10^-1 10⁰

proposed iterative MMSE detector with approx. I (BPSK,R

c=1/2, 2x2)

SNR_dB

PER

iter 1 iter 2 iter 3 iter 4 iter 5 iter 6

Fig. 4-11: Performance of proposed iterative MMSE detector with approximation I (BPSK, Rc=1/2, 2x2)

23 26 29 32 35 10^-1

10⁰

proposed iterative MMSE detector with approx. I (64-QAM,R

c=3/4, 2x2)

SNR_dB

PER

iter 1 iter 2 iter 3 iter 4 iter 5 iter 6

Fig. 4-12: Performance of proposed iterative MMSE detector with approximation I (64-QAM, Rc=3/4, 2x2)

28 31 34 37 40 10^-1

10⁰

proposed iterative MMSE detector with approx. I (64-QAM,R

c=3/4, 3x3)

SNR_dB

PER

iter 1 iter 2 iter 3 iter 4 iter 5 iter 6

Fig. 4-13: Performance of proposed iterative MMSE detector with approximation I (64-QAM, Rc=3/4, 3x3)

Case3: Observe the performance of the proposed iterative MMSE detector with

approximation II compared to the proposed iterative MMSE detector.

( ) ( )

^{apII H}

⁼

^H

( )

^H

⁺ ( )

^H

⁺

² R ^-1

p p p p p p p

n N

⎡

σ

⎤

⎢ ⎥

⎣ ⎦

g h h h H V H I

From the simulation result Fig. 4-14, we can find that the performance of the proposed iterative MMSE detector with approximation II is very close to the performance of the proposed iterative MMSE detector.

23 26 29 32 10^-1

10⁰

proposed iterative MMSE detector with approx. II (64-QAM,R

c=3/4, 2x2)

SNR_dB

PER

iter 1 iter 2 iter 3 iter 4 iter 5 iter 6

Fig. 4-14: Compare the performance of the proposed iterative MMSE detector with approximation II to the proposed iterative MMSE detector (64-QAM, Rc=3/4, 2x2)

Proposed iterative detector Proposed with Approx.II

Case4: Observe the performance of the proposed iterative MMSE detector with

approximation III compared to the proposed iterative MMSE detector.

( ) ( )

^{apIII H}

⁼

^H

( )

^H

⁺

² R ^-1

p p p p

n N

⎡

σ

⎤

⎢ ⎥

⎣ ⎦

g h h h I

From simulation result Fig. 4-15, we can find that the performance of the proposed iterative MMSE detector with approximation III by ignoring interference term is very close to the performance of the proposed iterative MMSE detector.

23 26 29 32 10^-1

10⁰

proposed iterative MMSE detector with approx. III (64-QAM,R

c=3/4, 2x2)

SNR_dB

PER

iter 1 iter 2 iter 3 iter 4 iter 5 iter 6

Fig. 4-15: Compare the performance of the proposed iterative MMSE detector with approximation III to the proposed iterative MMSE detector (64-QAM, Rc=3/4, 2x2)

Proposed iterative detector Proposed with Approx.III

Case5: Observe the performance of the proposed iterative MMSE detector with

approximation IV compared to the proposed iterative MMSE detector.

( ) ( )

^{gapIVp H}

⁼

^{hp H}

^⎡ _{⎢ ⎣}

^{h hp}

( )

^{p H}

^⎤ _{⎥ ⎦}

^-1

^=pinv ( )

^hp

From simulation result Fig. 4-16, we can find that the performance of the proposed iterative MMSE detector with approximation IV by ignoring interference and noise terms is very close to the performance of the proposed iterative MMSE detector.

23 26 29 32 10^-1

10⁰

proposed iterative MMSE detector with approx. IV (64-QAM,R_c=3/4, 2x2)

SNR_dB

PER

iter 1 iter 2 iter 3 iter 4 iter 5 iter 6

Fig. 4-16: Compare the performance of the proposed iterative MMSE detector with approximation IV to the proposed iterative MMSE detector (64-QAM, Rc=3/4, 2x2)

Proposed iterative detector Proposed with Approx.IV

4.4 Conclusions

There is 1dB enhancement at first iteration and about 2dB enhancement at more iteration in iterative MMSE detector. The performances of three methods of approximation are similar to the performance of iterative MMSE detector without approximation. That is because that in the inverse of the equation (4.47), the interference and noise term are very small compared to the first term

^{h h}

^tp

( )

^{tp H.}

However, if we use those methods of approximation, we can reduce the times of inverse computation from

N

_T⋅ ⋅

L N

_{s iteration}⋅

log M

₂ to

N without degrading the

_T performance.

Chapter 5:

Conclusions and Future Works

5.1 Conclusions

In this thesis, at first, we introduce to the system architectures of 802.11n proposal of TGn Sync and the channel models. Then, we derive the weight of bit metrics for MIMO BICM systems in the MMSE detector and the ZF detector. We analyze the performance of bit metric calculation with weighted gain and equal gain.

If we can present exactly the pdf of the interference and noise, there is about 3~4dB enhancement of performance. At lower modulation scheme, there is only about 1dB enhancement with pdf of the interference and noise by Gaussian approximation. By the way, the ZF detector has noise enhancement so the performance of MMSE detector is better than those of ZF detector about 1~4dB, especially at lower SNR.

At high SNR, the MMSE detector is similar as the ZF detector and makes more effort on interference suppression.

Besides, we design low complexity iterative MMSE detector with turbo principle and propose some methods of approximation to reduce computation complexity. From the simulation results, it proves that using weighted bit metrics can improve the performance. There is 1dB enhancement at first iteration and about 2dB enhancement at more iteration in iterative MMSE detector. Employing approximation of iterative MMSE detector can reduce the computation complexity without performance deterioration. That is because that in the inverse of the equation(4.47), the interference and noise term are very small compared to the first

term

^{h h}

^tp

( )

^{tp H}. However, if we use those methods of approximation, we can reduce the times of inverse computation from

N

_T⋅ ⋅

L N

_{s iteration}⋅

log M

₂ to

N without

_T degrading the performance.

5.2 Future Works

We combine detection and decoding to design a lower-complexity and higher-performance iterative signal detector based on MMSE criterion and turbo principle for MIMO BICM systems. We may consider advanced codes, such as turbo code and LDPC, to improve performance. We may design a iterative signal detector based on LDPC principle. We can joint channel estimation and decoding or detection to improve the ability of estimating channels. We can use geometrical approaches, such as sphere decoding and lattice decoding, to approximate ML detection.

Appendix A:

Multistage Detection for A Linear MMSE Receiver

To calculate the coefficients of adaptive linear detector based on MMSE Criterion,

G

^{MMSEk =arg min E}G_k

{ y

^.k－

s

^{.k 2}

}

^{=arg min E}G_k

{ G r

^{k .k}－

s

^{.k 2}

}

(A.1)

Find the minimum value of J, Assume the energy of signal is equal to 1.

{

^.

( )

^{. *}

}

²

E

s

^p_k

s

^p_k =

σ

_s=1 Then, the coefficient of linear MMSE detector is

^MMSEk

⁼ ( )

k ^H

^⎡ _⎢

k

( )

k ^H

⁺

σn² NR

^⎤ _⎥

^-1

⎣ ⎦

G H H H I (A.8)

Appendix B:

Multistage Detection for Iterative MMSE Receiver

To calculate the coefficients of adaptive linear iterative detector based on MMSE Criterion,

Find the minimum value of J,

^{p =}

( )

^{tp H E}

{ (

^{t tp tp(i)}

)(

^{t tp tp(i)}

)

^H

}

^E

{

^tp

⁽

^{t tp tp(i)}

⁾

^H

}

⁼⁰

( )( )

Appendix C:

Modulation-Coding Scheme (MCS)

The TGn Sync proposal augments the 802.11a MCS set through the use of multiple spatial streams and bandwidth extension. The MCS filed defines the modulation and coding scheme, as indicated in Table C-1. The proposal recommends a mandatory data of 243Mbps using two spatial streams in regulatory domains that permit 40MHz operation. In the future, their proposal supports scalability to 4 spatial streams, offering data rates in excess of 600Mbps.

GI = 800ns GI = 400ns

17 3 QPSK 1/2 36 81 40 90 18 3 QPSK 3/4 54 121.5 60 135 19 3 16-QAM 1/2 72 162 80 180 20 3 16-QAM 3/4 108 243 120 270 21 3 64-QAM 2/3 144 324 160 360 22 3 64-QAM 3/4 162 364.5 180 405 23 3 64-QAM 7/8 189 425.25 210 472.5 24 4 BPSK 1/2 24 54 26.67 60 25 4 QPSK 1/2 48 108 53.33 120 26 4 QPSK 3/4 72 162 80 180 27 4 16-QAM 1/2 96 216 106.67 240 28 4 16-QAM 3/4 144 324 160 360 29 4 64-QAM 2/3 192 432 213.33 480 30 4 64-QAM 3/4 216 486 240 540 31 4 64-QAM 7/8 252 567 280 630

32 1 BPSK 1/2 6 6.67

Table C-1: Modulation-coding scheme

Appendix D:

IEEE 802.11n Channel Model B

Tap index 1 2 3 4 5 6 7 8 9

Excess delay

[ns] 0 10 20 30 40 50 60 70 80

Cluster 1 Power [dB] ^{0 -5.4 -10.8 -16.2 -21.7}

AoA AoA [°] ^{4.3 4.3 4.3 4.3 4.3}

AS

(receiver) AS [°] ^{14.4 14.4 14.4 14.4 14.4}

AoD AoD [°] ^{225.1 225.1 225.1 225.1 225.1}

AS

(transmitter) AS [°] ^{14.4 14.4 14.4 14.4 14.4} Cluster 2 Power [dB] ^{-3.2 -6.3 -9.4 -12.5 -15.6 -18.7 -21.8}

AoA AoA [°] ^{118.4 118.4 118.4 118.4 118.4 118.4 118.4}

AS AS [°] ^{25.2 25.2 25.2 25.2 25.2 25.2 25.2}

AoD AoD [°] ^{106.5 106.5 106.5 106.5 106.5 106.5 106.5}

AS AS [°] ^{25.4 25.4 25.4 25.4 25.4 25.4 25.4}

References

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