CHAPTER 2 SYSTEM PLATFORM
2.3 C HANNEL M ODELS
2.3.1 Jakes’ Model
Time-variant channel effect can be modeled as a FIR filter with time-variant tap gains. For Jakes’ model, the variance of each tap gain obeys Rayleigh distribution.
Figure 2-9 shows an n-tap FIR filter with Rayleigh-distributed tap gains, and the corresponded velocity is 120km/hr, shown for50ms .
Figure 2-9 FIR filter with Rayleigh-distributed tap gains at 120km/hr
D D
w0(t) w1(t) wn-1(t)
...
...
s t
y t
50 ms
-
10dB 0dB -10dB -30dB
PhaseAmplitude 0
sinusoids [8], that is
description of the sum of sinusoids is referred to [8].Chapter 3
The Proposed Algorithm
In this chapter, an adaptive channel estimation technique is proposed. 22 SFBC for MIMO-OFDM systems is considered. The proposed algorithm consists of three parts, the channel estimation of initial channel, the decision-feedback adaptive channel estimation for estimating the noisy time-variant channel, and the frequency domain lose-pass filter for decreasing the SNR requirement.
Section 3.1 gives an overview of proposed decision-feedback adaptive channel estimation and the use PDCCH for channel estimation and frequency domain adaptive channel estimation is proposed in section 3.2 and 3.3, the22SFBC is proposed in 3.4, and the lose-pass filter is discussed in section 3.5.
3.1 Adaptive Channel Estimation Overview
The synchronized time-domain signals are transformed into frequency-domain OFDM symbols by FFT. Figure 3-1 describes the data path after FFT for 22 LTE MIMO-OFDM systems.
FFT
Figure 3-1 22 adaptive channel estimation block diagram
After FFT, OFDM symbols classified as Physical Downlink Control Channel (PDCCH) are used for channel estimation. The 22 SFBC decoder operates for every two payload symbols, as shown as r1 and r2 above. Then the decoded symbols, x1'
and x'2, are decided by decision unit. The functionality of the decision unit is illustrated in Figure 3-1, it uses QAM constellation for decisioning.
Decision Unit
The decided symbols are averaged with which of the second receiver. x1 and
x2
are the differences between decided symbols and decoded symbols, and they are used by adaptive channel estimation to obtain the variances of the time-variant channels. After adding up with h1andh2, the time-variant channels, h1' andh'2, are ready to update the channel information stored in channel estimation block. The usage of Low pass filter is to mitigate the influence of Additive White Gaussian Noise (AWGN), since the adaptive channel estimation algorithm is sensitive to noise.
The flow chart for adaptive channel estimation is show below.
PDCCH
Figure 3-3 Adaptive channel estimation flow chart
After use PDCCH to do initial channel estimation, the OFDM symbols can only be one of the following categories: reference signal (RS) for channel estimation, or adaptive channel estimation. If the N1th OFDM symbol has RS, use these RS to do interpolation, otherwise, use Nth OFDM symbol channel to do adaptive channel estimation and create N1th OFDM symbol channel.
3.2 Use PDCCH for Channel Estimation
Before adaptive channel estimation, it is necessary to estimation an OFDM symbol channel as initial channel. In this thesis, the PDCCH is used. Physical Downlink Control Channel (PDCCH) carries scheduling assignments and other control information. A physical control channel is transmitted on an aggregation of one of or several consecutive control channel elements (CCEs), where a control channel element corresponds to 9 resource element groups. The number of resource-element groups not assigned to PCFICH or PHICH isNREG. The CCEs available in the system are numbered from 0 andNCCE1, whereNCCE NREG/9. The PDCCH supports multiple formats as listed in Table 3-1.
Table 3-1: Supported PDCCH formats
PDCCH format
Number of CCEs
Number of resource-element
groups
Number of PDCCH bits
0 1 9 72
1 2 18 144
2 4 36 288
3 8 72 576
As above describe, PDCCH are known signals which do not carry any data. In transmitted resource block, shows in Figure3-4. The PDCCH are second OFDM symbol and third OFDM symbol. We use the third OFDM symbol and zero forcing to
R0
R0
R0
R0
R0
R0
R0
R0
I=0 I=6 I=0 I=6
PDCCH PDSCH
Third PDCCH OFDM symbol
Figure 3-4 PDCCH in resource block
3.3 Frequency Domain Adaptive Channel Estimation
Each noisy time-variant channel estimated by adaptive channel estimation is then filtered by Low pass filter. For 2x2 LTE MIMO-OFDM systems, In an OFDM symbol, have 600 subcarriers. After SFBC decoder, there are 300 subcarriers. We get the 50 subcarriers, show in Figure 3-5, as virtual pilot to be adaptive channel estimation, pass through the SFBC coder and interpolation to 600 subcarriers, and then filtered by Low pass filter.
OFDM symbol (600 subcarriers) Virtual pilot (50 subcarriers)
Figure 3-5 Virtual Pilots
3.4 Adaptive Channel Estimation for 2
2 SFBC
In this section, the adaptive channel estimation algorithm can be seen as a transformation of the variances of SFBC-decoded symbols into the variances of time-variant channels. When we finish the one-shot channel estimation, if without adaptive channel estimation, we should keep a large memory to store the channel information until the step of channel equalizer. In architecture level, it will result huge number of gate count. The adaptive channel estimation algorithm applies to each frequency component individually for MIMO-OFDM systems.
For each receiver, the received signal within one SFBC block contains two
rfindicates the received signal at frequency index f within SFBC block and the channel from transmitter j is calledhj, which are assumed to be known. x1andx2
stands for the transmitted 2 2 SFBC codeword which are wanted.
By defining 1* can be expressed in matrix form, that is
1 1 2 1
Due to time-variant channel effect, the channels of the consecutive SFBC block are not consistent with the previous ones. For the consecutive SFBC block, the channels are assumed to be h1' andh'2, which are defined as
Therefore, the received consecutive SFBC block in matrix form becomes
' '
Applying the decoding process again, that is, multiplying the inverted channel matrix H-1 on both left side of equation (3.5), hence
As shown above, in consequence of the time-variant channel effect, the decoded symbol X' contains a residual part with compared toX. The residual part X indicates the variance of the decoded symbol due to time-variant channel effect. X can be obtained if the time-variant channel effect is not strong enough to make for a decision error on the decoded symbol X'. That is, if the decision feedback result ofX', which is defined asXDF, is equal to the ideal decoded symbolX, then the relationship between X and H can be identified. By observing equation(3.6)
DF
H can be solved since it is the only unknown matrix in equation (3.7). Further more, by defining 1 that equation (3.8) uses the same process as which used in equation (3.3) for SFBC decoding.
3.5 First Order Low Pass Filter
Low pass filter is used on frequency domain to decrease the SNR requirements that adaptive channel estimation needed since it is sensitive to noise. The functionality of frequency domain Low pass filtering is illustrated below
a and b are vectors of low pass filter. It is show as Figure 3-6.
Z-1
Z-1 Z-1
( ) x m
( )
b n b(3) b(2) b(1)
( ) y m ( )
a n a(3) a(2)
1( )
Zn m Z2( )m Z m1( )
Figure 3-6 Low Pass Filter
Chapter 4
Simulation Results
To evaluate the proposed algorithm, a typical MIMO-OFDM system based on LTE is used as the reference design platform. Performance of the proposed 2 2 adaptive channel estimation under time-variant frequency-selective fast-fading channels is simulated. The parameters used in the simulation platform are: OFDM symbol length is 14 and 600 subcarriers in an OFDM symbol. The major parameters are summarized in Table 4-1.
Table 4-1 Simulation parameters
Parameter Value
Number of taps 6
Doppler speed 120km/s
Modulation 64-QAM
Equalization Zero-Forcing
FFT size 1024 Bytes
Signal Bandwidth 20 MHz
Subframe size 1ms
The results show that the best step-size in simulation is 0.1. Figure 4-2 expresses the Bit Error Rate (BER) performance with different step-size of the proposed adaptive channel estimation for 22 LTE MIMO-OFDM systems under time-variant channels for 120km/hr Jakes’ model.
Figure 4-1 MSE of OFDM symbol with step-size 0, 0.1, 0.15, 0.2
Figure 4-2 BER of OFDM symbol with step-size 0.1, 0.15, 0.2
Figure 4-3 addresses the Bit Error Rate (BER) performance of the proposed adaptive channel estimation 22 LTE MIMO-OFDM systems, which use the low pass filter to filter the noisy.
Figure 4-3 BER of OFDM symbol with Low pass filter
Figure 4-4 indicates the Bit Error Rate (BER) performance of the proposed adaptive channel estimation 22 LTE MIMO-OFDM systems, which use the different initial channel estimation. The first one is use first OFDM symbol’s reference signal, and be interpolation as initial channel, the other one use the PDCCH to estimate the initial channel. The results show that use PDCCH to estimate the initial channel has the better performance.
Figure 4-4 BER of OFDM symbol with two channel estimation methods.
The required SNR for BER of the proposed algorithm among various configurations are summarized in Table 4-3.
Table 4-3 Required SNR for BER (64-QAM modulation)
Simulation configurations
Jakes’ model 120km/hr with
step-size 0.1
Jakes’ model 120km/hr with
step-size 0.15
Jakes’ model 120km/hr with
step-size 0.2
22 MI
MO-OFDM systems One-shot channel estimation
One-tap compensation 27 dB 27 dB 27 dB
Adaptive channel estimation
Direct compensation 38 dB 40 dB 45 dB
Adaptive channel estimation
low pass filter compensation 29 dB 30 dB 33 dB
As introduction described, the contribution of proposed adaptive channel estimation is computing complexity reduced which compared with one-shot channel estimation. Table 4-2 exhibits the comparison between the one-shot channel and proposed adaptive channel estimation. The result shows that the one-shot channel estimation needs 600×14 storages to store the channel information, but proposed adaptive channel estimation only needs 600 storages. In channel estimation, the multiplier number of one-shot channel estimation needs 650000, but the proposed algorithm which use PDCCH only needs 900, and in adaptive channel estimation, one-shot channel estimation needs zero multiplier and proposed algorithm needs 75000 multipliers. For estimate channel, the total multiplier number of one-shot channel estimation is 650000, and proposed algorithm is 75900. The computing complexity of proposed algorithm is reduced.
Table 4-2 complexity comparison between two channel estimation Channel Estimation One-shot Adaptive
(use first OFDM symbol)
Adaptive (use PDCCH) Storages
600 × 14 600 600
Multiplier
(channel Estimation) 650000 1800 900
Multiplier (Adaptive channel Estimation)
0 75000 75000
Chapter 5
Conclusion and Future Work
5.1 Conclusion
In this thesis, adaptive channel estimation is proposed to oppose the Doppler effect under high velocity environments, and make full use of the time- and frequency-domain correlation of the frequency response of time-varying multipath fading channels without requirement of accurate channel statistics.
The presented 22 adaptive channel estimation scheme achieves. It is a prototype algorithm for SFBC-coded MIMO-OFDM systems. The distance between QAM constellation points dominates the maximum tolerance to time-variant channels;
therefore lower modulation scheme provides higher velocity tolerance. Further more, the proposed adaptive channel estimation algorithm shares the same process with SFBC decoding. It is a significant feature in hardware implementation phase.
5.2 Future Work
High QAM constellation like 256-QAM for higher data rate is going to be deployed. As a result, the distance between constellation points shrinks; therefore a robust decision scheme is demanded. Since QAM decisioning simply outputs the closest constellation points to the input as its decision, FEC-feedback decisioning can be considered.
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