channel estimation and combining orthogonal pilot design ... · domain. this kind of channel...

Channel Estimation and Combining Orthogonal

Pilot Design in MIMO-OFDM System

Wang Liping Engineering Technology Research Center of Optoelectronic Technology Appliance, AnHui Province, China

Email: [email protected]

Abstract—The current technology of high speed wireless

communication exist the following weaknesses, limited

bandwidth, bad transmitted channel, complex mobile

environment and the quality of service under limited service

time. The dispersing pilot pattern and the method of

channel estimation using in MIMO-OFDM system couldn’t

eliminate the same frequency interference caused by the

OFDM frequency among different transmit-receive antenna

couples. Aiming at these problems, we proposed a method of

MIMO-OFDM channel estimation combining orthogonal

pilot design. The data format of pilot is designed. It can

retain the orthogonality of different sub-carriers of OFDM

which are produced by different transmit-receive antenna

couples or transmit-receive antenna couple. In wireless

mobile environment, it can better estimate the channel

parameters without any prior channel information. This

method proposed in the paper has finished the design of the

completely orthogonal pilot data symbol among the

different subcarriers position of different transmitting-

receiving antenna pair. And the pilot data symbols are

distributed in the entire time-frequency grid of the channel

reasonably, therefore this paper uses a FFT unit whose

points are equal to the number of the OFDM subcarriers to

realize the least-square channel estimation algorithm. In the

process of channel estimation, the channel parameters

estimation precision has increased because of the noise

getting better inhibition, which reduces the least squares

estimate noise sensitivity to the requirements.

Index Terms—Orthogonal Pilot; Method of Channel

Estimation; Wireless Communication; OFDM

I. INTRODUCTION

With the demand of the rapid growth of the wireless

communication requirement, wireless technology is

experiencing unprecedented opportunities for

development. At the same time, any progress in the field

of wireless must be tightly around a subject, which is the

assignment of the progress must be able to better solve

the service quality problems the high-speed wireless

communication itself facing because of the tension of the

bandwidth, bad communication channel, the complex

mobile environment and limited service. The high rate of

the user equipment will introduce the frequency-selected

multipath fading in broadband channel, at the same time, the fast movement of the mobile terminal or the constant

change of the scattering body around the spread

environment makes the channel have the time channel

selective decline effect [1]. The joint selectivity on both

time domain and frequency domain causes the multipath

fading and the Doppler Effect, which will influence the

overall system performance seriously. Therefore, it is very important to model and the tracking estimate the

wireless channel variation and study out a new space-

time processing technique which can fight against the

frequency selective fading and the frequency selective

fading for the next generation of mobile communication

system.

Orthogonal frequency division multiplexing (OFDM)

[2] is a kind of multi-carrier technology having high

frequency spectrum utilization, which uses a certain

number of orthogonal narrow-band subcarriers (the

bandwidth of each subcarrier is very narrow, which can form the flat fading)to transmit the low rate data in

parallel and further realize the high speed data

transmission for the whole system. The Multiple Input

Multiple Output (MIMO) wireless communication system

[3] in the spread environment with abundant scattering

conditions can provide high data transmission rate with

no extending the transmission frequency band,

meanwhile MIMO can combine the space-time coding

technology or signal processing techniques to realization

diversity effectively, so as to improve the reliability of

the whole wireless mobile communication system, and

provides a good compromise platform for diversity and multiplexing. In order to make full use of the advantages

of MIMO-OFDM, the accurate channel estimation

technology is indispensable.

At present the channel estimation techniques [4] on the

MIMO-OFDM wireless communication system can be

roughly classified into the following categories: the least

squares algorithm (LS), the linear least mean-square error

method (LMMSE), maximum likelihood method (ML)

[5], [6] and all kinds of blind (completely blind or half

blind) estimation methods [7]. Among them the

computational complexity of the least square method is low, and the estimation precision is vulnerable to the

noise influence in the transmitting process, using the

various channel statistics to modify the least squares

estimating values can obtain a degree of performance

improvement, but in the actual mobile environment, it is

impossible to obtain a more realistic channel statistics

information [8, 9]. The Linear least mean-square error

method can obtain a good channel parameters estimation

precision when the second order statistics of the channel

is appropriately estimated approximately, but the

JOURNAL OF NETWORKS, VOL. 9, NO. 2, FEBRUARY 2014 451

© 2014 ACADEMY PUBLISHERdoi:10.4304/jnw.9.2.451-457

realization of the whole estimation algorithm needs to

solve the inversion operation of an large matrix whose

orders are proportional to the number of the subcarriers

and the number of the antennas, therefore the complexity

of the system is very high. The maximum likelihood

method uses Viterbi algorithm to realize the estimation of

the channel response parameters, the needed calculation

amount will increase rapidly with the increase of the

channel matrix [10]. The blind (or half blind) channel

estimation method can improve the frequency utilization

of the whole system at the maximum extent, which does not need to (or need only less number of) the pilot

expenses, however the realization of the whole estimation

algorithm needs a large number of actual transmitted data,

and needs to use the iterative calculation method and the

convergence rate of the algorithm limits the algorithm

applied to the real time-varying wireless mobile

environment [11].

According to the retrieval of the existing techniques

analysis literature, we found that the patent called a

dispersive pilot pattern and channel estimation method

used for MIMO-OFDM system [12], which has provide the channel estimation method and equipment to decrease

the pilot symbol number and improve the system

performance in the MIMO-OFDM system. For each

transmitting antenna of the OFDM transmitter, the pilot

symbols are coded, so that the antenna is unique. The

coded pilot symbol was inserted into the OFDM frame, in

order to form the diamond grid, and the diamond grid

used for different antenna will use the same frequency,

but in the time domain will deviate a single symbol from

each other on [13]. At the OFDM receiver, the channel

responses are estimated through the use of two-dimensional interpolation according to the diamond

center symbol of each of the diamond grid and the

estimated channel response in frequency domain is

smoothed. The channel response of the rest symbols are

estimated through the interpolation in the frequency

domain. This kind of channel estimation method at each

pilot symbol uses the least-square method to complete the

initial channel parameters estimation, can't overcome or

improve the effect of noise on the estimation precision,

and the pilot data are not designed specially [14].

Although the insertion point position for different

subcarriers deviate one symbol for different transmitting-receiving antenna, can't effectively eliminate the co-

channel interference caused by sharing the same

frequency between different transmitting-receiving

antennas.

For the limitations of existing technology, this paper

puts forward a channel estimation method combining the

design of the joint orthogonal pilot for the MIMO-OFDM

system, which has designed the pilot data format

maintaining the orthogonal property between different

OFDM subcarriers of different transmitting-receiving

antenna pair and same transmitting-receiving antenna pair, at the same time, the pilot symbols are inserted into the

data frame at the transmitter according to the polygon

form in the change of the OFDM subcarriers in

transmitting-receiving antenna pair. At the receiver the

biggest FFT extraction multiphase filter groups with low

complexity are only needed to realize the window

function of the noise at the subcarriers, in order to reduce

the noise variance of the channel parameters estimation at

the receiver, so as to improve the accuracy of channel

estimation [15-16].For channel parameters each the

output corresponding different transmitting-receiving

antenna pair and different subcarriers, the linear

interpolation on the frequency domain and time domain

can be completed simultaneously, does not need any prior

channel statistics information to better realize time-varying channel parameter estimation under the wireless

mobile environment [17].

II. PROPOSED SCHEME

This section will introduce the technical proposal

combining the design of orthogonal pilot with the channel

estimation method of the MIMO-OFDM system. And the

technical proposal will be implemented according to the

following steps:

At the transmitter, according to the actual channel

environment, the designed completely orthogonal pilot

data symbols with parallelogram are inserted into the data frame of each transmitting antenna according to the

transmitting-receiving antenna pair and the position of

subcarriers.

At each receiving antenna, the needing estimated pilot

data symbols corresponding to the transmitting-receiving

antenna pair are extracted, and then the pilot data

symbols are sent into the channel estimator, after the

filter group add window on the noise and the needing

estimated pilot data symbols corresponding to each

subcarrier center are output, and then we directly use the

least square method to calculate the sample value of the channel frequency domain coefficient of the

corresponding position.

The entire estimated channel coefficient values are

allocated to the transmitting-receiving antenna pair

needing estimated according to the insertion location as

mentioned before, and the estimated values of the

transmitting-receiving antenna pair are the coefficient

sampling values on the frequency domain in the real

antenna communication channel.

And then the coefficient sampling values of the

transmitting-receiving antenna pair on the frequency

domain will be interpolated linearly, the entire channel coefficients in data location can be obtained.

At last the estimated channel coefficient values are

sent to the signal processing unit of each receiving

antenna, the whole system balancing, space-time

decoding or the closed loop transmission function will be

completed.

The performance of MIMO communication system is

mainly restricted by mobile wireless channel, signal

propagation environment is very complex, including the

radiation power, reflection and diffraction or scattering

medium refraction, different propagation mechanisms have different effect on the path loss and fading model

[18]. Usually the wireless communication channel is

divided into two main categories: the first category is due

452 JOURNAL OF NETWORKS, VOL. 9, NO. 2, FEBRUARY 2014

© 2014 ACADEMY PUBLISHER

to fading caused by spatial distance, terrain and

physiognomy, called a path loss model, mainly depends

on the communication range, generally. The second class

is mainly due to wave scattering, fluctuation of the

received signal strength as a result of the rapid mobile

receiver, called the micro fading model, the time-varying

[19].According to the power loss of the inverse square

law, assuming power for P, receiving power, perfect

space-time channel propagation path, the received signal

power is expressed as:

(1)

Among them, G1 and G2 is the wavelength of the

transmitting antenna and receiving antenna power gain

respectively, d is the spread distance, in the residential

environment, the received power can be approximated as:

(2)

The h1 and hr represented the effective height of the

transmitting antenna and receiving antenna, and d2>>h1,

in the actual environment, path loss exponent in the range

of 2.5~6, depending on the terrain and vegetation status.

d can be understood as located in the transmitting antenna

far field reference distance, determined by the actual test and a small size [20]. When the transmitted signal

bandwidth is less than the channel bandwidth, and the

gain in bandwidth range and linear phase is constant,

channel into flat fading channel. There is a common

Rayleigh fading channel model and Rice Fading Channel

Model. The Rayleigh fading channel is divided into the

Rayleigh channel and Rayleigh channel, the channel

matrix is a unit variance and circularly symmetric

complex Gauss random matrix; and each subspace

correlated Rayleigh fading channel has a certain

correlation. Les channel is different to Les factor to define.

In the MIMO system, the transmitter pre-coding based

on processing, usually need to control the system

throughput and transmission power, which requires the

base station transmitter channel state information to the

user a certain master. In general there are two ways: one

is in the uplink, the user feedback channel state

information corresponding to the base station, this

method has important significance in this research; the

other is the reciprocity of duplex transmission (duality)

principle, the duality between the uplink multiple antenna

transmission channel and downlink multiple access channel estimation, backward in the research methods,

the following are introduced. Duplex channel reciprocity

in MIMO diagram was shown in Figure 1.

The base

station

The user

terminal

Uplink

Downlink

Figure 1. Duplex channel reciprocity in MIMO diagram

In the actual MIMO system, the number of various

interference and the presence of the limitation of the

antenna, the channel estimation method of application is

limited. However, based on the principle of reciprocity,

multiple antenna multiple access channel and MIMO

channel has duality in the capacity region and transmit

correlation matrix, and MIMO multiuser water-filling

power allocation behind research and also has the duality

of power allocation of transmission power of a base

station end restriction cooperation based on the M1MO,

has a very high research value. Based on the general

treatment of pre-coded MIMO system is shown in Figure

2.

Multi-

user

schedulin

g

Pre-

coding

Channel state

information

Uplink

feedback

User

User

Vector

quantization

Training

sequence

Channel

estimate

Multiple

antenna

processing

Decode

Figure 2. .Based on the general treatment of pre-coded MIMO system.

Multiple Input Multiple Output (MIMO)

communication is a key technology to improve the

throughput of wireless communication system in the

future. For transmit and multiple receive antennas end

point to point link, the throughput performance of MIMO

system with the minimum number of transmit and receive

antennas (degrees of freedom is relative to space) and

linear growth. However, because of space constraints, the mobile user terminal (Mobile, Station, MS) is usually

only one or two receive antenna, which limits the

application level of MIMO technology. In collaboration

with the MIMO system, a plurality of base stations (Base

Transceiver Station, BTS or BS) for communication with

a plurality of mobile user terminal, through the

cooperative pre-coding (down) or joint decoding

(upstream), greatly improve the overall performance of

the system. However, for a number of users of large scale

MIMO system, the overall capacity of uplink and

downlink by base station transmitting antenna limits, space degree of freedom is limited.

In the actual cellular multi-cell system deployment of

MIMO technology has the very big difference with the

theoretical research, because the performance of the

whole system is restricted by all kinds of interference

factors. Although the interference problem is inherent in

multi-cell system, but in a multiple antenna condition is

more significant, because the adjacent base station to the

user with multiple antenna interference condition is more

and more obvious, so the user terminal is difficult to

detect and suppress the, end users can only through to demodulate the signal linear technology is simple. In fact,

this is a virtual MIMO system, the base station multiple

cellular transmit antenna as a large array, the formation of

a generalized MIMO system, which can be pre-coding in

the downlink channel information feedback by the user,

through the pretreatment of the data at the transmitting



end, will transmit beam vector directivity transmission

for different users, and thus inhibit multi-cell co-channel

interference and improve the user acceptance of objective,

the signal-to-noise ratio of the receiving end this way,

using a simple linear demodulation can get useful data

signal strong, compared with the traditional MIMO

technology has obvious advantages in the complexity of

the above design.

The transmitting data goby channel encoding and

interleaving is processed with SC-FDMA technology, and

CP is added to it, the final signal is transmitted in the transmitter. Channel estimation utilizes the received pilot

to estimate the frequency response of the channel. From

the block diagram, we can know that both the calculation

of beam forming vector and the equalization of channel

fading are based on the conclusion of channel estimation.

Therefore channel estimation results will directly affects

the system performance. The receiver decomposes the

data in inverse process accordingly. For the subcarrier k ,

let h denote the channel from the transmitter to receiver

and x is the transmitting data. Figure 3 shows the basic

block diagram of modern mobile communication base station. The signal at the receiver is given by

(3)

where w is the beam forming vector, s denotes the sum

of all interference terms and n is the additive white

Gaussion noise with 2HE nn .

Software testing is to verify whether the difference

between target design requirements, in accordance with

the proposed to identify actual output and output theory

exists to design, locating and correcting incorrect to

reduce risk. However, only for the purpose of checking

and correcting the software defects are not test, through

the analysis of the abnormal output positioning reasons

for this defect, can let testers find out the test rules and effective strategies, and improve the effectiveness and

efficiency of test. This analysis can also detect

deficiencies in the software development process, so that

relevant personnel quickly to improve. Even if not

detected any defect, test analysis can also provide

reference for software quality evaluation. Communication

interface module

The

baseband

module

Radio

frequency

module

Feedback

module

The power supply

module

Environmental

monitoring module

Figure 3. The basic block diagram of modern mobile communication

base station.

The mobile communication base station has

experienced from analog to digital, from narrowband to

broadband development sharp change, the system

architecture with the evolution and development of the

function of the system constant. At present, the base

station of the latest generation of 3G mobile

communication with multi-carrier, high-efficient pre-

distortion digital power amplifier, a high performance

HSDPA, open architecture features. The base station is in

the form of the future, will undoubtedly toward functional

macro base station, more powerful and integrated volume

more portable, micro base station network more flexible

distributed base station 3 direction; at the same time the

base station in the framework, the evolution will be open,

modular products. As the base function is stronger, the

composition of the base station also at continuously

perfect, but the basic functional modules of the base

station from the digital communication.

III. THE SCHEME DETAILS

The main content combines the design of orthogonal

pilot with the channel estimation method of the MIMO-

OFDM system is that it will realize the sampling

estimation function of the independent channel frequency

response between the transmitting-receiving antenna pair

by using the completely orthogonal pilot data symbols on

different OFDM subcarriers of and different transmitting-

receiving antenna pair. In order to obtain the channel

coefficient values of different OFDM subcarriers and

different transmitting-receiving antenna pair with interpolation, the pilot inserting of the method will be

realized with polygon. The next time we will use the

linear interpolation on time domain and frequency

domain respectively to estimate the time-frequency

selective channel coefficient. The insert density of the

pilot data symbol between each transmitting-receiving

antenna pair is proportional to the time varying rate of the

channel and the biggest time delay spread. That is:

Each needing estimated sampling interval tN in time

axis between the transmitting-receiving antenna pair

satisfies:

(4)

where T is the symbol interval of the OFDM system and

df is the Doppler expansion value of the channel.

Each needing estimated sampling interval fN in

frequency axis between the transmitting-receiving

antenna pair satisfies:

(5)

where max is the time delay spread value of the channel

and cF is the frequency interval between the two

adjacent subcarriers.

The concrete realization steps of the channel

estimation part of the technical proposal combining the

design of orthogonal pilot with the channel estimation

method of the MIMO-OFDM system include the

following steps:

First, we design a 1M -order low pass filter 0h n ,

then the crack phase processing is carried out on 0h n :

1 1

0

MP

P lP

l o

n

E z h nP l z

(6)



Then each branch filter coefficient correspondingly

can be obtained. The pilot data symbols at the receiving

antenna will be filtered after the separated down sampling

and then the filtered data is sent to the FFT unit, where

FFT points are equal to the number of the OFDM

subcarriers. The data coming from the FFT unit will be

carried out estimation with the least squares least square

algorithm respectively and the estimated values are the

channel coefficient values corresponding to the

subcarriers position of the transmitting-receiving antenna

pair. When the sampling values of the channel on the frequency domain between the transmitting-receiving

antennas pair are available, the linear interpolation

processing in frequency domain and time domain will be

carried out directly, and then the entire channel

coefficient values can be obtained corresponding to the

transmitting-receiving antennas pair. In order to improve

the accuracy of the estimation, the data coming from the

FFT unit must carry out the threshold judgment

processing, that is only the data which is bigger than the

threshold value can be considered as the useful data. At

last the least squares estimation will be carried out. From this method, the pilot data designed on the

subcarriers position corresponding to the transmitting-

receiving antennas pair is selected the complex

exponential signal data which is same with that of the

center frequency of the subcarrier, but stagger with the

subcarriers of different transmitting-receiving antennas

pair that is:

21 1

0 0

, ( , ) , , 1,2npLP P jN

pilot pL ij

p p

X X e n p M i j

(7)

where , , 1,2ijM i j denote the position of the pilot data

used for the channel estimation at the time-frequency axis

between the transmitting antenna and the receiving antenna.

Through the fading influence of the MIMO wireless

mobile channel, the entire pilot data signal becomes the

following form:

21

0

( ) , ( , ) , , 1,2ij

npLP j H pLN

pilot ij pL ij

p

Y H pL e W n p M i j

(8)

where pLW is the additive Gaussian noise with zero mean

of the entire channel ijH .

The pilot data signal pilotY at the receiving antenna will

be sent to the channel estimation section, and the channel

coefficient will be estimated effectively via the least

squares algorithm, however the influence of the pLW item

on the accuracy of the channel estimation using the least

squares algorithm will decrease because of the filter

group.

IV. THE SPECIFIC IMPLEMENT MEAN

In this section we will use three figures to interpret at

the technical proposal combining the design of

orthogonal pilot with the channel estimation method of the MIMO-OFDM system.

As shown in Figure 4, the data needing transmitted

firstly is serial/parallel processed, and then is coded in the

space-time coding module, and constitute a data frame

combining with the pilot data symbols at corresponding

position. After all the data frames are processed at the

FFT unit respectively, they become the radio frequency

signal and will be sent out from the two antennas. At the

receiver the pilot data signal will firstly be extracted from

the signal of each receiving antenna the signal according

to insert position of the pilot signal which are processed

by the will pass their own channel attenuation function, and the extracted pilot data signal will be sent to the

channel estimation unit in the method. Through the

channel coefficient estimation and the linear interpolation

on time domain and frequency domain, we can obtain the

channel coefficient between vector between the

transmitting antenna and the receiving antenna and at last

the channel balance on frequency domain and space-time

decoding will be realized by using the estimated channel

vector.

S

/

P

T

I

M

E

C

O

D

I

N

G

I

N

S

E

R

T

IFFT

IFFT

Extract

pilot

FFT

FFT

B

A

L

A

N

C

E

P

/

S

Channel

estimation

Extract

pilot

Figure 4. The system work flowchart.

As shown in Figure 5, the order of the corrugated FIR

filter in the channel estimation section is 128 and the

down factor Q is 4, N is 64 and F is equal to 2. Each data

signal through the processing in the FFT unit will be estimated by using the least squares algorithm, and the

estimated data will be allocated according to the insert

position of the pilot signal and the linear interpolation on

time domain and frequency domain will be carried out.

At last the channel transfer matrix of the entire MIMO

system can be obtained. Frequency Nt

Nf

Tim

e(a)

(b)Pilot of transmitting antenna 1-receiving antenna 1Pilot of transmitting antenna 1-receiving antenna 2Pilot of transmitting antenna 2-receiving antenna 1Pilot of transmitting antenna 2-receiving antenna 2

The data needing transmitted

Figure 5. The diagram of the pilot inserting mean and position.

The study designs the pre-coding matrix interference

zero-forcing criterion (ZF), minimum mean square error

(MMSE), the minimum bit error rate criterion (MBER).



The maximum signal-to-noise ratio criterion (SINR) and

the maximum capacity standards, different Pair-wise

error design criteria corresponding to different pre-coding

scheme were carried out. In order to achieve and

algorithm for the complexity of the system constraints,

the objective function for system optimization is usually

the system capacity and BER performance. And, because

the solution error rate is extremely complex, usually by

some parameters and error rate closely as a substitute, for

example: the pairwise error probability (PEP), detection

of mean square error (Detection Mean-square Error), as well as the signal-to-noise ratio. The design of this pre-

coding is mainly based on the maximum system capacity

criterion.

V. SIMULATION RESULTS

In this section, we will provide the simulation results

to demonstrate the effectiveness of our proposals in this

paper. We assume the channel is a quasi-static flat fading

channel in our system. Without loss of generality, we

consider the BERas the performance measurement to

verify the advantage of our scheme in this paper.

In order to verify the system performance, the MIMO-OFDM system in TD-LTE scenario needs a multi-cell

interference model. As shown in Figure 6, this is a

cellular system with 19 cells. The round dot denotes the

base station in a hexagonal grid. We will select the

cooperative base stations from the second tier and the rest

are interference cells. For simplicity, in our MIMO-

OFDM system, we have simulated one cell where each

base station is equipped with 2 transmit antennas, and

each user is equipped2 receiving antennas.

Base Station Target cell

Figure 6. The multi-cell model

TABLE I. SIMULATION PARAMETERS

Parameters Assumption

Cellular layout Hexagonal grid, 19 cell sites

Inter-site distance 1000 m

Carrier frequency 2.6 GHz

Channel model SCME

System bandwidth 5 MHz

UE speed 3 km/h, 30 km/h

Modulation QPSK

Antenna configuration 2Tx/2 Rx

FFT size 512

Number of sub-carrier 256

Cyclic prefix 160 (pilot symbol),140 (data symbol)

SNR in uplink 10 dB

The simulation parameters of the MIMO-OFDM

system under TDD mode are presented in Table I. Spatial

Channel Model Extended (SCME) is considered as the channel model used in the simulations and the

modulation is Quadrature Phase-Shift Keying (QPSK).

Figure 7 presents the BER performance comparison for

various SNR. In the factual system and the simulation

system, the FFT size is larger than the available

subcarriers. From the simulation results, we know that the

performance of the proposed scheme termed combining

the design of orthogonal pilot with the channel estimation

method of the MIMO-OFDM system is better than the

conventional channel estimation scheme.

Figure 7. The comparison of new method and conventional method

As Figure 7 shows, in the MIMO-OFDM system, the

BER performance of the new method proposed in this

paper outperforms about 1dB at the BER of 110 from

the conventional scheme. The simulation results show

that, the proposed method outperforms the conventional

scheme in BER performance. F capacity of MIMO channel refers to all the elements

in the channel matrix H statistics on the total transmission

rate of the average value, can provide a characterization

of the wireless channel link average throughput, unknown

to the sender channel state information, can be expressed

as the mean capacity:

(9)

where R is the channel matrix rank, λ is positive

eigenvalue. The system capacity of MIMO traversal of

different antenna configurations with the SNR can be

seen, MIMO system make full use of the space system,

without sacrificing time and frequency resources, obtain space diversity gain is larger, and the diversity gain with

the day line (smaller number of transmitting and

receiving antenna the amount of increased linearly).Due

to the random channel completely average in the actual

system is very difficult to achieve, so, another

characterization of MIMO channel capacity parameter is

the outage capacity out, ensure the data transmission rate

and high reliability service, channel link rate performance

under different signal-to-noise ratio conditions

throughput characterized, reflects the stability of system

performance.

VI. CONCLUSION

The dispersing pilot pattern and the method of channel

estimation using in MIMO-OFDM system couldn’t

eliminate the same frequency interference caused by the

OFDM frequency among different transmit-receive

-6 -4 -2 0 2 4 610

-3

10-2

10-1

100

SNR(dB)

BE

R

conventional method

new method



antenna couples. Aiming at these problems, we proposed

a method of MIMO-OFDM channel estimation

combining orthogonal pilot design. The data format of

pilot is designed. It can maintain the orthogonality of

different sub-carriers of OFDM which are produced by

different transmit-receive antenna couples or transmit-

receive antenna couple. To achieve window function of

noise in sub-carriers, received terminal use a low

complex and max FFT to extract multi-phased filter

group. It can reduce the variance of noise in each output

of fitter group when estimate the channel parameters by least square and improve the precise of channel

estimation. The output terminal utilizes the linear

interpolation method to estimate the channel parameters

between different transmit-receive antenna couples and

different sub-carriers in frequency domain and time

domain. In wireless mobile environment, it can better

estimate the channel parameters without any prior

channel information.

This method proposed in the paper has finished the

design of the completely orthogonal pilot data symbol

among the different subcarriers position of different transmitting-receiving antenna pair. And the pilot data

symbols are distributed in the entire time-frequency grid

of the channel reasonably, therefore this paper uses a FFT

unit whose points are equal to the number of the OFDM

subcarriers to realize the least-square channel estimation

algorithm. In the process of channel estimation, the

channel parameters estimation precision has increased

because of the noise getting better inhibition, which

reduces the least squares estimate noise sensitivity to the

requirements. The proposed channel estimation algorithm

in the method can greatly reduce interference because the different antennas use the same frequency to transmit

data in the OFDM transmission system, which can be

applied wireless mobile MIMO-OFDM communication

system with double selective fading.

ACKNOWLEDGEMENT

The work was supported by Colleges and universities

natural fund in Anhui: environment friendly research and

industrialization of high power LED street light

(KJ2011Z373) and School fund project: high-power LED

lighting research and development and industrialization

(2011tlxy11zd).

REFERENCES

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“Performance degradation of OFDM systems due to Doppler spreading”, IEEE Transactions on Wireless Communications, vol. 5, pp. 1422 – 1432, 2006.

[2] T. Fusco and M. Tanda. “ML frequency offset and carrier phase estimation in OFDM systems with noncircular transmissions,” in Proc. EUSIPCO 2004, pp. 897–900, 2004.

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