Description

DETECTING METHOD OF MULTIPLE-INPUT MULTIPLE- OUTPUT SYSTEM

Technical Field

[1] The present invention relates to a detecting method of a multiple-input multiple- output system; and, more particularly, to a detecting method of a multiple-input multiple-output system, including the steps of: determining K optimal transmit (Tx) signals among all possible combinations of Tx signals at a receiver of the MIMO system, and calculating estimation values of the K optimal Tx signals; determining L antenna signals in which interference of the optimal antenna signals is removed, and calculating L residual Tx signal estimation values; and applying a maximum likelihood (ML) detection scheme to KxL estimated Tx signal candidate groups.

[2]

Background Art

[3] In recent years, as the wireless communication service is changing from a voice service to a high-quality multimedia service, studies on data transmission technologies are actively in progress in order to transmit a larger amount of data at a lower error rate more rapidly than ever.

[4] The data transmission, however, is greatly influenced by wireless communication environment, e.g., signal fading, interference, and noise. Specifically, the data transmission is influenced by serious signal distortion caused by combination of signals having different phases and amplitudes received through different paths due to multipath fading.

[5] To solve the signal fading, a multiple-input multiple-output (hereinafter, referred to as a MIMO) system has been proposed.

[6] Fig. 1 is a block diagram of a conventional MIMO system.

[7] Referring to Fig. 1, the MEMO system is a wireless communication system that transmits/receives different data through M transmit (Tx) antennas 1 and N receive (Rx) antennas 2, instead of using wide frequency bandwidth, in order to increase a data rate and a transmission capacity in the wireless communication environment with limited frequency resources.

[8] In the MEMO system, signals received at the Rx antennas 2 are expressed as Eq. 1 below.

[9]

[10] r=Hx+n

Eq.1

[11] where x is Tx signals transmitted differently through the M Tx antennas 1, H is multipath radio channels that the Tx signals x pass through before they are received at the Rx antennas 2, r is Rx signals received at the N Rx antennas 2 over the multipath radio channels H, and n is noise signals added to the Rx antennas 2.

[12] The Tx signals x are expressed as

, the Rx signals r are expressed as

T = [ T ₁ r ₂ ^• " r _N _ _γ r _N ]

, and the multi-path radio channels H are expressed as

H-I h ₁ h ₂ ^{• • •} h _Mλ h _M ]

[13] In addition, the noise signals n are expressed as

Tl = [ Tl ₁ Tl ₂ ' " Tl _Nλ TI _N ] and have a complex Gaussian distribution in which a mean of the respective components is zero and a variation is N /2 in each order. [14] Meanwhile, the receiver of the MEMO system uses a maximum likelihood (ML) detection scheme. The ML detection scheme is to select an input signal having a minimum squared Euclidean distance among the possible combinations of the Tx signals x. [15] Referring to Fig. 1 and Eq. 1, the ML detection scheme determines a solution

JC using Eq. 2 below. [16] [17]

X

Eq. 2

[18] In Eq. 2, C ^M is a vector set that the Tx signals x can have within a signal con-

M stellation. The number of elements of the vector set is L , where L is a constellation size.

[19] In the MEMO system, the ML detection scheme exhibits the optimal performance in terms of a bit error rate (BER). However, as can be seen from Eq. 2, the ML detection scheme must calculate the square of the Euclidean distance for all possible Tx signals and compare the calculated values from one another. Thus, the operation of Eq. 2 must be performed L times.

[20] As the constellation size L or the number M of the Tx antennas 1 increases, the processing time and complexity exponentially increase, so that the practical implementation is difficult.

[21] Meanwhile, a successive interference cancellation (SIC) scheme is proposed to reduce the complexity of the ML detection scheme. The SIC scheme is carried out as follows.

[22] In the first step, interference between Tx signals is cancelled by linearly assigning weight values to Rx signals, and the Tx signals are separated according to Tx antennas 1.

[23] In the second step, the interference-cancelled signals are randomly sorted, for example in ascending order of signal-to-noise ratio (SNR), and a signal to be first removed is determined.

[24] In the third step, an estimation value of the Tx signal is calculated through quantization of the determined signal into the signal constellation, and the influence of the calculated estimation value is removed from the Rx signal.

[25] Then, the first to third steps are repeated until all Tx signals are detected.

[26] Compared with the ML detection scheme, the SIC method can greatly reduce the amount of calculation and complexity. However, because noise increases when the interference between the Tx signals is removed in the first step, the BER performance is degraded.

[27] Further, because the entire performance of the SIC scheme is greatly dependent on the reliability of the initially detected signal, the sorting step plays an important role.

[28] That is, the ML detection scheme has a problem of the complexity and the SIC scheme has a problem of the degraded BER performance. Therefore, there is a demand for a detecting method that can provide the reduced complexity and the improved BER performance and can easily implement the MIMO system.

[29]

Disclosure of Invention Technical Problem

[30] It is, therefore, an object of the present invention to provide a detecting method of a

MIMO system, including the steps of: determining K optimal transmit (Tx) signals among all possible combinations of Tx signals at a receiver of the MEMO system, and

calculating estimation values of the K optimal Tx signals; determining L antenna signals in which interference of the optimal antenna signals is removed, and calculating L residual Tx signal estimation values; and applying a maximum likelihood (ML) detection scheme to KxL estimated Tx signal candidate groups.

[31]

Technical Solution

[32] In accordance with one aspect of the present invention, there is provided a detecting method of a MEMO system using multipath radio channels, including the steps of: a) canceling interference between transmit signals by assigning weight values to signals received through a plurality of antennas, and determining K optimal antenna signals through channel gain estimation, where K is an arbitrary positive integer; b) calculating L transmit signal estimation values by quantizing the optimal antenna signals according to a predefined constellation size of L, where L is an arbitrary positive integer; c) calculating L residual antenna signals, in which interference of the transmit signal estimation values is removed from the received signals using the L Tx signal estimation values; d) calculating L residual transmit signal estimation values by quantizing the L residual antenna signals according to the predefined constellation size; and e) creating KxL estimated transmit signal candidate groups by repeating the calculation of the L residual transmit signal estimation values for each of the K optimal antenna signals, and detecting transmit signals from the estimated transmit signal candidate groups.

[33]

Advantageous Effects

[34] In accordance with the present invention, the complexity of the ML detection scheme can be remarkably reduced and the BER performance can be greatly improved.

[35] Furthermore, the MIMO system capable of solving the problem of the limited frequency resources can be easily implemented.

[36]

Brief Description of the Drawings

[37] The above and other objects and features of the present invention will become apparent from the following description of the preferred embodiments given in conjunction with the accompanying drawings, in which:

[38] Fig. 1 is a block diagram of a conventional MIMO system; and

[39] Fig. 2 is a flowchart illustrating a detecting method of a MIMO system according to an embodiment of the present invention.

[40]

Best Mode for Carrying Out the Invention

[41] Other objects and aspects of the invention will become apparent from the following description of the embodiments with reference to the accompanying drawings, which is set forth hereinafter.

[42] Fig. 2 is a flowchart illustrating a detecting method of a MIMO system according to an embodiment of the present invention.

[43] Since the present invention is applied to the MIMO system of Fig. 1, a detailed description of the MEMO system will be omitted.

[44] Referring to Fig. 2, when the Rx antennas 2 in the receiver of the MIMO system receive signals in step SlOO, K (l≤K≤M) antenna signals having the highest reliability (hereinafter, referred to as "Optimal antenna signals" are determined among the possible Tx signals of the Tx antennas 1 in step SlOl. That is, the receiver of the MIMO system cancels the interference between the Tx signals by assigning weight values to the Rx signals and then determines the K optimal antenna signals by estimating channel gains using SNR, log likelihood ratio, or preamble.

[45] Step SlOl will be described in detail with reference to Eqs. 3 to 5 below.

[46]

[47]

W= (H ^H H+σJ ² ) ^{' X} H ^H , (MMSE)

[48]

Eq. 3 [49] [50] y = Wr= WHx= Wn

Eq. 4 [51] [52]

Eq. 5

[53] Eq. 3 represents a weight matrix W to be multiplied by the Rx signal r in order to cancel the interference between the Rx signals. The weight matrix W is expressed as

T

TV= [ λV 1 λV λV M\

[54] The weight matrix W is calculated using a minimum mean square error (MMSE) scheme or a zero forcing (ZF) scheme. Since the MMSE scheme and the ZF scheme are well known, their detailed description will be omitted.

[55] Eq. 4 represents the interference-cancelled signal y obtained by multiplying the Rx signal r by the weight matrix W of Eq. 3.

[56] Eq. 5 represents an n* signal y n extracted from the signal y. Since the number of the

Tx antennas 1 is M, l≤n≤M.

[57] Specifically, the receiver of the MIMO system determines the K optimal antenna signals using the SNRs or log likelihood ratios of the M extracted signals

{y\^ " ^n^ " ^M} given by Eq. 5. For convenience, it will be assumed that one of the K optimal antenna signals is an n* extracted signal y n and is applied to following equations. Since the ap- plication of the remaining (K-I) optimal antenna signals is identical to that of the extracted signal y n , its detailed description will be omitted.

[58] The receiver of the MIMO system determines the K optimal antenna signals among the Tx signals in step SlOl, and quantizes the K optimal antenna signals into all possible values of a predefined constellation size L to calculate L optimal Tx signal estimation values in step S 102. That is, the receiver of the MIMO system calculates the L optimal Tx signal estimation values with respect to the n extracted signal y n , and calculates L optimal Tx signal estimation values with respect to each of the remaining

(K-I) optimal antenna signals.

[59] Step S 102 will be described in more detail with reference to Eq. 6 below.

[60]

[61]

Eq. 6 [62] Eq. 6 represents the optimal Tx signal estimation value

^x n obtained by quantizing the n* extracted signal y n according to the predefined con- stellation size L.

[63] In this manner, the receiver of the MIMO system calculates the L optimal Tx signal estimation value

λ ^x n by substituting all values of the constellation, that is, the first to last signals whose

number is equal to the constellation size L.

[64] Meanwhile, the receiver of the MIMO system calculates the L optimal Tx signal estimation values with respect to each of the K optimal antenna signals in step S 102, and calculates L residual Rx signals in which the interference of the optimal Tx signal estimation values is cancelled from all Rx signals in step S 103.

[65] Step S 102 will be described in more detail with reference to Eqs. 7 to 10.

[66]

/\ r '=r-h _n x „

Eq. 7 [67] [68]

Eq. 8

[69] [70]

[71]

W=(H^H') ^{' λ} H' ^H , (ZF)

Eq. 9

[72] [73] y '= Wr '

Eq. 10 [74] Eq. 7 represents the cancellation of the interference of the optimal Tx signal estimation values, calculated in Eq. 6, from all Tx signals. Since the calculation of the optimal Tx signal estimation value

* n is performed as many times as the constellation size L in Eq. 7, the residual Rx signals r' are generated as many as the constellation size L. [75] Eq. 8 represents an interference-cancelled channel matrix H' for removing the influence of the optimal Tx signal estimation value from the multipath radio channels H. That is, it can be seen from Eq. 8 that h is removed.

[76] Eq. 9 represents a weight matrix W calculated using the multipath radio channel matrix H' of Eq. 8, in which the influence of the optimal Tx signal estimation value is removed. Since the weight matrix of Eq. 9 is calculated in the same manner as Eq. 8, its detailed description will be omitted. Using Eq. 9, the weight matrix W for canceling the interference of the Tx signals, in which the influence of the optimal Tx signal estimation signal is removed, can be obtained.

[77] Eq. 10 represents the residual antenna signal obtained by multiplying the weight matrix W of Eq. 9 by the residual Rx signal r' of Eq. 7. In Eq. 10, L residual antenna signals y' are calculated because the number of the residual Rx signals r' is L.

[78] Meanwhile, in step S 103, the receiver of the MIMO system calculates the L residual antenna signals in which the interference of the optimal Tx signal estimation values is removed. In step S 104, L estimated Tx signal values (hereinafter, referred to as residual Tx signal estimation values) are calculated by quantizing the residual antenna signals into the possible values of the predefined constellation size L.

[79] Step S 104 will be described below with reference to Eq. 11 below.

[80]

[81]

Eq. 11

[82] Eq. 11 represents the residual Tx signal estimation value calculated using the residual antenna signals. It can be seen that

is removed. [83] In addition, Eq. 11 determines the L residual Tx signal estimation values because the residual antenna signals y' are generated as many as the constellation size L. [84] When the receiver of the MEMO system selects the K optimal antenna signals in step SlOl, steps S 102 to S 104 are repeated for each of the K optimal antenna signals.

In other words, steps S 102 to S 104 are repeated until the L residual Tx signal estimation values are determined. [85] In step S 105, as the results of the K-time repetition, the receiver of the MEMO system generates estimated Tx signal candidate groups as many as the L residual Tx signal estimation values in each of the K optimal antenna signals. That is, the number of the estimated Tx signal candidate groups created is KxL, which is the product of the number of the optimal antenna signals and the number of the residual Tx signal estimation values. [86] In step S 106, the receiver of the MEMO system detects the Tx signals by applying

the ML detection scheme to the signals of the estimated Tx signal candidate groups. Since the ML detection scheme is well known, its detailed description will be omitted.

[87] The methods in accordance with the embodiments of the present invention can be realized as programs and stored in a computer-readable recording medium that can execute the programs. Examples of the computer-readable recording medium include CD-ROM, RAM, ROM, floppy disks, hard disks, magneto-optical disks and the like.

[88] The present application contains subject matter related to Korean patent application

Nos. 2005-0116134 and 2006-0047744, filed with the Korean Intellectual Property Office on December 1, 2005, and May 26, 2006, respectively, the entire contents of which is incorporated herein by reference.

[89] While the present invention has been described with respect to certain preferred embodiments, it will be apparent to those skilled in the art that various changes and modifications may be made without departing from the scope of the invention as defined in the following claims.