CN101944981A - Method for acquiring status information of medium- and long-term channel of communication system - Google Patents

Abstract

The invention discloses a method for acquiring status information of a medium- and long-term channel of a communication system, and belongs to the field of wireless communication. The method comprises that: 1) a base station computes an uplink space-time covariance matrix according to the status information of an uplink channel of a frequency division duplex communication system; and 2) the base station converts the uplink space-time covariance matrix according to the reciprocity between the uplink channel and a downlink channel of the frequency division duplex communication system so as to obtain the status information of a downlink medium- and long-term channel. Compared with the prior art, the method for acquiring the status information of the medium- and long-term channel of the communication system reduces the complexity of channel measurement at a UE end and the feedback overhead in the communication system.

Description

A method for acquiring long-term channel state information in a communication system

technical field

The invention relates to a method for acquiring channel state information, in particular to a method for acquiring long-term channel state information in a communication system, which is applicable to the field of wireless communication.

Background technique

Current research in the field of wireless communication shows that channel state information (CSI) is one of the important parameters that affect the performance of multiple-input multiple-output (MIMO) downlink systems. In an existing system, a set of channel state information is generally saved as a codebook to provide short-term channel state information for a base station (eNB) and a user terminal (UE). However, these codebooks are specially designed for specific transmitters. In this sense, they are fixed codebooks and cannot be changed adaptively as the channel changes. In order to adapt to the characteristics of the actual correlation channel, recent technical research points out that long-term channel state information (long-term CSI) such as space/frequency/time correlation matrix can be used to convert the codebook in the system to realize the existing codebook This reconfiguration. The acquisition of long-term channel state information plays a very important role in improving the performance of the communication system (reference: R1-091936, "Spatial Correlation Feedback to Support LTE-A MU-MIMO and CoMPOperations", Motorola, 3GPP RAN1#57, San Francisco , USA, May 4-8, 2009.; Literature: R1-100931, "Correlation-based feedback", Alcatel-Lucent Shanghai Bell, Alcatel-Lucent, San Francisco, USA, 22-26 February 2010; Literature: R1-100853 , "Channel reciprocity in FDD systems including systems with large duplex distance", Ericsson, ST-Ericsson, San Francisco, USA, 22-26 February 2010; Literature: R1-100205, "Use of Uplink Covariance Matrix for Down link MIMO in FDD", Motorola).

In a Time Division Duplex (TDD) system, the base station (eNB) and the user (UE) transmit information in the same frequency band, so the base station can obtain downlink channel information through the pilot information obtained from the uplink channel. However, in a frequency division duplex (FDD) system, the base station uses different frequency bands for transmission and reception, so the downlink channel information cannot be inferred from the received pilot information, which makes it more difficult to obtain CSI in the FDD system. The methods for the base station (eNB) to obtain long-term channel statistical information can generally be summarized into the following two categories:

The eNB receives the implicit channel characteristics of the mobile terminal (UE) feedback (reference: R1-091936, "SpatialCorrelation Feedback to Support LTE-A MU-MIMO and CoMP Operations", Motorola, 3GPP RAN1#57, San Francisco, USA, May 4-8, 2009.; Literature: R1-100931, "Correlation-based feedback", Alcatel-Lucent Shanghai Bell, Alcatel-Lucent, San Francisco, USA, 22-26 February 2010).

● eNB extracts statistical information by means of channel reciprocity (reference: R1-100853, "Channel reciprocity in FDD systems including systems with large duplex distance", Ericsson, ST-Ericsson, San Francisco, USA, 22-26 February 2010).

In the prior art, the eNB and the UE have a common codebook, and the UE feeds back the codeword index value in the codebook corresponding to the downlink channel to the eNB, so that the eNB can obtain short-term channel state information. At the same time, each UE calculates the spatial correlation matrix of the downlink channel by measuring the channel, and feeds it back to the eNB, so that the eNB realizes the acquisition of the long-term channel state information of the downlink channel. Each time the UE calculates and feeds back a new correlation matrix, the UE and the eNB perform codebook conversion according to the correlation matrix to obtain a reconfigured codebook, thereby determining an appropriate precoding method for multi-user MIMO. The spatial correlation matrix fed back includes spatial channel correlation information specific to each UE, which can improve the accuracy of channel feedback compared with a fixed codebook, thereby improving system performance.

However, the feedback method increases the complexity of channel measurement at the UE side, and increases the feedback overhead in the system. The measured data show that there is a strong correlation between the second-order statistical information of uplink and downlink channels, such as azimuth power spectrum (APS) and average fading gain. Since the spectrum exhibits a large degree of similarity in most locations, this implies that their long-term propagation characteristics are also similar. Based on this characteristic, the eNB can extract the statistical information of the downlink channel through the reciprocity of the downlink channel and the uplink channel (reference: R1-100853, "Channel reciprocity in FDD systems including systems with large duplex distance", Ericsson, ST-Ericsson , San Francisco, USA, 22-26 February 2010). The eNB can support complex channel estimation, so as to obtain uplink channel state information and calculate the correlation matrix of the uplink channel. Afterwards, the channel state information of the downlink channel is obtained through reciprocity transformation. However, current methods generally only utilize the spatial correlation characteristics of the channel, while ignoring the time correlation characteristics of the channel. For a multi-user MIMO system, it is extremely important to obtain the long-term spatial and temporal statistical correlation characteristics of the downlink channel to improve system performance.

Contents of the invention

In view of the problems existing in the prior art, the purpose of the present invention is to provide a method for obtaining long-term channel state information in a communication system. This method utilizes the reciprocity between the uplink and downlink channels of the FDD system, and provides - Time correlation matrix The method of calculating the downlink space-time correlation matrix through linear transformation can be extended to the case of different antenna structures and multiple antennas at the UE end. The invention can well make up the difference of the existing system, meet the system requirement, and achieve the purpose of improving the system performance.

Main contents of the present invention include:

1) Calculate the uplink space and time covariance matrix;

2) Conversion from uplink to downlink;

3) Expansion to other types of antenna structures;

4) Extension to the case of multi-antenna UE;

Technical scheme of the present invention is as follows:

A method for obtaining long-term channel state information in a communication system, the steps of which are:

1) The base station calculates the uplink space-time covariance matrix according to the uplink channel state information of the frequency division duplex communication system;

2) The base station transforms the uplink space-time covariance matrix according to the reciprocity between the uplink channel and the downlink channel of the frequency division duplex communication system, and obtains downlink medium and long-term channel state information.

A linear transformation matrix is used to transform the uplink space-time covariance matrix.

The linear transformation matrix is obtained by solving the error minimization method between the transformed downlink channel information matrix and the actual downlink channel information matrix.

The linear transformation matrix is an N×S dimensional matrix, where N is the number of antennas in the base station antenna array, and S is the number of scatterers around the user.

The antenna array of the base station is a uniform linear array.

The antenna array of the base station is a uniform circular array.

The antenna is a dual-polarized antenna or a uniform linear array or a uniform circular array.

The user terminal is configured with a single antenna.

The user terminal is configured with multiple antennas, and the base station uses the steering vector of the user terminal to establish an uplink channel.

The positive effect of the present invention is:

The present invention utilizes the reciprocity between the uplink and downlink of the FDD system, and provides a method for calculating the downlink space-time correlation matrix through linear transformation from the uplink space-time correlation matrix, which can be extended to UEs with different antenna structures and multiple antennas case. The invention reduces the complexity of channel measurement at the UE end and the feedback overhead in the communication system, can well make up for the differences of existing systems, meets system requirements, and achieves the purpose of improving system performance.

Description of drawings

Flow chart of the method of the present invention.

Detailed ways

In the present invention, an N-element antenna array is used at the eNB side, and a single antenna is used at the UE side, and a static narrowband channel between the two is considered. Assuming that there are S scatterers around the mobile user, the complex envelope of channel fading in a TDMA time slot m can be expressed as follows:

分别为第s个散射体的多普勒频移和随机相位偏移，α(θ_s)表示导向矢量。在只考虑最强的L条径(L≤S)的情况下，可以假设一个简化的离散AoA模型。Among them, T _s is the time slot duration, α _s is the signal amplitude, θ _s is the angle of arrival (AoA) observed by the eNB end, f _{d, s} and

are the Doppler frequency shift and random phase shift of the s-th scatterer, respectively, and α(θ _s ) represents the steering vector. In the case where only the strongest L diameter (L≤S) is considered, a simplified discrete AoA model can be assumed.

Assuming that a uniform linear array (ULA) is used, the array element spacing is d, and the direction is θ _s , then

$\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; 1</span>}\\ {\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\frac{\mathrm{<span\; class="notranslate">\; d</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span>}\\ <span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span>\\ {\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\frac{\mathrm{<span\; class="notranslate">\; d</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span>}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Among them, λ is the carrier wavelength.

The method process of the present invention is as shown in the figure, and concrete steps are as follows:

In the first step, the uplink space-time covariance matrix is calculated.

The space-time correlation matrix can be calculated as follows:

R(n)=E{h(m)h ^H (m+n)} (3)

m represents the sampling unit on the time axis, that is, the mth time slot of TDMA, n is a time sampling unit increment, E represents the function of statistical averaging, and R(n) is the uplink space-time covariance matrix,

Among them, the elements in row i and column j of the matrix can be obtained by formula (1) as follows:

${<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; S</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; sin</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}\mathrm{<span\; class="notranslate">\; no</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

It can be seen from the above formula that because λ and f _{d, s} are related to the value of this element, the correlation depends on the carrier frequency. Considering the uplink carrier frequency f _UL and the downlink carrier frequency f _DL , the uplink correlation function can be given by the following formula:

${<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; S</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; sin</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; UL</span>}}\mathrm{<span\; class="notranslate">\; no</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

分别表示在载波频率f_UL下的载波波长和多普勒频移。方便起见，我们给出如下定义：where, λ _UL and

denote the carrier wavelength and Doppler shift at the carrier frequency f _UL , respectively. For convenience, we give the following definitions:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>\stackrel{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}}{<span\; class="notranslate">\; =</span>}{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

The second step is the conversion from uplink to downlink.

和

(φ_s是用户前进方向和散射体s的方向之间的夹角，v表示UE的移动速度，c表示光速)对(5)式进行替换，通过(6)式可以得到如下表达式：It can be known from formula (5) that the difference between the uplink carrier frequency f _UL and the downlink carrier frequency f _DL causes the difference in the space-time correlation matrix between the uplink propagation channel and the downlink propagation channel. Nevertheless, as will be shown in the analysis below, the difference in the space-time correlation matrix can be ignored in some cases, or can be compensated in other cases using so-called frequency translation. use

and

(φ _s is the angle between the direction of the user and the direction of the scatterer s, v represents the moving speed of the UE, and c represents the speed of light) Equation (5) is replaced, and the following expression can be obtained through Equation (6):

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; S</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; df</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; sin</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; vf</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}\mathrm{<span\; class="notranslate">\; cos</span>}{\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}\mathrm{<span\; class="notranslate">\; no</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

和

的值非常小，载波频率的一个微小改变不会造成空间相关矩阵和时间相关矩阵很大的变化。设表示f_UL和f_DL之间的相对双工距离，对于f_UL和f_DL之间双工距离相对较小的某些情况(1＜Δ≤Δ₀，Δ₀为阈值)，可以得到：From (7), it can be observed that due to

and

The value of is very small, and a small change in the carrier frequency will not cause a large change in the spatial correlation matrix and the time correlation matrix. set up Indicates the relative duplex distance between f _UL and f _DL . For some cases where the duplex distance between f _UL and f _DL is relatively small (1<Δ≤Δ ₀ , Δ ₀ is the threshold), it can be obtained:

r _DL (k, n) ≈ r _UL (k, n). (8)

At the same time, for some other situations where the duplex distance between f _UL and f _DL is relatively large (Δ>Δ ₀ ), the correlation matrix of uplink and downlink has the following interdependent relationship:

r _DL (k, n) = r _UL (Δ·k, Δ·n) (9)

是相对双工距离。注意到上行和下行的相关矩阵通过Δ相互依赖，这被称为相关特性的信道互易性。in,

is the relative duplex distance. Note that the correlation matrices of uplink and downlink depend on each other by Δ, which is called the channel reciprocity of the correlation property.

The calculation of the downlink space-time covariance matrix is implemented based on reciprocity as follows. Using a linear transformation, the downlink channel can be estimated by the uplink channel as follows:

${\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Wherein, Ξ represents the transformation matrix of N×S,

downlink channel matrix

Uplink channel matrix

的情况下，即可利用线性变换矩阵Ξ计算得到下行信道矩阵

完成下行信道矩阵的获取。Wherein, n _UL and n _DL represent corresponding downlink and uplink time slots respectively. According to the above formula, the uplink channel matrix is known at the base station

In the case of , the downlink channel matrix can be obtained by using the linear transformation matrix Ξ to calculate

Complete the acquisition of the downlink channel matrix.

The linear transformation matrix Ξ can be obtained by minimizing the following value function

$\underset{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}{\mathrm{<span\; class="notranslate">\; min</span>}}\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

That is, finding an optimal linear transformation matrix Ξ minimizes the error between the calculated downlink channel information matrix and the actual downlink channel information matrix.

Differentiate the above formula with respect to Ξ and make it 0, the optimal transformation matrix can be obtained as follows:

$\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

Among them, (·) ^-1 represents the operation of inverse matrix, (·) ^H represents the conjugate transpose of matrix.

Considering the uplink channel estimation error, formula (12) can be further expressed as:

$\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

where ^σ2 represents the variance of the channel error and I represents the identity matrix.

Assuming that the uplink channel is determined by the strongest AoA angle θ _max , the wireless transmission model in formula (1) can be simplified as:

A linear transformation matrix can be expressed in the following compact format:

$\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; d</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; no</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; d</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; n</span>}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 15</span>}<span\; class="notranslate">\; )</span>$

φ_max为用户信号前进方向和具有最强AoA角度θ_max的散射体s的方向之间的夹角。in

φ _max is the angle between the forward direction of the user signal and the direction of the scatterer s with the strongest AoA angle θ _max .

If only spatial correlation is considered, formula (15) can be written as:

$\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;d</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;d</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; DL</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; )</span>$

If time dependence is considered, we can get:

$\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; no</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; UL</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; DL</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; n</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 17</span>}<span\; class="notranslate">\; )</span>$

It should be noted that the above results can be easily extended to any major AoA angle value.

Among them, diag( ) represents a diagonal matrix, n is the time slot label, f _{d, s} ^UL and f _{d, s} ^DL are the Doppler frequency shift of the s-th scatterer in the uplink channel and downlink channel, respectively.

According to these known parameters, the linear transformation matrix can be obtained. The estimated downlink covariance matrix can be written as:

R _DL = ΞR _UL Ξ ^H . (14)

That is, the downlink space-time covariance matrix R _DL can be obtained by using the uplink space-time covariance matrix R _UL and the linear transformation matrix Ξ through the above formula. The base station completes the acquisition of downlink long-term channel information.

The third step is the expansion of other types of antenna structures.

Similarly, for a uniform circular array (UCA), the corresponding steering vector can be written as:

where r represents the geometric radius.

By considering equations (1) and (3) jointly, it can be found that the structure of the antenna, such as uniform linear array (ULA), uniform circular array (UCA) or dual-polarized antenna (DPA), does not affect the uplink to Downside reciprocity. Therefore, the spatial and temporal correlation properties in (9) always hold regardless of the antenna structure.

The fourth step is to extend the multi-antenna UE case.

For R8 and higher versions, 2-4 transmit antennas are allowed to be configured on the UE side. If all antennas are activated to transmit measurement reference signals (SRS), downlink wireless channel parameters can be easily obtained through reciprocity. However, in order to save energy, in many cases only some of the transmit antennas transmit SRS and the others remain stationary. In this case, the following two methods can be used to obtain downlink correlation characteristics by means of channel reciprocity. The first method utilizes antenna exchange and uses a cyclic manner to send SRS in all antennas to reconstruct a complete downlink channel and extract downlink wireless channel parameters.

For the situation that the terminal does not support antenna switching, the second method can be used. In the second method, since the distance between antennas at the UE is very small, the propagation channels of different antenna elements should exhibit very similar channel characteristics. This means that the distribution of scatterers and the amplitude of the scattered field emitted by each scatterer are similar for different antenna elements at the UE end. Therefore, a reasonable assumption can be made that the different channel statistics with respect to different antenna elements at the UE only depend on the antenna spacing d _UE and the angle of departure (AoD) β _s observed from the UE. In this case, based on the partial uplink channel information obtained from the partially activated antenna units at the UE side, the base station can establish a complete uplink channel with the help of the steering vector α(β _s ) at the UE side,

Based on the expression of the channel reciprocity and the calculation process of the long-term CSI of the downlink channel in the case of single antenna, the complete downlink channel and its corresponding space and time correlation characteristics can be easily obtained.

Claims (9)

1. A method for obtaining long-term channel state information in a communication system, the steps of which are: 1) The base station calculates the uplink space-time covariance matrix according to the uplink channel state information of the frequency division duplex communication system; 2) The base station transforms the uplink space-time covariance matrix according to the reciprocity between the uplink channel and the downlink channel of the frequency division duplex communication system, and obtains downlink medium and long-term channel state information. 2. The method according to claim 1, characterized in that a linear transformation matrix is used to transform the uplink space-time covariance matrix. 3. The method according to claim 2, characterized in that the linear transformation matrix is obtained by minimizing the error between the transformed downlink channel information matrix and the actual downlink channel information matrix. 4. The method according to claim 3, wherein the linear transformation matrix is an N×S dimensional matrix, wherein N is the number of antennas in the base station antenna array, and S is the number of scatterers around the user. 5. The method according to claim 1, characterized in that the antenna array of the base station is a uniform linear array. 6. The method according to claim 1, wherein the antenna array of the base station is a uniform circular array. 7. The method according to claim 5 or 6, characterized in that the antenna is a dual-polarized antenna or a uniform linear array or a uniform circular array. 8. The method according to claim 5 or 6, characterized in that the user terminal is configured with a single antenna. 9. The method according to claim 5 or 6, wherein the user terminal is configured with multiple antennas, and the base station establishes an uplink channel by means of the steering vector of the user terminal.

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