In a wireless communications network employing a beamforming method, the quality of downlink signals received by a mobile station (MS) is determined by beamforming weighting vectors of a downlink channel. However, lack of information about channel coefficients of a downlink channel makes it difficult for a base transceiver station (BTS) in a frequency division duplex (FDD) network to obtain optimal beamforming weighting vectors for the downlink channel.
One way to address the issue is to develop a probing-and-feedback mechanism to obtain channel coefficients of a downlink channel. Employing a probing-and-feedback mechanism is not a preferred solution as it requires redesigning wireless communication protocols and incurs a large amount of overhead. In addition, a probing-and-feedback mechanism is only applicable to environments with slow fading channels.
Another way is to compute beamforming weighting vectors for a downlink channel by using a downlink channel covariance matrix that is transformed from an uplink channel covariance matrix. However, most of the algorithms that transform an uplink channel covariance matrix into a downlink channel covariance matrix suffer from high computation complexity.
The issue described above is related to downlink and uplink channels in a communications network employing FDD. Nonetheless, it would be obvious for a person of skills in the art to know the above description is also applicable to other transmitting and receiving channels. As such, what is desired is a method and system for transforming a covariance matrix of a receiving channel into a covariance matrix of a transmitting channel.
The present invention disclosed a method for transforming a covariance matrix of a receiving channel into a covariance matrix of a transmitting channel in a wireless network employing frequency division duplex. The method comprises calculating a speculative transformation matrix, generating a covariance matrix of a receiving channel, and transforming the covariance matrix of the receiving channel into a covariance matrix of a transmitting channel using the speculative transformation matrix.
The construction and method of operation of the invention, however, together with additional objects and advantages thereof, will be best understood from the following description of specific embodiments when read in connection with the accompanying drawings.
The drawings accompanying and forming part of this specification are included to depict certain aspects of the invention. The invention may be better understood by reference to one or more of these drawings in combination with the description presented herein. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale.
The following detailed description of the invention refers to the accompanying drawings. The description includes exemplary embodiments, not excluding other embodiments, and changes may be made to the embodiments described without departing from the spirit and scope of the invention. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims.
The present invention discloses a method that transforms a covariance matrix of a receiving channel into a covariance matrix of a transmitting channel in a wireless network employing frequency division duplex (FDD). Subsequently, the covariance matrix of the transmitting channel is used to compute beamforming weighting vectors of the transmitting channel.
The algorithm disclosed in the present invention improves the efficiency of computing beamforming weighting vectors in systems such as Macrocell/Microcell FDD OFDMA (Orthogonal Frequency Division Multiple Access). It performs well even when severe multi-path interference exists.
An array-steering vector A(θ,λ), also known as an array response vector, is defined according to the arrangement of an antenna array. It is a function of the direction of arrival (DOA) of receiving signals, denoted as θ, and the wavelength of a wireless channel, denoted as λ. If the antenna array is a uniform linear array (ULA), which is arranged linearly, then A(θ,λ) is equal to
where D is the distance between two adjacent antennas.
By contrast, if the antenna array is a uniform circular array (UCA), which is arranged circularly, then A(θ,λ) is equal to
where r is the radius of a circular array.
A response matrix is represented by the following equation: Q(θ)=A(θ,λ)AH(θ,λ) (1), where (.)H is a Hermitian operator. In a frequency division duplex wireless network, the response matrix of a transmitting channel is denoted as Qtx(θ)=A(θ,λtx)AH(θ,λtx) whereas the response matrix of a receiving channel is denoted as Qrx(θ)=A(θ,λrx)AH(θ,λrx).
Assuming that there are Nsector sectors in a cell, each sector is a pie shape. Each sector covers a region with a vertex angle of 2π/Nsector. In other words, it spans from angle −π/Nsector to angle π/Nsector. Let φ equal π/Nsector.
A sector is further divided into Na partitions of the same size. Each partition has a vertex angle of Δ and Δ=2φ/(Na−1). The transmitting and receiving response matrices of each partition are calculated according to equation 1. The DOA of the i-th partition, denoted as θ, equals (i−1)Δ−φ. The cumulative transmitting and receiving response matrices are defined by the following equations respectively.
In step 120, channel coefficients of a receiving channel in frequency domain for a desired wireless station is estimated to be Hrx=[Hrx,1 Hrx,2 . . . Hrx,M]T, where operator [*]T represents vector transpose and M is a total number of antennas in a transmitting wireless station. Let Rrx be a covariance matrix of a receiving channel. The instantaneous covariance matrix of a receiving channel is computed according to the following equation: Rrx=Hrx(Hrx)H. The average covariance matrix of the receiving channel is computed according to the following equation:
where Ne is the number of samples and Ne is between [1, ∝).
In step 130, the covariance matrix Rtx of a transmitting channel is transformed from the receiving covariance matrix Rrx according to the following equation: Rtx=RrxCT.
In step 140, the covariance matrix of the transmitting channel is used to compute a transmitting beamforming weighting vector wtx by finding the principal eigenvector of a channel covariance matrix problem. The principal eigenvector corresponds to a maximum eigenvalue.
Let X(t) be transmitting signals in frequency domain for a BTS. Transmitting signals in time domain for an antenna array are computed according to the following equation: s(t)=ifft(wtxHX(t)) where ifft is the Inverse discrete Fast Fourier Transform (IFFT).
The method disclosed in the present invention creates a fixed speculative transformation matrix for a transmission/reception system of an antenna array, generates an average covariance matrix of a receiving channel, and creates a covariance matrix of a transmitting channel by multiplying the average channel covariance matrix of the receiving channel with the fixed speculative transformation matrix.
The above illustration provides many different embodiments or embodiments for implementing different features of the invention. Specific embodiments of components and processes are described to help clarify the invention. These are, of course, merely embodiments and are not intended to limit the invention from that described in the claims.
Although the invention is illustrated and described herein as embodied in one or more specific examples, it is nevertheless not intended to be limited to the details shown, since various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the invention, as set forth in the following claims.
The present application claims the benefit of U.S. Provisional Application Ser. 60/854,216, which was filed on Oct. 24, 2006.
Number | Date | Country | |
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60854216 | Oct 2006 | US |