Signal compression for backhaul communications using linear transformations

A compression/decompression method for backhaul communication of a complex-valued radio signal between base stations and the network processing unit, such as a Central Processor of a Coordinated MultiPoint (CoMP) system, significantly reduces backhaul bandwidth. The spatial and temporal correlations of the wireless IQ signal are exploited in order to remove redundancy and substantially reduce signal bandwidth. Feature component signals of significance are extracted through linear transformation to form the radio signal, and are individually quantized, possibly at different bit rates in accordance with their relative importance. The transformation can either be pre-determined or computed in real-time based on the spatial and temporal statistics of the radio signal. In the latter case, the transformation matrix or matrices are also sent over the backhaul in order to allow the radio signal to be reconstructed at the receiving end. Different methods of generating the transformation matrices are proposed.

FIELD OF INVENTION

The present invention relates generally to wireless telecommunications, and in particular to a method of compressing signals for transmission on backhaul channels in wireless telecommunication networks.

BACKGROUND

The exponential growth in the demand of wireless data communications has put a tremendous pressure on the cellular network operators to improve the capacity of their communication networks. To improve the spectral efficiency of these networks, scarce radio resources have to be reused aggressively in neighboring cells. As a result, inter-cell interference has become a main source of signal disturbance, limiting not only the service quality of the cell-edge users, but also the overall system throughput.

Coordinated multi-point (CoMP) transmission or reception is one known means to effectively mitigate inter-cell interference.FIG. 1depicts a representative CoMP cluster10, in which User Equipment (UE)12receive wireless communication service in a number of conventional cells14. A base station or eNode B16transmits downlink RF signals to UE12(and receives uplink transmissions from the UE12) in each cell14. To avoid inter-cell interference, a central processor (CP)30coordinates downlink transmissions to, and possibly also uplink transmissions from, UE12in the cells14forming the CoMP cluster10. The CP30coordinates and optimizes transmissions to reduce or even avoid mutual interference among UE12. The benefit attainable by the deployment of CoMP systems hinges on how well such coordination can be performed by the CP30.

To enable the CP30to effectively coordinate transmission and/or reception at multiple cells14, timely signal information must be communicated between remote base station sites16and the CP30. However, the amount of information needed to send to or receive from each base station16can be overwhelming, especially when multiple antennas are deployed at each site. In general, the CP30generates, and must transmit to remote base stations16, the In-phase (I) and Quadrature (Q) components of complex-valued downlink signals to be transmitted by each antenna at each base station16. Additionally, each base station16must transmit to the CP30the complex-valued IQ signal received at each antenna.

In the standard Common Public Radio Interface (CPRI), each real-valued sample of the IQ backhaul signal would simply be quantized independently by a fixed number of bits (e.g., 16). CPRI transmission does not exploit any structure of the underlying backhaul signal, and is an inefficient way of representing wireless communication signals. CPRI transmission of IQ wireless signals thus places a large burden on the capacity of backhaul links, which may limit the performance otherwise achievable by CoMP systems.

Other systems may additionally perform precoding at a processing unit of a network, for transmission by base stations. All such systems require voluminous data transfer between the network processing unit and base stations.

SUMMARY

According to one or more embodiments described and claimed herein, a compression/decompression method reduces backhaul bandwith required for communicating a complex-valued radio signal between base stations and a processing unit, such as for example the Central Processor of a Coordinated MultiPoint (CoMP) system. The method exploits both the spatial and temporal correlation of the wireless IQ signal in order to remove redundancy and substantially reduce the capacity requirement of the backhaul links. Feature component signals of significance are extracted through linear transformation from the radio signal, and are individually quantized, possibly at different bit rates in accordance with their relative importance. The transformation can either be pre-determined or computed in real-time based on the spatial and temporal statistics of the radio signal. In the latter case, the transformation matrix or matrices are also sent over the backhaul in order to allow the radio signal to be reconstructed at the receiving end. Different methods of generating the transformation matrices are proposed.

One embodiment relates to a method of transmitting wireless communication signals across a backhaul channel between a base station and a network processing unit. A wireless communication signal is represented as a plurality of time-domain, complex-valued signal samples. At least one linear transformation of the signal samples is performed across one or more of time and antenna spatial domains to generate transformed signal coefficients. The transformed signal coefficients are quantized. The quantized transformed signal coefficients are transmitted across the backhaul. Quantization information is also transmitted across the backhaul.

Another embodiment relates to a method of receiving wireless communication signals across a backhaul channel between a base station and a network processing unit. Quantized transformed signal coefficients are received across the backhaul. Quantization information is received across the backhaul. Transformed signal coefficients are decoded from the quantized transformed signal coefficients based on the quantization information. At least one inverse linear transformation of the transformed signal coefficients is performed across one or more of time and antenna spatial domains to recover a plurality of time-domain, complex-valued signal samples corresponding to a wireless communication signal.

Yet another embodiment relates to a base station. The base station includes one or more antennas, and at least one front end circuit operative to front end process a wireless communication signal received from the antenna, and generate a plurality of time-domain, complex-valued signal samples. The base station further includes at least one transformation unit operative to perform a linear transformation of the signal samples across one or more of time and antenna spatial domains to generate transformed signal coefficients. The base station also includes a transmit bit allocation unit operative to determine a number of quantization bits for quantization of the transformed signal coefficients based on the variance of the coefficients. The base station further includes a quantizer operative to quantize the transformed signal coefficients using the number of bits determined by the bit allocation unit. The base station additionally includes a backhaul communication interface operative to transmit quantized transformed signal coefficients and quantization information across the backhaul to a network processing unit.

Still another embodiment relates to a network processing unit. The processing unit includes a backhaul communication interface operative to receive quantized transformed signal coefficients and quantization information from a plurality of base stations. The processing unit also includes a receive bit allocation unit operative to determine a number of quantization bits for decoding quantized transformed signal coefficients received from a base station, based on the received quantization information. The processing unit further includes a decoder operative to decode the received quantized transformed signal coefficients based on a number of bits determined by the bit allocation unit. The processing unit additionally includes at least one inverse transformation unit operative to perform an inverse linear transformation of the transformed signal coefficients across one or more of time and antenna spatial domains to generate a plurality of time-domain, complex-valued signal samples. The processing unit also includes a processing circuit operative to process the signal samples.

DETAILED DESCRIPTION

According to embodiments of the present invention, backhaul communications between a network processing unit30and base stations16in a wireless communication system10are significantly reduced. To provide context, and solely for the purpose of explanation herein, embodiments of the invention are described wherein the network processing unit30is a Central Processor (CP)30of a Coordinated MultiPoint (CoMP) system10. However, the invention is not limited to CoMP systems10, but may find applicability in any wireless communication system10in which a network processing unit30performs signal processing, such as precoding, on signals to be transmitted by base stations16, and which must be communicated to the base stations16over a backhaul communication link. Accordingly, all references herein to a CP30or a CoMP system10are to be understood as a non-limiting example of a network processing unit30in a wireless communication network10.

The backhaul communications are reduced by performing targeted compression on complex-valued IQ radio signals prior to backhaul transmission, and corresponding decompression on the signals following backhaul reception. The signal compression may be applied to both downlink signals to be transmitted by base station16antennas, and uplink signals received by base station16antennas. The compression methodology extracts feature component signals of significance through linear transformations in time and/or antenna spatial domains, and individually quantizes them. The transformation can either be pre-determined or computed in real-time based on the spatial and temporal statistics of the radio signal.

FIG. 2depicts a CP30and representative base station16, according to embodiments of the present invention. The CP30includes a CoMP processing unit32, signal compression unit100, signals decompression unit200, and a backhaul channel interface34. As known in the art, the CoMP processing unit32coordinates transmission to all UE12in the cells14comprising the CoMP cluster10, so as to reduce or eliminate mutual interference among the UE12. The backhaul interface34is operative to receive and transmit data from and to numerous base stations16across a high-speed, high-bandwidth communication channel. In some embodiments, the backhaul communication channel may comprise an optical channel. Even though the backhaul communication channel offers high bandwidth, the volume of data transfer between the CP30and base stations16necessary for effective CoMP system operation may easily overwhelm the capacity of the backhaul communication channel. Accordingly, according to embodiments of the present invention, RF IQ signal data transfer between the CP30and base stations16is compressed prior to transmission, by the compression unit200, and decompressed upon reception, by the decompression unit200.

The base station16, also depicted inFIG. 2, includes one or more antennas18and a corresponding number of duplexers20, front end receivers22, and transmitters26. The base station16also includes a compression unit100, decompression unit200, and a backhaul channel interface24. As known in the art, the base station16receives uplink wireless communication signals from UE12in the cell14. These signals are routed by duplexers20to a front end receiver22, which may include signal processing functions such as low noise amplification, filtering, analog to digital conversion, frequency downconversion, IQ separation, and the like. The processed received signals are compressed in the compression unit100and transmitted to the CP30across the backhaul communication channel via the backhaul interface24. Signals to be transmitted to the UE12are received by the base station16from the CP30, in compressed format, by the backhaul interface24. These signals are decompressed by the decompression unit200, and may be modulated, filtered, amplified, and the like by transmitter circuit26. The resulting RF signal is then routed by the duplexer20to an antenna18for transmission to UE12in the cell14. In general, separate signals may be received from the CP30for transmission on each antenna18.

In a typical wireless communication system, signals are transmitted in a multitude of time slots. Let T denote the duration of one such time slot. Within each time slot, the statistical behavior of the received or transmit signal remains roughly stationary, which is especially true when the base stations16in the network are synchronized. Let Tsdenote the sampling time period of the radio signal, and let N≡T/Tsbe the number of time samples within a time slot. Let y[n,m]=[y1[n,m],y2[n,m], . . . ,yna[n,m]]Tdenote an na-dimensional time-domain complex-valued sampled signal to be communicated through a backhaul link connecting a Central Processor30from or to a particular base station16, where nadenote the number of antennas at the base station16and nε{1,2, . . . ,N} denotes the sample time index within one time slot, mε{1,2, . . . ,M} denotes the time-slot index, and M denotes the maximum number of time slots over which the statistics of the signal remain roughly stationary. For example, in the Long Term Evolution (LTE) of UMTS/HSDPA, M is greater than or equal to 2, depending on the scheduling decision and user activity.

According to embodiments of the present invention, the backhaul signal is compressed by exploiting its spatial and temporal correlation through linear transformations. In one embodiment, two stages of linear transformations are applied to the sampled signal y[n,m]. The structure of the encoding unit100and decoding unit200are depicted inFIGS. 3 and 4, respectively. The first linear transformation Wt110applies to the time-domain sampled signals. The second linear transformation Wa120applies to the intermediate samples across the antennas. Then, the feature samples are quantized130, according to information from a bit allocation unit140, before being transported over the backhaul. Depending on a-priori knowledge of the underlying signal, the order of the linear transformation110,120is interchangeable. For example, the compression unit100can instead first exploit known structure in the spatial domain before processing the time-domain signals.

The linear transformation Wt110, which is applied to the time-domain sampled signals, is described first. Let Yi[m]≡[yi[l,m],yi[2,m],L,yi[N,m]]Tdenote a vector formed by stacking the time samples received from, or to be transmitted by, antenna i in time slot m. For example, the time-domain analysis and synthesis transformation, Wt110and Tt210(seeFIG. 4) respectively, may be selected as the Discrete Cosine Transform (DCT) and the inverse DCT matrices—both of which are a form of Fast Fourier Transform (FFT). Alternatively, the transformation matrices may be adaptively computed through the eigen-decomposition of the N×N covariance matrix RY=E[Yi[m]Yi[m]H] which can be approximately computed by

Let the eigen decomposition of RYbe UtΛtUtH, where ΛYis a diagonal matrix with the eigenvalues {λt,j}Nj=1of RYas its diagonal elements, and Utis an orthonormal matrix with the eigenvectors of RYas its columns. In this case, the analysis and synthesis transformation matrices110,210should be set as Wt=UtHand Tt=Ut, respectively.

The intermediate samples, {zi[n,m]}, generated after the time-domain transformation110are related to Yi[m] for each antenna i through

The intermediate samples can be compressed using a-priori information, such as nulls in the intermediate transform domain created by scheduling. In this case, the compression unit100can determine which transformed intermediate transform domain components should be quantized and which components should be excluded. A similar method can be applied to exploit time-domain scheduling information in, for example, a time-division multiplexed system, in which case, Wt110may simply be chosen as the N by N identity matrix.

After the time-domain transformation110, the intermediate samples are grouped across the spatial dimension, i.e. z[n,m]=[z1[n,m],z2[n,m],L,zna[n,m]]T, to prepare for transformation in the spatial domain.

The transformations in spatial domain120(i.e., exploiting correlation across antennas), are now described. Let Wa120and Ta220denote an analysis transformation matrix120of dimension Ka×naand a synthesis transformation matrix220of dimension na×Karespectively. Since we are interested in compressing the signal, the transformation matrices are designed such that Ka≦na. For each time sample n in time slot m, a feature vector x[n,m] of dimension Kais computed using Wa120given by
x[n,m]≡[x1[n,m],x2[n,m],L,xKa[n,m]]T=Waz[n,m]
and then quantize130each component feature xi[n,m] individually using a codebook Ciof size |Ci|=2bi, where bidenotes the number of bits140needed to index each code vector in Ci. Different codebook sizes can be assigned to different feature components depending on their relative significance (e.g., measured by their statistical variances). Associated with each bi-bit index for the codebook Ciis a reproduction codeword {circumflex over (x)}i[n,m]εCiof the feature component. The goal of the compression unit100is to select

x^⁡[n,m]≡[x^1⁡[n,m],x^2⁡[n,m],L,x^Ka⁡[n,m]]T∈C1×C2×L×CKa@∏i=1Ka⁢⁢Ci
such that Ta{circumflex over (x)}[n,m] closely approximates z[n,m]. For example, the compression unit100should compute

From the orthogonality principle, we have

Hence, for any given synthesis transform Ta220, the best analysis transform120is given by its pseudo inverse
Wa=(TaHTa)−1TaH,   (3)
which projects z[n,m] onto the column space of Ta. Substituting equation (3) into (2), we have

In general, the quantization of all feature components should be performed jointly over the product codebook

∏i=1Ka⁢Ci
with the joint distortion metric
d(x,y)=(x−y)HTaHTa(x−y)
to achieve the best performance.

In the special case where the columns of Ta220are chosen to be orthogonal, we have

x^i⁡[n,m]=∑i=1Ka⁢wi⁢argxi⁢⁢%∈Ci⁢⁢min⁢xi⁡[n,m]-x⁢⁢%2.
where widenotes the squared norm of the i-th column of Ta220. Hence, the compression unit100can quantize130each feature component xi[n,m] using Ciindividually with the per-feature distortion metric
d(x, y)=|x−y|2
without affecting the overall performance. This presents significant complexity reduction since joint optimization across all feature dimensions simultaneously is not required, as in the original formulation in equation (1).

In one embodiment of the spatial transform, Ta120is a square orthonormal matrix with Ka=na. In this case, also Wa=TaH. Ta120can be chosen to be a standard predetermined orthonormal matrix, such as the FFT or the DCT matrices. Alternatively, an orthonormal matrix can be adaptively computed that maximizes the energy compaction in fewer feature components. When the signal samples z[n,m] are Gaussian distributed, the best transform is the Karhunen-Loeve Transform (KLT) U whose columns are the eigenvectors of the signal covariance matrix Rz≡E[z[n,m]z[n,m]H], which can be computed approximately by averaging over different time samples and time slots as

Moreover, the eigenvalues {λi} of Rzalso provides the relative significance of the corresponding feature component {xi[n,m]}, which can be used to determine the number of bits {bi}140used to quantize {xi[n,m]}130. Specifically, λi=σi2, where σi2denotes the variance of the i-th transformed coefficient. There are a number of possible methods to determine {bi}140depending on the type of quantizer130used. For fixed-rate quantization, {b1}140can be computed using a high-resolution approximation as

bi≅btotalna+12⁢log⁡(σi2/(∏j=1na⁢⁢σj2)1/na)
where btotaldenotes the total number of bits available to index all components {{circumflex over (x)}i[n,m]}. The approximation can be further simplified by allocating equal number of bits to the first k components, where σk2>β and k<na. In this case, β is the minimum energy that determines if a feature component should be neglected. Alternatively, one can select the Breiman, Friedman, Olshen, and Stone (BFOS) algorithm to optimally allocate the bits for a given set of component codebooks {Ci}, as described in the paper by E. A. Riskin, “Optimal bit allocation via the generalized BFOS algorithm,” published in theIEEE Trans, Info Thy., vol 37, pp. 400-402, March 1991, the disclosure of which is incorporated herein by reference in its entirety. The quantizer130for each coefficient can also be a variable-rate quantizer130. In this case, it is preferred to use a quantizer130with a fixed step or cell size. The fidelity of the reproduced signal is determined by the choice of the step or cell size.

In one embodiment, the spatial transformation matrices120,220described above are computed in real time based on the spatial and temporal statistics of the radio signal, which are also measured from the radio signal. The coefficients of the spatial transformation matrices120,220are transmitted over the backhaul, to allow the radio signal to be reconstructed at the receiving end. The frequency at which such statistics and coefficients are computed depends on the stationarity of the underlying signal, which depends on the scheduling decisions and user activity. For example, in the LTE network, this period lasts at least 2 time slots. The representation of the spatial transform coefficients and the bit allocation information can be done using fixed-rate (for example, 8 bit per coefficient) quantization130.

FIG. 5depicts a method300of transmitting wireless communication signals across a backhaul channel between a base station16and a Central Processor30in a CoMP system10. A wireless communication signal is represented as a plurality of time-domain, complex-valued signal samples (block302), e.g., I and Q components. At least one linear transformation110,120of the signal samples is performed across one or more of time and antenna spatial domains to generate transformed signal coefficients (block304). As described above, a-priori knowledge of the signal characteristics may determine the order of the linear transformations110,120. The transformed signal coefficients are quantized (block306), in one embodiment using a variable number of quantization bits determined by a bit allocation unit140based on the variances of the transformed coefficients. The quantized transformed signal coefficients and quantization information (i.e., the number of quantization bits or the coefficient variances) are transmitted across the backhaul (block308), i.e., from the base station16to the CP30for uplink signals, or from the CP30to one or more base stations16for downlink signals. The method steps are repeated while the wireless signal statistics remain substantially stationary (block310). When the signal statistics have changed appreciably (block310), one or more linear transformation matrices110,120are updated (block312), and the method300repeats. A similar, inverse method is performed at the receiving one of the base station16or CP30.

Embodiments of the present invention provide an effective method to compress the digitized representations of complex-valued radio signals either received from or to be transmitted by each antenna at a remote base station16in a CoMP cluster10. The compression exploits spatial and temporal correlations in the radio signals, and extracts feature components from these signals. In some embodiments the method utilizes a-priori knowledge, such as known scheduling information, to neglect part of the signal component and reduce both computational and backhaul bandwidth burden.

Embodiments of the present invention can optimally compress the feature components of wireless communication signals according to known total backhaul bandwidth constraints. The bandwidth is then allocated to each feature component according to its significance. Moreover, in some embodiments, because feature components are sorted according to their significance, the backhaul network may adaptively reject lower impact feature components, to further preserve bandwidth in the backhaul links.