GB2467144A - Vector perturbation in a Multiple Input Multiple Output (MIMO) communication system with legacy terminals - Google Patents

Abstract

The invention relates to a method of processing information prior to transmission from a multi-antenna device in a multi-user MIMO network having at least one new user terminal supporting vector perturbation and at least one legacy user terminal which does not support vector perturbation, the information being a data vector comprising corresponding data for each antenna of the multi-antenna device. In one embodiment the data vector is precoded, the precoding comprising scaling the data vector by means of a precoding matrix defining pseudo inverse channel characteristics of the new and legacy user terminals, the characteristics of the new terminal being orthogonal to those of the legacy terminal, and applying a perturbation to the precoded information. The perturbation is expressible as a perturbation vector and comprises unperturbed data for the legacy terminal, thereby allowing the simultaneous transmission of information to the new and legacy user terminals. In a second embodiment (fig. 7), the perturbation vector comprises a diagonal matrix of perturbation elements and a complex integer vector, and at least one element corresponding to the legacy terminal is selected and scaled. In a third embodiment (fig. 9), a perturbation vector is selected by solving an integer least squares problem such that a normalization factor is minimized, and includes applying a constraint.

Description

WIIRELESS COMMUNICATIONS METHODS AND APPARATUS

Field of the Invention

The present invention is in the field of wireless communication and particularly, though not exclusively, the field of multiple input, multiple output (MIIMO) communications.

Background of the Invention

In multiple input multiple output (MIMO) systems employing precoding, channel knowledge is used at the transmitter in order to enhance link quality.

A conventional MIMO system, with n. transmit and R receive antennas, can be modelled mathematically in the complex narrowband notation as: y=Hx+n (1) where H is the R x channel matrix, x is the T xl transmit vector of complex symbols with an imposed transmit power constraint, for instance 1 without loss of generality, y is the R xl receive vector, and n is an x 1 zero-mean white Gaussian distributed noise vector with variance o.

Precoding can be also employed in OFDM systems. In such a system, it can be applied, for example, for each subcarrier separately or for a group of subcarriers. For example, an OFDM system having 512 subcarriers can have a joint transmit power constraint of 512, or an average transmit power constraint Ixl2 =1, without loss of generality.

Precoding can be achieved in several ways. For example, the Moore-Penrose pseudoinverse P H = H" (ifflH) can be applied at the transmitter side, which, in one network configuration, can be at a base station. If 12 = R' P becomes simply P if'. This precoding step is necessary for instance in multi-user MIMO systems, wherein each element of y will be assigned to an independent user terminal (UT), and therefore no cooperation will be possible between the UTs. In such a case, the precoding matrix P will suppress the inter-user interference; nevertheless the above technique may also be employed in a single-user MIMO system or a multi-user multi-antenna MIMO system, where one or more UTs have more than one receive antenna.

A further example of precoding is the regularised pseudoinverse P = II"' (pH + as set out in "A vector-perturbation technique for near-capacity multiantenna multiuser communication -part I: channel inversion and regularization," (C. B. Peel, B. M. Hochwald, and A. L. Swindlehurst, IEEE Trans. on Commun., vol. 53, no. 1, pp. 195- 202, Jan. 2005), hereinafter referred to as "Peel et al." , where a = Kcx is defined, and K is the number of spatial streams.

However, a drawback of precoding by means of the pseudoinverse channel matrix is that it can lead to an increase in transmitted power. This is addressed in "A vector-perturbation technique for near-capacity multiantenna multiuser communication---part II: perturbation," (B. M. Hochwald, C. B. Peel, and A. L. Swindlehurst, IEEE Trans. on Commun., vol. 53, no. 3, pp. 537-544, March 2005) hereinafter referred to as "Hochwald et al.". Variations in transmitted power are undesirable, particularly as they may violate performance constraints for a device. They may also lead to increased power consumption, which is an important factor in the design of a handheld or otherwise portable communications device.

To illustrate problems faced and identified in the prior art, an example will now be given. In this example, u denotes the symbols, prior to precoding, to be transmitted.

The vector is precoded by means of a precoding matrix P, which is chosen to be the Moore-Penrose pseudoinverse P ii, as s=Pu (2) The Moore-Penrose pseudoinverse is well known, but is particularly referenced in "On the reciprocal of the general algebraic matrix" (E. H. Moore; Bulletin of the American Mathematical Society 26: 394-395) and "A generalized inverse for matrices" (R.

Penrose; Proceedings of the Cambridge Philosophical Society 51: 406-4 13).

Prior to transmission, the precoded signal s has to be scaled in order to fulfil the power restriction xV2 = 1, such that x=_S (3) where y = Pu2 as set out in Peel et al.. This approach assumes perfect knowledge of at the receiver side.

The normalisation factor is often very large because of the large singular values of the precoding matrix P, i.e., of the pseudoinverse of the channel matrix H (such as noted in papers by Hochwald et al. and by Peel et al., cited above). This can cause noise amplification at the receiver side since the receive symbol vector y = J(Hx + n), or equivalently, -4= = (Hx + n), is impaired by a scaled Gaussian noise vector,Jn. In a .11 MIMO OFDM system, the instantaneous normalisation factor of a transmission can be determined for each subcarrier or the average normalisation factor can be determined as a mean value for all the subcarriers, or any other grouping of resources to resource blocks and the like. This may be, for example in an Orthogonal frequency-division multiple access (OFDMA) MIMO system, where different subcarriers are assigned to different groups of terminals, thus representing multiple multi-user MIIMO configurations.

Hochwald et al. suggests that one way of overcoming this noise amplification is to ensure that the transmitted data u does not lie along the singular values of if' (or H, as the case may be). This approach is also described in US73 17764. The idea is to allow u to be perturbed by a complex vector. The perturbed data vector is then: ü=u+rl (4) where r is a positive real number and I is a complex integer vector. The scalar T is selected to be sufficiently large that the receiver may apply element-wise a modulo function to y (5)

L TJ

to obtain ü, where [ ] rounds towards the nearest integer closest to zero. It will be noted that f(y1) is applied to real arid imaginary parts separately. It should be recognised by the reader that ü is not quantised and therefore contains additive noise.

Hochwald et al. also suggests that the constellation shift parameter r should be r=21c1 + (6) 2) where is the absolute value of the real or imaginary part of the constellation symbol with greatest magnitude, and is the smallest distance between two constellation symbols. It will be understood that the foregoing is set out for M-QAM constellations; non-square constellations such as PSK (Phase shift keying) or other, such as hexagonal constellations, may have a constellation shift parameter r that is essentially the distance between the centres of repeated equidistantly shifted constellations.

Figure 1 illustrates the modulo operation at the receiver side for a 16-QAM constellation. The received symbol, marked with an x', is shifted from the extended constellation (unfilled points) back to the original constellation (filled points), in which the symbol detection stage will be done. As will be appreciated by the reader, the average number of neighbouring points will be increased, as points of the original constellation which were previously considered to be at the edge of the constellation now have a complete set of neighbours. This has an impact on the error protection of the outer symbols. The shift parameter r, as the distance between the centres of the respective constellations, can lower this impact if it is chosen to be greater than defined in Equation 6.

In accordance with the above, for a given r, 1 can be selected in order to minimise r ls2, such that: 1 = arg mm1 JJP(u + rI' )1J2 (7) This is an integer least squares problem in the dimension of u, for the solution of which there exist a large number of algorithms. For instance, the reader is directed to "Closest point search in lattices" (E. Agrell, T. Eriksson, A. Vardy, and K. Zeger, IEEE Transactions on Information Theory, vol. 48, no. 8, pp. 2201-2214, Aug. 2002) and to the references noted in Hochwald et al., especially the Fincke-Pohst algorithm, which is used for space-time demodulation in "Lattice code decoder for space-time codes," (M.

0. Damen, A. Chkeif, and J.-C. Belfiore, IEEE Commun. Letters, vol. 4, pp. 161-163, May 2000), where it is called a sphere decoder. Because this algorithm can be used for encoding the data vector u, it is called a "sphere encoder".

If G is defined as the set: G={a-i-ibla,bEZ}, with i2 =-1, that is, the set of complex-valued integers, then an approximation oft can be calculated, and the perturbation vector is then given as approx = _TQTGK {T_1u}, where the quantisation function QGK {} rounds the K-dimensional vector towards the nearest complex-valued point of the K-dimensional integer lattice, scaled with t (depicted by VGK), where K is the number of spatial streams, i.e., the dimension of the vector u.

A practical implementation as an integer rounding function, indicated by G, can be appmx =-TQ {Tu} (8) Due to the denominator r, the complex-integer-rounding function operates in a scaled integer lattice.

This is as set out in "Lattice-reduction-aided broadcast precoding," (C. Windpassinger, R. F. H. Fischer, and 3. B. Huber, IEEE Trans. on Commun., vol. 52, no. 12, pp. 2057- 2060, Dec. 2004 -"Windpassinger et al.").

A number of lattice reduction algorithms exist. Any one of them can be used to calculate a transformation matrix, T, such that a reduced basis, P, is given by PT. The matrix T contains only complex integer entries and its determinant is Idet(T)I = 1 and thus is called a unimodular matrix.

The unimodular matrix T is given by means of a lattice reduction of the precoding matrix P with the LLL algorithm "Factoring Polynomials with Rational Coefficients" (A. Lenstra, H. Lenstra and L. Lovasz, Math Ann., Vol. 261, PP. 515-534, 1982.), but any other algorithm for reducing a lattice basis is also applicable.

The normalisation factor y is then determined, by means of a closest point approximation, as: = = P(u + ri)2 The complete transmission employing non-linear precoding can thus be formulated as YJ[H[J_J+flJ (10) with y being the receive signal of a single user or a plurality of users, each receiving one or more elements y, of the vector y.

A block diagram of a transmission train employing data perturbation is shown in figure 2. As illustrated in Figure 2, vector perturbation is carried out on the transmitted data u in a vector perturbation unit 20. The perturbed data is passed to be multiplied by the pseudo inverse H4 in block 22, which is equivalent to equation 2 set out above. The next block 24 represents division byJ, which is a normalisation step. The resultant vector x is re-multiplied by the channel matrix H (in block 26), to which is added a noise vector n. In block 28, the resultant vector y is re-multiplied by the square root of the normalisation factor y and then modulo r is applied to arrive at the perturbed data vector ü.

Finding the perturbation vector 1 can be done in several ways. For instance, the solution of 1 arg mm1 P(u + r1')I2 (11) is an integer least squares problem for which there exist a large number of solution methods, such as that disclosed in Agrell et a!. and also as disclosed in references contained in Hochwald et al. Moreover, "On the expected complexity of integer least-squares problems," (B. Hassibi and H. Vikalo, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002 (ICASSP 02), vol. 2, pp. 1497-1500) describes complexity in the context of sphere decoding.

Further, approximation by means of lattice reduction is introduced in Windpassinger et al. A Multi-User MIIMO (MU-MIIMO) system is a set of advanced M]MO system that exploits the availability of multiple independent terminals (or users) in order to enhance the communication capabilities of each individual terminal. Essentially, all the users in the system are coordinated for communications by considering the requirements of each user, such as scheduling algorithms, Quality of Service requirements, and so on. The fundamental advantage of a MTJ-MIMO system is that a direct gain in multiple access capacity can be achieved by allowing spatial multiplexing gain to be obtained at a base station, without having the need of multiple antenna terminals. This allows small sized terminals to be developed at a low cost, while keeping the implementation cost and complexity at the base station. An example of a MU-M1IMO scheme is Space-Division Multiple Access (SDMA), which allows a terminal to transmit (or receive) signal to (or from) multiple users in the same band simultaneously.

Linear precoding is essentially a generalisation of a conventional SDMA scheme, where users are assigned different precoding matrices at the transmitter. The precoders are designed jointly based on channel state information (CSI) of all the users, although other sources of information are equally possible, for example a code book entry.

It has been noted that non-linear precoding provides better performance than linear precoding in that the error rate performance can be improved through additional transmit signal processing. However, as described in the preceding paragraphs, non-linear precoding requires a nonlinear operation to be performed at the receiver in order to reconstruct the transmitted symbols.

An example of a practical implementation of a conventional MTJ-MIMO system 40 comprising a transmitter device 42, and a plurality of receiver devices 46, 48 is illustrated in figure 3.

In this example, the transmitter device 42 in figure 3 is operable to transmit signals to receiver devices 46, 48 in a wireless network through a wireless channel. In this illustrated example, three transmit antennas 44 are provided, though practical implementations may include more (or less) antennas depending on the application.

The receiver devices 46, 48 as shown in figure 3 include "legacy terminals" or (legacy users) 48 and "new terminals" (or new users) 46. The terms "legacy terminal(s)" and "legacy user(s)" are used interchangeably herein to describe a terminal which is not capable of performing a modulo operation to reconstruct a transmitted signal in which non-linear precoding is applied, or any other recognition and processing of vector perturbation employed at the transmitter. The term "legacy" is also commonly used in the field of wireless communications to refer to previous generation of hardware andlor software that continues to be used. One example of a legacy terminal is a second generation GSM (Global System for Mobile Communications) mobile terminal.

In contrast, the terms "new terminal(s)" and "new user(s)" are used interchangeably herein refers to a terminal that supports modulo operation, or any other recognition and processing of vector perturbation employed at the transmitter.

It will be appreciated by a person skilled in the art that a MTJ-MIMO system can only operate in one precoding mode, that is either in linear precoding or in non-linear precoding, at any one time andlor frequency. Therefore, the conventional method of transmitting signals in a network having both legacy terminals and new terminals is by transmitting signals to the legacy terminals (using linear precoding) and to the new terminals (using non-linear precoding) at different time or frequency. For example, the transmitter device transmits signals to the legacy terminals using linear precoding at time (t1), and subsequently transmits signals to the new terminals using non-linear precoding at time (t2). However, it is noted that this method does not fully utilise the bandwidth of the network efficiently.

It is further noted that one of the main problems encountered by a legacy terminal operating in a MU-MIMO system providing non-linear precoding is that the legacy terminal is not capable of unperturbing the perturbation shift vector of equation 4. One possible method of overcoming this problem is by setting the corresponding element of I to zero. However, this method can lead to a power normalisation higher than an average power normalisation, which is undesirable in terms of system performance in most applications.

Thus, it is desirable to provide a method of transmitting signals simultaneously to legacy terminals and new terminals in a network.

Summary of the Invention

In a first aspect of the present invention there is provided a method of processing information prior to emission from a multi-antenna device in a multi-user network having at least one user andlor at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna device, the method comprising precoding said data vector prior to emission thereof, the precoding comprising scaling said data vector by means of a precoding matrix defining pseudo inverse channel characteristics of said at least one user andlor at least one legacy user, said pseudo inverse channel characteristics of said at least one user being orthogonal to said pseudo inverse channel characteristics of said at least one legacy user, and applying a perturbation to said precoded information, said perturbation being expressible as a perturbation vector, wherein said perturbation vector comprises unperturbed data for said at least one legacy user thereby allowing simultaneous emission of information from the multi-antenna device to the at least one user and the at least one legacy user.

Use of the precoding matrix determined using the steps above can allow the transmitter device to transmit signals to the legacy terminals and new terminal simultaneously, without the need of unperturbing the perturbation shift vector at the legacy terminal.

The precoding matrix may be defined by applying a constraint to said pseudo inverse channel characteristics of said at least legacy user.

The step of applying the constraint may include the following: determining a column vector of precoding matrix representing said pseudo inverse channel characteristics of said legacy user, determining a channel matrix defining a channel between said multi-antenna device and said at least one user andlor at least one legacy user, and replacing said determined column with pseudo inverse of a row vector of said determined channel matrix, said row vector representing said channel characteristics of said legacy user.

The step of replacing may be expressible in the form p(.., 1) = (H(j, .)), for J = legacy' where P is a precoding matrix, and H is a channel matrix.

In a second aspect of the present invention there is provided a method of processing information prior to emission from a multi-antenna device in a multi-user network having at least one user and/or at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna device, the method comprising applying a perturbation to said data vector in order to generate a perturbed data vector, said perturbation being expressible as a perturbation vector comprising a diagonal matrix and a complex integer vector, said diagonal matrix being perturbation elements corresponding to said at least one user and/or at least one legacy user, selecting at least one of said perturbation elements corresponding to said at least one legacy user, scaling said selected at least one of said perturbation elements.

The scaling of said selected at least one of said perturbation elements may be performed on the basis of a scaling factor employed in said scaling.

The scaling factor may be a number substantially higher than 1, and is expressible in the form L >> 1, where L is a scaling factor.

The perturbation vector may be selected by solving an integer least squares problem such that a normalisation factor is minimised The solving of the integer least squares problem may include applying a further scaling factor to the integer least squares problem.

The integer least squares problem may be expressible in the form all arg mm1, IP(u + UFI2, where u + L)I' is a perturbed data vector, u is a data vector, is a diagonal matrix of positive real numbers, 1' is a complex integer vector, and P is a precoding matrix.

The further scaling factor may be applied to the integer least squares problem such that said integer least squares problem is expressible in the form all arg min u + where P = In an embodiment of the above aspect, the method may further comprise the step of determining a solution for solving said integer least squares problem.

In one example described herein, the solution may be a lattice reduction closest point approximation expressible in the form approx = -TQQK {t-' u}, where T is a lattice reduction transformation matrix.

The lattice reduction transformation matrix may be determined by applying a lattice reduction algorithm, and is expressible in the form t = LLL(P), where P P. One example of a lattice reduction algorithm is the LLL algorithm.

The perturbation elements may be a plurality of constellation shift parameter.

The plurality of constellation shift parameter may be distinct from each other.

In a third aspect of the present invention there is provided a method of processing information prior to emission from a multi-antenna device in a multi-user network having at least one user and/or at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna device, the method comprising applying a perturbation to said data vector in order to generate a perturbed data vector, said perturbation being expressible as a perturbation vector comprising a number and a complex integer vector, said complex integer vector comprising at least one element corresponding to said at least one user and/or at least one legacy user, said perturbation vector being selected by solving an integer least squares problem such that a normalisation factor is minimised, wherein said solving the integer least squares problem includes applying a constraint to the integer least squares problem.

The integer least squares problem may be expressible in the form 1 = arg mm1. P(u + 2,where u + ri' is a perturbed data vector, u is a data vector, ris --a positive real number, 1' is a complex integer vector, and P is a precoding matrix.

The applying of constraint may be performed by applying a zero constraint to said at least one element corresponding to said at least one legacy user, such that the integer least squares problem may be expressible in the form 1= arg mm1, + r In a fourth aspect of the present invention there is provided a signal processing apparatus for processing information for a multi-antenna wireless communication apparatus in a multi-user network having at least one user and/or at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna emission, the signal processing apparatus comprising a precoder for precoding said data vector, the precoder comprising scaling means for said data vector by means of a precoding matrix defining pseudo inverse channel characteristics of said at least one user andlor at least one legacy user, said pseudo inverse channel characteristics of said at least one user being orthogonal to said pseudo inverse channel characteristics of said at least one legacy user so as to allow simultaneous emission of information from the multi-antenna device to the at least one user and the at least one legacy user, and perturbations means for applying a perturbation to said precoded information, said perturbation being expressible as a perturbation vector.

The scaling means may be operable to apply a constraint to said pseudo inverse channel characteristics of said at least legacy user, thereby defining the precoding matrix.

The scaling means may be further operable to: determine a column vector of precoding matrix representing said pseudo inverse channel characteristics of said legacy user, determine a channel matrix defining a channel between said multi-antenna device and said at least one user andlor at least one legacy user, and replace said determined column with pseudo inverse of a row vector of said determined channel matrix, said row vector representing said channel characteristics of said legacy user.

The replaced determined colunm may be expressible in the form P(:, Iegacy)= (ii(j, :)), for j = 1legacy' where P is a precoding matrix, and H is a channel matrix.

In a fifth aspect of the present invention there is provided a signal processing apparatus for processing information for a multi-antenna wireless communication apparatus in a multi-user network having at least one user and/or at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna emission, the signal processing apparatus comprising perturbation means for applying a perturbation to said data vector in order to generate a perturbed data vector, said perturbation being expressible as a perturbation vector comprising a diagonal matrix and a complex integer vector, said diagonal matrix being perturbation elements corresponding to said at least one user and/or at least one legacy user, selection means for selecting at least one of said perturbation elements corresponding to said at least one legacy user, scaling means for scaling said selected at least one of said perturbation elements.

The scaling means may be operable to apply a scaling factor to said selected at least one of said perturbation elements.

The scaling factor may be a number substantially higher than 1, and is expressible in the form L >> 1, where L is a scaling factor.

The selection means may be operable to solve an integer least squares problem such that a normalisation factor is minimised.

The selection means may be further operable to apply a further scaling factor to the integer least squares problem so as to solve the integer least squares problem.

The integer least squares problem may be expressible in the form lall = arg mm1. P(u + where u + )1' is a perturbed data vector, u is a data vector, ) is a diagonal matrix of positive real numbers, 1' is a complex integer vector, and P is a precoding matrix.

The further scaling factor may be applied to the integer least squares problem such that said integer least squares problem is expressible in the form lall = arg mm1. (o u + ,,)02 where P = P0 In an embodiment of the above aspect, the signal processing apparatus further comprises means for determining a solution for solving said integer least squares problem.

In one example described herein, the solution may be a lattice reduction closest point approximation expressible in the form approx = "QGK {t'o u}, where T is a lattice reduction transformation matrix.

The lattice reduction transformation matrix may be determined by applying a lattice reduction algorithm, and is expressible in the form T = LLL(1), where f' = . One example of the lattice reduction algorithm is the LLL algorithm.

The perturbation elements may be a plurality of constellation shift parameter.

The plurality of constellation shift parameter may be distinct from each other.

In a sixth aspect of the present invention there is provided a signal processing apparatus for processing information for a multi-antenna wireless communication apparatus in a multi-user network having at least one user andlor at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna emission, the signal processing apparatus comprising perturbation means for applying a perturbation to said data vector in order to generate a perturbed data vector, said perturbation being expressible as a perturbation vector comprising a number and a complex integer vector, said complex integer vector comprising at least one element corresponding to said at least one user andlor at least one legacy user, and a selection means for selecting said perturbation vector by solving an integer least squares problem such that a normalisation factor is minimised, wherein said solving the integer least squares problem includes applying a constraint to the integer least squares problem by means of selection means.

The integer least squares problem may be expressible in the form 1 = arg mm1. P(u + , where u + ri' is a perturbed data vector, u is a data vector, ris a positive real number, 1' is a complex integer vector, and P is a precoding matrix.

The selection means is further operable to apply a zero constraint to said at least one element corresponding to said at least one legacy user, such that the integer least squares problem may be expressible in the form 1 = argmin1, P[u + An aspect of the invention provides a computer program product comprising computer executable instructions which, when executed by a computer, cause the computer to perform a method as set out above. The computer program product may be embodied in a carrier medium, which may be a storage medium or a signal medium. A storage medium may include optical storage meaiis, or magnetic storage means, or electronic storage means.

The above aspects of the invention can be incorporated into a specific hardware device, a general purpose device configure by suitable software, or a combination of both. The invention can be embodied in a software product, either as a complete software implementation of the invention, or as an add-on component for modification or enhancement of existing software (such as, as a plug in). Such a software product could be embodied in a carrier medium, such as a storage medium (e.g. an optical disk or a mass storage memory such as a FLASH memory) or a signal medium (such as a download). Specific hardware devices suitable for the embodiment of the invention could include an application specific device such as an ASIC, an FPGA or a DSP, or other dedicated functional hardware means. The reader will understand that none of the foregoing discussion of embodiment of the invention in software or hardware limits future implementation of the invention on yet to be discovered or defined means of execution.

Brief description of the drawings

Further aspects, features and advantages of the invention will become apparent from the following description of specific embodiments thereof with reference to the accompanying drawings, in which: Figure 1 illustrates a 16 QAM constellation having a modulo operation applied thereto; Figure 2 illustrates a block diagram of a transmission train employing data perturbation; Figure 3 illustrates an exemplary wireless communications network arrangement having legacy terminals and new terminals; Figure 4 illustrates an exemplary wireless communications device incorporating a specific embodiment of the invention; Figure 5 illustrates a flow diagram of a precoding method in accordance with a first embodiment of the invention; Figure 6 illustrates a comparison simulation result obtained using the method in accordance with the first embodiment of the present invention, and the method in

accordance with the prior art;

Figure 7 illustrates a flow diagram of a precoding method in accordance with a second embodiment of the invention; Figure 8 illustrates a comparison simulation result obtained using the method in accordance with the second embodiment of the present invention, and the method in

accordance with the prior art;

Figure 9 illustrates a flow diagram of a precoding method in accordance with a third embodiment of the invention; and Figure 10 illustrates a comparison simulation result obtained using the method in accordance with the third embodiment of the present invention, and the method in

accordance with the prior art.

Detailed Description

Specific embodiments of the present invention will be described in further detail on the basis of the attached diagrams. It will be appreciated that this is by way of example only, and should not be viewed as presenting any limitation on the scope of protection sought.

The present invention will now be described with reference to an implementation of a wireless communication device. Figure 4 illustrates schematically hardware operably configured (by means of software or application specific hardware components) as a wireless communication device 100.

The wireless communication device 100 illustrated in Figure 4 is generally capable of being used in a M1J-MIMO context, to establish a M1J-MIMO communications channel with one or more other devices and, in accordance with a specific embodiment of the invention, to take account of channel information so as to derive a pre-coding scheme appropriate to the quality of the channel. The reader will appreciate that the actual implementation of the wireless communication device is non-specific, in that it could be a base station or a user terminal.

The device 100 comprises a processor 120 operable to execute machine code instructions stored in a working memory 124 andlor retrievable from a mass storage device 122. By means of a general purpose bus 130, user operable input devices 136 are capable of communication with the processor 120. The user operable input devices 136 comprise, in this example, a keyboard and a mouse though it will be appreciated that any other input devices could also or alternatively be provided, such as another type of pointing device, a writing tablet, speech recognition means, or any other means by which a user input action can be interpreted and converted into data signals.

Audio/video output hardware devices 138 are further connected to the general purpose bus 130, for the output of information to a user. Audio/video output hardware devices 138 can include a visual display unit, a speaker or any other device capable of presenting information to a user.

Communications hardware devices 132, connected to the general purpose bus 130, are connected to antennas 134. In the illustrated embodiment in Figure 4, the working memory 124 stores user applications 126 which, when executed by the processor 120, cause the establishment of a user interface to enable communication of data to and from a user. The applications in this embodiment establish general purpose or specific computer implemented utilities that might habitually be used by a user.

Communications facilities 128 in accordance with the specific embodiment are also stored in the working memory 124, for establishing a communications protocol to enable data generated in the execution of one of the applications 126 to be processed and then passed to the communications hardware devices 132 for transmission and communication with another communications device. It will be understood that the software defining the applications 126 and the communications facilities 128 may be partly stored in the working memory 124 and the mass storage device 122, for convenience. A memory manager could optionally be provided to enable this to be managed effectively, to take account of the possible different speeds of access to data stored in the working memory 124 and the mass storage device 122.

On execution by the processor 120 of processor executable instructions corresponding with the communications facilities 128, the processor 120 is operable to establish communication with another device in accordance with a recognised communications protocol.

A method of performing non-linear precoded MU-MIMO transmission in a network comprising legacy terminals and new terminals according to a first embodiment of the invention will be discussed with reference to the flow chart of figure 5. The method in accordance with the first embodiment can be achieved by generating a precoding matrix based on the inverse channel such that at least one precoding vector (that is the vector corresponding to the legacy terminal) is orthogonal to the rest of the vectors in the precoding matrix.

Figure 5 illustrates the steps of generating a precoding matrix to support this method.

The method commences with an initialisation process (step Si -2) including determining the channel matrix, H, and defining the weight matrix, W. According to equation (2), the data vector, u, is precoded by means of a channel inverse precoder P = W' (or pseudoinverse) as S Pu The pseudoinverse for the precoding matrix is defined as (12) where ( )H denotes the Hermitian transpose of a matrix.

Accordingly, a weight matrix can be defined as W=P (13) In step S1-4, the colunm(s) in the weight matrix that correspond(s) to the transmit symbol of the legacy terminal is determined. In this example, one of the columns in the weight matrix, W, corresponds to the transmit symbol of the legacy terminal. It is further noted that in practical implementations the number of the columns in the weight matrix that correspond to the transmit symbol depends on the number of legacy terminals in the network. For example, all the columns of the weight matrix will correspond to the transmit symbols of all the legacy terminals, if the network consist of only legacy terminals.

In step S 1-6, the column is replaced by the pseudo inverse of the corresponding row vector of the channel matrix, such that W(.*,iiegacy)= (H(j,:)) for = 1legaey (14) Equation 14 can also be expressed in vector form as w,1 = (h) = h (hh)_l for j = (15) where w isthei-thcolunmofW, and h isthej-throwoflll,thatis h =ILI(j,.).

Essentially, the inner product of the column corresponding to the legacy terminal, 1Iegcy' and the rest of the columns of W is 0, that is they are orthogonal to each other.

Thus, this characteristic of the beam matrix can be expressed as for ji H(j,.)\V(.,i)= 0 for.1!= i, i iegacy H(j,.)W(.,i)z for j!=i, 1=1legacy Accordingly, a 4 x 4 effective channel matrix can be expressed as z 100 Ierr=11W:= 2 (16) Z3 0 1 0 z4 0 0 1 where z2, z3 and z4 represent the interference between the legacy terminal to other terminals in the network. It will be appreciated that the interference z2, z3 and z4 can be controlled by a user selection process, such that the transmitted signals can be recovered by the new terminals.

The closest lattice point approximation can be carried out to reduce the above lattice basis of the legacy terminal by means of a lattice reduction algorithm (for example: an LLL algorithm), such that the lattice transformation matrix is defined as T= A (17) 0 T where T is unimodular and is a 3 x 3 matrix, in this example. Therefore, by applying the precoding matrix generated using the method described above for the present invention results in a closest point approximation that allows unperturbed transmit symbol to be transmitted to the legacy terminal.

The precoding matrix determined using the steps above can allow the transmitter device to transmit signals to the legacy terminals and new terminal simultaneously, without the need of unperturbing the perturbation shift vector at the legacy terminal. This provides an advantage in that a guaranteed link quality for the legacy terminal can be provided, while the legacy terminal is unaware of the MU-MIMO non-linear precoding transmission in the network.

The performance of the precoding matrix generated using the above method will now be demonstrated by way of an example.

In this example, a 4 x 4 M1J-MIMO transmission is applied, and the level of inter-terminal (inter-user) interference between the legacy terminals and the new terminals is determined in order to demonstrate the performance of the precoding matrix. As described in the preceding paragraphs, the interference can be represented as z2, z3 and z4, which can be further characterised by the norm such that I iT 2 3 4 1 A defined number of channel realisations, N, is generated and the channel matrix, H, resulting in the smallest norm, [z2, , z4]T, is selected accordingly. In this example, the defined number of realisations are N = 1 (that is no selection), N 4, N 16, and N = 100.

The results depicted in figure 6 show the performance difference among different number of channel realisations having at least one legacy terminal, and the performance of the system without legacy terminal. The results shown in figure 6 is based on a 4 x 4 Iv[U-MUvIO system employing uncoded QPSK (Quadrature Phase Shift Keying) transmission, and the uncoded bit error rate (UBER) for all channel realisations (y-axis).

In figure 6, the curve labelled "LRA VP Precoding" represents the performance of pseudo inverse vector perturbation precoding employing lattice-reduction-aided closest point approximation for all the users (that is there is no legacy tenninal in the network).

For simulations which include a legacy terminal, the curves labelled "Target User" represent the performance of the legacy terminal, and the other curves represent the performance of the remaining terminals for different channel realisation numbers, N. The results in figure 6 demonstrate that the legacy terminal has similar uncoded bit error rate (UBER) performance for all the channel realisation numbers, N, as the new terminals receive non-linearly precoded spatial streams from the transmitter device.

The results clearly show that the method of the present invention is able to provide a legacy terminal with a linearly precoded transmission within a transmission employing vector perturbation precoding, thereby allowing simultaneous transmission of signals to legacy terminals and new terminals in a network.

A method in accordance with a second embodiment of the invention will be discussed with reference to the flow chart of figure 7.

Similarly, the data vector, u, is precoded by means of a channel inverse precoder P = H1 (or pseudoinverse) as s = Pu According to equation 4 above, the data vector is perturbed to avoid noise amplification on precoded data vector, such that: Li u + ii where r is a positive real number and 1 is a complex integer vector.

The solution for finding the perturbation vector I in a network comprising legacy terminals and new terminals can therefore be defined as lall = argmin1P(u + .c1'I2 (18) Essentially, the method in accordance with the present embodiment avoids perturbation of the element which corresponds to the legacy terminal by reducing the likelihood of a perturbation on this element. In this example, this element is referred to as element Ii.

This can be achieved, for example, by scaling the corresponding shift distance r of l by a large value (step S2-2) as ri=Lr1, L>>l (19) where r1 is the shift parameter in the first position of a constellation shift parameter matrix which is represented as r1 0 0 0 Or 0 :2.. : (20) O 0 where r1. are constellation shift parameters for K spatial streams.

According to equation 19, r1 can be scaled by a large scaling factor such that equation is expressed as: Lr1 0 0 0 TK] (21) where the first terminal is the legacy terminal in this example. It will be appreciated that there may be more than one legacy terminal in the matrix and ( , or the legacy terminal may be positioned in any positions other than the first.

In accordance with the above, the constellation shift parameter matrix of equation 21 can be applied to the solution of equation (18), such that all = arg min1 p + t 11)02 (22) In step S2-4, a scaling factor O.Q1 is applied to equation 22: tall = arg min1 i (i +i 11)02 (23) where is as expressed in equation 21.

Accordingly, the minimisation problem of equation 23 (step S2-6) can be expressed as: all =argmin1i(si1u+il)2 (24) such that the minimisation problem can be solved by: all arg min1 ( 1u + 11)02 (25) where f' Pi, and it will be appreciated that the closest point can be determined to the scaled lattice point u.

Essentially, equation 25 supports different modulo shift values for different spatial streams sharing one frequency resource, thereby allowing the perturbation vector 1 in a network comprising legacy terminals and new terminals to be solved.

Once the minimisation problem is solved, vector perturbation can be applied to the data vector in step 2-8.

It will be appreciated by the person skilled in the art that there are a large number of solution methods that can be employed to solve the problem of equation 25. For instance, a sphere encoder can be used to find the solution of the minimisation problem.

It can also be demonstrated that the minimisation problem of equation 25 can also be solved by lattice reduction aided closest point approximation as follows: approx _TQGK {t'u} (26) where T=LLL(P) (27) In this example, T is the lattice reduction transformation matrix obtained by a lattice reduction by means of the LLL algorithm applied to = P1. However, it will be appreciated that any other lattice reduction algorithm can also be employed.

The quantisation function as a simpler element-wise complex-valued rounding to the nearest Gaussian integer as approx = -TQK {t't u} (28) In a further example, the minimisation problem of equation 25 can also be solved by a method employing lattice reduction which has the capability of providing performance closer to an optimal solution previously described in UK patent application 0805306.8.

The result of the vector perturbation precoding method of the present embodiment in figure 8 is achieved by applying a large number value, L, to the constellation shift parameter of the legacy terminal. In this example, L = 15, however it will be appreciated that any number that is larger than 1 can be applied. The results depicted in figure 8 shows that the vector perturbation precoding method in accordance with the present embodiment is able to provide better performance than a conventional vector precoding method (such as lattice-reduction-aided vector perturbation precoding) in a MIU-MIMO system comprising legacy terminals and new terminals.

A method in accordance with a third embodiment of the invention will be discussed with reference to the flow chart of figure 9.

Referring to figure 9, in step S3-2, a zero-constraint is applied directly to the minimisation of the equation 11 such that the solution of the perturbation vector can be expressed as I arg mm1,. P(u + (29) where 1,' is the legacy terminal. Accordingly equation 29 can be expressed as 1 arg mm1, P [u + (30) and 1 argmin, [p []+r[]J2 (31) where p1 e CMXI, P E CMXKI, u1 e C,\ü E CK_IXI jt e Equation (31) can be reformulated such that I = arg mm1, p,u1 + Pu + rP1' arg mm1, y + ri'll2 (32) where y = p1u1 + Pu and the data perturbation vector is ro =Li (33) Alternatively, equation 32 can be formulated as I = argniin1, (+ri')D2 (34) where ü = P (p1u1 +ii).

One advantage of applying a constraint to the solution of the perturbation vector is that it reduces the dimensionality of the lattice search, and consequently reduces the computational complexity. For example, if there is only one legacy terminal in the system, the dimensionality of the lattice search can be reduced by one accordingly.

The pseudoinverse and the perturbation steps (steps S3-4, S3-6) of this embodiment are similar to that described above with reference to the flow chart of figure 7.

The comparison simulation result obtained using the method in accordance with the present embodiment is illustrated in figure 10. In figure 10, the curve labelled "LRA VP Precoding" represents the performance of pseudo inverse vector perturbation precoding employing lattice-reduction-aided closest point approximation for all the users (that is there is no legacy terminal in the network). In figure 10, the curves related to the "invention" demonstrate the performance of the precoding method in accordance with the present embodiment.

While the foregoing specific description of an embodiment of the invention has been provided for the benefit of the skilled reader, it will be understood that it should not be read as mandating any restriction on the scope of the invention. The invention should be considered as characterised by the claims appended hereto, as interpreted with reference to, but not bound by, the supporting description.

Claims (20)

1. CLAIMS: A method of processing information prior to emission from a multi-antenna device in a multi-user network having at least one user andlor at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna device, the method comprising precoding said data vector prior to emission thereof, the precoding comprising scaling said data vector by means of a precoding matrix defining pseudo inverse channel characteristics of said at least one user andlor at least one legacy user, said pseudo inverse channel characteristics of said at least one user being orthogonal to said pseudo inverse channel characteristics of said at least one legacy user; and applying a perturbation to said precoded information, said perturbation being expressible as a perturbation vector, wherein said perturbation vector comprises unperturbed data for said at least one legacy user thereby allowing simultaneous emission of information from the multi-antenna device to the at least one user and the at least one legacy user.
2. 2. A method according to claim 1 wherein the precoding matrix is defined by applying a constraint to said pseudo inverse channel characteristics of said at least legacy user.
3. 3. A method according to claim 2 wherein the step of applying the constraint includes the following: determining a colamn vector of precoding matrix representing said pseudo inverse channel characteristics of said legacy user; determining a channel matrix defining a channel between said multi-antenna device and said at least one user andlor at least one legacy user; and replacing said determined column with pseudo inverse of a row vector of said determined channel matrix, said row vector representing said channel characteristics of said legacy user.
4. 4. A method according to claim 3 wherein the step of replacing is expressible in the form P(.,Iiegacy)= (H(j,.)), for j = Iegacy' where P is a precoding matrix, and H is a channel matrix.
5. 5. A method of processing information prior to emission from a multi-antenna device in a multi-user network having at least one user andlor at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna device, the method comprising applying a perturbation to said data vector in order to generate a perturbed data vector, said perturbation being expressible as a perturbation vector comprising a diagonal matrix and a complex integer vector, said diagonal matrix being perturbation elements corresponding to said at least one user andlor at least one legacy user; selecting at least one of said perturbation elements corresponding to said at least one legacy user; and scaling said selected at least one of said perturbation elements.
6. 6. A method according to claim 5 wherein the scaling of said selected at least one of said perturbation elements is performed on the basis of a scaling factor employed in said scaling.
7. 7. A method according to claim 6 wherein the scaling factor includes a number substantially higher than 1, and is expressible on the form L >> 1, where L is a scaling factor.
8. 8. A method according to any one of claims 5 to 7 wherein the perturbation vector is selected by solving an integer least squares problem such that a nonnalisation factor is minimised
9. 9. A method according to claim 8 wherein said solving of the integer least squares problem includes applying a further scaling factor to the integer least squares problem.
10. 10. A method according to claim 9 wherein the integer least squares problem is expressible in the form L211 = arg mm1 IIP(u + where u + 1' is a perturbed data vector, u is a data vector, ( is a diagonal matrix of positive real numbers, 1' is a complex integer vector, and P is a precoding matrix.
11. 11. A method according to claim 9 or claim 10 wherein the further scaling factor is applied to the integer least squares problem such that said integer least squares problem is expressible in the form = argmin1 (1l u + 11)02 where P P0
12. 12. A method according to claim 11 further comprises the step of determining a solution for solving said integer least squares problem.
13. 13. A method according to claim 12 wherein the solution is a lattice reduction closest point approximation expressible in the form approx -TQK {t-'o -lu} where T is a lattice reduction transformation matrix.
14. 14. A method according to claim 13 wherein the lattice reduction transformation matrix may be determined by applying a LLL algorithm, arid is expressible in the form T = LLL(P), where P = P0.
15. 15. A method according to any one of claim 5 to 14 wherein the perturbation elements are a plurality of constellation shift parameter.
16. 16. A method according to claim 15 wherein the plurality of constellation shift parameter are distinct from each other.
17. 17. A method of processing information prior to emission from a multi-antenna device in a multi-user network having at least one user andlor at least one legacy user, said information being a data vector comprising corresponding data for each antenna of said multi-antenna device, the method comprising applying a perturbation to said data vector in order to generate a perturbed data vector, said perturbation being expressible as a perturbation vector comprising a number and a complex integer vector, said complex integer vector comprising at least one element corresponding to said at least one user andlor at least one legacy user, said perturbation vector being selected by solving a integer least squares problem such that a normalisation factor is minimised, wherein said solving the integer least squares problem includes applying a constraint to the integer least squares problem.
18. 18. A method according to claim 17 wherein the integer least squares problem is expressible in the form I = arg mm1 + . where u + rI' is a perturbed data vector, u is a data vector, r is a positive real number, 1' is a complex integer vector, and P is a precoding matrix.
19. 19. A method according to claim 17 or claim 18 wherein the applying of constraint may be performed by applying a zero constraint to said at least one element corresponding to said at least one legacy user, such that the integer least squares problem is expressible in the form 1= arg mm1, P [u + r
20. 20. A storage medium storing computer executable instructions which, when executed on general purpose computer controlled communications apparatus, cause the apparatus to become configured to perform the method of any of claims ito 19.