Concept for providing data detection information The present invention relates to data signal detection in received radio signals, more particularly but not exclusively to data signal detection in a multiple- input-multiple-output (abbreviated as MIMO) system, Background Multiple-Input Multiple-Output (MIMO) antenna techniques can significantly increase the data rates by exploiting spatial multiplexing, and/or achieve additional diversity gains by applying space-time coding, cf. Foschini, G. J. and Gans, M. J.; "On limits of wireless comunications in a fading environment when using multiple antennas," Wireless Personal Communications, vol.6, no.3, pp.3 1-335, Mar. 1998; Telatar, I . E.; "Capacity of multiantenna Gaussian channels," European Transactions on Telecommunications, vol.10, no.6, pp. 585-595, Nov. 1999; Alamouti, S. M.; "A simple transmit diversity technique for wireless communications," IEEE Journal on Selected Areas in Communications, vol.16, no.8, pp.1451—1458, Oct. 1998. Nevertheless, with the ever increasing demands for higher data throughput and higher spectral efficiency, new system requirements have been defined for the 4-th Generation (4G as abbreviation) and Beyond 4G (B4G as abbreviation) wireless networks. Thus, new key technologies need to be taken into account. For example, in Boudreau, G.; Panicker, J.; Ning Guo; Rui Chang; Neng Wang; Vrzic, S.; "Interference Coordination and Cancellation for 4G Networks," IEEE Communications Magazine, vol.47, no.4, pp.74-81, Apr. 2009, the authors show that interference coordination in 4G networks can be achieved by a combination of physical layer techniques such as Inter-Cell Interference Cancellation (ICIC), MIMO, Spatial Division Multiple Access (SDMA), adaptive beamforming, sphere decoding, cf. Larsson, E.G.; "MIMO Detection Methods: How They Work," IEEE Signal Processing Magazine, vol.26, no.3, pp.9 1-95, May 2009, Windpassinger, C ; Lampe, L.; Fischer, R.F.H.; Hehn, T.; "A performance study of MIMO detectors," IEEE Transactions on Wireless Communications, vol.5, no.8, pp.2004-2008, Aug. 2006, Hochwald, B.M.; ten Brink, S.; "Achieving Near- Capacity on a Multiple-Antenna Channel," IEEE Transactions on Communications, vol.51, no.3, pp.389-399, Mar. 2003. Other concepts are network MIMO, cf. Foschini, G.J.; Karakayali, K.; Valenzuela, R.A.; "Coordinating multiple antenna cellular networks to achieve enormous spectral efficiency," IEE Proceedings Communications, vol. 153, pp.548-555, Aug. 2006, Huang, H.; Trivellato, M.; Hottmen, A.; Shaft, M.; Smith, P.; Valenzuela, R.; "Increasing downlink cellular throughput with limited network MIMO coordination," IEEE Transactions on Wireless Communications, vol.8, no.6, pp.2983- 5 2989, Jun. 2009, and Coordinated Multi-Point transmission and reception (abbreviated as CoMP). Obviously, the challenges for high data throughput, high spectral efficiency, and better coverage have created new interest in large MIMO systems, with possibly distributed 0 antenna techniques Mohammed, S.K.; Zaki, A.; Chockalingam, A.; Rajan, B.S.; "High- Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation," IEEE Journal of Selected Topics in Signal Processing, vol.3, no.6, pp.958- 974, Dec. 2009, and thus refined considerations regarding the performance/complexity trade-off. On the other hand, thanks to technology and silicon integration advances, larger 5 antenna array configurations with power and cost efficient detection algorithms are becoming feasible. Linear MIMO detection algorithms, i.e. Zero-Forcing (abbreviated as ZF), or Minimum Mean Square Error (abbreviated as MMSE), are well known for their low computational 0 complexity, cf. McKay, M.R.; Collings, LB.; "Capacity and Performance of MIMO-BICM With Zero-Forcing Receivers," IEEE Transactions on Communications, vol.53, no.l, pp.74-83, Jan. 2005, Seethaler, D.; Matz, G.; Hlawatsch, F.; "An Efficient MMSE-Based Demodulator for MIMO Bit-Interleaved Coded Modulation," in Proceedings of IEEE Global Telecommunications Conference 2004 (GLOBECOM'04), pp.2455-2459, Nov.- 5 Dec. 2004. Linear detection algorithms may have low computational requirements. Nevertheless, the diversity order will decrease, if the number of transmission signal streams increases. In practice the MMSE algorithm suffers from noise estimation errors and serious degradation under block fading channel conditions, and ZF algorithms have to combat against noise amplification and the ill-conditioned channel matrix, if MR m =Qm H For the subset yields in a fifth step ,,( - or alternatively Based on the above, LLRs may be calculated in a sixth step, e.g. Log-Likelihood Ratios for APP detection, as In embodiments any highly reliable or high complexity detection approach may be used. From here, in a subsequent seventh step the gm receive signal streams in the m-th subset can be decoded. The eighth step may then close the for-loop started in the second step. In embodiments, most unreliable streams y , y and especially the signal streams z, cf. Figure 2, may be processed by multiple separate APP detections, cf. seventh step of Figure 7a. The reliability of these signal streams can be improved through additional diversity within the overlapped part of the signal subspace in Figure 2, i.e., by (though not statistically independent) adding the respective LLRs. This method is, thus, denoted as Overlapped Subspace Detection (OSD). Obviously, as a special case, Non-Overlapped Subspace Detection (NOSD) is obtained by choosing A ^BnC =A ]C= Figure 7b illustrates a flow chart of an embodiment of a method. In a first step 702 the apparatus 100 may pick out a number L of users to establish a M x MRMIMO system. In other words the apparatus 100 may further comprise means for selecting users to form a Ϊsystem. In a subsequent step 704 the M xMR channel matrix is estimated, which 5 can be composed of the individual smaller MIMO channel matrices of the individual users, on which the users may report the channel estimates to the apparatus 100 together with their receive signal vectors. In a following step 706 the apparatus 100 may create M subsets for all signal streams or data signals. There may be gm streams available in m-th subset, cf. Figure 7a first step. A counter C may be initiated in step 708 before a While- 10 loop is started in step 710, which terminates when the counter has reached the number M of subsets. In a step 712 in the While-loop the apparatus 100 may employ QRdecomposition for the m-th detection subset, regarding the reordered channel matrix, cf. Figure 7a steps 3 and 4. Subsequently in step 714, the apparatus 100 may employ APP detection for the m-th detection subset as shown in Figure 7a in steps 5-7. In a further step 15 716, the apparatus 0 may deliver the Log-Likelihood-Ratio (LLR) of the streams or data signals in the -t detection subset to the corresponding user. In step 718 the counter is increased for the next iteration of the While- loop starting at step 710. Once the While- loop has exited, in a subsequent step 720 for each of the L users, the LLR of their signal streams or data signals may be collected in a distributed manner, before they are combined and 20 decoded. In embodiments several variations are conceivable, which are indicated by the three additional branches indicated in the flow chart of Figure 7b by "A", "B" and "C" and which are linked to the flow chart shown in Figure 7c. Figures 7b and 7c illustrate the coordination between the apparatus 100 and a user in an embodiment. In step 712, after the 25 matrix decomposition, it can be checked whether the streams of an i-th user completely belong to the m-th subset in question, which is indicated by step 730 in Figure 7c. If this does not apply, the method may continue with step 714 as described above. If, however, the streams of the -th user completely belong to the subset, then, in a step 732, it can be checked whether said i-t user equipment is capable of APP detection. If that is not the 30 case, the method may continue with step 714 as described above. If the user has these capabilities, then in a step 734 the apparatus 00 may deliver the triangularized matrix R to the i-th user, as well as the processed receive signal vector. In such an embodiment the data detection information provided to user i may correspond to the matrix R and the processed or modified receive signal vector. The matrix R and the processed or modified receive signal vector can be considered as data detection information, as they correspond to a MIMO-subset comprising the modified receive signals of the user with reduced interference, as other streams not being comprised in this subset do not take effect. The modified user signals are therefore more reliable or they are detectable with a higher reliability from the subset compared to a detection from the full MIMO system. In a further step 736 said i-t user may deploy APP detection as explained in Figure 7a in steps 5-7. Subsequently in step 738, the z'-th user may deliver the LLRs of the streams of other users to the apparatus 100. In the following, point-to-point link layer simulation results will be presented to evaluate the performance of embodiments, using the above described OSD and NOSD algorithms, Channel coding is not taken into account for the simulations. Hence, for the simulation results the uncoded Bit Error Rate (BER as abbreviation) is considered right after the LLR computation of the MIMO detection. Figure 3 shows BER versus SNR. The quality of the LLR soft output is evaluated by computing the achievable rate of the equivalent bit channel, cf. Figure 4. For the proposed OSD and NOSD algorithms, the notation M x (g x g) indicates that M detection sets are created, each of which having g signal streams. The table shown in Figure 8a provides further simulation parameters. As modulation schemes QPSK (as abbreviation for Quadrature Phase Shift Keying) and 16QAM (as abbreviation for Quadrature Amplitude Modulation) are considered. The number MT of transmit signal streams or data signals detected was 4 or 8, a similar number of receive antennas was considered. I.i.d complex Gaussian distributed random variables have been used as channel entries. Furthermore, it was assumed that the channel is perfectly known at the receiver. As detection algorithms aside from the above embodiments, full  Mx APP and ZF MR per-antenna were considered. Additionally, the computational requirements for the respective algorithms were considered. Figure 8b shows a table summarizing the computational complexities, i.e. it illustrates a comparison of embodiments considering computational complexity in terms of real multiplications, additions and divisions. The complexity is quantified in terms of real additions, multiplications and divisions during the simulation. For the full M x M APP and full M x M R QRD-APP algorithms, simplifications, such as Jacobian logarithm and Max-log approximation are taken into account, cf. Hochwald, B.M. et al. Notice that the QR decomposition yields a triangular matrix with real entries on the diagonal of matrix , which provides a certain simplification is compared to the full APP algorithm. Compared to the hypothesis enumerations of the LLR soft output computations itself, the computational requirement of the QR decomposition is assumed to be negligible. In Figures 3 and 4 the uncoded BER and achievable rates are presented for a 4 x 4 MIMO system. Notice that the APP performance bound can be approached by the embodiment using OSD 2x(3x3) very closely, both for QPSK and 16QAM. It is well known that the diversity order of ZF and lull APP are 1 and MR, respectively, if MT=MR holds. As being a "hybrid approach", embodiments using the OSD algorithm may improve the diversity order compared to ZF. Figure 8c illustrates a performance comparison of embodiments considering normalized computational complexity. Figure 8c shows that the normalized complexity of OSD 2*(3*3) is approximately 29.5% and 7.44% of that of full APP with QPSK and 16QAM, respectively. Furthermore, considering a code rate 3/4, the gaps to the APP reference performance are 1.OdB and 0.9dB for QPSK and 16QAM, respectively, cf. Figure 4. This indicates that the embodiments using the OSD algorithm achieve a good trade-off between performance and complexity for higher order modulation schemes. Similarly, in Figures 5 and 6, the uncoded BER and achievable rates are studied for QPSK in an 8*8 MIMO system. Due to the higher number of transmit antennas, there are more degrees of freedom for embodiments to deploy OSD and NOSD algorithms. It can be observed that the OSD 3 (4*4) and OSD 2x(6*6) schemes deliver satisfactory results, with computational requirements approximately 0.32% and 5.09% of that of full APP detection. The corresponding gaps to the APP references are 2.7dB and 1.2dB for code rate 3/4. Again, the embodiment using the OSD algorithm achieves a good trade-off between performance and complexity for MIMO systems with a large number of transmit data streams. Embodiments may use one of the above methods for subspace detection, which may deploy QR decomposition to triangularize the effective MIMO channel matrix, allowing the creation of multiple subspace detection sets that can be individually processed by optimum APP detection. The detection of weak signal streams can be improved by appropriately choosing overlapping detection regions. Embodiments may therefore achieve a good trade-off between computational complexity and performance, with basic functional blocks being replicated as needed. For example, a 4*4 APP detection block can be used for the QRD-APP NOSD 2(4 4) as well as the QRD-APP OSD 3*(4><4) algorithm, with deterministic computational requirements, amenable to hardware implementation. As an alternative to QRD, embodiments may use other matrix triangularization approaches, e.g. Cholesky decomposition, which has low computational complexity. Embodiments may provide advantages on hardware designs. For instance, APP processing function block with g=4 is not relatively complex. The function block can be adapted to embodiments using QRD-APP NOSD 2x(4x4) and QRD-APP OSD 3x(4x4) algorithms. The corresponding computational requirement can be deterministic, which may be advantageous for hardware design. With the technology and silicon integration advances, M1MO systems may move more and more towards larger antenna array configurations (e.g., see AAA, as abbreviation for active antenna array). Embodiments may provide power and cost efficient detection algorithms for such systems. Large scale MIMO is expected to be deployed within the next 2-4 years within the LTE-Advanced (as abbreviation for long term evolution). LTE-R10 already has specified MIMO systems based on 8 antennas. A person of skill in the art would readily recognize that steps of various above-described methods can be performed by programmed computers. Herein, some embodiments are also intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, wherein said instructions perform some or all of the steps of said above-described methods. The program storage devices may be, e.g., digital memories, magnetic storage media such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The embodiments are also intended to cover computers programmed to perform said steps of the above-described methods. The description and drawings merely illustrate the principles of the invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples recited herein are principally intended expressly to be only for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor(s) to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass equivalents thereof. Functional blocks denoted as "means for ..." (performing a certain function) shall be understood as functional blocks comprising circuitry that is adapted for performing a certain function, respectively. Hence, a "means for s.th." may as well be understood as a "means being adapted or suited for s.th.". A means being adapted for performing a certain function does, hence, not imply that such means necessarily is performing said function (at a given time instant). The functions of the various elements shown in the Figures, including any functional blocks labeled as "means", "means for communicating", "means for determining", "means for providing", "means for generating", "means for calculating", "means for estimating", "means for detecting", "processors", may be provided through the use of dedicated hardware as e.g. "a transceiver", "a determiner", "a generator", "a calculator", "an estimator", "a detector", as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor" or "controller" should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and/or custom, may also be included. Similarly, any switches shown in the FIGS are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even 12 050858 30 manually, the particular technique being selectable by the implementer as more specifically understood from the context. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the invention. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. Claims 1. An apparatus (100) for providing first data detection information on a first data signal of a first mobile transceiver and second data detection information on a second data signal of a second mobile transceiver, comprising means ( 110) for communicating with the first mobile transceiver for receiving a first receive signal vector from the first mobile transceiver, the first receive signal having been received from a base station transceiver by the first mobile transceiver; means (120) for communicating with the second mobile transceiver for receiving a second receive signal vector from the second mobile transceiver, the second receive signal having been received from the base station transceiver by the second mobile transceiver; means (130) for determining the data detection information on the first data signal based on the first receive signal vector and the second receive signal vector and for determining the data detection information on the second data signal based on the first receive signal vector and the second receive signal vector; and means (140) for providing the data detection information on the first data signal to the first mobile transceiver and for providing the data detection information on the second data signal to the second mobile transceiver. 2. The apparatus (100) of claim 1, wherein the means for determining (130) is adapted for determining a first estimated radio channel between the first mobile transceiver and the base station transceiver and for determining a second estimated radio channel between the second mobile transceiver and the base station transceiver, and wherein the means for determining (130) is further adapted for determining the data detection information on the first data signal and the data detection information on the second data signal based on the information on the first estimated radio channel and the information on the second estimated radio channel. 3. The apparatus (100) of claim 2, wherein the means ( 110) for communicating is further adapted for receiving information on the first estimated radio channel between the first mobile transceiver and the base station transceiver from the first mobile transceiver and wherein the means (120) for communicating is further adapted for receiving information on the second estimated radio channel between the second mobile transceiver and the base station transceiver from the second mobile transceiver. 4. The apparatus (100) of claim 2, wherein the first estimated radio channel refers to a multiple-input-multiple-output radio channel between a plurality of transmit antennas at the base station transceiver and a plurality of receive antennas at the first mobile transceiver, and/or wherein the information on the second estimated radio channel refers to a multiple-input-multiple-output radio channel between the plurality of transmit antennas at the base station transceiver and a plurality of receive antennas at the second mobile station transceiver. 5. The apparatus (100) of claim 2, wherein the first receive signal vector refers to a plurality of receive signals received at a plurality of receive antennas at the first mobile transceiver, and/or wherein the second receive signal vector refers to a plurality of receive signals received at a plurality of receive antennas at the second mobile transceiver. 6. The apparatus (100) of claim 5, wherein the means (130) for determining is adapted for determining the first radio channel estimate as a first radio channel matrix and the second radio channel estimate as a second radio channel matrix, wherein the means (130) for determining is further adapted for determining a radio channel matrix H between the plurality of transmit antennas at the base station and the plurality of receive antennas at the first and the second mobile transceiver, the radio channel matrix H having at least one channel coefficient for each combination of one of the plurality of transmit antennas at the base station transceiver and one of the plurality of receive antennas at the first and second mobile transceiver, wherein the means (130) for determining is further adapted for determining subsets of data signals, and for determining one sub-matrix for each subset of data signals, wherein each of the data signals is comprised in at least one of the subsets, wherein the means (130) for determining further comprises means for generating modified received signals for each subset, further comprises means for calculating data detection information for each data signal in each subset, and wherein the means (130) for determining is further adapted for determining the data detection information on the first and second data signal based on the data detection information for each data signal in each subset. 7. The apparatus (100) of claim 6, wherein the means for determining (130) is adapted for determining the subsets by determining quality information on the first and second data signal based on the plurality of receive signal vectors and the channel matrix H , a submatrix being based on a subset of coefficients selected from the channel matrix based on the quality information. 8. The apparatus (100) of claim 7, wherein a sub-matrix is a triangular matrix based on a reordering operation on the channel matrix based on the quality information and a subsequent decomposition of the channel matrix H . 9. The apparatus (100) of claim 7, wherein a sub-matrix is a sub-matrix of a triangular matrix resulting from the decomposition of the reordered channel matrix H according to reordering operations, where a reordering operation is different for each subset. 10. The apparatus (100) of claim 8, wherein the reordering corresponds to a selection of a subset of transmit signals, the subset of transmit signals being selected based on the quality information, where in a first reordering operation transmit signals are selected among which the quality information indicates the best quality for the first data signal, and where in a second reordering operation transmit signals are selected among which the quality information indicates the best quality for the second data signal, or wherein the quality information indicates a better quality for the first data signal than for the second data signal and wherein the second data signal is selected in both reordering operations. 11. The apparatus (100) of claim 8, wherein the matrix decompositions correspond to a QR-decomposition or a Cholesky-decomposition and/or wherein the means for generating is adapted for generating the modified receive signals based on a result of a matrix decomposition. 12. The apparatus (100) of claim 11, wherein the means (140) for providing is adapted for providing a subset of the modified receive signals and at least a part of the result of the matrix decomposition to the first or second mobile transceiver as data detection information. 13. The apparatus (100) of claim 1, wherein the means for determining (130) is adapted for calculating reliability information or Log-Likelihood-Ratios of transmit symbols of the first and the second data signal as data detection information. 14. A method for providing first data detection information on a first data signal of a first mobile transceiver and second data detection information on a second data signal of a second mobile transceiver, comprising communicating (2 10) with the first mobile transceiver for receiving a first receive signal vector from the first mobile transceiver, the first receive signal being received from a base station transceiver by the first mobile transceiver; communicating (220) with the second mobile transceiver for receiving a second receive signal vector from the second transceiver, the second receive signal being received from the base station transceiver by the second mobile transceiver; determining (230) the data detection information on the first data signal based on the first receive signal vector and the second receive signal vector and for determining the data detection information on the second data signal based on the first receive signal vector and the second receive signal vector; providing (240) the data detection information on the first data signal to the first mobile transceiver; and providing (250) the data detection information on the second data signal to the second mobile transceiver. 15. A computer program having a program code for performing the method of claim 14 when the computer program is executed on a computer or processor.