Patent Application
20130243068
The invention relates to the field of wireless communication and more particularly to a process for performing near-ML detection in a receiver of a wireless MIMO communication system.
Wireless communications based on multiple antennas is a very promising technique which is subject to extensive investigations so as to take into advantage of the significant increase of data rate which may be obtained by such technique.
On the receiver side, two antennas 21 and 22 provides two RF signals which are received by receiver 20 which performs RF reception, demodulation and then detection of the two data streams before multiplexing it into one single data stream.
The MIMO configuration allows to get rid of the different obstacles (such as represented by obstacles 28 and 29 in figures) and thus increase the data rate of the communication.
Now considering the receiver of the communication, let us introduce a nT-transmit and nR-receive nT×nR MIMO system model such as: y=Hx+n, where y is the receive symbols vector, H the channel matrix, x the transmit symbols vector and n an additive white Gaussian noise.
Reduction of complexity in the MIMO detection can be achieved both by means of Sphere Decoders techniques or lattice reductions.
A first well known technique for finding the optimal Maximum Likelihood (ML) estimate {circumflex over (x)}ML by avoiding an exhaustive search is to examine only the lattice points that lie inside a sphere. This solution is denoted as the Sphere Decoder (SD) technique and, starting from the conventional ML equation
where H=QR, with the classical QR Decomposition (QRD) definitions, and d is the sphere constraint.
The SD principle has been introduced and leads to numerous implementation problems. In particular, it is a NP-hard problem. This aspect has been partially solved through the introduction of an efficient solution that lies in a Fixed Neighbourhood Size Algorithm (FNSA), commonly denoted as the K-Best, which offers a fixed complexity and a parallel implementation, thus making possible an implementation. However, this solution makes the detector to be sub-optimal since its leads to a performance loss compared to the ML detector. It is particularly true in the case of an inappropriate K according to the MIMO channel condition number, since the ML solution might be excluded from the search tree.
Since the complexity is fixed with such a detector, the exposed optimizations will induce a performance gain for a given neighbourhood size or a reduction of the neighbourhood size for a given Bit Error Rate (BER) goal. Common existing optimizations in the FNSA performance improvement are in particular the use of the Sorted QRD (SQRD) at the pre-processing step, and the Schnorr-Euchner (SE) enumeration strategy and the dynamic K-Best at the detection step.
However, although a neighbourhood sourly remains the one and only solution that achieves ML performance, it may lead to the use of a large size neighbourhood scan which would correspond to a dramatic increase of the computational complexity. This point is particularly true in the case of high order modulations.
Also, the SD must be fully processed for each transmit symbols vector detection over a given channel realization. A computational complexity reduction through adjacent-channel information re-use is not possible, even if the channel may be considered as constant over a certain block code size. Consequently, due to the SD's principle itself, the skilled man would have noticed the necessity of the computational complexity reduction of any SD-like detector for making it applicable in the LTE-A context.
A second technique which can be used for achieving near-ML performance is based on lattice reduction.
Document “Near-Maximum-Likelihood Detection of MIMO Systems using MMSE-Based Lattice-Reduction,” by Wübben, R. Böhnke, V. Kühm, and K.-D. Kammeyer, Communications, IEEE International Conference on, vol. 2, pp. 798-802, 2004 discloses the use of Lattice Reduction for the purpose of improving the conditioning of the channel matrix H and improving the efficiency of the detection process.
In particular the cited prior art document discloses the combination of a preprocessing phase depending on the channel followed by a linear MMSE equalization of the received symbols y.
In Wubben et al, the preprocessing phase comprises, in order to significantly reduce of the computational complexity the following steps :
{tilde over (H)}=HT
and to introduce a new vector Z=T−1x which results in
y=Hx+n=HTT
−1
x+n={tilde over (H)}z+n
The preprocessing phase suggested by Wübben of al. yields to both some efficiency for reducing the computational complexity of the detection process and some performance improvement.
However, the overall performance shows to be still far from the ideal ML-detection and it is therefore desirable to improve such preprocessing by an additional processing phase which significantly increases the performance of the detection while limiting the processing resources being required with the classical K-Best.
It is an object of the present invention to carry out a process of detection for a MIMO receiver which achieves near-ML performance while limiting complexity of the algorithm.
It is another object of the present invention to achieve a MIMO detection process for a wireless communication signal which combines effective SQRD decomposition, lattice reduction and a subsequent neighborhood search process for the purpose of achieving detection close to the Maximum Likelihood detection.
It is a further object of the present invention to achieve a new near-ML Sphere Detector for a Wireless MIMO receiver, that is centered—to the best of the authors' knowledge—on the best pseudo-linear solution and which takes advantage of the Lattice Reduction and also implies the use of small RDN scans for achieving the ML hard-decision performance, thus making the RDN study size independent on the order of the constellation.
It is another object of the present invention to achieve a receiver for a MIMO wireless communication system which incorporates effective mechanism of SQRD decomposition, lattice reduction as well as an efficient neighborhood search mechanism.
It is an additional object of the present invention of providing an efficient metric computation, which make the search phase to be done efficiently, thus providing a performance gain for a given computational complexity.
These and other objects of the invention are achieved by means of a detection process for a receiver of a wireless communication system based on Multiple-In-Multiple-Out antennas, the process involving:
The process is characterized by the fact that it further involves the steps of performing a neighborhood search with a search center being equal to the result {tilde over (z)} of said lattice reduction. Then the K-Best symbols are computed in accordance with a Partial Euclidian Distance (PED) defined in accordance with the following formula:
∥{tilde over (R)}({tilde over (z)}−z)∥2≦d2
Each layer can thus be detected, and each detected layer causes the update of the search center for the purpose of detecting the next layer.
When the estimation of {tilde over (z)} is achieved, the latter is multiplied by the matrix T then mapped onto the original constellation so as to generate the estimated value {tilde over (x)}.
In one embodiment, an extended model is applied for both the matrix H and the received symbol y in accordance with the formula below:
with H being the channel matrix, σ being the noise standard deviation, I being the identity matrix.
In one embodiment, the Lattice Reduction is based on the Lenstra-Lenstra-Lovasz (LLL) algorithm.
In one particular embodiment, the SQRD decomposition is a sorted QRD decomposition, with the rows of said upper triangular matrix that are sorted in accordance with the level of the Signal-to-Interference and Noise Ratio (SINR), said SQRD decomposition issuing Qext, Rext and a permutation matrix P.
The invention also achieves a Receiver for a wireless communication system based on Multiple-In-Multiple-Out antennas, said receiver processing observations symbols y derived from symbols x transmitted by an emitter through a channel H; is said receiver which comprises:
The receiver further comprises:
∥{tilde over (R)}({tilde over (z)}−z)∥2≦d2
In one embodiment, the preprocessing phase is applied on an extended model of the matrix H defined in accordance with the following formula:
With H being the channel matrix, σ being the standard deviation, I being the identity matrix.
Preferably, the lattice reduction is based on a Lenstra-Lenstra-Lovasz (LLL) algorithm.
Other features of one or more embodiments of the invention will be best understood by reference to the following detailed description when read in conjunction with the accompanying drawings.
a and 5b illustrate functional block of one particular embodiment.
a-7d illustrate the uncoded Bit Error Rate (BER) of the ODN ZF centred FNSA, of the ODN MMSE centred FNSA, of the RDN LRA-ZF centred FNSA, of the RDN LRA-MMSE, of the RDN LRA-MMSE-Extended centred FNSA and of the ML, for K={1, 2, 3, 4}, 4×4 complex Rayleigh channel, QPSK modulation on each layer.
a-8d illustrate the uncoded BER of the ODN ZF centred FNSA, of the ODN MMSE centred FNSA, of the RDN LRA-ZF centred FNSA, of the RDN LRA-MMSE centred FNSA, of the RDN LRA-MMSE-Extended centred FNSA and of the ML, for K={1, 2, 4, 16}, 4×4 complex Rayleigh channel, 16-QAM modulation on each layer.
a and 9b illustrate the uncoded BER of the strictly equivalent ODN MMSE centred FNSA, of the strictly equivalent RDN LRA-MMSE centred FNSA, of the strictly equivalent RDN LRA-MMSE-Extended centred FNSA, compared to the assumption respect in mean, and of the ML, for K={1, 2, 4, 16}, 4×4 complex Rayleigh channel, 16-QAM modulation on each layer. By the strictly equivalent designation, we means that the constant modulus assumption remains respected.
There will now be described one particularly embodiment of a process which is adapted to carry out a receiver for a MIMO wireless communication system, such as a Orthogonal Frequency Division Multiplex (OFDM).
In order to clarify the description of the process (II), some theoretical considerations will be introduced first (I).
The conventional and basic Sphere Detector is based on the Equation (1)—recalled above—which is centered on the received signal y.
In the case of a depth-first search algorithm, the Babai point is defined as the first solution that is given by the algorithm. For such conventional system, the induced Babai point is implicitly a Zero Forcing-Successive Interference Canceller (ZF-SIC) and, in the case of a Fixed Neighborhood Size Algorithm (FNSA), this definition is extended and is considered as the solution that would be directly reached, with no neighborhood study.
In opposition with the above mentioned Babai point, another relevant concept is the sphere search centre xC, namely the distance calculation in any equation of the form ∥xC−x∥2≦d2, where x is any possible signal, which is consistent with the equation of an (nT-1) sphere.
The embodiment which will be described in the process below, is based on the fixing of an appropriate search center which induces an already close-to-optimal Babai point, in other words a solution that is already close to the ML solution.
By fixing such a search center, the process described below offers the ML diversity but a tiny SNR offset even with an odered pseudo-linear equalization, which is to the best of the authors'knowledge the best pseudo-linear equalization in term of performance. It has been discovered that, by selection of an appropriate search center, the neighbourhood scan size can be significantly decreased while still reaching the ML estimate. In the case of a FNSA detector having a fixed neighbourhood size, it will yield a performance improvement for a given neighbourhood size or a reduction of the neighbourhood size for a given BER goal.
To achieve such performance, an equivalent metric has been defined (the definition following hereinafter) and show efficiency for the case of the Minimum Mean Square Error (MMSE)-SIC Babai point with an Original Domain Neighbourhood (ODN) ξn
Also, it has been implicitly done for the Lattice Reduction Aided (LRA)-ZF-SIC Babai point with a Reduced Domain Neighbourhood (RDN) Cn
This makes the layers in the symbols vector close to be independent and consequently reduce the interest of scanning a large neighbourhood in both the reduced and original domains to achieve the ML performance.
Therefore, there is achieved a new near-ML SD that can be advantageously centred on the best pseudo-linear solution that takes advantage of the LR-aid. The RDN study implies the use of small RDN and ODN scans for achieving the ML hard-decision performance and makes the ODN study size independent of the constellation order.
For the sake of clarity, one will now discuss different possible sphere centres.
A) ZF-SIC Babai Point, Original Domain Neighborhood
The known Sphere Decoder (SD) expression may be re-arranged, leading to an exact formula that has been firstly proposed by Wong et al [5] aiming at allowing optimizations for a VLSI implementation through an efficient Partial Euclidean distance (PED) expression and early pruned nodes [5]:
{circumflex over (x)}SIC-ZF=arg min∥ReZF∥2, (2)
where eZF=xZF−x and xZF=(HHH)−1HHy.
Equation (2) clearly exhibits the point that the conventional and basic SD is unconstrained ZF-centred and implicitly corresponds to a ZF-SIC procedure with an ODN study at each layer. It can be noticed that, in the case of a large ODN study, the ML performance is achieved since the computed metrics are exactly the ML metrics.
B) MMSE-SIC Babai Point, ODN Study
Another solution could be based on the choice of a closer-to-ML Babai point than the ZF-SIC, which is the case of the MMSE-SIC solution.
For sake of clearness with definitions, we say that two ML equations are equivalent if the lattice points argument outputs of the minimum distance are the same, even in the case of different metrics. Two ML equations are equivalent if:
arg minx∈ξ
where c is a constant.
In that respect, one may observe that Cui et al. [reference 2] proposed a general equivalent minimization problem: {circumflex over (x)}ML=arg min {∥y−Hx∥2+axHx}, by noticing that signals X have to be of constant modulus. Clearly, this assumption is respected in the case of QPSK modulations but is not directly applicable to 16-QAM and 64-QAM modulations, even if this assumption is not limiting since a QAM constellation can be considered as a linear sum of QPSK points [See reference 2 above 2].
Such expression has been applied to the FNSA by Wang et al. in the case of the unconstrained MMSE-centre which leads to a MMSE-SIC procedure with an ODN study at each layer [See reference 3 above]. In this case, the equivalent ML equation reads:
{circumflex over (x)}
MMSE-SIC=arg min(xMMSE−x)H(HHH+σ2I)(xMMSE−x) (4)
Through the use of the Cholesky Factorization (CF) of HHH+σ2I=UHU in the MMSE case (HHH=UHU in the ZF case), the ML expression equivalently rewrites:
{circumflex over (x)}
SIC=arg min{∥U({tilde over (x)}−x)∥2} (5)
where U is upper triangular with real diagonal elements and {tilde over (x)} is any (ZF or MMSE) unconstrained linear estimate.
In [3], the authors discuss that when nT is large, the constant modulus signal assumption becomes the time average of the nTxi entries. As depicted in
Consequently, the assumption may still be considered as fulfilled as nT increases. However, the detector robustness to this approximation must be checked by simulations, which is done in the
Nevertheless, in order to make the Equation (5) strictly equivalent to the ML metric, it is suggested to represent any M-QAM constellation as a weighted sum of QPSK constellations:
where x(M-QAM): is a nT symbols vector whose entries all belong to a M-QAM constellation and xi(QPSK)| is a nT symbols vector whose entries all belong to a QPSK constellation.
C) LRA-ZF-SIC Babai Point, RDN Study
The classical LRA-FNSA shows to be implicitly unconstrained LRA-ZF-centred, which leads to a LRA-ZF-SIC procedure with a RDN study at each layer. The exact formula has not been clearly provided but is implicitly used by any LRA-FNSA [See reference 4] and may even be considered as an incremental extension of Equation (2):
where {tilde over (R)} is the LLL-based LR algorithm output, eLRA-ZF=zLRA-ZF−z, and Cn
To the best of the author's knowledge, no convincing formula has been proposed until now. Even if Jalden et al. [6] proposed a LRA-MMSE-centred solution:
the introduced metrics are not equivalent to the ML expression. The corresponding detector is a sub-optimal solution that consists in a RDN study around the unconstrained LRA-MMSE solution. The solution output in this will be the constrained LRA-MMSE solution plus a list of solutions in the neighbourhood, generated according to a non-equivalent metric, which would be subsequently re-ordered according to the exact ML metric. However, the list is not generated according to the correct distance minimization criterion and would not lead to a near-ML solution. Consequently, the proposed detector does not offer acceptable uncoded BER performance in the sense that it would not lead to a near-ML solution. In particular, the ML performance is not reached in the case of a large neighbourhood study.
In order to improve the detection process, it is now suggested to use an unconstrained LRA-MMSE-centre. One recalls that, as known by the skilled man the LRA-MMSE solution is given by the formula below
LRA-MMSE=({tilde over (H)}H{tilde over (H)}+σn2THT)−1{tilde over (H)}Hy.
which is also indicated in formula (16) of the Wubbens reference.
The LRA-MMSE center leads to a LRA-MMSE-SIC procedure with a RDN study at each layer. The equivalent ML equation reads:
{circumflex over (z)}
LRA-SIC=arg min∥Ũ({tilde over (z)}−z)∥2 (9)
Where {tilde over (H)}H{tilde over (H)}σ2THT=ŨHŨ in the MMSE case ({tilde over (H)}H{tilde over (H)}=ŨHŨ in the ZF case) and by noting that Ũ is upper triangular with real diagonal elements and {tilde over (z)} is any LRA (ZF or MMSE) unconstrained linear estimate.
Proof: Let us introduce any term c′ s.t. ∥y−{tilde over (H)}z∥2+c′=∥Ũ({tilde over (z)}−z)∥2, where {tilde over (z)} is any LRA (ZF or MMSE) unconstrained linear estimate:
by introducing {tilde over (z)}={tilde over (G)}−1{tilde over (H)}Hy| and {tilde over (z)}H=yH{tilde over (H)}{tilde over (G)}−1, where {tilde over (G)}={tilde over (H)}H{tilde over (H)}| in the LRA-ZF case and {tilde over (G)}={tilde over (H)}H{tilde over (H)}+σ2THT in the LRA-MMSE case.
In the ZF case, {tilde over (H)}{tilde over (G)}−1{tilde over (H)}H={tilde over (H)}{tilde over (H)}−1({tilde over (H)}H)−1{tilde over (H)}H=I and {tilde over (G)}−{tilde over (H)}H{tilde over (H)}=0|, consequently c′=0| is a constant term.
In the MMSE case, c′=yH({tilde over (H)}({tilde over (H)}H{tilde over (H)}+σ2THT)−1{tilde over (H)}H−I)y+σ2zHTHTz which is a constant term in x iff the signal x entries are of constant modulus since σ2zHTHTz=σ2xHx|.
The formula introduced in (9) offers an equivalent metric which is the same than the MMSE metric introduced in (5). The difference, and in particular the interest in the. LRA case, relies on the neighbourhood study nature. In the case of a RDN study, the equivalent channel matrix {tilde over (H)} is considered and is remembered to be only roughly, and not exactly, orthogonal. Consequently, the symbols vector x detection layer by layer, independently, does not exactly correspond to its joint detection since the mutual influence of the transformed z signal is still present. This discussion exhibits the interest of SD-like techniques to still improve such a detector performance but also makes the skilled man feel that for achieving the ML performance, the RDN study size would be smaller than an ODN study size.
LRA-MMSE-OSIC Babai Point, RDN Study
All the solutions rely on the use of the CF that is an efficient pre-processing solution, but only usable in the case of a formula in a factored form. Although it is the case in our context, most of the studies have been conducted in the case of the always applicable QRD. In particular, the advantageous SIC performance optimizations such as ordering according to the corresponding decreasing SNR (from nT to 1) in the ZF-SQRD case and SINR in the MMSE-SQRD case have been proposed [7]. Moreover, a computational complexity reduction of the LLL-based LR algorithm has been proposed [7].
In the embodiment described below, it will be suggested to introduce the QRD, and more particularly the sorted SQRD, in the (LRA-)MMSE-(O)SIC cases.
The MMSE criterion is introduced through the consideration of an extended system model [7], namely by introducing the (nR+nr)×nr matrix Hext and the (nR+nT) vector yext such as:
This way, the pre-processing step is similar to the ZF-SQRD and the detection procedure equals that of LRA-ZF-SIC. The SQRD interest lies in the ordering of the detection symbols that can be ordered as a function of their S(I)NR and consequently limit the error propagation in SIC procedures. It has been shown by Wübben et al. that the optimum order offers a performance improvement, even if the ML diversity is still not reached. However, once the ML diversity is achieved through a LRA technique, the performance may be significantly improved with this solution [7].
Thus the LRA-MMSE-OSIC corresponds, to the best of the author's knowledge, to the best pseudo-linear detector in the literature, in particular in the case of 4×4 MIMO systems with QPSK modulations on each layer [7]. However, it may be shown that this solution does not offer convenient hard decision performance in the case of a higher order modulation or/and a larger antennas system.
Let us introduce the following notations: Qξ
is the quantification operator in the reduced domain constellation, α is the power normalization and scaling coefficient (i.e. 2/√{square root over (2)},2/√{square root over (10)},2√{square root over (42)}, for QPSK, 16-QAM and 64-QAM constellations, respectively) and
is a complex displacement vector.
The RDN LRA-MMSE-OSIC centred solution block-diagram which is proposed in the embodiment described below is depicted in
A LRA-SIC procedure, with no neighbourhood study, is described in Cn
Starting from the LRA-SIC principle, a neighbourhood is considered at each layer and leads to the RDN LRA-SIC FNSA block 50, which is depicted in
z
k
=
{{tilde over (z)}
k
},
{{tilde over (z)}
k}+1, {{tilde over (z)}k}+j,
{{tilde over (z)}k}−1.
{{tilde over (z)}k}−j, (1′
The SE strategy aims at finding the correct decision early, leading to a safe early termination criterion, which is not considered here for sake of readability in performance comparison. Also, all the corresponding PEDs are computed and then ordered. The K best solutions, namely with the lowest PED, in the reduced domain are stored (C{circumflex over (z)}) similarly to their corresponding Cumulative Euclidean Distances (CED) (Dtot)
The inputs/outputs of the different blocks are summarized as follows:
Block 51:
Block 52:
Block 53:
Block 54:
Block 55:
By adding the pre-processing steps, namely the LR block 48 and SQRD block 47 and the computation of a close-to-ML although linear unconstrained estimate (block 49), a complete description of a new process is given in
Block 47: (Extended SQRD)
Block 48: (Lattice Reduction)
Block 49: (LRA-MMSE Linear Equalizer)
Block 50: (Illustrated in
As a final step of the detector and in the case of a RDN-based SD, the list of possible symbols output has to be re-ordered according to the ML metrics in the original domain and duplicate solutions are removed. It is due to the presence of noise that makes some candidates to be mapped on non-legitimate constellation points in the reduced constellation, leading to this discussion when they are mapped back to the original constellation. The symbols vector associated to the minimal metric becomes the hard decision output of the detector and offers a near-ML solution.
Conclusions on Performances
The RDN LRA-MMSE-OSIC FNSA used by the process described is particularly efficient in the case of rank-deficient MIMO Systems, namely spatially correlated antennas systems, and for high order modulation which are considered points of the LTE-A norm [8].
Since the equivalent channel matrix in the LRA case is only roughly orthogonal, the mutual influence of the transformed Z is small but still present. That is the reason why a neighbourhood study in the original constellation domain improves the performance compared to a SIC. However, contrary to classical solutions that are not LRA, the necessary size for achieving the optimal performance is smaller.
a to 7d respectively illustrate uncoded BER of the ODN ZF-SIC centred FNSA, of the ODN MMSE-SIC centred FNSA, of the RDN LRA-ZF-SIC centred FNSA, of the RDN LRA-MMSE-SIC, of the RDN LRA-MMSE-OSIC centred FNSA and of the ML, for K={1, 2, 3, 4}, 4×4 complex Rayleigh channel, QPSK modulation on each layer.
Some notable points have to be highlighted from
It may also be noticed in
a-8b particularly illustrate uncoded BER of the ODN ZF-SIC centred FNSA, of the ODN MMSE-SIC centred FNSA, of the RDN LRA-ZF-SIC centred FNSA, of the RDN LRA-MMSE-SIC centred FNSA, of the RDN LRA-MMSE-OSIC centred FNSA and of the ML, for K={1, 2, 4, 16}, 4×4 complex Rayleigh channel, 16-QAM modulation on each layer.
a-8b show that there still exists a switching point from low to high SNR between LRA detectors and others. This aspect is removed through the use of better techniques. In particular, the SQRD in the RDN LRA-MMSE-OSIC FNSA offers ML diversity and the BER offset in low SNR has been importantly reduced compared to the RDN LRA-MMSE-SIC FNSA, leading now to a close-to-ML solution.
In both
By comparing the impact on LRA detectors performance of QPSK and 16-QAM modulations, two fundamental points must be discussed.
There implicitly exists a constraint from the QPSK constellation construction that eliminates nearby lattice points that do not belong to ξn
Also, the author recalls that the constant modulus constellation assumption has, in theory, to be fulfilled. It was not the case in
a and 9b illustrate uncoded BER of the strictly equivalent ODN MMSE-SIC centred FNSA, of the strictly equivalent RDN LRA-MMSE-SIC centred FNSA, of the strictly equivalent RDN LRA-MMSE-OSIC centred FNSA, compared to the assumption respect in mean, and of the ML, for K={1, 2, 4, 16}, 4×4 complex Rayleigh channel, 16-QAM modulation on each layer.
As depicted in
The proposed solution is particularly efficient for a large number of antennas and for high order constellations. It was not the case of the LRA-MMSE-OSIC that has been shown not to provide so good BER performances in 4×4 MIMO systems with a 16-QAM modulation on each layer, compared to the ML detection [9], while it was the case for 4×4 MIMO systems with QPSK modulation on each layer [7].
Complexity Considerations
By assuming the assumptions presented in Table 1, the computational complexities introduced in Table 2 can be demonstrated.
The RDN study is processed in an infinite lattice which would not lead to boundary control; however a finite set of displacements has been generated in a SE fashion in simulations. Its size has been fixed to an empirical value (N=5) and, although the use of a SE technique, the proposed solution does not any complexity reduction like early termination.
As exhibited in the table above, the computational complexities of RDN LRA-ZF/MMSE-(O)SIC FNSA detectors do not depend on the constellation order log2{M}. It may be checked in the numerical applications in Table 4 and it is the key point of the invention advantage over classical techniques for high order modulations such as 16(64)-QAM.
Overview of All the Aforementioned Techniques
For sake of clearness, the metrics computation formulas are summarized in Table 3.
The SNR loss compared to ML are given in Table 4. They have been measured for an uncoded BER of 10−4 in the case of the ML decoder.
For all the configurations given in Table 4, the numerical application of the corresponding computational complexity is given in Table 5 for a RDN size N=5.
Even if the proposed solution is two times more complex in the QPSK case, it offers near-ML performance and in particular a SNR gain of 0.3 dB at a BER of 10−4. The interesting point concerns higher order modulations: starting from the 16-QAM modulation, the estimated complexity of the proposed solution is ten times less complex than the classical one, for the same performance result. Identically, the performance is expected to be the same for a 64-QAM modulation, with the same complexity. In such a case, the complexity gain will increase importantly, in this particular case it is decreased of about a hundred times.
Similarly, the numerical application of the 16-QAM extension computational complexity is given in Table 6.
As an example, in the case of 16-QAM modulations, the computational complexities read 8MKnT2+4MKnT−4MK+3M for the ODN equivalent MMSE-(O)SIC and
8N min{K,N}nT2+60 min{K,N}nT+4N min{K,N}nT−4N min{K,N}+24 min{K,N}nT2+8 min{K,N}nRnT+2 min{K,N}nR+16nT2−32 min{K,N}+2N
for the RDN equivalent LRA-MMSE-(O)SIC, and with M=4 since a QPSK modulation is considered in this case.
As depicted in Table 6, the computational complexity is more important and consequently does not offer an effective solution.
With respect to
As described above, the particular embodiment successively involves a preprocessing phase (A)—only depending on the channel—, then followed by an loading phase (B) for processing the received observations and then completed by a phase of neighborhood search (C) for the purpose of achieving the detection.
The preprocessing phase starts with the assumption of the knowledge of the channel H which can be determined by any conventional means, such as for instance by the use of pilot or reference signals.
Also, it is assumed that the variance of the noise (σ2) is known.
Such parameters may be determined, for instance, after the receipt of a frame of symbols and can be repeatedly performed as soon as the channel varies.
The process then starts with a step 61 which consists in an SQRD decomposition in accordance with any conventional means. In one particular embodiment, the SQRD decomposition is particularly applied to a Hext channel which takes into account the noise contribution, that is to say according to the model below from the formula 10:
Step 1 then results in the generation of the following three parameters: Qext, Rext, and P, with P being a permutation matrix.
Then the process proceeds to a step 62 where a lattice reduction is applied for the purpose of improving the conditioning of the two components (Qext, Rext) of the channel matrix.
For that purpose, the embodiment uses more particularly the Korkine-Zolotareff or Lenstra-Lenstra-Lovasz (LLL) algorithm.
Step 62 thus issues the following variables {tilde over (Q)}ext, {tilde over (R)}ext, T with T being a permutation matrix which takes into account the permutations already accounted with matrix P, plus the additional changes resulting from the lattice reduction. As recalled in Wubbens reference, such matrix only includes complexes having real and imaginary parts being integers and has a determinant which absolute value is equal to 1.
This completes the pre-processing phase which only takes into account the H channel.
The so-called loading phase includes the processing of a determined number n of observation vectors y, with n depending on how varies the channel H. Generally speaking, when H is subject to fast variations (for instance because the mobile is moving within the cell), then the number n of observations will be reduced so as to allow more frequent update of the channel. Conversely, if the channel shows to be quite stable, then the number of observations Y to be loaded with the results of a same preprocessing phase A may be increased. Many variations may be considered for determining the proper number of observations to be considered during phase B. For instance, the consideration of the number of positive or negative acknowledgment may be used for determining whether the channel is rapidly changing, thus resulting in the need of initiating a new preprocessing phase. Such particular aspect is not part of the present invention and will not be further elaborated.
The loading phase starts with a step 63, which is the initialization of a loop for the purpose of loading successive observations, e.g. a set of n vectors Y.
Then, in a step 64, the process proceeds with the loading of the current observation vector y.
Then, in a step 65, the process proceeds with the execution of a Linear equalization which, in the particular embodiment being considered is based on a Lattice Reduction aided MMSE algorithm.
{tilde over (z)}LRA-MMSE
The third phase corresponds to the end of the so-called LOADING phase, and starts the real detection process. While the second phase was simply based on a linear equalization, that means the multiplication by a matrix, phase C now leads to a detection of the transmitted symbol.
In the Wubben's article, the detection is based—taking advantage of the triangular shape of the R matrix in the QR decomposition—on the use of a Successive Interference Canceller (SIC) for achieving quantification and thus the detection process.
Conversely, the embodiment which is now described deviates from that conventional teaching in that a neighborhood search is investigated in order to yield a predetermined number of possible symbols.
More particularly, in the embodiment which is considered, the result of phase B above is used for deriving the search center for the neighborhood search.
This results in the fact that the search is no longer performed in the original constellation, but is executed in “z” constellation resulting from a lattice reduction while WANG, in
Step 66 is the start of a FOR loop for the purpose of processing all the layers of the received symbols.
In a step 67, the process performs a search center update which particularly takes into account the value of the previous step 65 yielding {tilde over (z)}LRA-MMSE and also the result of the previous iteration on the last layers.
Then, in a step 68, the process proceeds with a shift and divide operation is applied on the value of the search center so as to achieve basic normalization of the power and scaling of the constellation.
Then, in a step 69, the process proceeds with the generation, for each layer, of all symbols to be investigated. Thanks to the previous shift-normalization step, such generation is based on the consideration of integers around the sphere center. The process then generates a list of integers and the computation of the partial Euclidean Distance with respect to the considered Sphere Center {tilde over (z)}LRA-MMSE, in accordance with the formula:
∥{tilde over (R)}({tilde over (z)}LRA-MMSE−Z)∥2<d2
The above generation of integer causes the selection of a predetermined number K of the integers giving the small PED distance hereinafter designated as the K-best solutions.
In one alternative embodiment, the process directly generates a list of ordered symbols giving increasing PED value, so that the selection of the K-best solutions is simply based on the generation of the first K values of the ordered list.
Different algorithms can be used for the purpose of generating the K-best, such as, for instance the so-called SCHNORR-EUCHNER algorithm.
In one particular embodiment, the process generates a set of 10 K-best possible integers per layer.
Then, in a step 70, the process proceeds to the processing of the next layer, and loops back to step 66 again.
When all the layers have been computed, then the process proceeds to a step 71 where the estimated value {circumflex over (z)} is
When the set of n observations vectors has been processed, then the process initializes a again for the purpose of performing a new pre-processing of the next frame.
Any OFDM standard supporting MIMO spatial-multiplexing mode, e.g. IEEE 802.16, IEEE 802.11, 3GPP LTE and 3GPP LTE-A, are linked to the invention. The invention is particularly advantageous in the case of a large number of antennas and consequently in the case of the 3GPP LTE-A standard.
The main advantage in the use of the generalized SD algorithm, to which this invention relates, is a performance improvement for a given neighbourhood size or a reduction of the neighbourhood size for a given BER goal and provides an advantage over competition in the popular MIMO-OFDM background:
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| Number | Date | Country | Kind |
|---|---|---|---|
| 10368044.3 | Nov 2010 | EP | regional |
| 11368018.5 | May 2011 | EP | regional |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/EP11/05971 | 11/29/2011 | WO | 00 | 5/21/2013 |
| Number | Date | Country | |
|---|---|---|---|
| 61488461 | May 2011 | US | |
| 61432820 | Jan 2011 | US |
