CN101383797B - Low complexity signal detecting method and device for MIMO system - Google Patents
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Abstract
The invention provides a low-complexity signal testing method and a signal testing device which are used in an MIMO system. The signal testing device tests the received singles according to the result of channel estimation, and then outputs the soft bit measure information of the received signal. The signal testing device comprises: a linear testing part which tests the received signal on the base of the result of the channel estimation so as to obtain the initial estimation signal, a constellation option part which creates candidate constellation subsets aiming at all sending code elements from the original constellation set on the basis of the initial estimation signal, and a soft-bit measure computing part which computes soft bit measures aiming at all the sending code elements according to the candidate constellations in the candidate constellation subset. The signal testing method and the signal testing device of the invention can realize the better performance than the liner tester as lowers the calculation complexity.
Description
Technical Field
The present invention relates to a signal detection technique in an MIMO system, and more particularly, to a low-complexity signal detection method and detection apparatus for an MIMO system, which reduces computational complexity while achieving good performance by combining a linear detection and a maximum likelihood detection principle.
Background
MIMO (multiple input multiple output) systems have received much attention as a solution to meet the high capacity demand of future wireless communication systems.
In the MIMO system, a transmitting side transmits signals using a plurality of antennas, and a receiving side receives signals using a plurality of antennas. Research shows that compared with the traditional single-antenna transmission method, the MIMO system can remarkably improve the channel capacity, thereby improving the information transmission rate. In addition, the more transmit and receive antennas a MIMO system employs, the higher the information transmission rate it can provide. Compared with time-frequency resources, spatial antenna resources are almost infinitely available, so that the MIMO technology effectively breaks through the bottleneck in the traditional research and becomes one of the core technologies of the next generation wireless communication system.
In a MIMO system, one possible transmission scheme is to simultaneously transmit a plurality of different data streams (spatial multiplexing or vertical layered space-time code (VBLAST)) ([ non-patent document 1 ]). In this case, all transmitted data streams will experience different channel parameters and be received overlapping at the receive antennas.
On the other hand, the continuously increasing demand for higher bit rates has led to the emergence of multicarrier transmission techniques, which can be realized by OFDM (orthogonal frequency division multiplexing) for broadband communication. The OFDM modulation technique divides the total available bandwidth into a number of equally spaced frequency bands. By applying an appropriate cyclic prefix, individual sub-channels can be made to exhibit flat fading channel characteristics. Combining MIMO technology and OFDM technology allows BLAST to be employed in frequency selective channels. Therefore, the MIMO-OFDM system based on BLAST detection algorithm is expected to be an alternative solution of the future mobile wireless system.
As a signal detection scheme in the MIMO system, various detection methods such as Zero Forcing detection method (ZF: Zero Forcing), minimum mean square error detection method (MMSE), VBLAST detection method, and maximum likelihood detection Method (MLD) have been proposed. ZF and MMSE are linear detection methods that are low in complexity but also poor in performance. VBLAST is a method that combines linear detection with successive signal interference cancellation and has better performance than the linear detection method. The MLD detection method calculates the distances between the received signal obtained by all possible transmission symbol combinations and the actual received signal, and detects the transmission symbol combination corresponding to the minimum distance as the most possible transmission symbol combination. MLD has superior characteristics compared to MMSE and VBLAST, but its computational complexity grows exponentially with the number of constellations and the number of transmitter antennas. To overcome this drawback, many sub-optimal non-linear detection methods have been proposed, such as iterative BLAST ([ non-patent document 2]), Sphere Decoding (SD) ([ non-patent document 3]), and QRM-MLD ([ non-patent document 4]), which can greatly reduce the computational effort but also cause considerable performance loss. While new detection methods for MIMO and MIMO-OFDM systems are still under investigation.
In most practical wireless systems, channel coding is applied to further enhance system performance. In coded MIMO systems, MLD is optimal, but complexity is highest. Non-linear detectors with hard decisions, such as BLAST and sphere decoding, perform better than linear detectors. For soft bit output information detectors, MMSE detection has better performance than VBLAST detection, since VBLAST detection can be affected by error propagation in the decision feedback process.
In view of the above, there remains a need for a tradeoff between soft bit output MMSE detector and MLD for MIMO systems. It is desirable to find other detectors with performance close to ML decoders and with lower complexity. To fully exploit the coding gain, a suitable soft information computation method for the decoder is critical for the wireless channel.
The following are references to the present invention, which are incorporated herein by reference as if fully set forth in the specification.
1. [ patent document 1 ]: love David J.et al, Low-complex resonant systems using a multidimensional QAMsgignaling (US 0052317A1)
2. [ patent document 2 ]: niu Huanging, et al, Method of soft bit methodology with direct matrix inversion MIMO detection (US 0227903A1)
3. [ patent document 3 ]: houur Srinath, et al, MIMO Decoding (US 0265465A1)
4. [ non-patent document 1 ]: J.Foschini, "layed space-time architecture for wireless communication in a mapping environment using multielementary antipenas", Bell Labs Tech.J.pp.41-59, Autemn 1996
5. [ non-patent document 2 ]: X.Li, H.Huang, G.Foschini, and R.A.Valenzela, "Effects of Iterative Detection and Decoding on the Performance of BLAST", in Proc.IEEEGLOBECOM' 00, pp.1061-1066, 2000
6. [ non-patent document 3 ]: viterbi and J.Boutros, "A Universal laser codec for facing Channels", IEEE Transactions on Information Theory, vol.45, No.5, pp.1639-1642, July 1999
7. [ non-patent document 4 ]: kawai, K.Higuchi, N.Maeda, M.Sawahashi, T.Ito, Y.Kakura, A.Ushirakawa, and H.seki, "lipid function for QRM-MLD capable of being used for soft-decision turbo decoding and its performance for OFCDM MIMO multiplexing in multiplexing channel", IEICE trans.Commun, vol, E.E 88-B, No.1, pp.47-57, Jan.2004
Disclosure of Invention
It is an object of the present invention to exploit potential performance improvements between soft-output MMSE detectors and ML detectors. By selecting smaller subsets of candidate QAM constellations, the computational complexity of LLR calculations is reduced.
According to a first aspect of the present invention, there is provided a signal detection method in a receiver for a wireless communication system, which performs channel estimation on a signal received by the receiver, detects a received signal according to a result of the channel estimation, and outputs soft bit measure information of the received signal, the signal detection method comprising the steps of: detecting a received signal using a linear detector based on a result of the channel estimation to obtain an initial estimation signal; generating a candidate constellation subset for each transmitted symbol from an original constellation set based on the initial estimation signal; and calculating a soft bit measure for each transmitted symbol according to the candidate constellation in the subset of candidate constellations.
In the above signal detection method, the linear detector includes a zero forcing detector and a minimum mean square error detector.
In the above signal detection method, the candidate constellation is selected based on a distance between the initial estimation signal and a constellation in the original constellation set.
In the above signal detection method, the number of candidate constellations in the candidate constellation subset may be fixed or variable according to the channel information and the signal-to-noise ratio value.
In the above-described signal detection method, the soft bit measure of each transmitted symbol is calculated by a maximum a posteriori probability (or simplified maximum a posteriori probability) processing module.
According to another aspect of the present invention, there is provided a signal detection apparatus for use in a receiver of a wireless communication system, which detects a received signal according to a result of channel estimation to output soft bit measure information of the received signal, the signal detection apparatus comprising: a linear detection unit that detects a received signal based on a result of the channel estimation to obtain an initial estimation signal; a constellation selection unit that generates a candidate constellation subset for each transmission symbol from an original constellation set based on the initial estimation signal; and a soft bit metric calculation unit that calculates a soft bit metric for each transmission symbol based on the candidate constellation in the candidate constellation subset.
In the above signal detection device, the linear detection section includes a zero forcing detector and a minimum mean square error detector.
In the above signal detection apparatus, the constellation selection section selects the candidate constellation based on a distance between the initial estimation signal and a constellation in an original constellation set.
In the above signal detection apparatus, the number of candidate constellations in the candidate constellation subset may be fixed or variable according to the channel information and the signal-to-noise ratio value.
In the above signal detection device, the soft bit metric calculation unit calculates the soft bit metric of each transmission symbol by a maximum a posteriori probability (or simplified maximum a posteriori probability) processing module.
In the signal detection method and the signal detection apparatus, the original constellation set may be a QAM constellation set or a BPSK constellation set.
The signal detection method and the signal detection device can be applied to MIMO, MIMO-OFDM, WiMAX or other wireless communication systems.
According to yet another aspect of the present invention, there is also provided a computer program comprising instructions for running on a computer to cause the computer to: detecting a received signal using a linear detector based on a result of the channel estimation to obtain an initial estimation signal; generating a candidate constellation subset for each transmitted symbol from an original constellation set based on the initial estimation signal; and calculating a soft bit measure for each transmitted symbol according to the candidate constellation in the subset of candidate constellations.
In the above computer program, the linear detector comprises a zero forcing detector and a minimum mean square error detector.
In the above computer program, the candidate constellation is selected based on a distance between the initial estimation signal and a constellation in the original constellation set.
In the above computer program, the number of candidate constellations in the subset of candidate constellations may be fixed or variable depending on the channel information and the signal-to-noise ratio value.
In the above computer program, the soft bit measure for each transmitted symbol is calculated by a maximum a posteriori probability (or simplified maximum a posteriori probability) processing module.
In the above computer program, the original constellation set may be a QAM constellation set or a BPSK constellation set.
The above-described computer program may be applied to a MIMO, MIMO-OFDM, WiMAX or other wireless communication system.
According to still another aspect of the present invention, there is also provided a readable recording medium containing the computer program described above, which is readable by a computer to load the computer program into the computer and to execute the computer program by the computer.
Compared with the prior art, the signal detection method and the signal detection device can realize the performance superior to that of a linear detector while reducing the calculation complexity.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention in further detail. Wherein:
fig. 1 shows a schematic structure of a receiver of a MIMO wireless communication system;
fig. 2 shows a schematic structure of a MIMO detector according to the present invention;
fig. 3 shows the number k of candidate constellations for a fixed number of constellations k in a 16QAM modulated 2 × 2MIMO system1＝k2A selection procedure for case 4;
fig. 4 shows a flow diagram for adaptively selecting candidate constellations in a 2 x 2MIMO system;
fig. 5 illustrates a process of adaptively selecting candidate constellations, in accordance with the present invention;
fig. 6 shows a performance comparison between the detection method according to the present invention and the MMSE, MLD detection method with a fixed number of candidate constellations; and
fig. 7 shows a comparison of the performance of the detection method according to the invention with the MMSE, MLD detection method in case of an adaptive selection of candidate constellations.
Detailed Description
The following describes a signal detection method and a detection apparatus according to the present invention, with reference to the accompanying drawings, by taking a MIMO communication system as an example. Those skilled in the art will appreciate that the present invention is not applicable only to MIMO communication systems, but may also be applied to MIMO-OFDM, WiMAX or other wireless communication systems.
In a MIMO communication system, when a plurality of different data streams are simultaneously transmitted in parallel, all the transmitted data streams experience different channel parameters. Having NtA transmitting antenna and NrA typical MIMO system for individual receive antennas can be modeled as:
y＝Hs+n (1)
where n is a noise vector with variance σ2； <math><mrow>
<mi>s</mi>
<mo>=</mo>
<mo>{</mo>
<msub>
<mi>s</mi>
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</msub>
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<mi>s</mi>
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</mrow></math> Are a transmit vector and a receive vector, respectively, H is Nr×NtA dimensional channel transmission characteristic matrix.
The probability of a specific implementation of the received vector is given by the following multidimensional gaussian distribution:
where | is the vector modulus.
For coded transmission, the detector must provide a soft bit output, i.e., an A Posteriori Probability (APP) for each transmitted bit given the received signal. These can be expressed as log-likelihood ratios (LLRs), as shown in the following equation:
wherein two "and" respectively denote p (y | s) atSi，j，±1＝{si|bi，jExpected value on. + -. 1}, bi，jBit j, s, representing the transmitting antenna ii，j，±1＝{si|bi，jIs ± 1} bi，jConstellation set when ± 1. The optimal scheme enables maximum likelihood bit measure detection at the expense of an exponential increase in computational complexity with the number of constellations and the number of transmit antennas.
The MIMO detector and the detection method thereof according to the present invention will be described below with reference to the drawings.
Fig. 1 shows a structure of a receiver of a MIMO wireless communication system. The receiver comprises Nr An RF part 100, NrADC part 101, Nr An FFT section 102, a synchronization and channel estimation section 103, a MIMO detector 104 and a channel decoder 105.
The RF section 100 and the ADC section 101 convert a radio frequency signal into a baseband signal. The baseband signal is input to the FFT section 102, and is converted into a frequency domain signal by the FFT section 102. The received signal vector y converted by the FFT section 102 is input to the MIMO detector 104 to reconstruct a transmission symbol. The channel decoder 105 then processes the transmit symbols reconstructed by the MIMO detector 104 to recover the original bit information. The synchronization and channel estimation unit 103 is configured to synchronize a plurality of channels, and perform channel estimation based on a pilot signal in a received signal or by using another method, for example, to estimate a current channel transmission characteristic matrix H.
Fig. 2 further illustrates a specific structure of the MIMO detector 104 according to the present invention.
As shown in fig. 2, the MIMO detector 104 includes a linear detection section 200, a constellation selection section 201, and a soft bit metric calculation section 202.
The detection method of the present invention will be described with reference to fig. 2.
First, the linear detection unit 200 obtains an estimate of the transmission signal based on the channel transmission characteristic matrix H and the reception vector y estimated by the synchronization and channel estimation unit 103. Here, the linear detection section 200 may be a ZF detector or an MMSE detector. The estimation results of the two detectors are respectively as follows:
wherein equation (4) represents the estimation result using the ZF detector, and (5) represents the estimation result using the MMSE detector,
is estimating a signal vector, HHIs a conjugate transpose of H, INIs an identity matrix of size N.
Next, the constellation selection section 201 determines a reduced candidate constellation subset for each transmission dimension instead of the entire QAM constellation from the above estimation result. The criterion of selection is based on estimated symbols iDistance to QAM constellation. The candidate constellation subset of size k for the ith transmit antenna is represented as:
wherein s isijIs the ith estimation data
iC is a BPSK or QAM constellation set of modulation symbols.
The number of candidate constellations k may be fixed or vary for different antennas.
Fig. 3 shows the number k of candidate constellation points for a fixed in a 16QAM modulated 2 × 2MIMO system1＝k2The selection process for the case of 4. Two are providedLinear estimation of the receive antenna data as
1And
2its position in the original constellation is indicated by an "x". The original constellation points are indicated by ". smallcircle". For each estimated symbol, sequentially searching the original constellation diagram for the constellation point with the minimum distance to the estimated symbol according to a formula (6), wherein the searched 4 candidate constellation points are respectively marked in the diagram. The dashed circle in fig. 3 represents the maximum radius of the search.
And selecting different numbers of candidate constellation points for different antennas by adopting a self-adaptive constellation point selection method according to the signal-to-noise ratio difference of each receiving antenna.
For the adaptive selection method, the number k of candidate constellations for different antennas depends on the channel information and the SNR value. Fig. 4 shows a flow chart of an adaptive selection method in a 2 x 2MIMO system. The specific description is as follows:
referring to equation (1), the 2 × 2MIMO system signal model can be expressed as:
wherein, respectively representing a transmitted signal vector, a received signal vector and a noise vector, representing a matrix of channel transmission characteristics.
Firstly, calculating the signal-to-interference-and-noise ratio (SINR) gamma of each antenna branch1、γ2。
If an MMSE detector is employed, the estimated signal can be expressed as:
wherein, <math><mrow>
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</mrow></math> of estimated signalsThe SINR is respectively:
the SINR calculation method for ZF detection is the same as MMSE detection.
Thereafter, γ is judged1/γ2Is greater than a predetermined threshold p (p > 1).
If gamma is1/γ2Greater than p, the initial candidate constellation number k for the first antenna branch is incremented1Increment Δ (Δ is an integer value) and make an initial candidate constellation number k for the second antenna branch2Decrement by Δ and then perform constellation selection.
If gamma is1/γ2If not greater than p, then determining gamma2/γ1Whether greater than p.
If gamma is2/γ1Greater than p, the initial candidate constellation number k for the first antenna branch is incremented1Decrement Δ and number k of initial candidate constellations for the second antenna branch2Increment delta and then perform constellation selection.
If gamma is2/γ1If the value is not greater than p, the constellation selection is directly carried out.
From the above, it can be seen that the subset size k of each antenna branchiDependent on gamma1、γ2And Δ.
The self-adaptive selection of candidate constellations with the candidate number can improve the performance of the detection method utilizing the channel information. For example, in a 2 × 2MIMO system, the computational complexity of 3 × 5 is less than that of 4 × 4. Fig. 5 shows the process of adaptively selecting candidate constellations, where the number of candidate constellations for antennas 1 and 2 is 3 and 5, respectively (initial parameter k)1＝k2＝4，Δ＝1)。
In addition, there are other selection criteria for adaptively selecting candidate constellations. For example, one possible method is to adjust k in the opposite direction of the method in FIG. 4iThe refined LLR calculation formula is then used.
After the constellation selection unit 201 selects the candidate constellation subset, the soft bit information calculation unit 202 calculates soft bit information.
bi，jThe LLR of (1) is:
wherein S isij，±1＝{si，|bi，jIs ± 1} bi，jThe selected constellation subset for transmit antenna i is ± 1. Regarding the bit soft information calculation, there are many simplified algorithms such as MaxLogMAP and the like (non-patent document 4) in addition to the classic method of formula (12)])。
Fig. 6 shows a comparison of the performance of the detection method according to the invention with a fixed number of candidate constellations and with a linear MMSE and an optimal MLD detection method. Fig. 7 shows a comparison of the performance of the detection method according to the invention with the MMSE, MLD detection method in case of an adaptive selection of candidate constellations. Wherein the abscissa is the signal-to-noise ratio of the receiving end, the ordinate is the bit error rate, and the constellation diagram is 16QAM modulation. The results show that the detection method of the invention is superior to the linear MMSE detection method in performance. In terms of computational complexity, the linear detector (ZF or MMSE) is the simplest, and MLD detection grows exponentially with the number of antennas and the number of constellation points, with greatly increasing complexity. The computational complexity of the present invention is higher than linear detectors but much lower than MLD detection methods.
It should be noted that the scope of the present invention also includes a computer program for executing the above-described signal detection method and a computer-readable recording medium in which the program is recorded. As the recording medium, a flexible disk readable by a computer, a hard disk, a semiconductor memory, a CD-ROM, a DVD, a magneto-optical disk (MO), and other media may be used here.
While only the preferred embodiment has been chosen to illustrate the present invention, it will be apparent to those skilled in the art from this disclosure that various changes and modifications can be made herein without departing from the scope of the invention as defined in the appended claims. The foregoing description of the embodiments is merely exemplary in nature and is in no way intended to limit the invention, which is defined by the appended claims and their equivalents.
Claims (8)
1. A signal detection method in a receiver for a wireless communication system, which performs channel estimation based on a signal received by the receiver and detects a received signal based on a result of the channel estimation to output soft bit measure information of the received signal, the signal detection method comprising the steps of:
detecting a received signal using a linear detector based on a result of the channel estimation to obtain an initial estimation signal;
generating a candidate constellation subset for each transmission symbol from an original constellation set based on distances between the initial estimation signal and constellations in the original constellation set, wherein the candidate constellation subset of size k for an ith transmission antenna is represented as:
wherein s isijIs the ith estimation data
C is the original constellation set; and
and calculating the soft bit measure aiming at each sending code element according to the candidate constellation in the candidate constellation subset.
2. The signal detection method of claim 1, wherein the linear detector comprises a zero forcing detector and a minimum mean square error detector.
3. The signal detection method of claim 1, wherein the number of candidate constellations in the subset of candidate constellations is fixed or varies according to channel information and a signal-to-noise ratio value.
4. The signal detection method of claim 1, wherein the soft bit measure for each transmitted symbol is calculated by a maximum a posteriori probability or a simplified maximum a posteriori probability processing module.
5. A signal detection apparatus in a receiver for a wireless communication system detects a received signal according to a result of channel estimation to output soft bit measure information of the received signal,
the signal detection device includes:
a linear detection unit that detects a received signal based on a result of the channel estimation to obtain an initial estimation signal;
a constellation selection section that generates a candidate constellation subset for each transmission symbol from the original constellation set based on a distance between the initial estimation signal and a constellation in the original constellation set, wherein the candidate constellation subset of size k for the i-th transmission antenna is represented as:
wherein s isijIs the ith estimation data
C is the original constellation set; and
a soft bit metric calculation unit that calculates a soft bit metric for each transmitted symbol based on the candidate constellation in the subset of candidate constellations.
6. The signal detection apparatus according to claim 5, wherein the linear detection section includes a zero forcing detector and a minimum mean square error detector.
7. The signal detection apparatus of claim 5, wherein the number of candidate constellations in the subset of candidate constellations is fixed or varies according to channel information and a signal-to-noise ratio value.
8. The signal detection apparatus according to claim 5, wherein the soft bit metric calculation section calculates the soft bit metric of each transmission symbol by a maximum a posteriori probability or a simplified maximum a posteriori probability processing module.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US8279965B2 (en) * | 2009-06-30 | 2012-10-02 | Hong Kong Applied Science And Technology Research Institute Co., Ltd. | Multiple antenna spatial multiplexing optimal detection |
| JP5691245B2 (en) * | 2010-05-27 | 2015-04-01 | 富士通株式会社 | Receiving apparatus and receiving method |
| JP5633271B2 (en) * | 2010-09-17 | 2014-12-03 | 富士通株式会社 | COMMUNICATION DEVICE, COMMUNICATION METHOD, AND COMMUNICATION SYSTEM |
| CN103378898A (en) * | 2012-04-24 | 2013-10-30 | 马维尔国际有限公司 | Signal detection method and apparatus in mimo system |
| CN103746728B (en) * | 2013-10-08 | 2016-07-27 | 北京科技大学 | The MIMO of a kind of mixed self-adapting receives detection method |
| CN104852784B (en) * | 2015-04-07 | 2018-02-16 | 浙江理工大学 | A kind of multiple cell mimo system upward signal detection method based on constellation structures |
| CN104796374B (en) * | 2015-04-27 | 2018-01-12 | 电子科技大学 | A kind of signal detecting method for carrier index modulation ofdm system |
| CN109672644A (en) * | 2017-10-13 | 2019-04-23 | 瑞昱半导体股份有限公司 | Multistage multiple-input and multiple-output detector and its method for detecting |
| CN109067683B (en) * | 2018-09-25 | 2020-12-01 | 郑州大学 | Blind detection and modulation constellation optimization method in wireless communication and storage medium |
| US11387870B2 (en) * | 2020-12-04 | 2022-07-12 | Nokia Technologies Oy | MIMO detector selection |
| CN112887231B (en) * | 2021-01-13 | 2022-09-09 | 深圳市极致汇仪科技有限公司 | Method and system for improving MIMO channel estimation |
| CN114389756B (en) * | 2022-01-20 | 2024-04-09 | 东南大学 | Uplink MIMO detection method based on packet ML detection and parallel iterative interference cancellation |
| CN115622665B (en) * | 2022-10-31 | 2024-06-25 | 电子科技大学 | MCMC-MIMO detection method, equipment and system based on self-adaptive probability calculation |
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