CN102833018A - SEESM (simplified exponential effective signal to interference and noise ratio mapping) implementation method based on order self-adaption mu law non-uniform quantizing - Google Patents

Abstract

The invention provides an SEESM (simplified exponential effective signal to interference and noise ratio mapping) implementation method based on order self-adaption mu law non-uniform quantizing. The method includes the following steps: firstly, determining maximum values and minimum values of sub-carriers SINR (signal to interference and noise ratio) in a system bandwidth, and adaptively acquiring a quantization order M applicable to a current channel condition based on the above maximum values and the minimum values; secondly, dividing subcarrier SINR range into M quantized intervals according to an M-fold mu law, and mapping subcarrier SINRs into corresponding quantized intervals; thirdly, uniformly quantizing the SINRs in a same quantized interval into an arithmetic average value of the SINRs in the quantized intervals; finally, using EESM(exponential effective signal to interference and noise ratio mapping)algorithm to acquire an equivalent SINR value according to the quantized value of the subcarrier SINRs. Index calculation times in the EESM algorithm can be reduced from subcarrier amount to quantization order, and on the premise of hardly losing system throughput rate performance, complexity of EESM realization is greatly lowered and timeliness of a system is improved.

Description

SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing

Technical field

The present invention relates to wireless communication field; Specifically be a kind of effective Signal to Interference plus Noise Ratio mapping of index (Simplified Exponential Effective Signal to Interference and Noise Ratio Mapping, implementation method SEESM) of restraining the simplification of non-uniform quantizing based on exponent number self adaptation μ.

Background technology

Two-forty, big bandwidth are just becoming the core feature of future broadband wireless communication systems; OFDM (Orthogonal Frequency Division Multiplex; OFDM) with its good spectrum efficiency and antagonism intersymbol interference (Iter-Symbol Interference, performance ISI) and become the support technology in the 4th generation wireless communication standard.

For single-carrier system; Can be easy to obtain Signal to Interference plus Noise Ratio (the Signal to Interference and Noise Ratio on each subchannel; SINR); (Adaptive Modulation and Coding AMC) waits link circuit self-adapting (Link Adaptive, LA) technology to carry out adaptive coding and modulating in view of the above.Yet for ofdm system; The time delay expansion makes broadband wireless channel experience frequency selective fading; The SINR of OFDM different sub carrier is different; For the channel characteristics with a plurality of subcarriers on the frequency domain resource piece integrates the SINR value that obtains an equivalence; The effective Signal to Interference plus Noise Ratio mapping of a kind of index (Exponential Effective Signal to Interference and Noise Ratio Mapping, EESM) method has been carried, its utilization index information measure function; With the SINR value of an equivalence of subcarrier SINR sequence boil down to that changes, thereby can the frequency-selective channel equivalence be carried out various technical research for flat fading channel.

The method that EESM calculates equivalent SINR value is following:

${\mathrm{\γ}}_{\mathrm{eff}}=-\mathrm{\β}\ln \left[\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\exp (-\frac{{\mathrm{\γ}}_i}{\mathrm{\β}})\right]---\left(1\right)$

In the formula (1), N is the ofdm system number of sub carrier wave, and i is a subcarrier number, γ _iBe the SINR value of i number of sub-carrier, β is and modulation coding mode (Modulation and Coding Scheme, MCS) relevant scale factor.

Though EESM has solved the mapping problems of frequency-selective channel to flat fading channel well; For the LA technology provides the application prerequisite, still, with reference to formula (1); We find that EESM need carry out the exponent arithmetic of sub-carrier number contents, and (The 3 with third generation partner program ^RdGeneration Partnership Project, and the Long Term Evolution of 3GPP) formulating (Long Term Evolution, LTE) the R9 consensus standard is an example; Stipulated the situation of 20MHz bandwidth in the standard; If the OFDM subcarrier spacing is 15KHz, this moment, the number of subcarrier was 1200, then used the EESM method and need calculate index 1200 times; The bulky and complex degree has brought great challenge for the hardware realization (especially the hardware of portable terminal is realized) of system like this.

Summary of the invention

The objective of the invention is in order to overcome the defective of the high complexity of above-mentioned EESM method, a kind of simplification EESM method based on exponent number self adaptation μ rule non-uniform quantizing is provided.The present invention at first confirms maximum and the minimum value of each subcarrier SINR in the system bandwidth, on this basis, obtains the quantification exponent number M that is applicable to current channel condition adaptively; Use M-folding μ rule that subcarrier SINR scope is divided into M quantized interval then, each subcarrier SINR is shone upon in the corresponding quantitative interval; To being in the SINR in the same quantized interval, be the arithmetic mean value of each SINR in this interval with its unified quantization; Quantized value to subcarrier SINR uses the EESM algorithm at last, obtains equivalent SINR value.The present invention can be reduced to the quantification exponent number from number of sub carrier wave with index calculation times in the EESM algorithm, under the prerequisite of loss system throughput performance hardly, has greatly reduced the implementation complexity of EESM, has improved the real-time of system.

Particularly, the present invention realizes through following technical scheme, the present invention includes following steps.

Step 1, search subcarrier SINR sequence, maximum and the minimum value of acquisition subcarrier SINR;

Step 2 according to maximum and the minimum value of subcarrier SINR, confirms to quantize exponent number M adaptively;

Step 3 according to M-folding μ rule, is confirmed the separation of quantized interval;

Step 4 according to the separation of quantized interval, is implemented to quantize to each subcarrier SINR;

Step 5 with the subcarrier SINR value that quantizes, is used the EESM algorithm, obtains equivalent SINR.

Preferably, in step 2, the described step of confirming to quantize exponent number adaptively, adaptive approach is following:

M=max{ρln[(exp(Γ _max)-exp(Γ _min))],1}

Wherein, Γ _Max, Γ _MinBe respectively to be maximum and the minimum value of the subcarrier SINR of unit with dB.

Preferably, in step 3, described separation according to the definite quantized interval of M-folding μ rule, concrete grammar is following:

$\left\{\begin{array}{c}{\mathrm{\λ}}_1={\mathrm{\γ}}_{\min },{\mathrm{\λ}}_{M+1}={\mathrm{\γ}}_{\max };\\ {\mathrm{\λ}}_i=(1-\mathrm{\κ}){\mathrm{\λ}}_1+{\mathrm{\κ\λ}}_{i+1},i=M,M-1,...\mathrm{3,2}\end{array}\right.$

Wherein, λ _iBe separation, γ _Max, γ _MinBe respectively maximum and the minimum value of subcarrier SINR, κ is the scale factor of nonuniform quantiza, carries out according to i order from big to small during iteration.

Preferably, in step 4, described quantification at first need be mapped into corresponding quantitative at interval with subcarrier SINR according to following rule:

For i number of sub-carrier SINR γ _i, when it satisfies:

γ _i∈(λ _p,λ _p+1],p=2,3,...M?or?γ _i∈[λ _p,λ _p+1],p=1

Then its mapping is got into p quantized interval; Wherein, M is for quantizing exponent number, λ _pBe the quantized interval separation.

Preferably, in step 4, described quantification, to being in the SINR in the same quantized interval, with their arithmetic mean value as the quantized value of this quantized interval.

Preferably, in step 5, described to quantizing subcarrier SINR application EESM algorithm, the final form that obtains equivalent SINR is:

${\mathrm{\γ}}_{\mathrm{eff}}=-\mathrm{\β}\ln \left(\frac{1}{N}\underset{p=1}{\overset{M}{\mathrm{\Σ}}}{M}_p\exp (-\frac{{\stackrel{\‾}{\mathrm{\γ}}}_p}{\mathrm{\β}})\right)$

Wherein, Be the quantized value of p quantized interval, M _pBe the number of SINR value in p the quantized interval, M is for quantizing exponent number, and N is the total number of sub-carriers order, and β is the scale factor relevant with MCS.

Preferably, said segmentation factor ρ=0.25.

Preferably, the scale factor κ of said nonuniform quantiza=0.5.

EESM compares with prior art; The beneficial effect that SEESM of the present invention has is: under the prerequisite of loss system throughput performance hardly; This method can select to quantize exponent number adaptively according to channel conditions, through the exact value of the SINR quantized value with SINR is replaced, makes that the index calculation times is reduced to the quantification exponent number from number of sub carrier wave in the EESM algorithm; Thereby greatly reduce the system hardware implementation complexity, improved the real-time of system.

Description of drawings

Fig. 1 is overall flow figure of the present invention;

Fig. 2 is in the embodiment scene, under the different channels bandwidth, SEESM algorithm of the present invention to the EESM algorithm in the approximate error of prediction aspect the equivalent SINR;

Fig. 3 is in the embodiment scene, under the different channels bandwidth, and the contrast of the complexity of SEESM algorithm of the present invention and EESM algorithm;

Fig. 4 is in the embodiment scene, and bandwidth is 1.4MHz, when time delay expands to 480ns, and SEESM algorithm of the present invention and the comparison of EESM algorithm on the system throughput performance.

Embodiment

Below in conjunction with accompanying drawing embodiments of the invention are elaborated, present embodiment is that prerequisite is implemented with method of the present invention, provided detailed execution mode and concrete operating process, but protection scope of the present invention is not limited to following embodiment.

In the present embodiment, the inventor chooses the descending point-to-point communication link of LTE R9 agreement as application scenarios, subscriber equipment (User Equipment; UE) receive from base station (Base Station; BS) information, (Channel State Information CSI) estimates UE to channel condition information; And it is fed back to BS, BS uses the LA technology in view of the above.

The concrete parameter of this embodiment application scenarios is as shown in table 1.About the WINNER II channel model that occurs in the table 1, it is in the WINNER of European Union project, to put forward.WINNER II channel model utilizes the ray addition method, through around BS and UE, shedding the scattering object group according to statistical law, simulates actual electromagnetic wave reflection, refraction etc., thereby realizes the simulation to actual channel.WINNER II channel model is measured through actual channel, has provided the statistical parameter of 15 kinds of scenes, and table 2 has provided the time delay spreading parameter of used 4 kinds of scenes in our emulation.

Table 1

The parameter title	Parameter value
		The link operational mode	The single-shot list is received pattern (SISO)
System bandwidth	[1.4、3、5、10、15、20]MHz

Subcarrier spacing	15KHz
		The downlink sub-carrier number	[72?180?300?600?900?1200]
The simulated channel model	WINNER II Model [seeing table 2 for details]
		Channel estimation methods	Perfect channel estimating
The UE translational speed	0m/s
		The HARQ maximum retransmission	0

Table 2

Sequence number	The scene code name	RMS time delay expansion (ns)	Coherence bandwidth [MHz]
				1	A1	?25	8.0
2	B1	?76	2.6
				3	B2	?480	0.4
4	C3	?630	0.3

Particularly, the process of whole embodiment is: at first, UE obtains the SINR value of each subcarrier through channel estimation technique; Then, UE uses the SINR of the SEESM algorithm of the present invention's proposition with all equivalences of subcarrier SINR boil down to _EffAt last, UE is according to SINR _EffConfirm that (Channel Quality Indicator CQI), feeds back to the foundation of BS as AMC with it in the channel quality indication.

More particularly, the concrete steps of present embodiment are following:

Step 1, UE obtains the SINR value of each subcarrier through channel estimation technique, and this step was carried before using the present invention, but non-the present invention's main contents, so will not give unnecessary details.

Step 2, shown in flow chart element among Fig. 11, search subcarrier SINR sequence { γ _i, (i=1,2...N), γ wherein _iBe the SINR of i number of sub-carrier, N is the total number of sub-carriers order, and is relevant with system bandwidth and subcarrier spacing, as shown in table 1.Obtain the maximum γ of subcarrier SINR thus _MaxWith minimum value γ _Min

Described subcarrier SINR sequence is meant, in whole broad band multicarrier channel, obtains the SINR on each subcarrier through channel estimating, and all subcarrier SINR arrange the sequence that forms according to the frequency band sequence of positions at its place.

Step 3 shown in flow chart element among Fig. 12, according to the fading range of the determined subcarrier SINR of step 2, is confirmed to quantize exponent number M adaptively, confirms method such as formula (2):

M=max{ρln[(exp(Γ _max)-exp(Γ _min))],1}（2）

In the formula (2), ρ is the segmentation factor, and empirical value is ρ=0.25, Γ _Max, Γ _MinBe respectively to be maximum and the minimum value of the subcarrier SINR of unit, i.e. Γ with dB _Max=10lg (γ _Max), Γ _Min=10lg (γ _Min), γ wherein _MaxAnd γ _MinBe maximum and the minimum value of the determined subcarrier SINR of step 2.

Said definite adaptively quantification exponent number is meant; Use the resulting quantification exponent number of formula (2) M; Can be according to channel width, the expansion of channel RMS time delay, channel signal to noise ratio (Signal to Noise Ratio; SNR) automatically adjustment reaches best the approaching of EESM algorithm performance with the quantification exponent number of minimum.

Step 4 shown in flow chart element among Fig. 13, is used M-folding μ rule, confirms the separation of quantized interval.Because whole interval is quantified as the M rank, in left and right sides end points was included in, then total M+1 separation according to order from small to large, was designated as λ respectively with them _i(i=1,2 ... M+1).Definite method of this M+1 separation such as formula (3):

$\left\{\begin{array}{c}{\mathrm{\λ}}_1={\mathrm{\γ}}_{\min },{\mathrm{\λ}}_{M+1}={\mathrm{\γ}}_{\max };\\ {\mathrm{\λ}}_i=(1-\mathrm{\κ}){\mathrm{\λ}}_1+{\mathrm{\κ\λ}}_{i+1},i=M,M-1,...\mathrm{3,2}\end{array}\right.---\left(3\right)$

In the formula (3), γ _Max, γ _MinBe respectively maximum and the minimum value of determined subcarrier SINR in the step 2, κ is the scale factor of nonuniform quantiza, and empirical value is κ=0.5, and M is the determined quantification exponent number of step 3.Formula (3) is an iterative computation formula, the initial value of confirming iteration by maximum and the minimum value of decline, and according to from big to small order, iteration is obtained each separation successively then.

Step 5 shown in flow chart element among Fig. 14, is accomplished the quantification to subcarrier SINR.At first each number of sub-carrier SINR is mapped into corresponding quantitative at interval, then the subcarrier SINR that is in same quantized interval is asked for arithmetic average, with the quantized value of this mean value as all SINR in this interval.

Described subcarrier SINR is mapped into corresponding quantitative at interval, and mapping ruler wherein is: for i number of sub-carrier SINR γ _i,, then its mapping is got into p quantized interval when it satisfies formula (5).

γ _i∈(λ _p,λ _p+1],p=2,3,...M?orγ _i∈[λ _p,λ _p+1],p=1 （5）

In the formula (5), M is for quantizing exponent number, λ _pBe the determined quantized interval separation of step 4.

Described the subcarrier SINR that is in same quantized interval is asked for arithmetic average, is meant:

Suppose that the SINR set of values in p the quantized interval is combined into { γ _{P, j}, j=1,2...M _p, M wherein _pBe SINR value number, the then arithmetic mean of p quantized interval in p the quantized interval

Calculate with following method:

${\stackrel{\‾}{\mathrm{\γ}}}_p=\frac{1}{M_p}\underset{j=1}{\overset{M_p}{\mathrm{\Σ}}}{\mathrm{\γ}}_{p,j}---\left(6\right)$

Step 6 shown in flow chart element among Fig. 15, is implemented the EESM algorithm to the quantized value of subcarrier SINR, obtains equivalent SINR.Concrete grammar such as formula (4):

${\mathrm{\γ}}_{\mathrm{eff}}=-\mathrm{\β}\ln \left(\frac{1}{N}\underset{p=1}{\overset{M}{\mathrm{\Σ}}}{M}_p\exp (-\frac{{\stackrel{\‾}{\mathrm{\γ}}}_p}{\mathrm{\β}})\right)---\left(4\right)$

In the formula (4), γ _EffBe equivalent SINR value, Be the quantized value of determined p the quantized interval of step 5, M _pBe SINR value number in p the quantized interval, M is the determined quantification exponent number of step 3, and N is the total number of sub-carriers order, and β is the scale factor relevant with MCS.

Because subcarrier SINR is quantized, the SINR value that is in the same quantized interval all uses same quantized value to represent, then uses the EESM algorithm for the SINR value in p the quantized interval, and the exponent arithmetic number of times is by M _pInferior being reduced to 1 time; Use the EESM algorithm for the SINR value in all quantized intervals, the exponent arithmetic number of times is reduced to M time by N time, and N is the total number of sub-carriers order, and M is for quantizing exponent number, and this just greatly reduces computational complexity, and it is more easy to make hardware realize.

Step 7, according to resulting equivalent SINR, (Additive White Gauss Noise, the AWGN) mapping curve of SINR and CQI under the channel obtains CQI and it is fed back to BS, as the usefulness of AMC to consult additive white Gaussian noise.The non-the present invention's of this step main contents will not be given unnecessary details here.

Fig. 2 has provided under the present embodiment scene, under 6 kinds of bandwidth of LTE R9 agreement regulation, uses the determined equivalent SINR of SEESM algorithm of the present invention, with the approximate error of using the determined equivalent SINR of EESM algorithm.Can find out that the present invention reduces along with the increase of system bandwidth the approximation ratio of EESM, reduce along with the increase of system's time delay expansion.Even if but under worst condition (maximum bandwidth, maximum delay expansion), approximate error can not surpass 2dB, can accomplish the prediction of equivalent SINR preferably.

Fig. 3 has provided under the present embodiment scene, under 6 kinds of bandwidth of LTE R9 agreement regulation, and SEESM algorithm of the present invention and EESM algorithm, the comparison on the Index for Calculation number of times.Can find out, the outstanding advantage of the present invention aspect the reduction computation complexity, and this advantage is more obvious along with the increase of system bandwidth.

Fig. 4 has provided under the present embodiment scene, is 1.4MHz in bandwidth, and time delay expands under the situation of 480ns, and the SEESM algorithm that system uses the present invention to propose is with use EESM algorithm, the comparison on the system throughput performance.Can find out that SEESM has approached the EESM algorithm well aspect throughput.

Claims (8)

1. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing is characterized in that, may further comprise the steps:

Step 1, search subcarrier SINR sequence, maximum and the minimum value of acquisition subcarrier SINR;

Step 2 according to maximum and the minimum value of subcarrier SINR, confirms to quantize exponent number M adaptively;

Step 3 according to M-folding μ rule, is confirmed the separation of quantized interval;

Step 4 according to the separation of quantized interval, is implemented to quantize to each subcarrier SINR;

Step 5 with the subcarrier SINR value that quantizes, is used the EESM algorithm, obtains equivalent SINR.

2. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing according to claim 1 is characterized in that, in step 2, and the described step of confirming to quantize exponent number adaptively, adaptive approach is following:

M=max{ρln[(exp(Γ _max)-exp(Γ _min))],1}

Wherein, Γ _Max, Γ _MinBe respectively to be maximum and the minimum value of the subcarrier SINR of unit with dB.

3. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing according to claim 1 is characterized in that, in step 3, and described separation according to the definite quantized interval of M-folding μ rule, concrete grammar is following:

$\left\{\begin{array}{c}{\mathrm{\λ}}_1={\mathrm{\γ}}_{\min },{\mathrm{\λ}}_{M+1}={\mathrm{\γ}}_{\max };\\ {\mathrm{\λ}}_i=(1-\mathrm{\κ}){\mathrm{\λ}}_1+{\mathrm{\κ\λ}}_{i+1},i=M,M-1,...\mathrm{3,2}\end{array}\right.$

Wherein, λ _iBe separation, γ _Max, γ _MinBe respectively maximum and the minimum value of subcarrier SINR, κ is the scale factor of nonuniform quantiza, carries out according to i order from big to small during iteration.

4. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing according to claim 1 is characterized in that, in step 4, described quantification at first need be mapped into corresponding quantitative at interval with subcarrier SINR according to following rule:

Value γ for i number of sub-carrier SINR _i, when it satisfies:

γ _i∈(λ _p,λ _p+1],p=2,3,...M or γ _i∈[λ _p,λ _p+1],p=1

Then its mapping is got into p quantized interval; Wherein, M is for quantizing exponent number, λ _pBe the quantized interval separation.

5. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing according to claim 1; It is characterized in that, in step 4, described quantification; To being in the SINR in the same quantized interval, with their arithmetic mean value as the quantized value of this quantized interval.

6. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing according to claim 1 is characterized in that, in step 5, described to quantizing subcarrier SINR application EESM algorithm, the final form that obtains equivalent SINR is:

${\mathrm{\γ}}_{\mathrm{eff}}=-\mathrm{\β}\ln \left(\frac{1}{N}\underset{p=1}{\overset{M}{\mathrm{\Σ}}}{M}_p\exp (-\frac{{\stackrel{\‾}{\mathrm{\γ}}}_p}{\mathrm{\β}})\right)$

Wherein, Be the quantized value of p quantized interval, M _pBe the number of SINR value in p the quantized interval, M is for quantizing exponent number, and N is the total number of sub-carriers order, and β is the scale factor relevant with MCS.

7. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing according to claim 2 is characterized in that said segmentation factor ρ=0.25.

8. the SEESM implementation method based on exponent number self adaptation μ rule non-uniform quantizing according to claim 3 is characterized in that the scale factor κ of said nonuniform quantiza=0.5.

Images