FAST SCAN OF NB-IoT SIGNALS IN NETWORKS

Abstract

The invention discloses a method for fast detection scan of NB-IoT signals in networks. The object of the invention to provide a scanning procedure which is reliable and very fast in order to reduce the search time and hence the power consumption will be solved by a method for fast detection scan of NB-IoT signals in a network by applying a higher sampling rate than 240 kHz and observing a received signal at a receive bandwidth around a magnitude wider than the NB-IoT signal bandwidth of 180 kHz, wherein a set of 2M+1 NB-IoT signals each having a different E-UTRA absolute radio frequency channel number (EARFCN) can be observed simultaneously, whereas M is a natural number and 2M+1 indicates the number of concurrently observed channels.

Claims

1. A method for fast detection scan of NB-IoT signals by applying a higher sampling rate than 240 kHz and observing a received signal at a receive bandwidth around a magnitude wider than the NB-IoT signal bandwidth of 180 kHz, wherein a set of 2M+1 NB-IoT signals each having a different E-UTRA absolute radio frequency channel number (EARFCN) can be observed simultaneously, and wherein M is natural number and 2M+1 indicates the number of observed channels.

2. The method for fast detection scan of NB-IoT signals according to claim 1, whereas the method comprises a first stage, at which an average part of a cyclic prefix of a baseband signal is removed.

3. The method for fast detection scan of NB-IoT signals according to claim 2, wherein the method comprises a second stage, at which time domain vectors of N IQ samples are collected in time, such that an average time difference of the time domain vectors is equal to one LTE OFDM symbol, whereas Nis the length of the vector.

4. The method for fast detection scan of NB-IoT signals according to claim 3, wherein the method comprises a third stage, at which a vector in the frequency domain of each time domain vector is computed, applying a Discrete Fourier Transform (DFT), forming a DFT vector, respectively.

5. The method for fast detection scan of NB-IoT signals according to claim 4, wherein the third stage is based on a Fast Fourier Transformation (FFT).

6. The method for fast detection scan of NB-IoT signals according to claim 4, wherein the method comprises a fourth stage, at which a Hadamard product of a current DFT vector with a conjugate of a previous DFT vector is computed.

7. The method for fast detection scan of NB-IoT signals according to claim 6, wherein index points W.sub.m of the Hadamard products are added with regard to a frequency content of the received NB-IoT signal of the m-th EARFCN.

8. The method for fast detection scan of NB-IoT signals according to claim 6, wherein the collection of Hadamard products are averaged over a time period of multiple LTE frames.

9. The method for fast detection scan of NB-IoT signals according to claim 6, wherein the method comprises a fifth stage, at which a cover code of NPSS contained in the NB-IoT signal is taken into consideration with regard to the Hadamard product in order to calculate output values.

10. The method for fast detection scan of NB-IoT signals according to claim 4, wherein the time domain vector is separated by one half of an LTE OFDM symbol in time, the frequency domain vectors are computed based on the half of the LTE OFDM symbols, the Hadamard product is performed with a corresponding alternate half of an LTE OFDM symbol in time, thus the average time difference of the Hadamard products is again one OFDM symbol period.

11. The method for fast detection scan of NB-IoT signals according to claim 9, wherein 2M+1 peak values are computed with regard to calculated output values according to the cover code of NPSS contained in the NB-IoT signal and the Hadamard product.

12. The method for fast detection scan of NB-IoT signals according to claim 9, wherein a single reference value is computed with regard to the output values of the fifth stage.

13. The method for fast detection scan of NB-IoT signals according to claim 11, wherein a post processing is performed by re-scanning the peak values at a different center frequency, whereas a minimum peak value is taken from the initial scan at an initial center frequency and a second scan at the different center frequency.

Description

[0042] The appended drawings show

[0043] FIG. 1 A scan of multiple NB-IoT signals simultaneously;

[0044] FIG. 2 Half symbol correlation in relation to complete OFDM symbols;

[0045] FIG. 3 Correlation result in the presence of a noisy NB-IoT signal at m.sub.opt.

[0046] FIG. 1 shows a frequency interval

[00008]$-\frac{BW}{2}-M\Delta \le f\le M\Delta +\frac{BW}{2},\Delta =100\mathrm{kHz}$

which supports a simultaneous scan of EARFCN −M≤m≤−1 and 1≤m≤M in addition to the EARFCN for m=0 relative to the carrier frequency f.sub.c.

[0047] Applying a higher sampling rate f.sub.s and thus observing the signal at a higher receive bandwidth relative to the signal bandwidth BW gives rise to a parallel search of multiple EARFCNs in a single run of NPSS search.

[0048] Let {x(k)} be the discrete time baseband signal obtained at some sampling frequency f.sub.s≥BW+2MΔ. A first required processing part is to remove the average part of the cyclic prefix from the sequence of incoming data samples x(k). Assuming a typical LTE sampling rate

[00009]$f_s=\frac{1}{K}30.72\mathrm{MHz}$

is used, where K is an integer value. For K=16, a sampling rate of 1920 kHz provides sufficient oversampling for M≤8. In this case, the average cyclic prefix can be removed by discarding Q(s=n mod 7) IQ samples, once the n-th chunk of 128 IQ samples has been captured, where {Q(s)}.sub.s=0.sup.6={10,9,9,9,9,9,9}. Alternatively, a slightly lower sampling rate f

[00010]$f_s=\frac{14}{15K}30.72\mathrm{MHz}$

can be used. In this case, the process of periodically discarding samples as described above can be avoided, since the lowering of the sample rate performs already the cyclic prefix removal.

[0049] Without loss of generality, let K=16. Regardless of the method applied for average cyclic prefix removal, each LTE frame consists of 140 consecutive time domain OFDM symbol vectors x.sub.n of the form x.sub.n=[x(k+n.Math.128),x(k+n.Math.128+1), . . . , x(k+n.Math.128+127)].sup.T for n=0,1, . . . , 139. By using a Discrete Fourier Transformation of length N=128 one can obtain a corresponding frequency domain vector X.sub.n=DFT.sub.128(x.sub.n), which is more efficient than applying a bank of local discrete time mixers. Let F.sub.n=X.sub.n.sup.∘X.sub.n−1* (Eq. 3) be the Hadamard product of the n-th OFDM symbol vector X.sub.n with the conjugate of the previous OFDM symbol vector X.sub.n−1* in time. Due to the good auto-correlation properties of the Zadoff-Chu sequence, the magnitude |F.sub.n| for the symbols n belonging to the NPSS is relative large, provided the sampling is aligned with regard to the boundary of an OFDM symbol.

[0050] However, since the symbol boundary is unknown while performing a NPSS signal detection, the magnitude |F.sub.n| strongly depends on the initial sampling point. This dependency can be relaxed by considering a set of time shifted vectors x.sub.n.sup.p=[x(k+n.Math.128+v.sub.p),x(k+n.Math.128+v.sub.p+1), . . . , x(k+n.Math.128+v.sub.p+127)].sup.T for p=0,1,2,3 with v.sub.p=p.Math.32 and computing corresponding values XP and F.sub.n.sup.p, respectively.

[0051] Referring to FIG. 2. A simpler approach is to consider half symbols rather than complete OFDM symbols. Let x.sub.n=[x(k+n.Math.64),x(k+n.Math.64+1), . . . , x(k+n.Math.64+63)].sup.T be the n-th half symbol for n=0,1, . . . , 279 belonging to a complete LTE frame. Let X.sub.n=DFT.sub.64(x.sub.n), and similar to Eq. 3, let F.sub.n be defined as F.sub.n=X.sub.n.sup.∘X.sub.n−2* (Eq. 4). In this case, the dependency of |F.sub.n| with regard to the initial sampling point is considerably relaxed, since at least one portion of a half symbol is always contained in a complete OFDM symbol, regardless of the initial sampling point. This is shown in FIG. 2. The complete OFDM symbols S.sub.k−1 and S.sub.k at the radio interface obtain some offset with regard to the random symbol boundary of X.sub.n. As can be seen, the half symbols of the pair (X.sub.n−2, X.sub.n) are contained within the adjacent OFDM symbols S.sub.k−1 and S.sub.k, whereas the half symbols of (X.sub.n−1, X.sub.n+1) are not.

[0052] Note that the k-th element X.sub.n (k) of the vector X.sub.n corresponds to an estimate of the frequency content

[00011]$\hat{X}=\left(e^{\left(j\frac{2\pi .Math.k}{64}\right)}\right)$

of the baseband signal. In order to reduce the memory requirements, it is sufficient to store values A.sub.n,m=Σ.sub.k∈W.sub.mF.sub.n(k), −M≤m≤M, n=0,1, . . . , 279 where W.sub.m is the set of frequency indices belonging to the main spectral part of the NB-IoT signal with regard to the m-th EARFCN.

[0053] To improve the reliability of the NPSS signal detection, A.sub.n,m can be averaged over multiple LTE frames v=1, . . . , N.sub.F according to B.sub.n,m(v)=αB.sub.n,m(v−1)+βA.sub.n,m(v) (Eq. 5) with B.sub.n,m(0)=0 and 0≤α,β≤1.

[0054] The update of B.sub.n,m may be performed in real-time while receiving the I/Q samples. For the n.sub.x-th chunk of N samples, the computational complexity of updating B.sub.n,m is approximately given as follows: N log.sub.2N applying the Fast Fourier Transform (FFT), N multiplications, (2M+1)|W.sub.m| additions have to be performed. Hence, storage of B.sub.n,m is required for n=0,1, . . . , 279 and −M≤m≤M, while performing an in-place update over several frame periods. Storage of X.sub.n and X.sub.n−2 is only locally required for step n=n.sub.x up to a depth of 3.

[0055] According to Eq. 1, the NPSS uses a specific cover code. With regard to Eq. 5 let C.sub.n,m=|Σ.sub.l=0.sup.9S(l+1)S(l)B.sub.(2l+1)mod 280,m(v=N.sub.F)|.sup.2≥0 (Eq. 6) and let P.sub.peak(m)=max.sub.{0≤n≤279}C.sub.n,m {−M≤m≤M}.

[0056] This means that for each channel value {−M≤m≤M} a dedicated peak value P.sub.peak(m) is computed. Let P.sub.ref be a common reference value according to

[00012]$P_{ref}=\frac{T}{280}{.Math.}_{n=0}^{279}{C}_{n,m_{\mathrm{opt}}}$

with m.sub.opt taken from (m.sub.opt, n.sub.opt)=arg-max.sub.{−M≤m≤M, 0≤n≤279} C.sub.n,m and T being a scaling factor. The presence of a NPSS can be considered if P.sub.peak (m)≥P.sub.ref (Eq. 7). This reference value performs an automatic calibration with regard to the dynamic range of the baseband signal {x(k)}. The scaling factor T should be chosen such that the false alarm rate is very low. A conservative value for T with regard to a very low false alarm rate is found by

[00013]$\frac{P_{\mathrm{peak}}\left(m\right)}{P_{ref}}\approx \frac{1}{4}\left\{-M\le m\le M\right\}$

in the presence of a pure Gaussian noise random signal.

[0057] Note that the argument of the complex number B.sub.n;m is proportional to the carrier frequency offset, see Eq. 4 making the estimation very robust with regard to an unknown carrier frequency offset. A channel raster offset and a possibly large frequency offset due to crystal tolerances of the user equipment (UE) (up to 25 ppm) are covered by the method of this invention.

[0058] The computation of C.sub.n;m can be performed in an additional post-processing part, once the RF interface has already been turned off. Since all information of C.sub.n;m can be obtained from B.sub.n;m, explicit storage of C.sub.n;m is not required in order to compute P.sub.peak (m) and P.sub.ref.

[0059] FIG. 3 shows a plot of a measurement result of an embodiment of the invention, applying N.sub.F=16 frames for averaging with parameters α=β=1. Scanning has been performed at f.sub.c=806 MHz, observing 11 channels (M=5). The RF interface operated at low input power (10 log.sub.10 SNR<0) with 10 ppm offset from the reference clock.

[0060] Note that deliberately a set of 2M+1 peak values P.sub.peak(m) are computed, and not only P.sub.peak (m=m.sub.opt). This is outlined in the following. Depending on the properties of the radio part, the proposed algorithm may incorrectly indicate additional NB-IoT signals in the vicinity of the correctly detected EARFCN. This may be, for instance, caused due to I/Q mismatch of the receive path of a low IF receiver. In order to mitigate those unwanted false positive detections, it is useful to re-invoke the complete scan but using a different center frequency f.sub.c+f.sub.c. The frequency offset f.sub.o should be a multiple of 100 kHz and be chosen such that the found peaks can be re-measured using a single snapshot. Taking the minimum of the peak values with regard to the previous measurement, may retrieve the false peak candidates from the true candidate of the EARFCN.