TECHNICAL FIELD

The present disclosure relates to the field of Narrowband Internet of Things (NB-loT), a 3GPP standard designed for low power and low bit rate devices. More particular, the disclosure relates to a band scan and cell search algorithm for identification of a Narrowband Primary Synchronization Signal (NPSS).

BACKGROUND NB-loT is the fifth-generation of mobile communication technologies standard developed within the 3rd Generation Partnership Project, 3GPP. One of the purposes is to improve the Universal Mobile Telecommunication System (UMTS) standard to cope with future requirements in terms of improved services such as higher data rates, improved efficiency, and lowered costs. In a typical UMT system, wireless devices or terminals also known as mobile stations and/or user equipment units (UEs) communicate via a radio access network (RAN) to one or more core networks.

When a UE, such as a wireless communication device, is powered on it will attempt to connect to a network and the first process in this attempt is to initiate a frequency band scan, time/frequency sync and later a cell search. There are pre-determined frequencies bands in which the UE will perform the frequency band scan and while scanning it will look for a Narrowband Primary Synchronization Signal (NPSS). The NPSS is used by the NB-loT UE to perform timing synchronization and to estimate the frequency offset. The UE collects data within the frequency band looking for the NPSS, which has a known pattern. There are several known methods of detecting the NPSS. The band scan is completed once all requested frequency bands have been scanned. The frequency or frequencies where the NPSS is detected are added to a list with the frequency and the corresponding Received Signal Strength Indication (RSSI) value. The purpose of band scan and the cell search is to find frequencies to enable communication with a network. Many of the NB-loT UEs are low powered with limited battery power supply and therefore it is essential that the search algorithm is efficient, precise and fast to not drain battery power from the UE. Figure 1 shows 40ms of NB-loT data and the signal of interest, the NPSS. The NPSS sequence is located in subframe 5 and its periodicity is 10ms. The NB-loT data further comprises the signal Narrowband Physical Broadcast Channel (NPBCH), the Physical Downlink Shared Channel (PDSCH), and the Narrowband Secondary Synchronization Signal (NSSS-1) among others. The NPSS sequence is generated at each 3 ^rd OFDM symbol. Figure 1 shows an example of in-band deployment, where NPSS occupy the last 11 OFDM symbols of subframe 5 and is punctured by Long Term Evolution Cell-Specific Reference signal (LTE CRS).

It is known, as disclosed in document "3GPP TSG RAN WG1 NB-loT Ad-Hoc no 2 (2016-03), Rl- 161897", to detect the presence of NPSS by using a method based on partial autocorrelation. The incoming signal is passed through a low-pass filter and then the signal is correlated with a local stored sequence to detect the NPSS. The basic steps include autocorrelation at 240 kHz for coarse timing offset by accumulation across multiple 10 ms time periods or windows, cross correlation with multiple hypotheses test at 1,92 MHz for fine timing detection by

accumulation across 10 ms windows and a frequency offset estimation.

The correlation with the NPSS sequence is one of the most computationally intensive parts of the cell search. This correlation needs to be performed over a periodicity of NPSS. The lowest complexity is exhibited by correlating after a certain number of accumulations instead of correlating after every 10 ms windows.

SUMMARY

With the above description in mind, then, an object of the present invention is to provide methods and devices which seeks to mitigate, alleviate or eliminate one or more of the above- identified deficiencies in the art and disadvantages singly or in any combination and allows for a computationally cheap and efficient band scan and cell search algorithm for identification based on the NPSS. The object is achieved by a method for scanning a frequency band to detect a NPSS. The method comprising receiving a signal within the frequency band, processing the received signal, accumulating the processed signal across a predetermined time period and detecting the presence of NPSS and determining the timing of the detected NPSS within the

accumulated signal. The received signal is processed by performing partial autocorrelation of the signal by using a partial autocorrelation algorithm and performing cross-correlation of the partially auto-correlated data by using a cross-correlation algorithm and a cross-correlation mask, s(n). The proposed method allows for a computationally cheap and efficient band scan and cell search algorithm for identification based on the NPSS. The algorithms help the UE to synchronize to the base station in frequency and time, allowing the UE to connect and communicate with the network. Many NB-loT UE is low powered with limited battery power supply and it is essential that the search algorithm is efficient, precise and fast to not drain battery power from the UE.

According to some aspects, s(n) is defined according to the formula: s(n) = S(n) * S(n + 1), wherein n= 0 to 10 and S(n) is [1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1].

According to some aspects, the processing comprises performing low-pass filtration of the received signal. The low-pass filtration is provided for reducing the effect of wideband noise.

According to some aspects, the processing comprises performing decimation of the received signal. The decimation provides for reducing the buffer sizes, e.g. if decimation by 8 is applied to 1,92MHz data it becomes 240 kHz.

According to some aspects, the decimation is performed in sequence with the filtration.

According to some aspects the predetermined time period is 10ms. This allows the signal to be accumulated multiple times over windows of 10 ms which is desirable to lower Signal-to-Noise Ratio (SNR). According to some aspects the accumulating comprises saving the processed signal to a memory.

According to some aspects, the disclosure proposes a computer program product comprising a computer readable medium, having thereon a computer program comprising program instructions, the computer program being loadable in a data-processing unit and configured to cause execution of the method described below and above.

According to some aspects, the disclosure proposes a wireless communication device having means for performing each step of the method described below and above. The device comprises a receiver configured to receive a signal within a frequency band and a processing unit. The processing unit is configured to process the received signal by performing partial autocorrelation of the received signal by using a partial autocorrelation algorithm in a partial autocorrelation circuitry and performing cross-correlation of the partially autocorrelated signal by using a cross-correlation algorithm and a predetermined cross-correlation mask, s(n), in a cross-correlation circuitry. The processing unit is configured to accumulate the processed signal over a predetermined time period in an accumulator and to detect a NPSS, and determine the timing of the detected NPSS within the accumulated signal in an analysing circuitry.

According to some aspects, the processing unit is configured to process the signal by performing low-pass filtration of the received signal in a low-pass filter.

According to some aspects, the processing unit is configured to process the signal by performing decimation of the received signal in a decimation circuitry.

According to some aspects, the processing unit is configured to save the processed data to a memory when accumulating the processed signal.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particular description of the example embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the example embodiments.

Figure 1 shows 40ms of NB-loT signal;

Figure 2 is a flowchart illustrating embodiments of method steps; Figure 3 is a flowchart illustrating embodiments of method steps; Figure 4 is a flowchart illustrating embodiments of method steps; Figure 5 is a flowchart illustrating embodiments of method steps; FIG. 6 is a flowchart illustrating embodiments of method steps; FIG. 7 is a flowchart illustrating embodiments of method steps; Figure 8a shows S(n), the cover code;

Figure 8b shows s(n)= S(n)*S(n+l), the cross-correlation mask; Fig 9 shows the processed signal in the time domain; Fig 10 shows detection probability; Fig 11 shows mean T-statistic values; Fig 12 shows detection probability; and

Fig 13 illustrates an embodiment of a wireless communication device, UE.

DETAILED DESCRIPTION

Aspects of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. The method and device disclosed herein can, however, be realized in many different forms and should not be construed as being limited to the aspects set forth herein. Like numbers in the drawings refer to like elements throughout.

Some of the example embodiments presented herein are directed to a method, computer program and a device for scanning a frequency band to detect a Narrowband Primary Synchronization Signal, NPSS. As part of the development of the example embodiments presented herein, a problem will first be identified and discussed.

Figure 1 shows 40ms of NB-loT data and the signal of interest, the NPSS. The NPSS is located in subframe 5 and its periodicity is 10 ms. This allows the signal to be accumulated multiple times over windows of 10ms which is desirable to lower the Signal to Noise Ratio, SNR. The NPSS sequence d|(nj is generated using a Zadoff-Chu sequence of length N _Z c = 11 and with root sequence index u = 5. It is multiplied with the code cover pattern, S(l), giving n = 0, . . ,10; Z = 0, ... ,10 (1) where

5(0 = [1,1,1,1, -1, -1,1,1,1, -1,1] (2)

This will create a matrix with 11 x 11 values, as shown in figure 1, which is transformed into the time domain before being transmitted. These 11 x 11 values can be related to the NPSS in figure 1 where the subcarriers (frequency) are listed on the vertical axis and cover code pattern (time) on the horizontal axis. The NPSS periodicity is 10ms and is developed to have good correlation properties. These properties are used in band scan to find the frequency and in cell search for time synchronization.

It is known that the detection of the NPSS can be performed in different ways and one way is to accumulate data over time periods or over windows of 10ms and then processes it.

Finding the correct frequency and timing is essential for performing a band scan and a cell search and eventually connecting to the network. This procedure should be as efficient as possible and consume minimal amount of time and energy.

Current solutions suggest that 10ms of data is accumulated before it can start being processed. This is not desirable since this requires that a large amount of data must be saved before processing and this will require a large memory capacity.

The present invention is directed to an efficient band scan and cell search procedure for the 3GPP technology referred to as NB-loT. Once the signal processing which includes filtration, partial autocorrelation and cross-correlation has been performed the data is saved to the memory and accumulated over windows of 10ms and once the accumulation is complete the data is ready to be analysed to detect the presence of the NPSS sequence.

The main advantage with the proposed method is that less data will need to be buffered before being analysed and the reason for this is that the data processing is streamed and the already processed data is saved to a memory, RAM. As soon as all the buffers have been filled with data, the processed data will be saved and accumulated over time periods or time windows of 10ms.

Referring now to figure 2, where an exemplary method for scanning a frequency band to detect a Narrowband Primary Synchronization Signal (NPSS) is presented.

At step SO the method comprises receiving a streamed or saved signal within the frequency band. The signal is a Narrowband Internet of Things (NB-loT) signal.

At step SI, the method comprises processing the received signal by performing partial autocorrelation of the signal at step S13 by using a partial autocorrelation algorithm and by performing cross-correlation of the partially autocorrelated data at step S14 by using a cross- correlation algorithm and a cross-correlation mask, s(n).

The partial autocorrelation function is defined as

(m) =∑™ ^{sym 1} x (n) * x ₂ (n) ( 3 ) where a(m) is the partial autocorrelation, N _sym is the number of samples per symbol and x(n) is the 1/Q. data in time domain. An example of how the summation can be calculated recursively is shown in formula 4 and visualized in figure 6. Data flows through the system from "data in" to "data out", the boxes marked (i) is buffers and the boxes marked (ii) are arithmetic's.

a(m) = a{m - \)- _Xl (m - l)* x ₂ (m - l) + x ₁ (m - l + N^ )* x ₂ (m - l + N _sym ) The cross-correlation function is defined as ,τη+ΙΙΝ 's.ym-1

p(m) =∑ a(n) * s(n— m) (5) where (n) is the partial autocorrelation, p(m) is the cross-correlation and s(n) the cross- correlation mask. This summation can be calculated similarly to formula 4.

In one embodiment, the partial autocorrelation and/or the cross-correlation can be implemented as recursive algorithms. An example of how the recursive implementation can be executed is shown in figure 7. Data flows through the system from "data in" to "data out". The boxes marked (i) are buffers and the boxes marked (ii) are arithmetic's.

The sign sequence from formula 2 is visualized in figure 8a. In figure 8b the sign sequence from figure 6a is shifted one symbol and multiplied by itself according to the formula: s(n) = S(n) * S(n + 1) (6)

In embodiment, S (n) = [1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1], which gives S(n+1)= [1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 0] and thus s (n) = [1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 0].

The pattern, s(n), is used as the cross-correlation mask and the reason why it works is because it undergoes a similar process as the partially autocorrelated data from formula 3 does. It is due to this that the cross-correlation mask and the autocorrelated data will give a high output when they match well in the cross-correlation.

In one embodiment, at step Sll, the step of processing comprises performing low-pass filtration of the received signal for reducing the effect of wideband noise.

In one embodiment, processing comprises performing decimation, as shown in figure 3 at step S12, of the received signal. In one embodiment, decimation is performed in sequence with the filtration. Decimation is used to reduce the buffer sizes, e.g. if decimation by 8 is applied to 1,92MHz data it becomes 240 kHz.

In one embodiment, the decimation step can also be performed after the cross-correlation or not at all, and then the method becomes slightly more robust at low SNR. At step S2, the method comprises accumulating the processed signal across a predetermined time period. In one embodiment, accumulating comprises saving, at step S21 of the processed signal to a memory. In one embodiment, the predetermined time period is 10ms and all multiples of 10 ms, due to the periodicity of the NPSS. At step S3, the method comprises detecting the presence of a NPSS and at step S31, as shown in figure 4, determining the timing of the detected NPSS within the accumulated signal.

Referring now to figure 9, in order to determine if the received signal is a NPSS signal a T- statistic value will be calculated. The ratio of the top peak, tpl, and the second top peak, tp2, will give an impression of how well the cross-correlation matched the partially auto correlated data.

Once all data has been accumulated N _acc times the T-statistic ratio will be calculated and all the T values will be saved. This will be repeated N _A f times until the entire frequency band has been scanned. Once the data has been processed and all T values are collected the analysis can take place. There are three steps in this process, as shown in figure 5:

At step 32 - Frequency - The processed and accumulated data is analysed to check if there is a NPSS signal or not in the collected data. It is performed in the three following steps (321, 322 and 323):

At sub step 321 - Find peaks and calculate T-statistic value. The ratio of the highest peak, tpl, and the second highest peak, tp2, is calculated. An example of what the data can look like after the data processing is shown in figure 9. Here the highest peak, tpl, is around 5.1 and the second highest, tp2, about 1.7. This would give a T-statistic value of 5, 1/1, 7 = 3.

At sub step 322 - Analyse T-statistic values. Once data has been collected within the band scanned and all the T-statistic values have been calculated they are analysed. A value is only considered to contain the NPSS if it is above a certain threshold. This threshold is calculated using the false alarm probability P ₀ which is set to a certain value, e.g. 1% or 0,1%.

At sub step 323 - Update parameters or return result. BW, f _lt f ₂ , and Af are updated and then the frequency band sweep is repeated with new parameters or if the outcome is satisfactory the results will be returned and the band scan is completed. Figure 9 illustrates an example of peak detection. The height of the peak shows clearly that the NPSS is present in the analysed data set.

At step S31 - Timing - Once a signal is found in the data set it needs to be determined where it is in time so that the UE can synchronize with the base station. This is done in three steps as follows: At sub step S311 - Find peaks and calculate T-statistic value. The same procedure as at sub step 321 is performed.

At sub step S312 - Analyse T-statistic value. If the value is above a certain threshold the peak is considered to be correct. If it is not above the threshold additional accumulations might be necessary.

At sub step S313 - Determine coarse timing. If the T-statistic value is above the threshold the time (x-value) of the peak is noted. The time gives us where the NPSS is located in the measured data and since the NPSS is known to be in sub frame 5 the timing can be estimated. In the example data in figure 9 the peak's x-value is at around 3ms which is approximate 2ms away from sub frame 5 giving us the coarse timing estimation.

The number of operations per sample will be the same whether the data is decimated or not. The buffer sizes can be reduced if decimation is applied. Below follows an example for how large buffer sizes are at 240 kHz and a time estimate of how long it takes for the buffers to fill.

In one example, the filter has 16 coefficients, 16 real-complex multiplications and 16 complex additions, 16 buffered values and t _f n _t = 0,0083ms. The partial autocorrelation has 1 complex multiplication, 2 complex additions/subtractions, 35 buffered values and t _ct = 0,143ms. The cross-correlation has 5 real-complex multiplications, 8 complex additions/subtractions, 171 buffered values and t _p = 0,714ms. The accumulation has 1 complex addition, 2400 complex values saved to RAM and t _to tai = _a cc-10 + t| _ag ms, where t| _ag = tfn _t + 1„ + t _p = 0,865ms. The data processing algorithm requires only 100 operations (46 multiplications and 54 additions/subtractions) per sample giving 24 MOPs from the abovementioned example. When the data is accumulated multiple times over 10ms windows the algorithm becomes very robust to noise which can be seen in figure 10 along with the corresponding mean T-statistic values in figure 11. The different curves represent different amount of accumulations. The more accumulations, the higher probability to find a NPSS at lower SNR. These calculations are performed at a sampling rate of 240 kHz. Figure 10 illustrates the probability of detection for 1, 2, 4, 8, 16 and 32 accumulations at different SNR and at 240 kHz. The false alarm probability Pfa is set to 1%. Figure 11 illustrates the mean T-statistic value for 1, 2, 4, 8, 16 and 32 accumulations at different SNR. An example of how the decimation affects the result can be seen in figure 12 where the data processing at 1,92MHz performs better than at 240 kHz. The number of operations per sample is the same as for 240 kHz however it requires larger buffer sizes and as 8 times more data is analysed the complexity becomes 192 MOPs. In this example the T-statistic value is not affected when decimated before being saved to the RAM. Figure 12 illustrates the probability of detection at 1,92MHz and 240 kHz sampling rate. The false alarm probability P _fa is set to 1%.

Figure 13 illustrates an example of a wireless communication device 100 which may incorporate some of the example embodiments discussed above. As shown in Figure 13, the wireless communication device 100 comprises a processing unit 120 configured to process the received signal. The processing unit comprises a partial autocorrelation circuitry 122 configured to perform partial autocorrelation of the received signal by using a partial autocorrelation algorithm. The processing unit comprises a cross-correlation circuitry 123 configured to perform cross-correlation of the partially auto correlated signal by using a cross- correlation algorithm and a predetermined cross-correlation mask, s(n). The device comprises an accumulator 130 configured to accumulate the processed signal over a predetermined time period. The device comprises an analysing circuitry 140 configured to detect a Narrowband Primary Synchronization Signal, NPSS, and to determining the timing of the detected NPSS within the accumulated signal.

In one embodiment, the processing unit 120 comprises a low-pass filter 121 configured to perform low-pass filtration of the received signal.

In one embodiment, the processing unit 120 comprises a decimation circuitry 124 configured to perform decimation of the received signal.

In one embodiment, the processing unit 120 is configured to save the processed data to a memory 150 when accumulating the processed signal. Aspects of the disclosure are described with reference to the drawings, e.g., block diagrams and/or flowcharts. It is understood that several entities in the drawings, e.g., blocks of the block diagrams, and also combinations of entities in the drawings, can be implemented by computer program instructions, which instructions can be stored in a computer-readable memory, and also loaded onto a computer or other programmable data processing apparatus. Such computer program instructions can be provided to a processor of a general-purpose computer, a special purpose computer and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.

In one aspect, a computer program product is provided. The computer program product comprises a computer readable medium, having thereon a computer program comprising program instructions, the computer program being loadable in a data-processing unit and configured to cause execution of the method described above when the computer program is run by the data-processing unit.

In some implementations and according to some aspects of the disclosure, the functions or steps noted in the blocks can occur out of the order noted in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality/acts involved. Also, the functions or steps noted in the blocks can according to some aspects of the disclosure be executed continuously in a loop.

In the drawings and specification, there have been disclosed exemplary aspects of the disclosure. However, many variations and modifications can be made to these aspects without substantially departing from the principles of the present disclosure. Thus, the disclosure should be regarded as illustrative rather than restrictive, and not as being limited to the particular aspects discussed above. Accordingly, although specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation.

It should be noted that although terminology from 3GPP NB-loT has been used herein to explain the example embodiments, this should not be seen as limiting the scope of the example embodiments to only the aforementioned system. Other wireless systems, including WCDMA, WiMax, UMB and GSM, may also benefit from the example embodiments disclosed herein.

It should be noted that the word "comprising" does not necessarily exclude the presence of other elements or steps than those listed and the words "a" or "an" preceding an element do not exclude the presence of a plurality of such elements. It should further be noted that any reference signs do not limit the scope of the claims, that the example embodiments may be implemented at least in part by means of both hardware and software, and that several "means", "units" or "devices" may be represented by the same item of hardware.

A "wireless communication device" as the term may be used herein, is to be broadly interpreted to include a radiotelephone having ability for Internet/intranet access, web browser, organizer, calendar, a camera (e.g., video and/or still image camera), a sound recorder (e.g., a microphone), and/or global positioning system (GPS) receiver; a personal communications system (PCS) user equipment that may combine a cellular radiotelephone with data processing; a personal digital assistant (PDA) that can include a radiotelephone or wireless communication system, a laptop, a camera (e.g., video and/or still image camera) having communication ability and any other computation or communication device capable of transceiving, such as a personal computer, a home entertainment system, a television, etc. Furthermore, a device may be interpreted as any number of antennas or antenna elements.

In the drawings and specification, there have been disclosed exemplary embodiments. However, many variations and modifications can be made to these embodiments. Accordingly, although specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation, the scope of the embodiments being defined by the following claims.