JP2018137558A - Signal transmitting method and reception signal detecting method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a signal transmitting method that allocates channels in consideration of a channel occupancy rate from information provided for an aggregation station by each radio terminal by using a probability inference technique in the case where it occurs in a probabilistic manner that a channel allocated to certain information is occupied by a primary system at a next moment and the information is not transmitted in real time.SOLUTION: A signal transmitting method includes the steps of, at an information aggregation station: finding an occupancy rate of a primary system at each channel; finding an expected value of difference between a value of information with a high appearance probability and an estimated value of information to be transmitted at next clock time at each radio terminal or each channel; finding a summation of products of the occupancy rate, the expected value, and a weighting coefficient; and determining the weighting coefficient under conditions of making the summation minimum. It further includes a step of allocating channels to each radio terminal MS without duplication in advance.SELECTED DRAWING: Figure 4

Description

  The present disclosure relates to a signal transmission method and a reception signal detection method for transmitting sensor information via a channel to which a primary system is preferentially assigned in advance.

  The range of application of wireless communication systems has expanded, and as represented by the Internet of Things (IoT), sensors are equipped with wireless functions to easily grasp the status of various things and people, and sensors that easily indicate these conditions. Attention has been focused on wireless sensor networks that aggregate information. For example, a collective aggregation type wireless sensor network in which a plurality of users share the same channel has been proposed (Patent Document 1, Non-Patent Document 1). On the other hand, cellular data systems such as smartphones and large-capacity data communication represented by WiFi are also continuously developed. As a result, the depletion of frequency resources, which are resources of wireless communication systems, has become more serious.

  For this reason, shared use of the same frequency resource is being promoted by controlling mutual interference among a plurality of wireless systems. As a representative example, in the 2.4 GHz band called ISM-BAND, shared use of frequency resources by various wireless systems such as the Zigbee standard used for wireless LAN, Bluetooth (registered trademark) and sensor networks is being promoted. In recent years, a lot of systems for specific low-power wireless communication systems of 900MHz called SubGHz band have appeared, and wireless systems for transmitting small amounts of information such as sensor information can share frequency resources as well as ISM-BAND. It is being advanced.

  In general, from the viewpoint of shared use of frequency resources, each radio system is required to avoid serious interference with other radio systems and to use frequency resources in accordance with the requirements of its own radio system. In particular, in cellular systems, WiFi, Zigbee, etc., wireless systems that transmit information from a large number of terminals to one central station are targeted. At this time, a channel obtained by dividing frequency resources by a certain bandwidth is defined, and an environment is assumed in which each system selectively uses a channel as a minimum unit. Here, a database that grasps the usage status of each channel by the system is assumed. This database comprehensively observes the occupation ratio, which is the average utilization of each channel, and provides the occupation ratio to the central station. Then, the aggregation station determines channel usage based on the channel occupancy rate information provided from this database.

  For example, a method of constructing a database for observing the usage status of each channel for a predetermined time in advance and providing a statistical model characterizing the usage status of the channel to each wireless system has been studied (Non-Patent Document 2).

  In addition, a method for collecting the usage information of the primary system of each channel in the database and statistically modeling the usage status of each channel is also being studied. In this case, an occupation ratio that is an average usage time of each channel is used (Non-Patent Document 3).

Japanese Patent Laying-Open No. 2015-162879

Taku et al., “Threshold setting and multi-antenna reception for frequency offset compensation in a batch collection type wireless sensor network” IEICE Technical Report, vol. 114, no. 491, SR2014-114, pp. 23-29, 2015 March Takeo Fujii, “Visualization of Radio Waves by Hierarchical Spectrum Database” IEICE Tech. Bulletin, SR2014-119, March. 2015 Wakao, Tahisa, Fujii, Kashimori, Handa, “Highly accurate occupancy measurement method that can follow time changes”, IEICE Technical Report, vol. 112, no. 153, SR2012-27, pp. 43-48, 2012 July

  However, even in these methods, there is a probability that a channel assigned to certain information is occupied by another user at the next moment and information is not transmitted. Further, when the sensor information is required to have real-time information, the problem becomes more serious.

  In view of the above problems, an object of one aspect of the present disclosure is to provide information on the future by using probability inference technology from information provided by each terminal to the aggregation station, even if the channel is occupied by the primary system in advance. Establishing a notification model, predicting the importance of future information, and allocating channels that take into account the channel occupancy rate to minimize the impact of information loss and received signal detection It aims to provide a method.

  A signal transmission method according to an aspect of the present disclosure is a signal transmission method in which a plurality of wireless terminals transmit information whose occurrence probability can be predicted based on a probability distribution via a plurality of channels, and the primary system in each channel Obtaining an occupancy ratio, obtaining an expected value of a difference between a value of information having a high appearance probability and a predicted value of information transmitted at the next time for each wireless terminal or channel, and the occupancy ratio Determining the sum of products of the expected value and the weighting factor, and determining the weighting factor under a condition that minimizes the sum.

  A step of allocating a channel to each wireless terminal in advance without duplication, and the weighting factor may be a function that limits the number of channels available to each wireless terminal to one.

  The weighting factor may be a function that limits information transmitted to the channel at the next time to one for each channel.

  The information may be sensor information.

  The probability distribution may take a maximum value in the average value of the information.

  The probability distribution may be approximated by a Gaussian distribution.

  The occupation ratio may be obtained by performing carrier sense.

  The probability distribution may be obtained based on past information and updated in a timely manner.

  The reception signal detection method according to an aspect of the present disclosure supplements information based on the probability distribution when reception of information transmitted by the signal transmission method fails.

  According to one aspect of the present disclosure, channel allocation is performed such that the sum of expected values of prediction errors is minimized, and as a result, the occupancy is relatively low for information that deviates greatly from prediction (the probability of successful transmission). High quality information communication quality can be achieved even with a channel assigned to the primary system and occupied by the primary system. That is, it is possible to notify necessary information in real time while suppressing the frequency of use of frequency resources.

Wireless communication model according to one aspect of the present disclosure Explanatory drawing of the frequency channel occupation rate which concerns on 1 aspect of this indication Flowchart according to one aspect of the present disclosure (MS side) Flowchart according to one aspect of the present disclosure (BS side) The graph which shows the information tendency in one example of this indication The graph which shows the result of one example of this indication The graph which shows the result of one example of this indication

  Hereinafter, an embodiment (hereinafter, this embodiment) according to one aspect of the present disclosure will be described in detail with reference to the drawings.

(Outline of assumed wireless communication system)
FIG. 1 shows a conceptual diagram of a wireless communication system in the present embodiment. A plurality of wireless terminals 101 and 102 (hereinafter referred to as MS) are distributed in a certain area, and there is one information aggregation station 104 (hereinafter referred to as BS). Each MS 101, 102 transmits information held individually to the BS 104. Note that the content of the transmitted information is information such as temperature and humidity detected by a sensor. Such sensor information generally has a probability distribution approximated by a Gaussian distribution.

  Such a method of aggregating information from a large number of MSs to a BS is called an uplink method in a cellular method. However, in the present embodiment, it is assumed that the present invention can also be applied to a downlink scheme in which the BS broadcasts each information to each MS. However, in order to simplify the explanation, an uplink system is assumed. Information transmission on the uplink is equivalent to a star network configuration in which each MS 101, 102 holds sensor information and transmits information to the BS 104 when a wireless sensor network is assumed.

  In wireless communication, in general, wideband frequency resources are divided into narrowband frequency resources called channels. The relationship between the band and the frequency is shown in FIG. In a star type network, the MSs 101 and 102 select one or a plurality of channels from many channels and transmit information. Here, in order to simplify the explanation, it is assumed that each MS selects one channel. Needless to say, even when a plurality of channels are selected, the following scheme can be applied.

  Further, in the present embodiment, it is assumed that an existing wireless system exists. For example, in the vicinity of the 2.4 GHz band or 5 GHz called ISM-band, or 900 MHz band called sub-GHz, since many wireless systems coexist, the frequency sharing which secures the channel of the own station while suppressing interference between systems Is progressing. Therefore, even in the system assumed in the present embodiment, the frequency channels used by these existing systems are not used at the same time in order to avoid inter-system mutual interference. In fact, in wireless LAN, by detecting signals called carrier sense before transmitting radio waves, channel use by existing systems is detected, simultaneous access by multiple systems to the same frequency channel is avoided, and mutual interference is suppressed. doing. Also in this embodiment, it is assumed that each MS 101, 102 performs carrier sense of all channels, uses the channel when the existing system is not using, and stops information transmission when the existing system uses it. .

  Similarly, in the existing system, it is assumed that MS and BS exist in an area where the area is developed. In carrier sense, signal power is greatly attenuated due to fading and shadowing peculiar to the propagation path. Therefore, it is determined that access is not made to the existing system, or that the existing system is not accessed. Therefore, the occurrence of a mutual alarm between systems which is generally called a hidden terminal problem and an unused state of an unnecessary channel called an exposed terminal problem occur. In this embodiment, it is assumed that false alarms and false detections do not occur for the sake of simplification of explanation, but it is of course applicable to the assumed environment. Hereinafter, such an existing system is referred to as a primary system 201, 202 (PS) in the present embodiment.

  These PSs 201 and 202 are considered to have different time lengths, frequency of use, and time intervals of use, depending on the applications used. An image in which each PS uses a channel is shown in FIG. In the primary system, a method of constructing a database 105 (spectrum database) for observing the usage status of each channel for a predetermined time in advance and providing a statistical model characterizing the usage status of the channel to each wireless system is being studied (non-non-existing) Patent Document 2). Also in this embodiment, it is assumed that a statistical model of each channel is provided to the BS from the database 105 observed for a certain period of time.

  The BS 104 determines a channel usage policy as a channel usage method based on the statistical model of each channel provided from the database 105. Here, it is assumed that the channel usage policy is determined by a coefficient w indicating channel usage as described later, and is equivalent to determining the channel usage weight. Then, the BS 104 notifies the channel usage weights to the MSs 101 and 102, and each MS uses the channel according to the channel usage weight (w). The method according to the present embodiment is excellent in frequency sharing, and in order to achieve high information notification quality, this channel usage weight policy is mainly determined. Hereinafter, determination of the channel usage weight w will be described.

(Determination of channel usage weight)
Information notified from each of the MSs 101 and 102 is provided to the BS 104, and information trend analysis proceeds. Analysis methods include modeling the information distribution with a Gaussian probability distribution, Kalman filtering to calculate the moving average and variance in the time axis direction, and EM algorithm to model with the minimum distribution error condition with past data based on the Gaussian mixture model Known probabilistic reasoning techniques such as can be applied.

By probabilistic reasoning, the BS 104 obtains an average value and a variance value of information to be predicted. Here, when the total number of MSs 101 and 102 is K, the average value of information at the i-th notification time for the k-th MS is m _k (i) and the variance is s ² _k (i). . In addition, the predicted value of the information at the next time is x (i + 1), and the probability that the predicted value occurs is p _k (x (i + 1)). Where x (i + 1) is an irregular variable that follows a Gaussian distribution with mean value m _k (i) and variance value s ² _k (i) based on past information, and x (i + 1) ~ N (m _k (i), s ² _k (i)). At each notification time, the BS 104 continues to update the average value and the variance value, which are predicted values, from the newly obtained information by probability inference.

(Use prediction model of each channel provided from the database)
In the database 105, the usage information of the primary system of each channel is aggregated and the usage status of each channel is statistically modeled. In the primary system, as described above, the occupation ratio that is the average usage time of each channel is used (Non-Patent Document 3). The occupation rate is defined as the time rate that the primary system (PS) is accessing. That is, when a signal is released by the PSs 201 and 202, carrier sensing is performed in which the other wireless device determines that the PS is using when detection energy equal to or higher than a certain reception power is received. This carrier sense is observed for a certain period of time, and a value obtained by normalizing the judgment result per one carrier sense trial is calculated as an occupation rate. Here, the carrier sense may be performed by the MS 101 or the MS 102, and the result of the carrier sense performed by another wireless device may be provided to the database. Let rm (i) be the occupation ratio for the m-th channel (m = 1, 2,..., M, M: the total number of channels) at the i-th time.

(Assignment design policy: Channel usage weight design based on minimum prediction error criteria)
The BS 104 regards the information of each of the MSs 101 and 102 at the i-th time as a random variable according to a Gaussian distribution that is uniquely obtained from the average value m _k (i) and the variance value s ² _k (i). Predict the appearance probability of the information of each MS 101, 102 in (i + 1). Further, by obtaining the occupancy rate information of the primary system from the database 105, prediction information on near-future channel usage is obtained. Using this information prediction and channel usage prediction, a design policy for each MS to use a channel is established.

First, in information prediction modeled with a Gaussian probability distribution, the appearance probability of information indicating the average value m _k (i) is the highest. Therefore, even if reception is temporarily interrupted because the channel is occupied by the PS or the MS does not emit information, the BS 104 is relatively accurate if the average value is regarded as information transmitted by the MS. Can be used to supplement discontinuous information. However, even if the appearance probability is low, the information held by the MS may actually differ greatly from the predicted value of the BS 104. In this case, an error occurs in information recognition in the BS 104. In order to suppress the error, it is necessary to preferentially notify the BS of information having a large difference from the predicted value. The method will be described below.

First, the difference information between the prediction information x (i + 1) at the (i + 1) th time (next time) in the near future and the average value of the information at the kth MS is (m _k (i) −x ( i + 1)) ² Here, if a channel with low PS usage frequency, that is, a low occupancy channel is assigned to an MS transmitting information with large error information, the error information of the kth MS with respect to the prediction information x (i + 1) The expected value e (k, i) is derived as follows using the probability p _k (x (i + 1)) that the predicted value occurs.

  Here, in the kth MS, the weighting factor w (k, x (i + 1), m indicating the assignment to the mth channel with respect to the random variable x (i + 1) indicating the prediction information at the next time. ). This weighting factor takes either 1 or 0. Here, 1 means assignment and 0 means no assignment. Considering this, the channel allocation method in the present embodiment is given as a minimization problem that minimizes the sum of expected values of error information.

The total sum F of expected values of error information can be written as follows using equation (1) and weighting factors w (k, x (i + 1), m).
Here, the weight coefficient w (k, x (i + 1), m) is expressed by the following equations (3) to (6).
As described above, r _m (i) represents the occupation ratio of the m-th channel at time i.

  The channel assignment weighting factor w (m, x (i + 1), k) is determined so as to minimize Equation (2). Expressions (3) to (6) are constraint expressions that determine the requirements of the wireless system. Equation (3) indicates whether or not the m-th channel of w (m, k) is allocated in the k-th MS, 1 indicates allocation, and 0 indicates non-allocation. Equation (4) means that the k-th MS can use only one channel when transmitting x (i + 1) information. Equation (5) means that the number of MSs that can send information of x (i + 1) using the m-th channel is one. Equation (6) means that when the k-th MS uses the m-th channel, it is limited to transmitting only one piece of information.

  As a specific example, in LTE, WiFi, etc., channels are allocated to each MS without duplication, so that each information can be transmitted with orthogonal resources. That is, since each MS can transmit arbitrary information on each channel, there is no restriction on the equation (6), and it can be dealt with by changing the channel allocation weight to two variables (that is, w (m, k)).

  On the other hand, in a collective aggregation type wireless sensor network (Non-patent Document 1) in which a plurality of users share the same channel, it is not necessary to limit the number of MSs used for one channel to one, and there is a restriction of equation (5). Disappear. Therefore, the weighting coefficient can be changed to two variables like w (m, x (i + 1)). As described above, the proposed system can be applied to various systems by selecting the constraint equation and increasing / decreasing the weight variable.

In this optimal problem, ∀r _m (i) ≧ 0, and the coefficients for the allocation weight w (m, x (i + 1), k) are all positive. In other words, since the expected difference information is a positive linear sum with respect to w (m, x (i + 1), k), this optimal problem is therefore a linear programming problem. As a result, channel assignment can be determined so that the difference between the predicted value and the actual information is minimized.

(flowchart)
3 and 4 show a processing flow of a series of procedures in the present embodiment. 3 shows a processing flow on the MS side, and FIG. 4 shows a processing flow on the BS side. First, the MSs 101 and 102 perform carrier sense on the channel used for information transmission (step 11 in FIG. 3, only numbers in the figure), and notify the BS when the primary system (PS) is using it. Skip (step 12). When the primary system is not in use, information x (i + 1) is notified from either or both of the MSs 101 and 102 to the BS 104 (step 13). At this time, since there is no past (i <0) statistical data immediately after the start of communication, the information notification may be continued for a certain period of time.

  On the other hand, the BS 104 performs processing to separate information for each MS (FIG. 4, step 21). This is because the MIMO technology is used for all of the collective aggregation methods, LTE, and WiFi, and there is a possibility that the information of the MS is interfering. It should be noted that this process can be omitted when each MS uses each channel independently.

Next, the BS 104 calculates values of average m _k (i) and variance s ² _k (i) as statistics from past information (step 23). Furthermore, the occupancy rate information r _m (i) of each channel is obtained from the server (step 24), and the information is modeled with a Gaussian probability distribution. Through these series of processes, the occurrence probability p _k (x (i + 1)) of each information is calculated from the obtained average and variance (step 25).

Furthermore, the BS 104 assumes an average m _k (i) having the maximum information occurrence probability as a transmission information predicted value x (i + 1) at the next time, and expects an expected value e of difference information from the predicted value of each information. Calculate (k, i) (= (m _k (i) -x (i + 1)) ² ), channel assignment weight w (k, x (i + 1), m) In the case of a wireless sensor network, w (m, x (i + 1)) may be designed) (step 26). The determined allocation weight is notified to all MSs 101 and 102 (step 27).

  Each MS changes the transmission method according to the received allocation weight w (k, x (i + 1), m) (step 15). Finally, each MS determines whether or not the prescribed number of times has been reached, for example, the information required by the BS has been obtained (step 16). If not, the process returns to step 11.

  The above steps are repeated to continue information transmission. Note that the channel allocation update frequency in the MS 104 may be updated every time the information is notified, or may be periodically performed at regular intervals.

  Hereinafter, the results of evaluating the effectiveness of the above embodiment by computer simulation will be shown as examples.

Example 1
Assuming the collective information aggregation type wireless sensor network of FIG. 1, the number of channels is set to 256, and a model in which sensor information (Data) of the MS 101 and the MS 102 continuously changes as time passes is defined as shown in FIG. Such a tendency is recognized in sensor information such as temperature and humidity in the wireless sensor network. Since the resolution of sensor information (Data) is 0.1, the dynamic range of information is 25.6 at the maximum. As an information disturbance model, a Gaussian distribution with an average 0 variance of 0.1 is assumed and is independent of time. Also, the occupation rate of each channel is a uniform random number from 0 to 1, and it is assumed that a first in first out (FIFO) type memory is used for statistical modeling of information, and the memory length is 1000 It was.

  FIG. 6 shows the calculation result as a cumulative distribution function (CDF) characteristic with respect to the notification success probability. In the figure, when the white circle (◯) is randomly assigned without considering the information probability model and the channel occupancy (conventional method), the black diamond (♦) is the channel assignment method of this embodiment. It is the result obtained by. As a comparative example, a case is also shown in which sensor information that is expected to occur most frequently among information probability models is preferentially assigned to a channel with a low occupancy rate (×).

  In the case of the conventional method (random), information is sent to a channel that is completely occupied by the primary with a certain probability, and as a result, notification fails. On the other hand, in the method according to the present embodiment, the notification success rate is slightly improved. However, at first glance, the comparative example achieves the highest notification success probability. That is, in the method of the present embodiment, the notification success probability is suppressed to about 10% lower than the comparative example at CDF = 0.1. In other words, the frequency of notifications to the BS is suppressed by about 10%. In the comparative example, since the most frequently generated information is assigned to the safest channel, the success rate is naturally high.

  However, from the viewpoint of minimizing the prediction error, the evaluation of the method of this example and the comparative example are reversed. FIG. 7 shows the CDF characteristics in the square error between the information notified by each MS 101 and 102 and the information recognized by the BS 104. From the figure, it can be seen that the method (♦) of this example achieves a lower square error characteristic than the comparative example (x). In other words, the information that occurs most frequently is, in other words, average information that is most easily predicted, and even if such information notification fails, interpolation can be performed with high accuracy. Conversely, information that deviates significantly from the average has a low occurrence frequency but a large prediction error. In the method of the present embodiment, the success rate of notification of such information with a large prediction error can be increased.

  As described above, it is considered that the method of the present embodiment can achieve high quality information communication quality while suppressing information transmission to a lesser extent than the conventional method and the comparative method. This can be said to have achieved high frequency utilization efficiency from the viewpoint of notifying the necessary information while suppressing the frequency of use of frequency resources.

  INDUSTRIAL APPLICABILITY The present invention can be used in a system that wirelessly transmits weather information such as temperature and humidity, air pollutants, traffic volume, and other sensor information via a low priority channel. This is particularly effective for IoT wireless sensor networks.

101 Wireless terminal (MS)
102 Wireless terminal (MS)
104 Information Aggregation Station (BS)
105 database

Claims (9)

A signal transmission method in which a plurality of wireless terminals transmit information whose occurrence probability can be predicted by a probability distribution through a plurality of channels,
Determining the primary system occupancy in each channel;
Obtaining an expected value of a difference between a value of information having a high appearance probability in each wireless terminal or each channel and a predicted value of information transmitted at the next time;
Obtaining a sum of products of the occupation ratio, the expected value, and a weighting factor;
A signal transmission method including the step of determining the weighting factor under a condition that minimizes the sum.   2. The signal transmission method according to claim 1, further comprising the step of assigning a channel to each wireless terminal in advance without duplication, wherein the weighting factor is a function that limits a channel that can be used by each wireless terminal to one.   2. The signal transmission method according to claim 1, wherein the weighting factor is a function that limits information transmitted to the channel at the next time to one for each channel.   The signal transmission method according to claim 1, wherein the information is sensor information.   The signal transmission method according to claim 1, wherein the probability distribution takes a maximum value in an average value of the information.   The signal transmission method according to claim 5, wherein the probability distribution is approximated by a Gaussian distribution.   The signal transmission method according to claim 1, wherein the occupation ratio is obtained by executing carrier sense.   The signal transmission method according to claim 1, wherein the probability distribution is obtained based on past information and updated in a timely manner. A received signal detection method for receiving information notified by the signal transmission method according to claim 1, comprising: a step of complementing information based on the probability distribution when information reception fails.