KR102216515B1 - Effective SNR mapping and link adaptation strategy and apparatus for next-generation underwater acoustic communications networks - Google Patents

Abstract

Suggested are an efficient SNR mapping-based link adaptation method for a next-generation underwater communication network and an apparatus thereof. According to the present invention, the suggested efficient SNR mapping-based link adaptation method for a next-generation underwater communication network includes the following steps of: defining priority rules for selecting an MCS level by analyzing a link adaptation procedure based on an adaptive modulation mode in accordance with a UAC network channel characteristic; and allocating a resource by selecting a plurality of MCS levels with respect to a link adaption procedure on a UAC network, based on the defined priority rules.

Description

An efficient SNR mapping and link adaptation strategy and apparatus for next-generation underwater acoustic communications networks.

The present invention relates to an efficient SNR mapping-based link adaptation method and apparatus for a next generation underwater communication network.

The interest in research on the next generation underwater sensor network for marine exploration has increased because of the increased interest in use in oceanography, commercial operations in marine areas, and military surveillance. The underwater base station controller communicates with the underwater base station (UBS) in the form of a surface buoy, and the UBS transmits the underwater sensor node and transceiver information through acoustic communication.

Next-generation underwater sensor networks have great potential for observing and exploring the underwater environment. Therefore, Underwater Acoustic Communications (UAC) is widely regarded as the only feasible approach to long-distance underwater communication, and has been widely adopted in a variety of scenarios. The demands for quality of service include communication between submarines, underwater security surveillance, scientific data collection at submarine observatory, marine oil exploration by autonomous underwater vehicles, and data exchange in underwater sensor networks for environmental monitoring. Occurs in civilian applications. However, it is important to deal with the frequency and time-selective fading caused by the long delay and large Doppler spread, which are the resistive characteristics of the underwater acoustic channel. In addition, fading, Doppler spreading and multipath propagation have a serious impact on UAC network (UACN: Underwater Acoustic Network) performance. Unlike typical wireless channels, transmission losses in UAC networks depend on the signal frequency as well as the distance between the transmitter and receiver. The absorption loss due to the signal frequency is because the acoustic energy is transferred as heat. Therefore, the underwater channel presents a daunting challenge when compared to the general ground channel model.

An object of the present invention is to provide a link adaptation method and apparatus for an orthogonal frequency division multiple access method used in an Underwater Acoustic Communications Network (UACN). Effective Signal-to-Noise Ratio (SNR) Mapping (EESM) and its UACN to represent the multi-state channel of the link-level simulation as a single-state channel of the system-level simulation and represent the multi-subcarrier SNR as an efficient SNR. We present an approach to beta calibration for application.

In one aspect, the efficient SNR mapping-based link adaptation method for a next-generation underwater communication network proposed in the present invention is for selecting an MCS level by analyzing a link adaptation procedure based on an adaptive modulation scheme according to UAC network channel characteristics. And allocating resources by selecting a plurality of MCS levels for a link adaptation procedure in the UAC network, based on the defining a priority rule and the defined priority rule.

The step of defining the priority rule for selecting the MCS level by analyzing the link adaptation procedure based on the adaptive modulation scheme according to the UAC network channel characteristics is the SNR for the MCS level using the SNR versus BLER curve based on the EESM algorithm. Threshold values are assigned, and UAC network channel characteristics including transmission loss, ambient noise, and multipath fading effects are analyzed.

Based on the defined priority rules, the step of selecting a plurality of MCS levels for the link adaptation procedure in the UAC network is the step of abstracting the physical layer using the EESM algorithm in the UAC network system, and the input data of the physical layer. Compressing to a single value of effective SNR (SNReff), obtaining AWGN equivalent SNR through EESM algorithm, mapping to BLER through AWGN link performance curve of AWGN equivalent SNR, and EESM algorithm to generate L2S interface model. And performing correction of the beta calibration coefficient through.

In order to generate the L2S interface model, the correction of the beta calibration coefficient through the EESM algorithm is performed by calculating the SNR (SNR _AWGN ) and the effective SNR (SNReff) experienced by the data subcarrier of the packet at the receiver side, and the effective SNR. Calculate the beta value that minimizes the difference between (SNReff) and the experienced SNR (SNR _AWGN ), correct the beta value to minimize the mean squared error, and query the effective SNR (SNReff) for the specified MCS AWGN-PER Map to packet error rate or BLER using the table.

In another aspect, the link adaptation apparatus based on efficient SNR mapping for a next generation underwater communication network proposed by the present invention analyzes a link adaptation procedure based on an adaptive modulation scheme according to UAC network channel characteristics, and selects an MCS level. An analysis unit defining a priority rule for performing the operation and a scheduling unit allocating resources by selecting a plurality of MCS levels for a link adaptation procedure in the UAC network based on the defined priority rule.

According to embodiments of the present invention, an effective signal-to-noise ratio (SNR) mapping (EESM) for an Underwater Acoustic Communications Network (UACN) and a beta calibration approach for UACN application thereof By providing a method and providing a link adaptation method and apparatus for an orthogonal frequency division multiple access method, it is possible to improve system performance such as system throughput by performing a link adaptation procedure according to the link state of an underwater acoustic communication network.

1 is a diagram showing the architecture of a step UAC network layout according to an embodiment of the present invention.
2 is a flowchart illustrating an efficient SNR mapping-based link adaptation method for a next generation underwater communication network according to an embodiment of the present invention.
FIG. 3 is a flowchart illustrating a process of allocating resources by selecting a plurality of MCS levels for a link adaptation procedure in a UAC network according to an embodiment of the present invention.
4 is a diagram showing four types of ambient noise according to an embodiment of the present invention.
5 is a diagram showing a fading envelope and a phase of a 12-path fading channel model according to an embodiment of the present invention.
6 is a diagram showing the configuration of an efficient SNR mapping-based link adaptation apparatus for a next generation underwater communication network according to an embodiment of the present invention.
7 is a diagram showing a curve of BPSK according to an embodiment of the present invention.
8 is a diagram showing a QPSK curve according to an embodiment of the present invention.
9 is a diagram showing a curve of 16QAM according to an embodiment of the present invention.
10 is a diagram for explaining an MCS level according to an embodiment of the present invention.
11 is a diagram illustrating a discarded MCS level based on a priority rule according to an embodiment of the present invention.
12 is a diagram showing a throughput curve based on 13 MCS levels according to an embodiment of the present invention.
13 is a diagram showing a BLER curve of a final MCS level according to an embodiment of the present invention.
14 is a diagram for explaining time domain scheduling for RR-based UBS selection and PF-based UBS selection according to an embodiment of the present invention.
15 is a diagram illustrating a UAC network SLS throughput based on an individual MCS level and a link adaptation curve according to an embodiment of the present invention.

Due to extreme limitations on the available bandwidth in UAC networks, the concept of frequency reuse and cellular to improve coverage and capacity of UAC networks is more important. In the present invention, the focus is on a cellular UAC network architecture in which the central cell is an area of interest and the first layer is regarded as an area causing interference. In fact, the evaluation for Link-Level Simulation (LLS) was measured as SNR vs. Block Error Rate (BLER). In order to achieve BLER in SLS without performing actual calculations, the BLER curve obtained by LLS in the Additional White Gaussian Noise (AWGN) channel related to the corresponding modulation and coding scheme (MCS) level Use. To predict BLER in a fading channel, an AWGN equivalent signal-to-interference-plus-noise ratio (SINR) is required. In the present invention, an Exponential Effective SNR Mapping (EESM) method is adopted for the L2S interface of a UAC network. The physical (PHY) layer is abstracted by considering the configuration of a single input, single-output (SISO) antenna with the AWGN channel. The effect of the fading channel or AWGN will be almost the same after averaging the different SNRs using the EESM method, which is why we use the AWGN channel. This work determines the layer crossover, effective SNR mapping, and link adaptation strategy for the next-generation UAC network for clarity. The Medium Access Control (MAC) layer handles logical channel multiplexing, uplink and downlink scheduling, and the like. The PHY layer controls coding/decoding, modulation/demodulation, multi-antenna mapping, and additional PHY layer functions. Link adaptation affects both MAC and Radio Link Control (RLC) processing. Error detection and correction, coding and modulation, and the attachment of periodic redundancy checks for transmission of result signals are all performed by the PHY layer. The use of an inter-layer approach in UAC networks is based on the common functioning of multiple layers. The information of the PHY layer is used in a layer crossing method in an upper layer to perform link adaptation based on various channel conditions. The channel quality indicator (CQI) manager of the PHY layer processes each received CQI report for scheduling and link adaptation purposes. The MAC layer includes a packet scheduler, but the exchange of information is performed at both upper and lower layers. For layer cross-optimization, beta values are corrected for three modulation schemes, and an optimal beta value for L2S mapping is found in the PHY layer by using this optimization parameter effect in the MAC layer for scheduling purposes in the UAC network.

The present invention provides a link level and system level study of a downlink using an orthogonal frequency division multiple access technique for Underwater Acoustic Communications (UAC). An approach to link-level-to-system-level (L2S) mapping at the link level that provides an abstraction model of link-level performance accessed by system-level simulation (SLS). Present.

In the present invention, the exponential effective signal-to-noise ratio (SNR) mapping (Exponential Effective SNR Mapping (EESM)) method is adopted to determine how multi-state channels are integrated into a single state in SLS, and effective SNR is multiplexed. The reason why the characteristics of the subcarrier SNR can be expressed is described in detail. In addition, the beta calibration procedure for UAC networks is detailed. Simulation results are provided to verify the beta calibration of the UAC network. In addition, a link adaptation strategy is adopted by evaluating the system throughput based on a proportional fair (PF) scheduler at the system level. Therefore, the simulation results confirm the effectiveness of the link adaptation strategy for the UAC network. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

According to an embodiment of the present invention, a general procedure for L2S mapping and link adaptation in SLS is adopted for UAC networks. This task abstracts the PHY layer for complete UAC SLS development by first addressing the L2S mapping problem in LLS. Also, the main concern is to inform the effect of the link adaptation approach in consideration of the practical environment of the submarine communication system.

LLS mainly focuses on finding the beta value for the L2S interface of the UAC network using the SNR vs. BLER curve based on the EESM algorithm. In the present invention, beta correction is performed for the UAC network for three modulation schemes (binary phase shift modulation [BPSK], quadrature phase shift modulation [QPSK], and 16 phase quadrature amplitude modulation [16QAM]). Sufficient fading channel recognition is taken to correct for the beta value and confirm the mean squared error. The beta value representing the least mean squared error is chosen as the optimal value.

In SLS, the link adaptation strategy is analyzed based on the adaptive modulation scheme according to the UAC network channel characteristics. In the present invention, an important priority rule for selecting an MCS level is defined. Based on this priority rule, the present invention selects the last 9 MCS levels for the link adaptation procedure in the UAC network.

Due to the limitation of available bandwidth, the present invention uses the concept of frequency reuse and cellular layout structure to increase the bandwidth efficiency of the UAC network system.

In the prior art, a sweep-spread carrier method has been proposed to improve SNR. Sweep is considered as a carrier signal that allows separate multipath arrivals by converting the time delay into a frequency redistribution scheme. The main difference in this work is the sweep-spread technique for UAC networks, which is a completely different approach from the design according to the embodiment of the present invention. In the prior art, five individual modulation and coding pairs are considered as a limited number of transmission modes. The effective SNR is considered as an indicator of the operating mode different from the input SNR and pilot SNR. The main difference between this task is that the system uses different sized MCS levels. In the prior art, a signal-based feedback channel and an adaptation mechanism of real-time PHY layer communication parameters or smooth switching between other technologies such as orthogonal frequency division multiplexing and direct sequence spread spectrum are designed.

1 is a diagram illustrating an architecture of a one-stage UAC network layout according to an embodiment of the present invention.

There are many contributions of wireless-based network topology and the concept of frequency reuse in terrestrial networks. The tremendous performance of cellular networks is enough motivation to consider the cellular and frequency reuse concept of UAC systems. The UAC network is still in the development stage, so a lot of interest and meaningful research are needed in this field. Because of bandwidth limitations, cellular based network architectures for UAC networks are useful. According to an embodiment of the present invention, a network structure in the form of a downlink cellular in a UAC network is emphasized.

The layout of the UAC network structure is based on the basic cellular concept of spatial frequency reuse. 1 shows the considered architecture of the first stage UAC network layout. The red circle represents the underwater base station controller or buoy and the green square represents the UBS. The distance between sites between the underwater base station controllers is 40 km, and three UBSs are connected to one underwater base station controller. Yellow areas indicate areas of interest. The center cell is an area of interest in which three UBSs are connected to the submarine base station controller via a fixed distance, i.e., short (1 km), medium (5 km), and long (10 km) acoustic communication link 1 (underground base station controller and UBS). it means. The term region of interest means that scheduling, interruption, and throughput calculation are performed only for users existing in the region. The region of interest varies depending on the assumption of the scenario according to the embodiment of the present invention. In the first-stage UAC network scenario, it is assumed that the center cell is an ROI as described in yellow in FIG. 1. The blue area is the interference providing area. In other words, UBS is not considered in step 1.

The UAC network channel model has a serious impact on system performance due to its resistive nature. Therefore, it is important to consider transmission loss, ambient noise, and multipath fading effects when analyzing link adaptation of SLS in order to increase the bandwidth efficiency of UAC networks. In practice, the main purpose of considering cellular type architecture in UAC network channel model is to maximize coverage and capacity within limited bandwidth resources. The main difference between terrestrial and underwater networks is to consider an underwater channel model. However, Equations (1) to (8) and numerical values corresponding thereto are well known in the prior art. In the present invention, the underwater channel model parameters of the proposed system were verified using this equation. Therefore, it is necessary to maintain the information (ie, equations (1) to (8) and corresponding values) for reliability of the system model.

2 is a flowchart illustrating an efficient SNR mapping-based link adaptation method for a next generation underwater communication network according to an embodiment of the present invention.

The proposed link adaptation method based on efficient SNR mapping for a generation underwater communication network is the step of analyzing a link adaptation procedure based on an adaptive modulation scheme according to UAC network channel characteristics, and defining a priority rule for selecting an MCS level ( 210) and a step 220 of allocating resources by selecting a plurality of MCS levels for the link adaptation procedure in the UAC network based on the defined priority rule.

In step 210, a link adaptation procedure is analyzed based on an adaptive modulation scheme according to UAC network channel characteristics, and a priority rule for selecting an MCS level is defined. The SNR versus BLER curve based on the EESM algorithm is used to give an SNR threshold for the MCS level, and the UAC network channel characteristics including transmission loss, ambient noise and multipath fading effects are analyzed.

Based on the priority rule defined in step 220, resources are allocated by selecting a plurality of MCS levels for the link adaptation procedure in the UAC network. A process of allocating resources by selecting an MCS level will be described in more detail with reference to FIG. 3.

FIG. 3 is a flowchart illustrating a process of allocating resources by selecting a plurality of MCS levels for a link adaptation procedure in a UAC network according to an embodiment of the present invention.

Step 220 is a step (310) of performing abstraction of the physical layer using the EESM algorithm in the UAC network system, step (320) of compressing the input data of the physical layer into a single value of effective SNR (SNReff), EESM algorithm Obtaining the AWGN equivalent SNR through the AWGN equivalent SNR, mapping to the BLER through the AWGN link performance curve of the AWGN equivalent SNR (330) and performing correction of the beta calibration coefficient through the EESM algorithm to generate the L2S interface model (340) ).

In step 340 of performing correction of the beta calibration coefficient through the EESM algorithm to generate the L2S interface model, the SNR (SNR _AWGN ) and the effective SNR (SNReff) experienced by the data subcarrier of the packet are calculated at the receiver side. , The beta value that minimizes the difference between the effective SNR (SNReff) and the experienced SNR (SNR _AWGN ) is calculated, and the beta value is corrected to minimize the mean square error. Thereafter, the effective SNR (SNReff) is mapped to the packet error rate or BLER using the AWGN-PER inquiry table corresponding to the designated MCS.

In the UAC network system, beta value correction is performed for binary phase shift modulation, quadrature phase shift modulation, and 16 phase quadrature amplitude modulation. To check the mean square error, fading channel recognition is taken, and the beta calibration coefficient is corrected with a beta value representing the least mean square error.

In addition, in the step of mapping the effective SNR (SNReff) to the packet error rate or BLER using the AWGN-PER inquiry table corresponding to the specified MCS, the link adaptation strategy based on the adaptive modulation method according to the channel characteristics in the UAC network system Is analyzed, a priority rule for selecting an MCS level is defined, and a plurality of final MCS levels for the link adaptation procedure are selected based on the defined priority rule.

Transmission losses in UAC networks are due to geometric spreading losses and attenuation. The transmission loss model according to an embodiment of the present invention can be expressed as follows.

수학식(1)

Equation (1)

Where A ₀ , k and α(f) are unit normalization constants, diffusion coefficients, and absorption coefficients, respectively. The transmission loss expression [decibel] can be expressed as follows.

(2)

(2)

In (2), the first term [k.10 logd] represents the diffusion loss (the value of the practical diffusion coefficient is k = 1.5), and the second term [d.10 logα(f)] represents the absorption loss. The absorption coefficient is due to the signal frequency and can be empirically expressed using Thorp's equation (3), which expresses α(f)[dB/m] as a function of f[kHz].

(3)

(3)

4 is a diagram showing four types of ambient noise according to an embodiment of the present invention.

4(a) and 4(b) show absorption coefficient loss and complete transmission loss or path loss, respectively. Absorption loss and transmission loss increase with frequency.

In the UAC network channel model, ambient noise is natural. The empirical formula for the main noise source with power spectral density can be expressed as

수학식(4)

Equation (4)

Where N _t , N _s , N _w , and N _th represent turbulence, transport, wind and thermal noise, respectively. The total noise power density is as follows.

수학식(5)

Equation (5)

Figure 4(c) shows the four types of ambient noise (turbulence, transport, wind and heat) individually. In transport noise, s is the transport activity coefficient, which is between 0 and 1 for low and high activity, respectively. For wind noise, w represents the wind speed (m) in seconds, and comes from the surface motion caused by the wind speed wave. Therefore, the total ambient noise is the sum of the total power spectral density of turbulence, transport, wind, and thermal noise as shown in FIG. 4(d).

According to an embodiment of the present invention, a Matlab-based LLS and SLS platform was built, and a 12-path Rician fading channel was used in the UAC network. A fading channel according to an embodiment of the present invention has 12 path delays, and all paths have different delay profiles and K values. This fading channel model is based on one of the channels measured in Korean waters where the channel model parameters are listed in Table 1.

<Table 1>

Also, the Doppler spread and maximum delay values are 4Hz and 17.78ms, respectively. And the maximum excess delay, coherence bandwidth and coherence time are 14.78ms, 16Hz, and 250ms, respectively. It should be noted that the data collection experiment is only performed on the fading channel model. The simulation uses the data from the fading channel model. Details of the experimental environment for collecting fading channel data are as follows.

1) Data collection experiments were conducted from December 9, 2017 to December 13, 2017.

2) The experiment was conducted in the Korean Sea (Pohang Sea, Geoje-si, Gyeongnam).

-Latitude and longitude coordinates are 34° North Latitude 49' 59.13, 128° 45 East Longitude 13.83

3) In the case of the experimental setup, the depth of the transmitter (transmission) and the receiver (manual) was set to 25 m and 5 m, respectively. Experiments were conducted based on different distances between the transmitter and receiver, i.e. 500m, 1km, 1.5km, 2km, 5km, and 10km.

4) In the case of channel measurement, the frequency range of 10kHz ~ 14kHz was considered, linear frequency modulation (LFM) and Zadoff-Chu signal were used, and the signal bandwidth was 4kHz.

5) The transmitting side periodically transmitted the signal, and the receiving side received the signal to estimate the impulse response of the channel. The channel was measured by varying the distance between the transmitting side and the receiving side.

The mean squared (rms) delay spread can be calculated as:

수학식(6)

Equation (6)

는 평균 초과 지연을 나타낸다.here

Represents the average excess delay.

5 is a diagram showing a fading envelope and a phase of a 12-path fading channel model according to an embodiment of the present invention.

5(a) and 5(b) provide a fading envelope and a phase of a 12-path fading channel model, respectively.

Receiver structure, coding scheme or feedback issues are suitable for simulation at the link level, but it is impractical to consider cell planning, scheduling or interference issues in this type of simulation. However, taking into account all the problems between the submarine base station controller and the UBS link is not feasible in running system level simulations. Because it requires a huge amount of computational power. Therefore, in order to reduce the complexity of the problem, the present invention abstracts the physical layer by capturing the fundamental characteristics in the SLS with high accuracy. Then, the SLS side can easily analyze the performance of the entire network. In the present invention, the abstraction of the physical layer is achieved using the EESM algorithm in the UAC network system.

In the L2S mapping procedure, the PHY layer input data is generally compressed into a single value called effective SNR (SNReff). The AWGN equivalent SNR is obtained by the EESM method and is mapped to the BLER through the AWGN link performance curve. Moreover, correction and optimization of the beta calibration coefficient provides a more accurate L2S interface model.

For accurate L2S mapping, a beta adjustment factor is needed in the UAC system. However, a lot of simulation is needed to determine the beta adjustment coefficient that provides the smallest difference between the estimated BLER and the actual BLER. Specifically, the SNReff points should be the same as the AWGN curve of each MCS. In the present invention, a beta value for L2S mapping is found using the EESM approach. UAC network 12-path Rician fading channel model is used to achieve different channel realization.

UAC network LLS is used to simulate a total of 10 channels realization according to the 12 path Rician fading channel model for the same MCS. The detailed steps of the beta calibration procedure are as follows.

Step 1] The receiver calculates the SNR value experienced by the data subcarrier of the packet [SNR1, ... ,SNRn]. All SNR values belonging to a specific packet are compressed into a single scalar value.

Step 2] Calculate the SNReff as follows.

수학식(7)

Equation (7)

To compare the two values obtained from SNReff and SNRAWGN, find the best beta value that minimizes the difference between the calculated SNR and the actual effective SNR.

수학식(8)

Equation (8)

here,

수학식(9)

Equation (9)

수학식(10)

Equation (10)

to be.

Equations (9) and (10) represent vectors as elements corresponding to channel realization.

Step 3] Many random channels need to be realized; Then, the beta value is corrected to minimize the mean squared error. The beta value that provides the minimum difference is chosen as the optimal value.

Step 4] Map the SNReff to the packet error rate (PER) or BLER using the AWGN-PER inquiry table corresponding to the designated MCS. The following Algorithm 1 shows the implementation details of the beta correction procedure on the LLS side of the UAC network.

According to Algorithm 1, the beta calibration procedure is easy to understand. Basically, the BLER curve under the AWGN channel model should be calculated at the assumed MCS level. Next, it is necessary to realize a plurality of channels in a specific channel model, and in the present invention, a simulation is performed to achieve various channels in a 12-path Rician fading channel model. Then, the SNR value of the existing subcarrier for each channel realization is calculated. Then, the SNReff or SNReesm value is calculated using the EESM channel realization. Finally, the difference between SNRAWGN and SNReff for the corresponding BLER is calculated. After that, the process requires iterations over various beta values to get the smallest difference. In the present invention, the EESM method of L2S mapping is summarized for a UAC network that provides EESM beta correction for three modulation schemes of BPSK, QPSK, and 16QAM through a 12-path Rician fading channel. Accordingly, the implementation steps of the beta correction procedure in Algorithm I for UAC network according to an embodiment of the present invention are summarized.

It should be noted that Algorithm I presents the implementation steps of the beta correction. Set values of specific parameters for performing beta correction, such as the number of fading channels realized (10). It presents more accurate implementation steps for common scenarios.

6 is a diagram showing the configuration of an efficient SNR mapping-based link adaptation apparatus for a next generation underwater communication network according to an embodiment of the present invention.

An efficient SNR mapping-based link adaptation device 600 for a proposed generation underwater communication network includes an analysis unit 610 and a scheduling unit 620.

The analysis unit 610 analyzes a link adaptation procedure based on an adaptive modulation scheme according to a UAC network channel characteristic, and defines a priority rule for selecting an MCS level.

The analysis unit 610 assigns an SNR threshold for the MCS level using the SNR versus BLER curve based on the EESM algorithm, and analyzes UAC network channel characteristics including transmission loss, ambient noise, and multipath fading effects.

The scheduling unit 620 allocates resources by selecting a plurality of MCS levels for the link adaptation procedure in the UAC network based on the defined priority rule.

The scheduling unit 620 performs abstraction of the physical layer using the EESM algorithm in the UAC network system, and compresses the input data of the physical layer into a single value of effective SNR (SNReff). Thereafter, the AWGN equivalent SNR is obtained through the EESM algorithm, mapped to the BLER through the AWGN link performance curve of the AWGN equivalent SNR, and the beta calibration coefficient is corrected through the EESM algorithm to generate the L2S interface model.

The scheduling unit 620 calculates the SNR (SNR _AWGN ) and the effective SNR (SNReff) experienced by the data subcarrier of the packet at the receiver side to perform correction of the calibration coefficient, and calculates the effective SNR (SNReff) and the experienced SNR. Calculate the beta value that minimizes the difference from (SNR _AWGN ), correct the beta value to minimize the mean square error, and determine the effective SNR (SNReff) by using the AWGN-PER query table corresponding to the specified MCS to determine the packet error rate or Mapped to BLER.

7 is a diagram showing curves of three modulation schemes (BPSK, QPSK, and 16QAM) according to an embodiment of the present invention.

In the present invention, the beta values of three modulation schemes (BPSK, QPSK, and 16QAM) are found using the EESM approach to the UAC network. The corrected beta value is used to achieve accurate L2S mapping in the UAC network. Fig. 7(a) shows the actual curve for BPSK, and Fig. 7(b) shows the corrected EESM curve. When scattered around the AWGN curve (solid line), scattered marks in Fig. 7(b) indicate the realization of distinct channels. This proves the validity of the abstraction model because the estimated BLER is very close to the simulated BLER according to AWGN.

8 is a diagram showing a QPSK curve according to an embodiment of the present invention.

Fig. 8(a) shows the actual curve of QPSK, while Fig. 8(b) shows the corrected EESM curve.

9 is a diagram showing a curve of 16QAM according to an embodiment of the present invention.

Fig. 9(a) shows an actual curve for 16QAM, and Fig. 9(b) shows a corrected EESM curve. Therefore, as shown in Figs. 7(b), 8(b), and 9(b), the solid line represents the reference SNR vs. BLER curve below the AWGN, while the scattered display shows the BLER obtained through the 12-path Rician channel model. Show.

The increase in variance in the high SNR range is due to fading in channel realization. In Figs. 7, 8 and 9, the BLER curves of the fading channel realization overlap at low SNR, but hardly spread at high SNR due to high BLER. As the dispersion of effective SNR increases, it is imitating the realization of fading channels. Thus, the variance of the effective SNR is low at low BLER and vice versa.

Moreover, the reason for taking 10 realizations of the fading channel model is quite obvious. 7, 8, and 9, it can be easily seen that the curves of the Rician fading channel overlap each other. This means that realization of 10 fading channels in a UAC system is sufficient for system level mapping at the link level.

10 is a diagram illustrating an MCS level according to an embodiment of the present invention.

According to an embodiment of the present invention, a link adaptation strategy is adopted based on an adaptive modulation scheme for a UAC network. In general, high SNR mimics higher order modulation schemes that provide high spectral efficiency. Conversely, low SNR follows a low-order modulation scheme. Low SNR decreases spectral efficiency, but is robust against transmission errors.

In the present invention, the BPSK, QPSK, and 16QAM modulation schemes of a constant code rate (1/2) are examined in the link adaptation strategy. In fact, a data repetition pattern (RP) corresponding to each modulation scheme is considered. In terms of LLS, several important data RPs (1, 3, 4, 8, 9, 12, 24, 36, 72) are considered. Therefore, there are a total of 27 MCS levels based on the combination of the three modulation schemes and RP shown in FIG. 10. However, there is overlap between the different MCS levels, and therefore, some important priority rules for selecting the MCS level in FIG. 10 are assumed as follows.

Select an MCS level with a difference of at least 1dB.

When the difference between different MSC levels (BPSK, QPSK, 16QAM) is less than 1dB, the BLER selects a better MCS.

If several MSC levels overlap, a lower MCS is selected.

If the same MCS level has different RPs (eg, BPSK RP1, BPSK RP3) and the difference is less than 1dB, the BLER selects a better MCS.

If RP is different for each MCS level, but the difference in BLER is very small, a low MCS is selected.

It does not affect the system throughput (e.g. BPSK RP72, BPSK RP36, BPSK RP24, QPSK RP72, etc.), so discard the lower MCS with the larger RP in the lower SNR range.

The RP pattern maintains a low MCS level because it has a large impact on the system throughput (eg BPSK RP1, QPSKRP3, 16QAM RP3).

11 is a diagram illustrating a discarded MCS level based on a priority rule according to an embodiment of the present invention.

The discarded MCS level based on the priority rule is shown in FIG. 11. Among the 27 MCS levels, 13 MCSs remain by applying the priority rule for selecting an MCS level. Now the remaining 13 MCS levels of SLS are utilized to analyze the system throughput at each MCS level.

12 is a diagram showing a throughput curve based on 13 MCS levels according to an embodiment of the present invention.

In FIG. 12, it can be easily seen that the high MCS level overlaps with the low MCS level (for example, because QPSK RP12 overlaps BPSK RP3) and thus needs to be discarded. Therefore, based on the SLS throughput curve, four more MCS levels (BPSK RP8, QPSK RP12, QPSK RP4 and 16QAM RP12) are discarded. After discarding these four MCS levels, the BLER curve of the final MCS level is shown in FIG. 13.

13 is a diagram illustrating a BLER curve of a final MCS level according to an embodiment of the present invention.

In addition, SNR-to-CQI mapping based on the last 9 MCS levels for adaptive modulation in the UAC network SLS is shown in Table 2.

By evaluating throughput in the UAC network SLS using PF scheduling in the frequency domain, the last nine MCS levels are utilized in the link adaptation strategy. In addition, link adaptation is considered in the frequency domain, and schedules are performed according to round robin (RR) UBS selection and PF UBS selection in the time domain. In the RR-based UBS selection strategy, one frame is allocated to one UBS in each time slot according to the RR UBS selection, while link adaptation is performed in the frequency domain for the corresponding UBS time slot. In the case of the PF-based UBS selection strategy, one frame is allocated to the UBS in each slot according to the PF UBS selection, and link adaptation is also performed in the frequency domain for the corresponding UBS time slot.

14 is a diagram for explaining time domain scheduling for RR-based UBS selection and PF-based UBS selection according to an embodiment of the present invention.

14(a) and 14(b) show details of time domain scheduling for RR-based UBS selection and PF-based UBS selection, respectively. Implementation details of the link adaptation procedure for adaptive modulation based on the nine MCS levels determined in the UAC network SLS are presented in Algorithm II.

Note that the adaptive modulation process is frequency domain based while allocating frames to each time slot. Therefore, scheduling is performed based on the PF rule for adaptive modulation in the frequency domain of the UAC system. The implementation steps of the link adaptation procedure are described in Algorithm II.

The computational complexity is measured by taking into account the basic work required to execute the procedure and indicates that it is a function of the problem size K (total number of resource blocks). The complexity of the water-filling adaptive modulation and coding model represented by T(K) operation is specified as follows.

수학식(11)

Equation (11)

Here, M represents multiple input, multiple output (MIMO) transmission mode, ζ is the coding rate, K is the total number of resource blocks or subcarriers, SEF and SET are valid and E-tightening subroutines respectively. Represents the number of iterative searches to obtain a solution for. In the worst case, these two parameters (SEF and SET) can be incremented to K, as determined by the initial bit allocation.

For block adaptive modulation and code models, the complexity is described as follows.

수학식(12)

Equation (12)

Here, L represents the MCS level (L = 1,..., Lmax): The dependence of Equation (12) on the resource block or subcarrier is derived from the calculation of the instantaneous Bhattacharyya coefficient in the bit error probability estimation. The computational complexity of the link adaptation strategy adopted for the UAC network in the present invention is described as follows.

수학식(13)

Equation (13)

Here, P is the number of repetition patterns (P = 1,..., Pmax), K is the total number of resource blocks, and M is the MIMO transmission mode for space and time. In the present invention, one antenna is used for the transmitter and one antenna is used for the receiver. Due to the low complexity of this approach, battery degradation will not be an issue. Therefore, Equation (13) represents continuous selection of the MCS level and depends on link adaptation.

15 is a diagram illustrating a UAC network SLS throughput based on an individual MCS level and a link adaptation curve according to an embodiment of the present invention.

15 provides UAC network SLS throughput based on individual MCS levels and link adaptation curves. It is easy to see that the bold red and brown curves mimic the adaptive modulation strategy based on the final 9 MCS levels. Thus, the bold red and brown curves show the effect of link adaptation in UAC network SLS based on PF and RR UBS selection, respectively. Link adaptation with PF UBS selection provides better UBS performance than link adaptation with RR UBS selection.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It can be implemented using one or more general purpose computers or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. Can be embodyed. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

Although the embodiments have been described by the limited embodiments and drawings as described above, various modifications and variations can be made from the above description to those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the claims to be described later.

Claims (8)

Analyzing a link adaptation procedure based on an adaptive modulation scheme according to a UAC network channel characteristic, and defining a priority rule for selecting an MCS level; And
Allocating resources by selecting a plurality of MCS levels for the link adaptation procedure in the UAC network based on the defined priority rules
Including,
Based on the defined priority rule, the step of selecting a plurality of MCS levels for the link adaptation procedure in the UAC network,
Performing abstraction of a physical layer using an EESM algorithm in a UAC network system;
Compressing the input data of the physical layer into a single value of effective SNR (SNReff);
Obtaining an AWGN equivalent SNR through an EESM algorithm, and mapping to a BLER through an AWGN link performance curve of the AWGN equivalent SNR; And
Compensating the beta calibration coefficient through the EESM algorithm to generate the L2S interface model
SNR mapping-based link adaptation method comprising a. The method of claim 1,
Analyzing a link adaptation procedure based on an adaptive modulation scheme according to UAC network channel characteristics, and defining a priority rule for selecting an MCS level,
SNR versus BLER curve based on the EESM algorithm is used to assign SNR thresholds for MCS levels, and to analyze UAC network channel characteristics including transmission loss, ambient noise and multipath fading effects.
Link adaptation method based on SNR mapping. delete The method of claim 1,
Compensating the beta calibration coefficient through the EESM algorithm to generate the L2S interface model,
By calculating the SNR (SNR _AWGN ) and the effective SNR (SNReff) experienced by the data subcarrier of the packet at the receiver side, a beta value that minimizes the difference between the effective SNR (SNReff) and the experienced SNR (SNR _AWGN ) is obtained. In order to minimize the mean square error, the beta value is corrected, and the effective SNR (SNReff) is mapped to the packet error rate or BLER using the AWGN-PER lookup table corresponding to the specified MCS.
Link adaptation method based on SNR mapping. An analysis unit for defining a priority rule for selecting an MCS level by analyzing a link adaptation procedure based on an adaptive modulation scheme according to a UAC network channel characteristic; And
A scheduling unit that allocates resources by selecting multiple MCS levels for link adaptation procedures in the UAC network based on the defined priority rules
Including,
The scheduling department,
Abstraction of the physical layer using the EESM algorithm in the UAC network system;
Compressing the input data of the physical layer into a single value of effective SNR (SNReff);
Obtaining AWGN equivalent SNR through EESM algorithm, and mapping to BLER through AWGN link performance curve of AWGN equivalent SNR;
To generate the L2S interface model, the beta calibration coefficient is corrected through the EESM algorithm.
Link adaptation device based on SNR mapping. The method of claim 5,
The analysis department,
SNR versus BLER curve based on the EESM algorithm is used to assign SNR thresholds for MCS levels, and to analyze UAC network channel characteristics including transmission loss, ambient noise and multipath fading effects.
Link adaptation device based on SNR mapping. delete The method of claim 5,
The scheduling department,
In order to perform correction of the calibration coefficient, the receiver side calculates the SNR (SNR _AWGN ) and the effective SNR (SNReff) experienced by the data subcarriers of the packet, and the effective SNR (SNReff) and the experienced SNR (SNR _AWGN ) are It calculates the beta value that minimizes the difference, corrects the beta value to minimize the mean square error, and maps the effective SNR (SNReff) to the packet error rate or BLER using the AWGN-PER lookup table corresponding to the specified MCS.
Link adaptation device based on SNR mapping.

Images