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ANALYSIS AND OPTIMIZATION OF DIGITAL COMMUNICATION SYSTEM

1.3 Selecting the type of modulation and calculating transmission quality characteristics

Application

INTRODUCTION

Life modern society unthinkable without widely ramified information transmission systems. Without it, industry, agriculture, and transport would not be able to function.

Further development of all aspects of our society’s activities is unthinkable without the widest implementation automated systems control, the most important part of which is the communication system for exchanging information, as well as devices for storing and processing it.

Transfer, storage and processing of information take place not only when using technical devices. A normal conversation is an exchange of information. There are many different forms of presentation and storage of information, such as books, floppy disks, hard drives, etc.

Information transmission technology, perhaps more than any other technology, influences the formation of the structure of the world community. The past decade has seen revolutionary changes in the Internet and with it radical and often unpredictable changes in the way business is done on a global scale. From this follows a completely logical conclusion that without knowledge of the fundamentals of the theory of signal transmission, the creation of new advanced communication systems and their operation is impossible. Therefore, its study is an integral part of students’ theoretical training.

Transmitting a message from one point to another is the basis of the theory and technology of communication. The course “Telecommunication Theory” studies unified methods for solving various problems that arise when transmitting information from its source to the recipient.

1.1 Block diagram digital system communications

In a number of practical cases, the problem of transmitting continuous messages through a discrete communication channel arises. This problem is solved by using a digital communication system. One such system is a system for transmitting continuous messages using pulse code modulation (PCM) and harmonic carrier manipulation. The block diagram of such a system is shown in Fig. 1. It consists of a message source (IS), an analog-to-digital converter (ADC), a binary discrete communication channel (DCC), An integral part which is a continuous communication channel (NCC), a digital-to-analog converter (DAC) and a message recipient (MS). Each of the above parts of the system contains a number of other elements. Let's look at them in more detail.

A message source is some object or system, information about the state or behavior of which must be transmitted over a certain distance. The information that is transmitted from the IS is unexpected for the recipient. Therefore, its quantitative measure in telecommunication theory is expressed through the statistical (probabilistic) characteristics of messages (signals). A message is a physical form of representing information. Often messages are provided in the form of a time-varying current or voltage that represents the information being transmitted.

Figure 1.1 – Block diagram of a digital communication system

In the transmitter (IS), the message is first filtered in order to limit its spectrum to a certain upper frequency f B. This is necessary for the effective representation of the low-pass filter response x(t) as a sequence of samples x k = x(kT), k = 0, 1, 2, . .., which are observed at the output of the sampler. Note that filtering is associated with the introduction of an error e f (t), which reflects that part of the message that is attenuated by the low-pass filter. Further readings (x k) are quantized per level. The quantization process is associated with the nonlinear transformation of continuous-valued samples (x k) into discrete-valued ones (x k l), which also introduce an error, which is called the quantization error (noise) e sq (t). Quantum levels (y k = x k l ) are then encoded with a binary non-redundant (primitive) or noise-resistant code.

The sequence of code combinations (b k l) forms a PCM signal, which is sent to the modulator - a device that is designed to match the message source with the communication line. The modulator forms line signal S(t, b i), which represents the electrical or electromagnetic vibrations, capable of propagating over a communication line and uniquely associated with the message being transmitted (in in this case with PCM signal). The signal S(t, b i) is created as a result of discrete modulation (manipulation) - the process of changing one or more parameters of the carrier according to the PCM signal. When using a harmonic carrier U Н (t) = U m cos(2pf Н t+j 0), signals are distinguished: amplitude, frequency and phase manipulation (AM, FM and FM).

To prevent out-of-band emissions in single-channel communications or when organizing multi-channel communications, as well as to establish the desired signal-to-noise ratio at the receiver input, the linear signal is filtered and amplified in the output stage of the IC.

The signal S(t) from the output of the IC enters the communication line, where it is affected by noise n(t). At the receiver input (R) there is a mixture of z(t) = s(t) + n(t) of the transmitted signal and noise, which is filtered in the input stage of the R and fed to the demodulator (detector).

During demodulation, the law of change in the information parameter is extracted from the received signal, which in our case is proportional to the PCM signal. In this case, to recognize the transmitted binary signals, a decision device (DE) is connected to the output of the demodulator. When transmitting binary signals b i, i = 0, 1 via the DCS, the presence of interference in the NCS leads to ambiguous decisions (errors) of the switchgear, which in turn causes a discrepancy between the transmitted and received code combinations.

Finally, to restore the transmitted continuous message a(t), i.e. After receiving its estimate, the received code combinations are subjected to decoding, interpolation and low-pass filtering. In this case, the L-th levels, m = 1 ... L-1, are restored in the decoder using binary code combinations.

The presence of errors in the binary DCS leads to transmission errors in the L-th DCS and the occurrence of transmission noise e P (t). The combined effect of filtering error, quantization and transmission noise leads to ambiguity between the transmitted and received messages.

1.2 Determination of ADC and DAC parameters

The time sampling interval T d is selected based on Kotelnikov’s theorem. The inverse quantity to Td - sampling frequency fd = 1/Td is selected from the condition

f d ≥ 2F m, (1.1)

where F m - maximum frequency primary signal (message).

Increasing the sampling rate makes it possible to simplify the input low-pass filter (LPF) of the ADC, which limits the spectrum of the primary signal, and the output LPF of the DAC, which restores the continuous signal from the sample. But increasing the sampling rate leads to a decrease in the duration of the binary symbols at the ADC output, which requires an undesirable expansion of the communication channel bandwidth for transmitting these symbols. Typically, the parameters of the input low-pass filter of the ADC and the output low-pass filter of the DAC are chosen to be the same.

In Fig. 1.2 presents: S(f) - spectrum of readings, which are displayed by narrow pulses, S a (f) - spectrum of continuous message a(t), A(f) - operating attenuation of the low-pass filter.

In order for the low-pass filter to not introduce linear distortions into a continuous signal, the limiting frequencies of the low-pass filter passbands must satisfy the condition

f 1 ≥ F m (1.2)

In order to eliminate the overlap of the spectra S a (f) and S a (f-f D), and also to ensure the attenuation of the composite S a (f-f D) by the restoring low-pass filter, the limiting frequencies of the low-pass filter stop bands must satisfy the condition

f 2 ≤ (f D - F m)(1.3)

Figure 1.2 - Spectrum of samples and frequency response of attenuation of ADC and DAC filters

So that the low-pass filters are not too complex, the ratio of the limiting frequencies is selected from the condition

f 2 / f 1 = 1.3 ... 1.1.(1.4)

After substituting relations (1.2) and (1.3) into (1.4), you can select the sampling frequency f D.

In a PCM digital transmission system, the noise power at the DAC output is defined as

,(1.5)

where is the average quantization noise power;

Average noise power of measurement errors.

(1.6)

The quantization noise power is expressed in terms of the quantization step size Dx:

.(1.7)

The quantization step depends on the number of quantization levels N:

Dx = U max / (N-1)(1.8)

From expression (1.8) we determine the minimum possible number of quantization levels:

(1.9)

The length of the binary primitive code at the ADC output is an integer:

m = log 2 N .(1.10)

Therefore, the number of quantization levels N is chosen as an integer power of 2, at which

N ≥ N m i n .(1.11)

The duration of a binary symbol (bit) at the ADC output is determined as

T b = T D / m.(1.12)

The average amount of information transmitted over a communication channel per unit time - the information transmission speed H t - is determined by the formula

,(1.13)

where is the sample transmission rate;

– entropy.

, (1.14)

where is the signal level distribution law, is the number of quantization levels.

The sampling rate is equal to the sampling frequency:

.(1.15)

1.3 Modulation

We choose the type of modulation so that the speed of information transmission after modulation is not less than the productivity of the source, i.e.

,

where is the modulation rate,

Number of signal positions.

For AM, FM, OFM, KAM

Channel bandwidth.

,

where is the number of subchannels.

Then ,

After determining the number of signal positions M, we calculate the error probabilities

Probability of error with AM-M:

,

Probability of error with FM-M:

Probability of error during OFM-M:

Probability of error with KAM-M:

M = 2 k, k is an even number.

Probability of error with OFDM:

where η is the number of amplitude levels;

M = 2 k, k is an even number.

The choice of modulation method is carried out in accordance with the criterion of minimum error probability.

1.4 Selecting the type of noise-resistant code and determining the length of the code combination

Noise-resistant, or redundant, coding is used to detect and/or correct errors that occur during transmission over a discrete channel. A distinctive property of error-correcting coding is that the redundancy of the source formed by the output of the encoder is greater than the redundancy of the source at the input of the encoder. Noise-resistant coding is used in various communication systems, when storing and transmitting data in computer networks, in household and professional audio and video equipment based on digital recording.

If economical coding reduces the redundancy of the message source, then error-correcting coding, on the contrary, consists of the purposeful introduction of redundancy in order to make it possible to detect and (or) correct errors that occur during transmission over the communication channel.

n=m+k – code combination length;

m – number of information symbols (bits);

k – number of check characters (digits);

Of particular importance for characterizing the correcting properties of the code is the minimum code distance d min, determined by pairwise comparison of all code combinations, which is called the Hamming distance.

In a non-redundant code, all combinations are allowed, and, therefore, its minimum code distance is equal to one - d min = 1. Therefore, it is enough for one symbol to be distorted for another allowed combination to be accepted instead of the transmitted combination. In order for the code to have corrective properties, it is necessary to introduce some redundancy into it, which would ensure a minimum distance between any two allowed combinations of at least two - d min > 2.

The minimum code distance is the most important characteristic noise-resistant codes, indicating the guaranteed number of errors detected or corrected by a given code.

When using binary codes, only discrete distortions are taken into account, in which one goes to zero (1 → 0) or zero goes to one (0 → 1). The transition 1 → 0 or 0 → 1 in only one element of the codeword is called a single error (single distortion). In general, the error multiplicity refers to the number of positions of the code combination at which, due to interference, some symbols were replaced by others. Double (t = 2) and multiple (t > 2) distortions of elements in the code combination within 0 are possible< t < n.

The minimum code distance is the main parameter characterizing the correcting capabilities of a given code. If the code is used only to detect errors of multiplicity t 0 , then it is necessary and sufficient that the minimum code distance be equal to

d min > t 0 + 1.(1.29)

In this case, no combination of t 0 errors can transform one allowed code combination into another allowed one. Thus, the condition for detecting all errors with multiplicity t 0 can be written as:

t 0 ≤ d min - 1.(1.30)

In order to be able to correct all errors with a factor of t or less, it is necessary to have a minimum distance that satisfies the condition:

In this case, any code combination with the number of errors t differs from each allowed combination in at least t and + 1 positions. If condition (1.31) is not met, it is possible that errors of multiplicity t will distort the transmitted combination so that it becomes closer to one of the allowed combinations than to the transmitted one or even turns into another allowed combination. In accordance with this, the condition for correcting all errors with a multiplicity of no more than t can be written as:

t and ≤ (d min - 1) / 2 .(1.32)

From (1.29) and (1.31) it follows that if the code corrects all errors with a multiplicity of t and, then the number of errors that it can detect is equal to t 0 = 2∙t and. It should be noted that relations (1.29) and (1.31) establish only the guaranteed minimum number of detected or corrected errors for a given d min and do not limit the possibility of detecting errors of higher multiplicity. For example, simplest code with parity check with d min = 2 allows you to detect not only single errors, but also any odd number of errors within t 0< n.

The length of the code combination n must be chosen in such a way as to provide the greatest throughput of the communication channel. When using a correcting code, the code combination contains n bits, of which m bits are information bits, and k bits are verification bits.

The redundancy of the correction code is the quantity

,(1.33)

whence follows

.(1.34)

This value shows what part of the total number of symbols of the code combination are information symbols. In coding theory, the value of B m is called the relative code rate. If the productivity of the information source is equal to H t symbols per second, then the transmission speed after encoding this information will be equal to

since in the encoded sequence, out of every n symbols, only m symbols are informational.

If the communication system uses binary signals (signals of type "1" and "0") and each unit element carries no more than one bit of information, then there is a relationship between the information transmission rate and the modulation rate

where V is the information transmission rate, bit/s; B - modulation speed, Baud.

Obviously, the smaller k, the more the m/n ratio approaches 1, the less V differs from B, i.e. the higher the throughput of the communication system.

It is also known that for cyclic codes with a minimum code distance d min = 3 the following relation holds true:

k³log 2 (n+1).(1.37)

It can be seen that the larger n, the closer the m/n ratio is to 1. So, for example, with n = 7, k = 3, m = 4, m/n = 0.571; with n = 255, k = 8, m = 247, m/n = 0.964; with n = 1023, k = 10, m = 1013, m/n = 0.990.

The above statement is also true for large d min, although there are no exact relationships for the connections between m and n. There are only upper and lower bounds that establish the relationship between the maximum possible minimum distance of the correction code and its redundancy.

Thus, the Plotkin bound gives the upper limit of the code distance d min for a given number of bits n in the code combination and the number of information bits m, and for binary codes:

(1.38)

At .(1.39)

The Hamming upper bound sets the maximum possible number of allowed code combinations (2 m) of any error-correcting code for given values ​​of n and d min:

,(1.40)

where is the number of combinations of n elements based on i elements.

From here you can get an expression for estimating the number of check characters:

.(1.41)

For values ​​(d min /n) ≤ 0.3, the difference between the Hamming limit and the Plotkin limit is relatively small.

Varshamov-Hilbert boundary for large values n defines a lower bound for the number of check bits required to ensure a given code distance:

All of the above estimates give an idea of ​​the upper bound of the number d min for fixed values ​​of n and m or a lower estimate of the number of check symbols k for given m and d min .

From the foregoing, we can conclude that from the point of view of introducing constant redundancy into the code combination, it is advantageous to choose long code combinations, since with increasing n the relative throughput

R = V/B = m/n(1.43)

increases, tending to the limit equal to 1.

In real communication channels there is interference, leading to errors in code combinations. When an error is detected by the decoding device in systems with POS, a group of code combinations is asked again. During the questioning useful information is not transmitted, so the speed of information transmission decreases.

It can be shown that in this case

,(1.44)

where P oo is the probability of detecting an error by the decoder (probability of asking again):

;(1.45)

P pp - probability of correct reception (error-free reception) of the code combination;

M - transmitter storage capacity in the number of code combinations

,(1.46)

where t p is the signal propagation time along the communication channel, s;

tk – time of transmission of a code combination of n bits, s.

Sign< >means that when calculating M, you should take the larger nearest integer value.

The signal propagation time over the communication channel and the code combination transmission time are calculated in accordance with the expressions

where L is the distance between terminal stations, km;

c is the speed of signal propagation along the communication channel, km/s (c = 3x10 5);

B - modulation speed, Baud.

If there are errors in the communication channel, the value of R is a function of P 0, n, k, B, L, s. Consequently, there is an optimal n (for given P 0, B, L, c), at which the relative throughput will be maximum.

To calculate the optimal values ​​of n, k, m, it is most convenient to use a mathematical modeling software package, such as MathLab or MathCAD, by plotting the dependence R(n) in it. The optimal value will be when R(n) is maximum. When determining the values ​​of n, k, m, it is also necessary to ensure that the following conditions are met:

where is the equivalent probability of a single bit reception error when using error-correcting coding with POC.

The value can be determined using the relation that when transmitting without the use of noise-resistant coding, the probability of erroneous registration of a code combination P 0kk of length n is equal to

.(1.48)

At the same time, when using noise-resistant coding

,(1.49)

where is the probability of undetected errors

;(1.50)

Probability of detected errors

.(1.51)

In addition to fulfilling condition (1.47), it is necessary to ensure

V ³ H t . (1.52)

From the above it follows that the process of searching for values ​​of B, n, m, k is iterative and it is most convenient to arrange it in the form of a table, a sample of which is given in Table. 1.2

Table 1.2

To detect errors, we select a cyclic code. Of all the known noise-resistant codes, cyclic codes are the simplest and most efficient. These codes can be used both to detect and correct independent errors and, in particular, to detect and correct serial errors. Their main property is that each code combination can be obtained by cyclically rearranging the symbols of combinations belonging to the same code.

Cyclic codes significantly simplify the description of a linear code, since for them, instead of specifying the elements of the binary matrix P, it is necessary to specify (n-k+1) binary coefficients of the polynomial g(D). They also simplify the encoding and decoding procedure for error detection. Indeed, to implement encoding, it is enough to multiply polynomials, which is implemented using a linear register containing k memory cells and having feedback connections corresponding to the polynomial h(D).

The cyclic code is guaranteed to detect multiplicity errors and correct them. Therefore, in systems with a decisive feedback cyclic code coding is used.

When an error is detected on the receiving side, a request is sent via the reverse communication channel to the block in which it was detected, and then this block is retransmitted. This continues until this block will not be accepted without an error detected. Such a system is called a decision feedback system (DFS), since the decision to accept a block or to retransmit it is made at the receiving side. The system with POC are in an efficient way increasing the noise immunity of information transmission.

When describing the encoding and decoding procedure with a cyclic code, it is convenient to use a mathematical apparatus based on comparing a set of code words with a set of power polynomials. This apparatus makes it possible to identify simpler encoding and decoding operations for a cyclic code.

Among all the polynomials corresponding to the codewords of the cyclic code, there is a non-zero polynomial P(x) of the smallest degree. This polynomial completely determines the corresponding code and is therefore called generating.

The degree of the generating polynomial P(x) is equal to n - m, the free term is always equal to one.

The generating polynomial is the divisor of all polynomials corresponding to the codewords of the cyclic code.

The zero combination necessarily belongs to any linear cyclic code and can be written as (x n Å 1) mod (x n Å 1) = 0. Therefore, the generating polynomial P(x) must be a divisor of the binomial x n Å 1.

This gives constructive possibilities for constructing a cyclic code of a given length n: any polynomial that is a divisor of the binomial x n Å 1 can be used as a generator.

When constructing cyclic codes, they use tables of decomposition of binomials x n Å 1 into irreducible polynomials, i.e. polynomials that cannot be represented as a product of two other polynomials (see Appendix A).

Any irreducible polynomial included in the expansion of the binomial x n Å 1, as well as any product of irreducible polynomials, can be chosen as a generating polynomial, which gives the corresponding cyclic code.

To construct a systematic cyclic code, the following rule for constructing code words is used

where R(x) is the remainder of m(x)×x n - m divided by P(x).

The degree of R(x) is obviously less than (n - m), and therefore in the code word the first m symbols will coincide with information ones, and the last n - m symbols will be verification ones.

The decoding procedure for cyclic codes can be based on the property of their divisibility without remainder by the generating polynomial P(x).

In error detection mode, if the received sequence is divided evenly by P(x), it is concluded that there is no error or it is not detected. Otherwise, the combination is rejected.

In error correction mode, the decoder calculates the remainder R(x) from dividing the received sequence F¢(x) by P(x). This remainder is called the syndrome. The received polynomial F¢(x) is the modulo two sum of the transmitted word F(x) and the error vector E error (x):

Then the syndrome S(x) = F¢(x) modP(x), since by definition of the cyclic code F(x) mod P(x) = 0. A certain syndrome S(x) can be associated with a certain error vector E osh(x). Then the transmitted word F(x) is found by adding .

However, the same syndrome can correspond to 2 m different error vectors. Let us assume that the syndrome S 1 (x) corresponds to the error vector E 1 (x). But all error vectors equal to the sum E 1 (x) Å F(x), where F(x) is any code word, will give the same syndrome. Therefore, by assigning the error vector E 1 (x) to the syndrome S 1 (x), we will carry out correct decoding in the case when the error vector is actually equal to E 1 (x), in all other 2 m - 1 cases the decoding will be erroneous.

To reduce the probability of a decoding error, from all possible error vectors that give the same syndrome, the most probable one in a given channel should be selected as the one to be corrected.

For example, for a DSC, in which the probability P 0 of erroneous reception of a binary symbol is much less than the probability (1 - P 0) of correct reception, the probability of the appearance of error vectors decreases with increasing their weight i. In this case, the error vector with the smaller weight should be corrected first.

If only all error vectors of weight i and less can be corrected by the code, then any error vector of weight from i + 1 to n will lead to erroneous decoding.

The probability of erroneous decoding will be equal to the probability P n (>i) of the appearance of error vectors of weight i + 1 or more in a given channel. For DSC this probability will be equal to

.

The total number of different error vectors that a cyclic code can correct is equal to the number of non-zero syndromes – 2 n - m - 1.

In the course project, it is necessary, based on the value of k calculated in the previous paragraph, to select a generating polynomial according to the table given in Appendix A. Based on the selected generating polynomial, it is necessary to develop an encoder and decoder circuit for the case of an error detection.

1.5 Digital communication system performance indicators

Digital communication systems are characterized by quality indicators, one of which is the fidelity (correctness) of transmission.

To assess the efficiency of a communication system, the coefficient of utilization of the communication channel by power (energy efficiency) and the coefficient of utilization of the channel by frequency band (frequency efficiency) are introduced:

where V is the information transmission speed;

Signal-to-noise ratio at the demodulator input

; (1.55)

Frequency bandwidth occupied by the signal

, (1.56)

where M is the number of signal positions.

A generalized characteristic is the channel utilization rate in terms of throughput (information efficiency):

For a continuous communication channel taking into account Shannon’s formula

we get the following expression

. (1.58)

According to Shannon’s theorems for h=1, we can obtain the relationship between b and g:

b=g/(2 g - 1), (1.59)

which is called the Shannon boundary, which represents the best exchange between b and g in a continuous channel. It is convenient to depict this dependence as a curve on the b - g plane (Fig. 1.6).

Figure 1.6 - Shannon boundary

The efficiency of the system can be increased by increasing the speed of information transfer (increasing the entropy of messages). The entropy of messages depends on the probability distribution law. Therefore, to improve efficiency, it is necessary to redistribute the densities of message elements.

Eliminating or weakening the relationships between message elements can also improve the efficiency of systems.

Finally, improvements in system efficiency can be achieved through appropriate coding choices that save time during message transmission.

In the course project, it is necessary to mark the efficiency of the designed digital communication system with a dot on the constructed graph (Fig. 1.6).

1. Guidelines for course design in the discipline “Theory of Electrical Connection” Bidny Yu.M., Zolotarev V.A., Omelchenko A.V. - Kharkov: KNURE, 2008.

2. Omelchenko V.A., Sannikov V.G. Theory of electrical communication. Parts 1, 2, 3. - K.: ISDO, 2001.

3. Theory of electrical communication: Textbook for universities / A.G. Zyuko. D.D. Klovsky, V.I. Korzhik, M.V. Nazarov; Ed. D.D. Klokovsky. – M.: Radio and communications. 1998.

4. Peterson W., Weldon E. Error Correcting Codes / Translation, from English. edited by R.L. Dobrushina and S.I. Samoilenko. - M-: Mir, 1999. - 596 p.

5. Andreev B.S. Theory of nonlinear electrical circuits. Textbook manual for universities. - M.: Radio and communication, 1999. - 280 p.

APPLICATION

Table of irreducible generating polynomials of degree m

23.05.2016

The goal of any operator is to provide its customers with coverage and services of higher quality than its competitors. Stable signal anywhere in the city, high speed data transfers and a large package of services are one of the main ways to attract new customers and increase profits.

If you conduct a more in-depth analysis of the situation, you can discover other factors that influence the increase in operator profitability. These include a significant reduction in network maintenance costs, minimizing risks and ensuring uninterrupted operation of the entire system.

But any, even minor, increase in operator efficiency is preceded by long-term preparation. Optimization of communication networks begins with an audit and analysis of their current state, and for this, operators attract outsourcing companies.

What does an audit of communication services include?

The exact list of work is determined by the final goals that the operator needs to achieve: modernize the network, improve the quality of coverage in a specific area, etc. As a rule, companies providing such services to operators are able to perform research of any complexity.

We contacted the company Modern technologies communications”, which has experience working with federal operators. According to management, the company has innovative equipment and a large suite of software for data analysis. Thanks to this, optimization of communication systems is carried out as efficiently as possible, since the operator receives objective information on a huge number of parameters.

During network exploration, the following can be performed:

benchmarking, or comparative assessment of several operators;

analysis of customer statistics;

configuration analysis (an audit of objects in sectors is carried out, heights, azimuths, inclination angles, etc. are measured);

verification of frequency-territorial plans;

The last point implies a whole range of field research. Based on the results obtained, measures to improve the quality of voice and data transmission are subsequently developed. Among the main measured parameters:

• parameters of availability, retention of voice services;
• parameters of radio channels;
• Location Update;
• MeanOptionScore;
• data transfer rate;
• quality of radio coverage in UMTS, LTE, GSM standards.

Cellular Optimization

After receiving a large volume of measurement results, they are processed using specialized software. The Modern Communication Technologies company uses the products Anite Nemo Analyzer, Actix, Ascom Tems Discovery. All programs allow maximum visualization of information in the report, thanks to which the operator receives a clear picture of the network status.

Technical optimization of communication networks allows the operator company to use available resources more efficiently, reduce system support costs, and solve many internal problems. In addition, the quality of the services provided is noticeably improved, which makes it possible to attract new subscribers and retain existing ones.

The quality of operation of information transmission systems is characterized by a combination of a large number of indicators, the main of which are noise immunity, speed, throughput, range, electromagnetic compatibility, weight and dimensions of equipment, cost, and environmental compatibility.

The set of system quality indicators can be written as a vector

The best (optimal) system is considered to be one that corresponds to the largest (smallest) value of a certain function

from private quality indicators The value is called efficiency or a general indicator of the quality of the system, and the function is the target function of the system (quality criterion).

One of the central points of the methodology for optimal design or comparison of systems is the formation of efficiency assessments - the target functions of the system. Such assessments are absolutely necessary in system studies related to tasks such as choice better system from among existing ones, assessing the level of system development in relation to modern world standards, determining the optimal version of a new (designed) system, etc.

In the simplest cases, the effectiveness of systems is assessed by individual most significant parameters, for example, speed, channel bandwidth, signal-to-noise ratio, etc.

In general, a systematic approach is required, in which efficiency is assessed as a whole based on a set of parameters. In this case, first of all, it is necessary to take into account all the most essential parameters of the systems. The desire to take into account all parameters, including small and secondary ones, leads to complication of the target function (quality criterion) and makes the assessment results difficult to see. At the same time, excessively limiting the number of parameters taken into account can lead to the criterion being too rough.

Any assessment of the effectiveness of systems is carried out with the aim of adopting definite decision. Thus, during design it is necessary to determine the set of system parameters at which the greatest efficiency is achieved.

Quantitative assessment of effectiveness must satisfy certain requirements. It must sufficiently fully characterize the system as a whole and have a clear physical meaning and have the necessary flexibility and versatility. Evaluation of the effectiveness of the system should be

constructive - suitable for both analysis and synthesis of systems. Finally, the performance measure must be simple enough to calculate and easy to use in practice. A common method is to evaluate efficiency in the form of a linear function

where is the number of parameters (indicators) taken into account; weighting coefficients; - relative values ​​of the parameters taken into account.

With this definition of the parameters included in the sum (11.27), the value can be determined in the range from 0 to 1. The best system will be the one for which the value is larger.

The choice of weighting coefficients X is, to a certain extent, arbitrary. The same applies to the number of parameters taken into account. However, the share of arbitrariness can be brought to a minimum by developing a rational methodology for finding these coefficients (for example, the methodology of expert assessments). The absolute values ​​of the weights are not important; Only the relative weights are significant.

Modern complex communication systems cannot always be comprehensively characterized by one single indicator. An assessment based on several indicators can be more complete and at the same time more substantive, allowing one to characterize various properties of the system. It is clear that a large number of indicators is unacceptable. It is necessary to have several indicators characterizing the main most essential properties of the system: informational, technical, economic, etc. In many cases, it is enough to limit ourselves to two indicators, for example, noise immunity and transmission speed, frequency and energy efficiency, technical effect and costs.

The final decision, as a rule, is based not only on quantitative calculation data, but also on experience, intuition and other heuristic categories, as well as on additional considerations that could not be taken into account when constructing a mathematical model.

In the general case, the problem of optimizing the SPI is reduced to finding the maximum of the objective function when the system (its structure or the values ​​of its parameters) varies, taking into account the initial data and restrictions on the structure and parameters of the system.

If the objective function is given and the set of admissible systems (or their variants) is determined, then optimization is reduced to the problem of discrete selection from a finite number given systems, i.e. to choosing a system that corresponds to the largest (smallest) of the values

A more complex task is the problem of optimizing (synthesis) the structure of the system. If the structure of the system can be described quite completely by known functions with a finite number of parameters, then the problem comes down to optimizing these parameters. In the special case when the objective function and all functions that define the constraints linearly depend on the parameters, the problem is reduced to linear programming. In some

In some cases, it is possible to solve the problem analytically based on the methods of functional analysis.

In general, solving the SPI optimization problem may turn out to be complex and unsuitable for decision making. Therefore, they usually resort to a step-by-step optimization procedure. First, for example, optimization is carried out according to information parameters, and then - according to technical and economic indicators. At the first stage, it is determined block diagram system, allowing you to evaluate its main potential characteristics, select modulation and coding methods, and a method of signal processing in the receiver. Then the operating algorithms and parameters of individual system blocks (modem, channel codec, source codec, etc.) are determined. The final stage is the design of the system.

Technical and economic analysis is based on at least two indicators: effect and costs. At the same time, the main principles for determining the effectiveness of SPI can be the principle of maximum effect or the principle of minimum costs

Costs are usually taken as the given annual costs per unit of production (in our case, the cost of transmitting one bit per second).

SPI optimization.

The useful effect (product) in SPI is the amount of information delivered to the consumer per unit of time (transmission speed) for a given transmission fidelity, i.e. the average transmission speed over a channel in a communication network with the probability of an error. This speed is usually called the system capacity and is denoted in contrast to the Shannon channel capacity C. If C is a theoretical concept that characterizes the limiting capabilities of the channel, that is, a technical characteristic that depends on real characteristics and equipment of this system.

By definition

Here is the number of bits of information transmitted over a channel in a communication network during the time where is the transmission time (duration) of the message; delay time, including waiting time; source codec efficiency, message (source) redundancy, channel efficiency calculated taking into account the correction code, type of modulation and losses in the channel, channel codec efficiency, - modulation efficiency, network efficiency.

Taking into account expressions (11.4) and (11.28) we have

where according to (11.23) and (11.24)

With; - This is what real quantity information that is delivered to the consumer per unit of time at a given transmission quality

When optimizing the SPI, expression (11.29) for can be taken as the objective function. Then the task will be to find a communication system that delivers the maximum of this function under given conditions and restrictions. Mathematically, this is a nonlinear problem, and in some cases linear programming. In some special cases, the problem is solved analytically, as a problem of searching for the extremum of a functional. In cases where it is necessary to ensure a given sufficiently high value, the choice of system is carried out by analysis (comparison) possible options, satisfying the specified requirements. The required value of C in these cases is achieved by a compromise choice of indicators included in expression (11.29), taking into account technical and economic requirements.

The problem of optimizing SPI arises both when developing new and when improving existing systems. In many cases, it is posed as a task to increase the effectiveness of the SPI. The solution to such a problem is not unambiguous. A high (or necessary) value of C according to (11.29) can be achieved in various ways.

Let's consider this using the example of a discrete message transmission system (SDTS). We will assume that the communication network in which the SPDS in question must operate is known (its efficiency is specified. The source of messages is usually also known (its redundancy is specified. The required fidelity (error) of transmission rpop is also specified.

Bandwidth channel C is information resource systems. It is usually specified or selected based on existing standards. There are options here when choosing. According to Shannon's formula, the value is completely determined by the energy resource and frequency resource. The choice of channel frequency band is very limited and is regulated by international agreements. As for the energy resource, it depends on the power of the transmitter and the noise temperature of the receiver, and in radio systems, on the antenna gain O. where A is a constant coefficient. This implies the possibility of varying the values ​​to obtain the required value of C. Thus, the use of highly directional antennas can significantly improve the channel energy for a given transmitter and receiver.

With the selected value of C and given values, increasing the efficiency of the SPI is reduced to increasing the efficiency of the channel. According to (11.4), information efficiency depends on the energy efficiency and specific speed y, which can be calculated using formulas (11.8) and (11.9). Then, for a given error probability and the calculated value of channel energy using exchange nomograms (Fig. 11.6), you can choose the type of modulation and coding method.

Fundamentals of optimization of information transmission systems, selection and principles of signal generation.

For radio channels with limited frequency and energy resources, the most important task is to use these resources effectively. This means to provide maximum speed transmission of information from the message source when given parameters resource and reliability of message transmission.

In the modern theory of information transmission systems, it is customary to first optimize the communication system as a whole. Then the remaining elements of the system, in particular the receiver, are optimized, provided that the type of signals has already been selected.

When optimizing the system, we look for best view signal for a given radio channel and the corresponding optimal reception method.

“The founder of optimization of communication systems in general is K. Shannon, who proved the theorem:

“If a communication channel with a finite frequency response and additive white Gaussian noise (AWGN) has a capacity of “C”, and the source performance is equal to H′(A), then when H′(A) ≤ C, coding is possible that ensures the transmission of messages over this channel with arbitrarily small errors and at a speed arbitrarily close to the “C” value:

[bit/s], (3.1)

Where ∆f k– bandwidth of the rectangular frequency response of the communication channel;

R s- average signal power;

R w =N 0· ∆f k; (3.2)

N 0· - one-sided spectral density of AWGN.

For a discrete channel and random source encoding, this theorem can be written in a different form

where is the average probability of decoding error over a set of codes;

T- duration of the code block of the enlarged message source.

Since, [С−Н ′ (А) ≥ 0] according to the conditions of the theorem, then with increasing T(by enlarging the source) and at H ′ (A)→C the value T→∞ and the decoding delay of the enlarged source code increases.

From (3.3) we can conclude conclusions:

- the longer the encoded message segment (T) and the less efficient

the channel capacity is used (the larger the difference [C-H ′ (A)]), the higher the reliability of the connection (1-);

- there is a possibility of exchange between the efficiency of use, the values ​​of C, and T (decoding delay).

a) Let us analyze the capacity (3.1).

"C" can be increased by increasing ∆f k And R s. It must be taken into account that the power R w(3.2) also depends on ∆f k.

Based on the known relationship (at α=2, β = e) can be written down

Let's find the limit value depending on the band ∆f k and build a throughput graph.

At ∆f k→∞ . Then we expand the function log(1+x) in the Maclaurin series (i.e. at the point X=0) , which at x→0 equals ln(1+x)≈x. As a result we get

Let's plot function (3.4) depending on ∆f k with normalization on both axes N 0 /P c.

Fig.3.1. Graph of normalized channel capacity.

At R s / R w=1 in (3.1) → WITH= ∆f k. Taking into account the normalization along the axes of the graph, this equality corresponds to the point (C N 0 /P c =P w/P c=1) with coordinates (1,1).

The throughput increases noticeably with increasing ∆f k until P s /P w ≥1 and tends to the limit of 1.44 R s /N 0, i.e. maximum value parameter C occurs as h →0.

b) Let's find Shannon's boundary values ​​for specific bandwidth and energy costs at the information transmission rateR max = C .

By definition, the specific bandwidth costs in the communication channel are equal to

where R is the information transmission rate (bit/s) in the channel. Attempts to reduce these specific costs are associated with additional energy costs, characterized by the value of specific energy costs

Where E b- energy spent on transmitting 1 bit of information;

T 0- time of transmission of 1 bit over the communication channel (duration of the channel symbol T ks);

Let's find the dependence of specific energy costs on specific strip costs. To do this, we express the quantities included in (3.1), assuming WITH=Rmax :

Substituting these values ​​into (3.1) and dividing it by WITH we get

Based on the definition of logarithm log 2 N=a meaning N=2a can be written from where, taking the root of both sides, we get

As a result expression

determines the relationship between specific energy and bandwidth consumption in a channel with AWGN and finite frequency response. At the same time, because

then from (3.5) we obtain the dependence for the signal-to-noise ratio (SNR):

Thus, in a communication channel with a finite frequency response and AWGN, it is possible to implement an infinite number of different optimal systems. Spectrally efficient systems (baseband spectrum of the baseband signal) require a correspondingly increased SNR. Energy efficient systems require low SNR but must be wideband.

Real systems have values ​​that lie on the graph in Fig. 3.2 above the Shannon limits. Comparing real systems with potential ones, it is possible to evaluate the reserve for improving the parameters of the communication system.

Alexey Ukolov - about what tricks mobile operators use and how small businesses can save on telephone calls

Mobile operators are constantly introducing new tariffs to the market. And understanding them can be quite difficult. Using the Tariffer service, subscribers can compare their current tariff with other offers on the market and choose the best option for themselves. If for individuals the benefit from switching to another tariff can amount to several hundred rubles per month, then the savings for large companies can amount to hundreds of thousands of rubles. About what tricks there are mobile operators and how to track communication expenses in real time, Alexey Ukolov, the founder of the Tariffer service, told the portal site.

35 years old, entrepreneur from Samara, founder and CEO of the service "Tarifer"(selection of optimal tariff plans cellular communications). Education: International Market Institute (Faculty of Economics and Management). The Tariffer service was launched in 2007. In 2008, the service received the Best Soft award, and in 2009, the Microsoft Business Start award.

Looking for the best offer

The idea for the Tariffer service came from Alexey Ukolov’s brother, Dmitry, in 2007. At that time, he worked as a programmer in one of the Samara companies. By this time, Alexey himself had experience in various projects in the field of trade - not always successful. After discussing the idea, the brothers decided to make trial version a service that would analyze the details of calls and select the most suitable tariff for a particular person.

“The idea was on the surface. Many people had problems choosing a tariff. At that moment the market was quite wild. Then there was a real leapfrog with tariffs. There were both per-minute and per-second rates. Some tariffs included some kind of packages. There was also a connection fee. In general, there were many nuances that needed to be taken into account and which were difficult for many people to understand,” says Alexey Ukolov.

In the new project, Dmitry took on programming, and Alexey took on all other issues. In 2008, another partner joined the project - Kirill Nasedkin. He had his own web design studio and took over the development of the site.

Creating a test version of the service took several months. And we spent several months creating the website. In 2008, a year and a half after work on the project began, the first version of Tarifer.ru was launched. And at the end of the same year, the site was redone, and came to a version similar to the current one.

The company has competitors in Russia, but they are few. They manually or semi-manually help clients analyze their costs and select new tariff nal plan. “Our advantage over them is in technology, in this regard we are much stronger,” says Alexey Ukolov.

We live on our own

The founding fathers launched and developed their project exclusively with their own funds - they never attracted borrowed money. About a million rubles were spent on developing a website prototype. The service brought in its first revenue in 2009, and in 2010 “Tarifer” reached payback.

The growth of the project was facilitated by the victory in the Microsoft Business Start competition for Russian startups in 2009. For the victory, “Tarifer” received a grant of 1 million rubles. He allowed, among other things, to hire the first employees. A programmer and a technical support specialist came to the company to work with the tariff plan database.

The addition of new employees to the team accelerated the development of the service. At that time, the founders of the company decided to focus on the corporate market, as it was more promising for their type of activity. Towards the end of 2009, Tarifer acquired its first corporate clients.

Finding them was not difficult, since the company set a fairly low price for its corporate product - several thousand rubles, regardless of the size of the client company and the number of SIM cards being “calculated”. But it soon became clear to the founders of the project that with such a pricing policy it was simply unprofitable to work with large companies, and the price list was revised. Naturally, selling became more difficult, and in 2011 the company hired a sales manager. Previously, these functions were performed by Alexey Ukolov himself, and, as he himself admits, he did not always have enough time for this. With the arrival of the new manager, sales increased significantly.

"Tariff" for individuals

Private clients can independently choose the most favorable mobile communication tariff for themselves. The client must enter his phone number and password from personal account cellular operator. If a person does not know this password, the program will help him “enter the account” - you just need to follow the instructions. The service “downloads” call details for the month and analyzes it. Based on the analysis, the most favorable tariffs are recommended to the client - both from “our own” and “foreign” operators.

The database of tariff plans is supplemented and updated daily. It includes both federal and all regional tariffs of the “Big Four” operators: Beeline, MTS, Megafon and Tele2.

“Operators are constantly introducing new tariffs. And we regularly make improvements to the calculation algorithm, changing something all the time. Now the main trend is the transition to package tariffs. In them, in addition to the actual telephone communication Internet use is included under certain conditions. And we have “sharpened” all our tools to work effectively with package tariffs,” says Alexey.

Corporate program

Corporate clients of Tarifer can choose one of two programs for using the service. The first is cost analysis. The program determines in which areas communication costs are higher than the company average. A company's telephone costs are broken down by employee, department, and cost source.

The second program is directly optimizing costs, that is, selecting the most favorable tariffs. The program analyzes all client numbers and selects the most advantageous offer for each number.

When working with companies, Tarifer uses two schemes. The company can purchase from Tarifer the necessary software, and continue to do everything yourself. A company employee working with this program will have to upload call details into it, build reports and select tariff plans. For example, this option is used if corporate security rules do not allow transferring data to third parties.

But most convenient for corporate clients An option for cooperation is to delegate all functions of analyzing communication costs and selecting tariffs to the Tariffer service. In this case, the client simply sends all the bills for communication, and then the company’s specialists work with them.

The program can also work with individual tariff plans, which many corporate clients have. These tariffs are “non-public”, that is, they are not presented on the operator’s websites. The customer provides a description of their pricing plan, and the terms of that plan are added to that specific client's program. The customer can see this option in his program and use it in calculations.

One of the main difficulties when working with corporations is the need to download call details from the personal account of your telecom operator. Tarifer specialists are now working to collect all data from operators’ personal accounts automatically. Then the client will only need to provide his login and password for his personal account on the operator’s website.

Online monitoring

Until recently, tariff selection schemes were aimed at analyzing communication costs that had already taken place over the past month. But in the near future, Tarifer is launching another technology for corporate clients - monitoring. It allows the client to track and adjust costs in real time.

New technology allows you to see how much employees spent on mobile communications and internet in current month to this minute, and how much they are spending at the moment. If the program “notices” that communication costs have increased sharply, it sends an SMS alert to the client.

The service is needed primarily to prevent unplanned communication costs, especially in roaming. For example, a person forgot to turn off the Internet. In roaming, the subscriber may even practically not use the Internet or communications. But thanks to rounding rules and certain operator tricks, he will receive an unexpectedly large bill at the end of the month. And since the company has a common balance for all numbers, this may be discovered late. And the communication bill will be an unpleasant surprise for the company.

It is not always possible to shift these costs to the employee for legal reasons. Therefore, companies often end up with hundreds of thousands of rubles due to the fact that someone forgot to turn off the Internet while roaming or used some services incorrectly.

“The director of the company that we are currently servicing under our monitoring program has gone abroad. And there I used the Internet, after which a bill for 160,000 rubles came to this number. The company is not very large, and this amount is significant for them. Unfortunately, at that time this client did not have a monitoring program connected. And they did not understand where such a sum for communication came from. Now they can see in real time the reason for increased costs and prevent them in time,” Alexey gives an example.

Data on sharply increased expenses enters the Tarifera system 15 minutes after the client begins to overspend. It takes about 10 more minutes to react to the situation and inform the client about it. A message about expenses can be sent to both the embezzler himself and his company. This way, the customer can see and stop unscheduled communication charges within half an hour after they started.

The service began working in test mode two months ago. Currently, Tarifer serves about 50 companies under this program, and the first reviews of the service are the most positive.

Clients

Tarifer considers companies with 30 or more employees as corporate clients. If the company has no more than 15-20 numbers, then all calculations can be done manually in a few hours. With a number of 30 numbers or more, the volume of data is already quite serious. And the company already needs to make a decision: either it allocates a specialist to work with them, or attracts an “outside” contractor.

In total, Tarifer has about 400 corporate users. Each company has from 50 to 5000 people. From the top 10 largest Russian companies four use the services of “Tarifer”.

The number of private clients who have used the service since its founding is about two hundred thousand people. Now the service website receives about 200-300 orders per day from individuals.

If we are talking about an average company of 100 people spending 500 rubles per month on one telephone number, then its savings after calculating the “Tariff” can amount to about 10-15 thousand rubles per month.

But the amount of savings largely depends on the structure and type of activity of the company. If this is a trading company whose representatives spend a lot of time in the regions and use roaming, then their communication costs are many times higher. And then her savings from working with Tarifera solutions are several times greater than those of other companies.

Tarifer employees periodically call their clients to find out their opinions about the service. “I myself from time to time take a random list of clients, call and ask them what can be improved and corrected in the service. In particular, thanks to such communication, we now have a “real-time monitoring” service,” says Alexey Ukolov.

Prices for services

For individuals service for a long time was free. But now a fee has been introduced for the service - and calculate optimal tariff Anyone can do it for 140 rubles. The website has a tariff plan calculator into which you can enter all the parameters of your communication use. The average savings for private clients after using the service and switching to a new tariff is 37%.

Payment occurs after all calculations and report preparation. However, if during the calculation it turns out that the client’s current tariff is the most profitable, Tariffer does not charge him and all analytics are provided to the client as a bonus.

Users who are well versed in the variety of tariff plans can choose a tariff for themselves for free. Posted on the website in open access full base all current offers of the “Big Four” operators (including regional tariffs).

The cost of servicing companies depends on the number of their employees and the required functionality. The price varies from 10 to 20 rubles per month for each company SIM card, depending on the services included in the service (just cost analysis or analysis + selection of new tariff plans).

"Pitfalls"

Any business built on working with the corporate sector faces the problem of coordination within client companies. In large structures, the decision-making system is usually multi-stage. It happens that negotiations with some large companies lasts up to several years.

In addition, not all companies have an urgent need for “tariff” savings. For many companies, communication costs are a small expense item compared to the overall budget. And many managers simply do not want to spend time seriously studying the issue of corporate tariffs.

But even in those companies where the issue of reducing communication costs is acute, not everyone is happy with Tariffer’s offers. The employee responsible for corporate mobile communications is not always interested in saving the company money. Therefore, the task of Tarifer managers is, whenever possible, to contact the top officials of companies interested in saving.

Some existing Tarifer clients almost never use the tariff selection function. It is important for them that the data on corporate communications were sorted. And the service helps them store information about all calls that were made with corporate numbers. Among these clients there are many branches of Western corporations.

Operator Tricks

Tarifer strives for its customers to pay less for communications. The task of mobile operators is exactly the opposite - to increase fees from customers. To achieve this, the Big Four have many tricks and tricks, one of the main ones being “archive tariff plans”.

The meaning of the trick is quite simple. The client chooses a tariff plan, connects and uses it. After some time, the cellular company sends this plan “to the archive.” Moreover, the operator may still have current tariff with exactly the same name. For example, three years ago the client connected to the “July” tariff. Now the operator has a tariff with exactly the same name, but with different conditions. And the tariff, which the client has been using for 3 years, has long been archived, and now it is called “July-2013”.

The benefit for the operator is that the “archive” tariff is usually made more expensive than the current tariff plan. In any subscription service agreement it is written that the operator has the right to change the terms of the tariff without informing the client about it. The subscriber can see among the operator company's offers a tariff with the same name as his own. But in fact, this is no longer his tariff, and he is served on the archived version, which is most likely less profitable.

“We just recently dealt with such a case. The client said that we recommended his own tariff, and at the same time promised savings. We began to look into it, and it turned out that he was on the archived version of the same tariff, which included a much smaller package of services. There are not enough services there - and the client overpays a lot of money, because the tariff is actually not the same. So if a subscriber has been served on the same tariff for several years, it makes sense for him to check if there is now a more profitable option,” recommends Alexey.

When a new operator enters the market, it often attracts users with low prices. At the same time, other operators are forced to adapt, reduce prices, and thus the situation in the cellular communications market is changing. But, having gained a foothold in the market, newcomers usually begin to gradually raise prices, and the overall market situation returns to its original state.

Now the strategy of operators entering new markets can be seen in the example of Tele2. Around the moment when this operator began to conquer Moscow with low tariffs, prices in the regions began to rise.

“Another feature of Tele2 is that they actually have low “front-end” tariffs, that is, numbers that the client pays attention to. But on various kinds“additional services” (long-distance, roaming, etc.) their prices are no longer the most favorable,” Alexey reveals his secrets.

Promotion

Since Tarifer has two different audiences - private and corporate, there are also two websites. Services for individuals are posted - a tariff calculator and the all-Russian tariff base. A is the main website of the project, which contains both corporate solutions and links to tarifer.net.

Over time, tarifer.ru will become a site only for corporate users, and tarifer.net - for private users. The website tarifer.ru is currently under redesign. His new version scheduled to launch at the end of August.

The founders of Tarifer chose direct sales as the main way to promote their services. Managers contact potential clients through cold calling. The company has a “two-level” sales department. “First-level” managers work like a call center. Their task is to call and initially communicate with the client. If the client shows interest, he is transferred to a more professional sales manager.

Team

In total, the Tarifer company employs about 30 people. The “head” office and developers are located in Samara, and the company has a sales office in Moscow. 10 people make up the sales department, the rest are developers, administrative staff and technical support staff.

Technical support receives calls and requests from customers. In addition, she has a large scope of work related to data updating. This is support and replenishment of the base of tariff plans, support of the base telephone numbers and recognition of all billing detail formats that are possible for operators.

Despite the impressive work experience, the project has not yet acquired its own mobile application. It is planned to be made in two versions: for individuals and for corporate clients.

Most current plan for the coming months – further development of the monitoring service (tracking and adjustment of expenses in real time). While it can be used in a test version, its “commercial” launch will begin in the fall.

“We are planning a serious development of this service so that it also tracks the balances of current service packages. Gradually, we will reduce all our services to one interface, everything will be built on the basis of monitoring,” sums up Alexey Ukolov.