Constructing Radio Environment Map Model for a Cognitive Communication System Based on LTE YahorAdamovskiy e.adamovsky@psu.by Polotsk State University
Blohina St., 29 211440 Novopolotsk Republic of Belarus
RykhardBohush r.bogush@psu.by Polotsk State University
Blohina St., 29 211440 Novopolotsk Republic of Belarus
ValeryChertkov v.chertkov@psu.by Polotsk State University
Blohina St., 29 211440 Novopolotsk Republic of Belarus
Constructing Radio Environment Map Model for a Cognitive Communication System Based on LTE FB230315B90A54CDD62857B09E321118 GROBID - A machine learning software for extracting information from scholarly documents Radio environment map Long-Term Evolution imitation model

The telecommunication systems actual development direction is the use of cognitive radio technology and dynamic spectrum access to solve the spectrum scarcity problem. Storage and processing of spatiotemporal data should be carried out using a radio environment map (REM). A two-level REM generation model for the LTE system and an algorithm for implementing the model are presented. A method for calculating REM at a grid node is shown, taking into account losses during signal propagation. The software implementation of the model was made using the MatLab package. Approaches to increasing the speed of its operation are described. The results of the REM generation software implementation algorithm are demonstrated, which confirm the correctness of the developed model.
Introduction

The extension of telecommunication systems is the problem of the spectrum scarcity [1]. Primary Users (PUs) use dedicated frequencies, but with normal traffic only partial spectrum usage. Currently, the radio frequency spectrum is distributed among various services, so solving the problem spectrum scarcity is an actual direction in the communication systems development.

It is proposed to use the technology of Dynamic Spectrum Access (DSA) [2] to solve the spectrum scarcity problem. The licensed frequency band is opened to Secondary Users (SU) at times when channels are not used by PUs.

The Cognitive Radio (CR) is used to implement DSA. The CR device can perceive radio environment and adapt the communication parameters using previous experience. The main task of CR is to identify white spaces in the spectrum for the use of free frequencies. The white spaces configuration can be predicted based on radio environment probe. The obtained data about radio environment can improve the CR system efficiency. At the moment, there are communication systems implementations uses cognitive radio conception [3,4].

For storage and processing of CR data, a radio environment map (REM) is used. REM is a spatiotemporal database of all network radio activities [5]. The REM task is the constant information exchange with cognitive devices. REM construction is carried out by direct and indirect methods [2]. REM construction basic principles and its structure: storage and acquisition unit, REM manager, measurement capable devices [1,6].

The generated REM quality indicators can be quantified using RMSEthe difference between the power values of the calculated and actual REM levels [1]. The radio environment state is calculated and predicted based on the REM data. Using the software simulation model output, optimal REM parameters can be extracted.

The purpose of this paper is the REM database formation model implementation of the 4G LTE communication system for the state predicting possibility of the radio environment by a cognitive system.

REM model for LTE system

A model is proposed for generating information about the dynamics of a frequency resource use in the REM form. The model implements the 4G LTE communication system functioning with some simplifications:

1. Signals propagate in an urban area, they contain only user information. 2. The radiation power of stations and devices does not change; radiation directivity is uniform. Frequency bands provided by the LTE TS 36.101 specification are licensed. Each band is characterized by transmission mode (frequency and time division, FDD and TDD), lower and upper frequencies. For downlink, Orthogonal Frequency-Division Multiplexing (OFDM) is used, and for uplink -Single-Carrier FDMA (SC-FDMA).

The bands are divided into carriers from 1.4 to 20 MHz, which corresponds to 6 to 100 resource blocks (RB). One RB is 180 kHz in 15 kHz steps and consists of resource elements (RE) or OFDM symbols.

In the simplified model, the frequency resource is divided into π‘“π‘Ÿπ‘’π‘ž_π‘›π‘’π‘š subcarriers, starting at π‘“π‘Ÿπ‘’π‘ž_π‘ π‘‘π‘Žπ‘Ÿπ‘‘ and stepping at π‘“π‘Ÿπ‘’π‘ž_𝑠𝑑𝑒𝑝 in the π‘“π‘Ÿπ‘’π‘ž_π‘π‘Žπ‘›π‘‘ (kHz) vector.

The signal attenuation depends on the frequency 𝑓 (MHz) and the distance to the source 𝑑 (km). The loss equation 𝐿 is used, it includes the coefficient 𝛼 and the constant 𝑙 0 [8], their possible values are given in Table 1.

𝐿(𝑑𝐡) = 𝑙 0 + 20 log 10 𝑓 + 𝛼 log 10 𝑑

(1) The simplified model uses a single attenuation factor π‘“π‘Ÿπ‘’π‘ž_π‘Žπ‘‘π‘‘ for the middle frequency of the modeled range.

A matrix of attenuation coefficients (π‘Žπ‘‘π‘‘) is formed (2) based on (1), the size of which is [(2𝑁 π‘₯ βˆ’ 1); (2𝑁 𝑦 βˆ’ 1)] elements, where the values 𝑁 π‘₯ and 𝑁 𝑦 are their number horizontally and vertically on REM in π‘”π‘Ÿπ‘–π‘‘_𝑠𝑖𝑧𝑒 with π‘”π‘Ÿπ‘–π‘‘_𝑠𝑑𝑒𝑝.

π‘Žπ‘‘π‘‘ π‘₯𝑦 = 10 𝑙 0 +π‘“π‘Ÿπ‘’π‘ž π‘Žπ‘‘π‘‘ +𝛼 log 10 √|𝑁 π‘₯ +1βˆ’π‘₯| 2 +|𝑁 𝑦 +1βˆ’π‘¦| 2 (2)

In practice, receivers have limited sensitivity and cannot receive signals that are too weak. In our model, the threshold noise level π‘π‘œπ‘€_π‘‘β„Žπ‘Ÿπ‘’π‘  is implemented.

The global level contains the initial data for the model presented in Table 2.

[2Γ—grid_size(1)-1] [2Γ—grid_size(2)-1] - [9.6Γ—10 -11 … 1.9Γ—10 -10 ]

Attenuation matrix eNodeB forms a cell with a local identifier. In the simplified model, each eNodeBS is assigned a unique π‘’π‘π‘œπ‘‘π‘’π΅π‘†. 𝑁𝐢𝑒𝑙𝑙𝐼𝐷 number and a string name π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘›π‘Žπ‘šπ‘’. An eNodeBS object is characterized by a position in space (two coordinates π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘π‘œπ‘ π‘–π‘‘π‘–π‘œπ‘›).

In the model, eNodeB can operate in a given frequency range (π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘“π‘Ÿπ‘’π‘ž_π‘π‘Žπ‘›π‘‘), which determines the operating frequencies from the π‘“π‘Ÿπ‘’π‘ž_π‘π‘Žπ‘›π‘‘ set.

Abonents are connected to the eNodeB and constantly keep in touch. Each UE is registered upon connection and is identified by a temporary data (M-TMSI, S-TMSI, GUTI) and a static global identifier (IMSI). In a simplified model, each eNodeB stores connected UEs identifiers π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘ˆπΈ_𝑅𝑁𝑇𝐼 and allocated resource blocks for downlink π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘ˆπΈ_𝐷𝐿_𝑅𝐡 and for uplink π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘ˆπΈ_π‘ˆπΏ_𝑅𝐡.

In the simplified model, the eNodeB emits with a constant power (π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘π‘œπ‘€π‘’π‘Ÿπ΅π‘†).

The eNodeB object parameters are presented in Table 3. The UE has a unique IMSI number assigned to its SIM card. In the simplified model, UEs are assigned sequence numbers π‘ˆπΈ. 𝑅𝑁𝑇𝐼 and names π‘ˆπΈ. π‘›π‘Žπ‘šπ‘’ by analogy with the eNodeB.

The UE searches for a communication channel with the best reception and connects to the station. Synchronization is carried out using the Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). For the uplink, the common PUSCH is used to transmit user data. The control channel PUCCH is transmitted independently and is used to transmit the channel quality indicator CQI, request to obtain a available resources schedule. The random access channel PRACH is used to request the communication initialization upon transition to the active mode.

In the simplified model, the UE must know the number of its own workstation π‘ˆπΈ. 𝑁𝐢𝑒𝑙𝑙𝐼𝐷 and its carrier frequency number π‘ˆπΈ. π‘“π‘Ÿπ‘’π‘ž_π‘π‘Žπ‘›π‘‘. Information about neighboring stations is contained in the π‘ˆπΈ. π‘π‘Žπ‘ π‘’. The device receives and stores information about the allocated resources in π‘ˆπΈ. 𝑅𝐡. The UE is in one of two states: idle mode and data transmission, parameter π‘ˆπΈ. π‘ π‘‘π‘Žπ‘‘π‘’π‘ .

Devices may change the π‘ˆπΈ. π‘π‘œπ‘ π‘–π‘‘π‘–π‘œπ‘› parameter over time. The movement schedule is set using an array π‘ˆπΈ. π‘‘π‘Ÿπ‘Žπ‘—π‘’π‘π‘‘π‘œπ‘Ÿπ‘¦ of coordinates with a time reference. The π‘ˆπΈ. π‘‘π‘Žπ‘‘π‘Žπ‘‡π‘Ÿπ‘Žπ‘›π‘ π‘“π‘’π‘Ÿ list data transfer sessions is formed with the start time and the session duration.

In the simplified model, the device radiation power is fixed by the parameter π‘ˆπΈ. π‘šπ‘Žπ‘₯π‘ƒπ‘œπ‘€π‘’π‘Ÿ. UE device parameters are presented in Table 4. The scheme and objects relationships in the simulation model are shown in Figure 1. PU devices (UE1) move on the grid, choose the nearest eNodeB for connection and use the allocated frequency resource. SU devices (SU1, SU2) for communication must determine the white spaces configuration by REM cells analyzing.

Model software implementation

The model is implemented in MatLab (version R2020b) according to the algorithm in Figure 2. The algorithm includes component initialization, UEs schedule generation, memory allocation for The 1800 Hz band is used because it is the most common for 4G networks. The transmission mode is FDD, the channel width is 75 MHz. To reduce computational complexity, 25 RB (5 MHz) are used each for the uplink and downlink, which corresponds to π‘“π‘Ÿπ‘’π‘ž_π‘›π‘’π‘š = 2002 subcarriers.

The used map size π‘”π‘Ÿπ‘–π‘‘_𝑠𝑖𝑧𝑒 is 20Γ—20 cells with a π‘”π‘Ÿπ‘–π‘‘_𝑠𝑑𝑒𝑝 = 250 m, which gives enough accuracy to calculate the UE movement between eNodeB cells. The distance between stations is 1 km. The number of simulated UEs may vary depending on the scenario.

Various behavior scenarios are assigned to the UEs, examples are presented in Table 5. An example of implemented schedules is shown in Figure 3. Figure 3(a) shows the movement key points of one of one UE during the day. Figure 3 At each iteration, the UE is checked and reconnected. The decision is made by the maximum signal level, which is calculated as the average value of the reference OFDM symbols [1]:

𝑃 π‘’π‘π‘œπ‘‘π‘’π΅ = βˆ‘ 𝑃 𝑅𝑆𝑅𝑃 𝑖 𝑁 𝑅𝑆𝑅𝑃 𝑖=1 𝑁 𝑅𝑆𝑅𝑃

where 𝑃 𝑅𝑆𝑅𝑃 𝑖 -OFDM reference symbol level, 𝑁 𝑅𝑆𝑅𝑃the reference OFDM symbol index in the signal from the eNodeB. The connection is carried out by distributing the eNodeB downlink resource (π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘ˆπΈ_𝐷𝐿_𝑅𝐡) between the connected UEs. The station has 𝑁 𝐷𝐿 = 250 RB for downlink, 25 RB per subframe. The eNodeB downlink resource is calculated by random permutation, function π‘Ÿπ‘Žπ‘›π‘‘π‘π‘’π‘Ÿπ‘š():

𝑅𝐡 𝐷𝐿 = π‘Ÿπ‘Žπ‘›π‘‘π‘π‘’π‘Ÿπ‘š(𝑁 𝐷𝐿 , 𝑁 𝐷𝐿_π‘ˆπΈ , 𝐢𝑁𝑇 π‘ˆπΈ )

where 𝑁 𝐷𝐿total resource blocks number (250 RB) for each eNodeB; 𝐢𝑁𝑇 π‘ˆπΈtotal UEs number; 𝑁 𝐷𝐿_π‘ˆπΈthe RB number allocated for each UE:

𝑁 𝐷𝐿_π‘ˆπΈ = [ 𝑁 𝐷𝐿 βˆ’1 𝐢𝑁𝑇 π‘ˆπΈ ]

where [β€’]the integer part. Each device is randomly allocated an equal number of RB, which depends on the connected UEs total number: where i -UE index.

The uplink resource distribution (π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘ˆπΈ_π‘ˆπΏ_𝑅𝐡) between the connected UEs is carried out differently, because a frequency diversity multiplexing scheme with transmission on a SC-FDMA is used. where 𝑅𝐡 π‘ˆπΏvector of RB numbers in subframe, which is formed similarly (4). Next, the resource grids generation for all objects is performed. Signals are placed on the REM in cells with the corresponding parent objects coordinates.

To generate eNodeB resource grids, the function of the LTE Waveform Generator package π‘™π‘‘π‘’π‘…π‘€πΆπ·πΏπ‘‡π‘œπ‘œπ‘™(), is used. The eNodeB configuration (Table 2) and information bits are transmitted to it, the data transmission status of each UE is taken into account. Figure 4 shows an example of a downlink resource grid.

Results

The presented model version includes 7 eNodeB and 30 UE objects. The stations form a hexagonal structure, as shown in Figure 5. The model formed a REM for 30 days, which is 259.2Γ—10 6 frames. As a result, 54246 files (145 GB) were created, the total operating time was 72 hours on a computer with an Intel Core i9-10900/2.80 GHz CPU, 64 GB RAM. Figure 6 analysis shows that near the eNodeB with many connected UEs the resource is limitedthere are fewer free subcarriers. Moving away from stations makes the spectrum freer, and the free frequencies number increases.

The generated data comparison in frames was performed to confirm the correctness of the model. The generated REM analysis in frame 100524000 is presented, the eNodeBs and UEs location is shown in Figure 7. Figure 7 shows three eNodeBs (B1, B2, B4). Station B1 occupies the second downlink band and the second uplink band, B2 occupies the third band, and B4 occupies the first band. All UEs do not transmit data and use one allocated RB in each direction. Figure 8 shows the REM cell state inside the labeled area in Figure 7 (coordinates (6, 10) -at the A22 location) in frame 100524000. The RMSE values were calculated to assess the generated REM quality: the difference between the estimated and actual eNodeB location (meters); the difference between the actual and expected signal level emitted by one UE (dBm) [2]. The average calculated value of RMSE parameters was 15.11 m and 7.83 dBm. The obtained RMSE values correspond to a sufficiently accurate REM for further application and are consistent with the results in [2,6].

Figure 1 :Figure 1: A cognitive system simulation model representation based on LTE
Figure 2 :Figure 2: REM generation algorithm
Figure 3 :(b) shows the device communication sessions. UE scenarios: position change according to the "Courier" scenario (a), sequence of sessions according to the "User" scenario (b)
π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘ˆπΈ_𝐷𝐿_𝑅𝐡(𝑖) = { 𝑅𝐡 𝐷𝐿 (𝑗), 𝑗 = 𝑁 𝐷𝐿_π‘ˆπΈ Γ— (𝑖 βˆ’ 1) + 1 ΠΏΡ€ΠΈ π‘ˆπΈ. π‘ π‘‘π‘Žπ‘‘π‘’π‘  = π‘“π‘Žπ‘™π‘ π‘’ 𝑅𝐡 𝐷𝐿 (𝑗), 𝑗 = 𝑁 𝐷𝐿_π‘ˆπΈ Γ— (𝑖 βˆ’ 1) + 1, 𝑁 𝐷𝐿_π‘ˆπΈ Γ— 𝑖 Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ… ΠΏΡ€ΠΈ π‘ˆπΈ. π‘ π‘‘π‘Žπ‘‘π‘’π‘  = π‘‘π‘Ÿπ‘’π‘’ , 𝑖 = 1, 𝐢𝑁𝑇 π‘ˆπΈ Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ… (6)
π‘’π‘π‘œπ‘‘π‘’π΅π‘†. π‘ˆπΈ_π‘ˆπΏ_𝑅𝐡(𝑖) = {𝑅𝐡 π‘ˆπΏ (𝑗), 𝑗 = 𝑁 π‘ˆπΏ_π‘ˆπΈ Γ— (𝑖 βˆ’ 1) + 1 ΠΏΡ€ΠΈ π‘ˆπΈ. π‘ π‘‘π‘Žπ‘‘π‘’π‘  = π‘“π‘Žπ‘™π‘ π‘’ 𝑅𝐡 π‘ˆπΏ (𝑗), 𝑗 = 𝑁 π‘ˆπΏ_π‘ˆπΈ Γ— (𝑖 βˆ’ 1) + 1, 𝑁 π‘ˆπΏ_π‘ˆπΈ Γ— 𝑖 Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ… ΠΏΡ€ΠΈ π‘ˆπΈ. π‘ π‘‘π‘Žπ‘‘π‘’π‘  = π‘‘π‘Ÿπ‘’π‘’ , 𝑖 = 1, 𝐢𝑁𝑇 π‘ˆπΈ Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…Μ…(7)
Figure 4 :Figure 4: eNodeB downlink resource grid
Figure 5 :Figure 6 :Figure 5: An example of the eNodeB and UE objects initial location on the map in the model
Figure 7 :Figure 7: Schematic location of eNodeB and UE objects on the REM and radio visibility area at the point (6, 10)
Figure 8 :Figure 8Figure 8: Radio environment between the UE and the workstation at the point (6, 10)
Table 1Possible parameter values for the signal loss equation (1)Coefficient 𝛼ParameterConstant 𝑙 0Description20.020.0Free space [8]40.227.7Suburban area [2]35.038.4Urban area [2]
Table 2Model global parametersParameterData typeUnitSample dataDescriptionfreq_num-2002Number of subcarriersfreq_startUint1720000Frequency band initial valuefreq_stepkHz15Subcarrier spacingfreq_banduint [freq_num]10 4 Γ— [172 … 183.5]List of LTE subcarriersfreq_att grid_sizeFloat Uint [2]-9.6Γ—10 -11 [20 20]Attenuation factor REM grid sizegrid_stepFloatm250.0REM grid steppow_thresFloatdBm-85.0Interference thresholdFloatattenuation
Table 3eNodeB object parameters for the model (local level)ParameterData typeUnitSample dataDescriptioneNodeBS.nameStringStation#1Station nameeNodeBS.NCellID eNodeBS.positionUint Uint [2]-1 [9 12]Station ID Station cell coordinateseNodeBS.freq_bandUint2Station band numbereNodeBS.powerBSFloatdBm43Station radiation powereNodeBS.UE_RNTIUint [𝑁 π‘ˆπΈ ][3 … 2]Connected UEseNodeBS.UE_UL_RBUint [𝑁 π‘ˆπΈ ] [ ]-[3…16] [2…11]Uplink RB array for UEseNodeBS.UE_DL_RBUint [𝑁 π‘ˆπΈ ] [ ][3…166] [2…241]Downlink RB array for UEs
Table 4UE object parameters for the model (local level)ParameterData typeUnitSample dataDescriptionUE.nameStringUE#1UE nameUE.RNTIUint1UE IDUE.position UE.NCellID UE.freq_bandUint [2] Uint-[7 1] 1 1Current UE cell coordinates Workstation ID Workstation band numberUE.RBUint [ ][3 … 13]RB numbers allocated by the workstationUE.maxPowerFloatdBm26UE radiation powerUE.status UE.baseBool Int [ ]-true [1 3 4]UE data transfer status List of nearest stationsUE.trajectory
(Int [3]) [ ] time-cell [0 9 2; …] Device movement schedule UE.dataTransfer (Int [2]) [ ] time-time [9 69; …] Device session schedule
Table 5Implemented movement and session scenariosScenarioDescriptionParameterValuesmodemStationary device--Move-mentWorkerA mobile phone moves twice between the points "home" and "work" on weekdays (5/2). Stays at home for weekends.ta -activity start tb -activity end [xh, yh] -"home" point coordinates [xw, yw] -"work" point coordinates0 … 20 h ta … 24 h [1..grid_size(1), 1..grid_size(2)]Mobile phone constantlyta -activity start0 … 20 hCouriermoving during working hours on weekdays (2/2). Stays at home for weekends.tb -activity end td -time between sessionsta … 24 h 100 … 1000 sThe device continuouslyta -activity start0 … 20 hHubtransmits data at a certain interval of time every day.tb -activity endta … 24 hSessionUserThe device performs sessions with variable intervals and durations.ta -activity start tb -activity end td -time between sessions tl -session duration0 … 20 h ta … 24 h 3 … 30 min 6 … 600 s
Conclusion

The paper presents the developed simulation model of REM generation for the LTE system. Model software implementation was made using the MatLab application package.

The results of the REM generation with a quality assessment are presented: the average RMSE values were 15.11 m and 7.83 dBm, these values suitable for further application. The one output file average size (1 frame) was 3 MB, each frame processing lasted about 1 ms.

The obtained results confirm the correctness of the developed model. The results of using the model is intended for use in the design and complex modeling of cognitive communication systems based on LTE.

Radio Environment Maps: The Survey of Construction Method MPesko 10.3837/tiis.2014.11.008 KSII Transactions on Internet and Information Systems 8 2014 Indirect Methods for Constructing Radio Environment Map SAlfattani AYongacoglu 10.1109/CCECE.2018.8447654 Proceedings of the IEEE Canadian Conference on Electrical & Computer Engineering the IEEE Canadian Conference on Electrical & Computer Engineering 2018 Working Group on Wireless Regional Area Networks 2019 802 TAdame 10.1109/MWC.2014.7000982 11AH: the WiFi approach for M2M communications IEEE Wireless Communications Magazine 2014 802 Cognitive radio technology BFette 2006 Elsevier Inc Radio environment maps for military cognitive networks: density of small-scale sensor network vs. map quality MSuchaΕ„ski 10.1186/s13638-020-01803-4 Cognitive Radio-Oriented Wireless Networks 189 2019 Radio Environment Map for the Cognitive Radio Network Simulator PBednarek DBicki JLopatka 10.24425/118145 International Journal of Electronics and Telecommunications 64 2018 Free Space Path Loss: details & calculator 2022 Spectrum Awareness for Cognitive Radios Supported by Radio Environment Maps: Zonal Approach PKaniewski 10.3390/app11072910 Applied Sciences 11 7 2021