of Korea Sungho Jeon, Seong-Man Min, Dawoon Chung, Kangsoo Kim, Jahoon Ku, and Kyungbok Lee July 19, 2023 (Session 2) Current & Future Korean BroadcastingSystem | Department of MediaTransmission Global Digital Terrestrial Transmission Workshop 2023 @ Busan, South Korea
Second-generation terrestrial broadcasting system First-generation terrestrial broadcasting system + → 1TV Simulcast 2TV Simulcast Visual 2FM EWS Emergency Warning Service TPEG Traffic information 9-3 Visual 1Radio (suspended) 9-1 KBS1 UHD 9-2 KBS disaster-focused channel IBB Interactive Service Visual 2FM KBS NEWS24 Ultra HD + Interactive Mobile HD/MMS/Datacasting AEAT Emergency Alert Service By deploying only one ATSC 3.0 system, various services are available DATA 7-1 KBS2 UHD AEAT Emergency Alert Service DATA
MBC, 지역민방 Seoul Daejeon Jeonju Gwangju Cheongju Busan Ulsan (2020.12.) UHD broadcasting network expansion in progress according to new Korean government policy plan ATSC 1.0 DTV switch-off 2020 2019 2021 2027 2023 2022 UHD innovation service (multi-channel/mobile/interactive) start 2017 World's first ATSC 3.0 terrestrial UHD broadcasting started 2017.05. 2017.12. 2017.12. 2017.12. 2017.12. Daegu 2017.12. 2023. Q3. 2023. Q3. Jeju 2022.02. Continuous expansion of service area Changwon Chuncheon 2024. Q1. 2024. Q1.
Note that with the advent of advanced 2nd generation transmission and modulations systems an additional block is to introduced between service multiplex and transport, the so-called Gateway. 1st Generation Terrestrial Broadcasting System Exciter Exciter 2nd Generation Terrestrial Broadcasting System ITU-R Rec. BT.1877 ITU-R Rec. BT.1306
ATSC3.0 Exciter GPS ATSC3.0 Exciter GPS Broadcast Gateway PTP ✓ SFN requires all devices to “use (synchronize) the same clock” based on GPS signal or PTP time. ✓ All transmitters must parse the Timing Packet and Preamble Packet among the input signals and set the transmitter to the same value. = Transmitter must be set by using STL Interface Transmission parameters are not set in individual transmitters ∴ All transmission parameter settings are ONLY on Broadcast Gateway! GPS ATSC3.0 Exciter SFN Condition #2 Same Time SFN Condition #1 Same Data SFN Condition #3 Same Frequency * PTP = IEEE1588v2 PTP(Precision Time Protocol) ATSC 3.0 Single Frequency Network
(B) Exciter (A) Exciter (B) TACU ATSC 3.0 Television Master control room Broadband IP Network (Point A) STLTP Microwave L2/L3 Switch (A) L2/L3 Switch (B) Remote device synchronized by PTP RF On-air (Point A) STLTP (Point B) RF Remote device synchronized by GPS PTP GPS Transmission site Central server collecting data JSON over VPN JSON over VPN ATSC 3.0 measuring Points A(STLTP), and B(RF) and corresponding inputs to the monitoring device
te":"4x8","stltp_ip":"239.255.9.30","txid_seed":"1", "network_delay_min":"4.8","stltp_port":5000,"network _delay_now":"9.2","max_net_delay":"599.8","packet_dr op_count":0,"wakeup_bit":"00","stltp_bitrate":184104 10,"l1d_bsid":1041,"l1d_version":1,"network_delay_ma x":"23.0","stl_fec_repair_count":0,"txid_injection_l vl":"21.0","txid_group":0}}} Remote monitoring device transmits measurement values in JSON format every second DB Server WEB Server ✓ Data collection and system integration ✓ Analysis of collected monitoring data Example of JSON specification for collecting data from central data collection server configuration and remote monitoring device
devices Dashboard displaying data collected every second by a central server (Point A) STLTP network_delay_now (Point A) STLTP packet_drop_count stl_fec_repair_count (Point B) RF x: rf_rssi, y: rf_mer (Point B) RF rf_status
type Meric Failure phenomenon (1) Mismatch stltp_ip stltp_port l1d_bsid l1d_version max_net_delay txid_seed txid_injection_lvl wakeup_bit stl_fec_state rf_frequency A matching issue between the BGW output multicast stream and the transmitter is occurring -> Not a normal transmission/reception situation (2) Event of interest packet_drop_count This is a situation in which data for RF signal generation cannot be normally received, and RF MUTE occurs when the amount of occurrence is high. stl_fec_repair_count The number of recovered packets does not affect actual broadcasting, but it can be confirmed that instability exists on the current transmission link. rf_fer This is the number of received error packets that remain even after all error corrections have passed. If a value other than 0 is observed, viewing inconveniences such as broken screens occur on the TV set.
type Meric Failure phenomenon (3) Concept drift network_delay_max network_delay_min This is an item representing the maximum or minimum value within the last 24 hours. It continues to be constant and then detects a change in value. In particular, whether the maximum value exceeds the MND is directly related to the occurrence of the SFN failure situation. (4) Point outlier stltp_bitrate In case of exceeding the maximum, Network Overflow -> a large amount of packet CRC errors -> RF MUTE occurs when packet drops exceed the allowable value If the minimum is not reached, If the BGW output is muted (or if the encoding of a specific channel is abnormal). rf_rssi The number of recovered packets does not affect actual broadcasting, but it can be confirmed that instability exists on the current transmission link. rf_mer This is the number of received error packets that remain even after all error corrections have passed. If a value other than 0 is observed, viewing inconveniences such as broken screens occur on the TV set.
+3σ μ Stltp_bitrate [Mbps] 30 29 25 STLTP output bit rate @ BGW AVG = 26.958 Mbps STD = 0.156 Mbps Capacity limit Anomaly Detection Framework for Time-Series Data based on 3-Sigma Rule The 3-sigma rule can be applied to detect anomaly situations that fall outside of 99.7% range. 1) stltp_bitrate:
μ Outlier +3σ -3σ μ mean AVG = 16.458 ms STD = 0.670ms Anomaly Detection Framework for Time-Series Data based on 3-Sigma Rule The 3-sigma rule can be applied to detect anomaly situations that fall outside of 99.7% range. 2) network_delay_now: +6σ = 4.02ms -6σ
Framework based on Mahalanobis Distance by combining two highly correlated metrics By transforming highly correlated time-series data (left) into a single plane (right) and applying Mahalanobis distance, various outlier points can be more clearly detected. A screen shot of a real-time monitoring system displaying data from Dashboard #2 (Case Study) Constant rf_rssi values, Decreasing rf_mer values
conditions and conducting real-world validation. International Journal of Data Science and Analytics (2021) 12:297–331 https://doi.org/10.1007/s41060-021-00265-1