TY - GEN
T1 - Sparse vector coding for ultra short packet transmission
AU - Ji, Hyoungju
AU - Kim, Sangtae
AU - Shim, Byonghyo
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIP)(2014R1A5A1011478).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/23
Y1 - 2018/10/23
N2 - Massive machine type communications (mMTC) and mission-critical MTC are new service categories in 5G to support Internet of Things (IoT). MTC-based services, such as sensing, metering, and monitoring requires small volume of information in most cases. Since the current data transmission principle requires long codeblock to maximize the coding gain and hence novel transmission mechanism to support short packet transmission is required. In this paper, we propose a new type of data transmission scheme suitable for the ultra short packet transmission, called sparse vector coding (SVC). Key idea behind the proposed technique is to transmit the information after the sparse transformation. By mapping the information into the sparse vector and then transmitting it after the non-orthogonal random spreading, we cast the symbol detection problem into the sparse signal recovery problem in compressed sensing. Through performance analysis and simulations, we show that SVC is very effective in short packet transmissions.
AB - Massive machine type communications (mMTC) and mission-critical MTC are new service categories in 5G to support Internet of Things (IoT). MTC-based services, such as sensing, metering, and monitoring requires small volume of information in most cases. Since the current data transmission principle requires long codeblock to maximize the coding gain and hence novel transmission mechanism to support short packet transmission is required. In this paper, we propose a new type of data transmission scheme suitable for the ultra short packet transmission, called sparse vector coding (SVC). Key idea behind the proposed technique is to transmit the information after the sparse transformation. By mapping the information into the sparse vector and then transmitting it after the non-orthogonal random spreading, we cast the symbol detection problem into the sparse signal recovery problem in compressed sensing. Through performance analysis and simulations, we show that SVC is very effective in short packet transmissions.
UR - http://www.scopus.com/inward/record.url?scp=85055001906&partnerID=8YFLogxK
U2 - 10.1109/ITA.2018.8503179
DO - 10.1109/ITA.2018.8503179
M3 - Conference contribution
AN - SCOPUS:85055001906
T3 - 2018 Information Theory and Applications Workshop, ITA 2018
BT - 2018 Information Theory and Applications Workshop, ITA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Information Theory and Applications Workshop, ITA 2018
Y2 - 11 February 2018 through 16 February 2018
ER -