TY - GEN
T1 - Sparse vector coding for short packet transmission in massive machine type communications
AU - Ji, Hyoungju
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 - 2019/2/1
Y1 - 2019/2/1
N2 - Massive machine type communications (mMTC) is a service category in 5G to support Internet of Things (IoT). Typically, mMTC-based services require small volume of information. Since the current data transmission principle requires long codeblock to maximize the coding gain and hence is not adequate for short packet transmission, multiplexing mechanism to support short packet transmission in mMTC is required. In this paper, we propose a new type of uplink data transmission scheme suitable for the mMTC, 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 random non-orthogonal spreading, we cast the symbol detection problem into the sparse signal recovery problem in compressed sensing. We show from the simulations in the LTE uplink scenario and massive access scenario in 5G that the proposed SVC scheme outperforms conventional approaches and is very effective in short packet transmissions.
AB - Massive machine type communications (mMTC) is a service category in 5G to support Internet of Things (IoT). Typically, mMTC-based services require small volume of information. Since the current data transmission principle requires long codeblock to maximize the coding gain and hence is not adequate for short packet transmission, multiplexing mechanism to support short packet transmission in mMTC is required. In this paper, we propose a new type of uplink data transmission scheme suitable for the mMTC, 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 random non-orthogonal spreading, we cast the symbol detection problem into the sparse signal recovery problem in compressed sensing. We show from the simulations in the LTE uplink scenario and massive access scenario in 5G that the proposed SVC scheme outperforms conventional approaches and is very effective in short packet transmissions.
KW - 5G
KW - Ultra short packet
KW - machine type communications
KW - sparse vector coding
UR - http://www.scopus.com/inward/record.url?scp=85062875026&partnerID=8YFLogxK
U2 - 10.1109/APCC.2018.8633508
DO - 10.1109/APCC.2018.8633508
M3 - Conference contribution
AN - SCOPUS:85062875026
T3 - 2018 24th Asia-Pacific Conference on Communications, APCC 2018
SP - 137
EP - 140
BT - 2018 24th Asia-Pacific Conference on Communications, APCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th Asia-Pacific Conference on Communications, APCC 2018
Y2 - 12 November 2018 through 14 November 2018
ER -