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Yaoguang Zhong

dblp:193/9322 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-5682-8788ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless networking · 70% Internet of things and sensor networks · 23% Physical-layer communications · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless networking › random access
ALOHA
0.612022
ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022
Internet of things and sensor networks
LPWAN
0.612022
ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022
Wireless networking
medium access control
0.612022
ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022
Wireless networking
network capacity
0.612022
ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022
Physical-layer communications
zadoff-chu sequences
0.212022
ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022

Methods — techniques the papers use, named apart from their topics

testbed experiments · 0.6simulation · 0.6
YearPublicationVenuePosition
2022 ZCNET: Achieving High Capacity in Low Power Wide Area Networks
abstract
In this paper, a novel LPWAN technology, ZCNET, is proposed, which achieves significantly higher network capacity than existing solutions, such as LoRa, Sigfox, and RPMA. The capacity boost of ZCNET is mainly due to two reasons. First, a ZCNET node transmits signals that occupy a small fraction of the signal space, resulting in a low collision probability. Second, ZCNET supports 8 parallel root channels within a single frequency channel by using 8 Zadoff-Chu (ZC) root sequences. The root channels do not severely interfere with each other, mainly because the interference power is spread evenly over the entire signal space. A simple ALOHA-style protocol is used for medium access, with which a node randomly chooses the root channel and the range it occupies within the root channel. ZCNET has been extensively tested with both real-world experiments on the USRP and simulations, and the results confirm that ZCNET achieves significant gains over LoRa, Sigfox, and RPMA. ZCNET will likely better accommodate the explosive growth of IoT network sizes and meet the demand of IoT applications.
Raghav Rathi, Steven Perez, Jumana Bukhari, Yaoguang Zhong
IEEE/ACM Trans. Netw.5