3GPP Release 15 Early Data Transmission

Höglund, Andreas; Van, Dung Pham; Tirronen, Tuomas; Liberg, Olof; Sui, Yutao; Yavuz, Émre A.

doi:10.1109/mcomstd.2018.1800002

Cited by 68 publications

(33 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Resource management & Scheduling [25]- [27] V Synchronization [28]- [37] III.A Uplink demodulation [38]- [47] IV.A & IV.B Downlink demodulation [48]- [53] IV.B Energy Saving [54]- [57] V.H & VI.B Positioning [58]- [60] VI.A Challenges & perspectives [17], [20]- [22], [61]- [64] I Release 13 [65]- [77] I to V 3GPP references Release 14 [78]- [85] VI.A Release 15 [86]- [92] I & VI.B…”

Section: Topicsmentioning

confidence: 99%

“…The design parameters corresponding usually used for contention resolution in legacy procedure). This feature allows further reduction of the signaling overhead and message latency while increasing battery lifetime [57]. In contrast, if the UE has more data to send, it switches to the optimized procedure of Release 13 (i.e., CP and UP CIoT data transfer).…”

Section: B Release 15 Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A Tutorial on NB-IoT Physical Layer Design

Kanj¹,

Savaux²,

Guen³

2020

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

The Internet of things (IoT) is transforming the whole of society. It represents the next evolution of the Internet and will significantly improve the ability to gather and analyze data, as well as the ability to control devices remotely. In this respect, the usage of connected devices is continuously growing with the expansion of the applications being offered to individuals and industries. To address IoT market needs, many low-power wide-area (LPWA) technologies have been developed, some operating on licensed frequencies (e.g., narrowband-IoT [NB-IoT] and Long-Term Evolution-M [LTE-M]), and others on unlicensed frequencies (e.g., LoRa, Sigfox, etc.). In this paper, we address the Release 13 of the NB-IoT 3rd generation partnership project (3GPP) standardized LPWA technology and provide a tutorial on its physical layer (PHY) design. Specifically, we focus on the characteristics and the scheduling of downlink and uplink physical channels at the NB-IoT base station side and the user equipment (UE) side. The goal is to help readers easily understand the NB-IoT system without having to read all the 3GPP specifications or the state-of-the-art papers that generally describe the system. To this end, each presented concept is followed by examples and concrete use-cases to further aid in the reader's comprehension. Finally, we briefly describe and highlight the new features added to the NB-IoT system in Releases 14 and 15.

show abstract

Section: Topicsmentioning

confidence: 99%

Section: B Release 15 Featuresmentioning

confidence: 99%

A Tutorial on NB-IoT Physical Layer Design

Kanj¹,

Savaux²,

Guen³

2020

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…After the frozen of 5G mobile network specification Release 15 on June 2018, the first nationwide commercial 5G network launched in South Korea on April 2019 [1]. Meanwhile, the study of roadmap and enabling technologies beyond the 5G mobile network are in progress in both the academic and telecommunication industries.…”

Section: Introductionmentioning

confidence: 99%

AI Inspired Intelligent Resource Management in Future Wireless Network

Yang

Xiao

2020

IEEE Access

View full text Add to dashboard Cite

In order to improve network performance, including reducing computation delay, transmission delay and bandwidth consumption, edge computing and caching technologies are introduced to the fifth-generation wireless network (5G). However, the volume of edge resources is limited, while the number and complexity of tasks in the network are increasing sharply. Therefore, how to provide the most efficient service for network users with limited resources is an urgent problem to be solved. Thus, improving the utilization rate of communication, computing and caching resources in the network is an important issue. The diversification of network resources brings difficulties to network management. The joint resource allocation problem is difficult to be solved by traditional approaches. With the development of Artificial Intelligence (AI) technology, these AI algorithms have been applied to joint resource allocation problems to solve complex decision-making problems. In this paper, we first summarize the AI-based joint resources allocation schemes. Then, an AI-assisted intelligent wireless network architecture is proposed. Finally, based on the proposed architecture, we use deep Q-network (DQN) algorithm to figure out the complex and high-dimensional joint resource allocation problem. Simulation results show that the algorithm has good convergence characteristics, proposed architecture and the joint resource allocation scheme achieve better performance compared to other resource allocation schemes. INDEX TERMS Artificial intelligence, deep Q-network, resource management, edge computing and caching, the fifth-generation wireless network (5G).

show abstract

“…The high operation frequencies allow small antennas to be employed on the User Equipment (UE) side, which means that manufacturers can also implement a large number of antennas on the mobile devices [5], increasing the mobile network capacity. The 3rd Generation Partnership Project (3GPP) Release 15 [6] has defined the Physical Layer (PHY) for the eMBB scenario, called New Radio (NR) [7], but 5G is more than just higher data rates and the research activities regarding 5G networks is growing.…”

Section: Introductionmentioning

confidence: 99%