Physical Uplink Control Channel Design for 5G New Radio

Kundu, Lopamudra; Xiong, Gang; Cho, Joonyoung

doi:10.1109/5gwf.2018.8517042

Cited by 20 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In case of error in the first transmission, the 1 bit NACK feedback could be transmitted in a span of « 6 symbols according the Physical Uplink Control Channel (PUCCH) Format 1 in 5G NR [38]. Assuming the same transmission rate for the NACK packet, the extra delay due to the NACK packet would be Δ = 6 1 .…”

Section: B Average Latencymentioning

confidence: 99%

“…The infinite series in (38) can be truncated to a finite sum of terms and we evaluate the accuracy of the expression noting that the accuracy increases with the number of terms. But, it is noticed that when testing for different system parameters ( , , , ), the accuracy for expanding 1 term is 92.7%, 2 terms is 99% and 99.9% for 3 terms only.…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

See 1 more Smart Citation

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Shehab

Alves

Jorswieck

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption. We analyze the EEE of ultrareliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in quasi-static Nakagami-(and Rayleigh as sub-case) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the QoS constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultra-reliability using one transmission consumes huge amount of power, which is not applicable for energy limited IoT devices. In this context, accounting for empty buffer probability in machine type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the non-empty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

show abstract

Section: B Average Latencymentioning

confidence: 99%

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Shehab

Alves

Jorswieck

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

Section: B Average Latencymentioning

confidence: 99%

Effective Energy Efficiency of Ultra-reliable Low Latency Communication

Shehab¹,

Alves²,

Jorswieck³

et al. 2021

Preprint

View full text Add to dashboard Cite

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption. We analyze the EEE of ultrareliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in quasi-static Nakagami-𝑚 (and Rayleigh as sub-case) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the QoS constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultra-reliability using one transmission consumes huge amount of power, which is not applicable for energy limited IoT devices. In this context, accounting for empty buffer probability in machine type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the non-empty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

show abstract

“…UE (mobile subscribers) and gNodeB (base station). gNodeB is connected to 5G Core-Network in the backend [1]. According to 3GPP series of standards related to 5G NR, the connection from gNodeB to User-Equipment is known as downlink, which have PBCH, PDSCH and PDCCH channels for carrying different control/data information.…”

Section: Physical Uplink Control Channel (Pucch)mentioning

confidence: 99%

Development of a verification environment to capture the functional coverage of PUCCH features in 5G User Equipment Simulator

Gowda¹,

Segu²,

Gupta³

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

View full text Add to dashboard Cite

The fifth generation of wireless mobile network, known as 5G NR (New Radio) is in the process of being designed and deployed. NR follows 3GPP series of standards for wireless network which includes gNB (base station) and UE (user equipment). The connection from User Equipment to gNodeB is known as Uplink, which includes PUCCH channel which is used to convey Control Information to the gNB. The features of the PUCCH in baseband of the UE simulator are modelled using HDL and implemented in FPGA (SoC). This paper proposes a way to develop environment that can be used to capture the functional coverage for the verification. The environment we developed is by using SystemVerilog with UVM framework. We used Python and Perl scripts to automate the process of verification. All the features which are intended to implement based on the specification can be exercised. Which will give 100% coverage, that is closer to the realworld application. This will enables telecommunication industry to make their product more reliable and less time in design cycle. Our work involves the development of the testbench environment and scripts which automatically captures the functional coverage of the feature-based verification process. This can also be helpful in the functional coverage of different channels in any Baseband unit and this environment is not limited to 5G, can be used for 4G LTE or in future 6G.

show abstract

Physical Uplink Control Channel Design for 5G New Radio

Cited by 20 publications

References 3 publications

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Effective Energy Efficiency of Ultrareliable Low-Latency Communication

Effective Energy Efficiency of Ultra-reliable Low Latency Communication

Development of a verification environment to capture the functional coverage of PUCCH features in 5G User Equipment Simulator

Contact Info

Product

Resources

About