Dynamic Resource Allocation Framework for MooD (MBMS Operation On-Demand)

Kaliski, Rafael; Chou, Ching-Chun; Meng, Hsiang-Yun; Wei, Hung‐Yu

doi:10.1109/tbc.2016.2590829

Cited by 15 publications

(10 citation statements)

References 28 publications

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“…This method provides a higher spectral efficiency than only using the worst case UE's MCS for all video layers, i.e., the MCS associated with the receiver whom exhibits the weakest SINR. Methods to dynamically allocate resources among MBMS flows (MBMS on Demand) are studied in [15]. Kaliski et al [15] used a Medium Access Control (MAC) layer ILP resource allocation methodology to allocate RBs, the basic unit used for resource allocation, among all MBMS flows every Transmission Time Interval (TTI).…”

Section: Related Workmentioning

confidence: 99%

“…Methods to dynamically allocate resources among MBMS flows (MBMS on Demand) are studied in [15]. Kaliski et al [15] used a Medium Access Control (MAC) layer ILP resource allocation methodology to allocate RBs, the basic unit used for resource allocation, among all MBMS flows every Transmission Time Interval (TTI).…”

Section: Related Workmentioning

confidence: 99%

“…For all simulations we consider 4 different numerology configurations. The first three (15) c RB H > c RB M > c RB L , Fig. 4 Illustrative example of Relay and IoT devices, 50 meters distance, variable delay spread and latency requirements.…”

Section: Simulationmentioning

confidence: 99%

“…Note: The desired multicast flow latency labels are H (High latency), M (Medium latency), and (Low latency). In this example, the multicast QoS Flow data is the same In terms of comparisons, the ILP implementation can be viewed as an implementation of the Throughput Oriented scheme presented in [15]. As [15] does not account for delay spread, we adjust the RB capacity to reflect the impact of max delay spread based on (1) and (2).…”

Section: Simulationmentioning

confidence: 99%

“…In this example, the multicast QoS Flow data is the same In terms of comparisons, the ILP implementation can be viewed as an implementation of the Throughput Oriented scheme presented in [15]. As [15] does not account for delay spread, we adjust the RB capacity to reflect the impact of max delay spread based on (1) and (2). Similarly, we compare our proposed NOMA Multicast Flow Migration scheme, N5GMFM, against the OMA Multicast Flow Migration scheme presented in our previous work [14].…”

Section: Simulationmentioning

confidence: 99%

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D2D NOMA Groupcast in 5G Heterogeneous Networks

Kaliski

2021

SN COMPUT. SCI.

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In traditional 5G latency-driven assignment methodologies, a radio carrier's numerology is typically fixed for any given QoS flow, as directed by the QoS requirements. Unfortunately, a drawback to 5G's radio carrier numerology assignment approach is that it does not account for the impact of multi-path/delay spread. As such 5G is unable to efficiently utilize the allocated radio spectrum in high delay spread radio environments, thus potentially limiting both massive Machine Type Communication (mMTC) and ultra Reliable Low Latency Communication's (uRLLC) ability to meet each data flow's Quality of Service (QoS) requirements; low latency flows are more sensitive to high delay spread. When considering power-domain NOMA resource allocation, the impact of delay spread can only be partially mitigated. In this research work we derive an optimal QoS to 5G radio carrier numerology mapping methodology for NOMA-enabled 5G Heterogeneous groupcast networks, subject to the constraints imposed by the UEs and UE-to-Network Relay. Compared to legacy multicast methodologies, our methodology can achieve up to a 4.7× higher resource block capacity while minimizing each resource block's latency deviation from its original latency requirements, i.e., compared to legacy methodologies, our methodology reduces QoS flow latency while minimizing resource block latency deviation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%