Potential new requirements needed to support the use case result of the following parameters:
AI/ML model size.
Accuracy of the model.
latency constraint of the application or service.
number of concurrent downloads, i.e. number of UEs requesting AI/ML model downloads within same time window.
The number of concurrent downloads further depends on the density of UE in the covered area and the covered area size.
, Table 18.104.22.168-2
and Table 22.214.171.124-3
contain KPI for different aspects of the real-time media editing use case.
From Table 126.96.36.199-1
, AI/ML models currently available to elaborate the use case have sizes that vary from 3.2 MB to 536 MB.
As indicated in the use case in clause 6.1
, it can be noted that the size of AI/ML models can be reduced prior to transmission with dedicated model compression techniques. On the contrary, AI/ML models with more neural network layers and more complex architectures arise to solve more complex tasks and to improve accuracy. This trend is expected to continue in the coming years. Typical model sizes in the range of 3 MB to 500 MB appear to be a reasonable compromise to consider for this use case.
In the following, two categories are considered for AI/ML model sizes:
AI/ML model sizes below 64 MB, which can be associated to models optimized for fast transmission,
AI/ML model sizes below 500 MB, which can be associated to models optimized for higher accuracy.
Maximum latency in function of application or service:
Videocall service: end-to-end latency below 200 ms,
Video recording, video streaming, and object recognition applications: latency below 1 s.
User experienced DL data rate results from above AI/ML models' sizes and maximum latency values are summarized in the Table 188.8.131.52-2
As indicated above, the number of concurrent downloads is a third parameter to determine potential new requirements. This corresponds to the maximum number of UEs requesting a AI/ML model download in a same time window and same covered area/cell.
The case of a concert hall is an illustration of the scenario, "Broadband access in a crowd"
from TS 22.261
. This scenario assumes an overall user density of 500 000 UE / km2 (i.e. 0.5 UE / m2) and an activity factor of 30 %.
In the concert hall case, it is also assumed that only a part of the UEs intends to request AI/ML model downloads. Moreover, only a subpart will request AI/ML model download during the same time window in the same cell. The activity factor is finally estimated to 1 % (i.e. % of UE requesting an AI/ML model download within same time window).
Typical number of UEs in a concert hall varies from ~1000 seats to ~ 5000 seats.
Based on these figures and UE activity assumption, the number of concurrent downloads is estimated as given in Table 184.108.40.206-3
From Table 220.127.116.11-2
and Table 18.104.22.168-3
, requirements on the covered area are estimated as follows:
Another approach to estimate the number of concurrent downloads is to estimate the number of different AI/ML models requested by UEs instead of the number of UEs requesting AI/ML models. The AI/ML models can then be broadcast/multicast to multiple UEs. The number of different AI/ML models depends on the accuracy expectations of the AI/ML models, the execution environments and the hardware characteristics of end devices. When the number of UE requesting AI/ML models is very high, the number of different AI/ML models can remain smaller. This approach is well suited for very large crowd.
The number of concurrent downloads when transmitted in broadcast/multicast to many UEs can be estimated between 1 (i.e. all UEs request the same AI/ML model) and 50 (i.e. all UEs request different AI/ML models).
The 5G system shall support the download of AI/ML models with a latency below 1s and a user experienced data rate of 512 Mb/s.
The 5G system shall support the download of AI/ML models with a latency below 1 s and a user experienced data rate of 4 Gb/s.
The 5G system shall support the parallel download of up to 50 AI/ML models with a latency below 1 s.
The 5G system should support the functionality to broadcast/multicast a same AI/ML model to many UEs with a latency below 1 s.