An extract from the Open RAN RIC and Apps report and workshop.
1.
O-RAN defined rApps and xApps
1.1.
Traffic steering rApp and xApp
Traditional RAN provides few mechanisms to load balance and
force traffic on specific radio paths. Most deployments see overlaps of
coverage between different cells in the same spectrum, as well as other
spectrum layered in, allowing performance, coverage, density and latency scenarios
to coexist. The methods by which a UE is connected to a specific cell and a
specific channel are mostly static, based on location of the UE, signal
strength, service profile and the parameters to handover a connection from one
cell to another, or within the same cell from one bearer to another or from one
sector to another. The implementation is vendor specific and statically configured.
Figure 9: Overlapping cells and traffic steering
Non-RT RIC and rApps offer the possibility to change these
handover and assignments programmatically and dynamically, taking advantage of
policies that can be varied (power optimization, quality optimization,
performance or coverage optimization…) and that can change over time.
Additionally, the use of AI/ML technology can provide predictive input
capability for the selection or creation of policies allowing a preferable
outcome.
The traffic steering rApp is a means to design and select
traffic profile policies and to dynamically allow the operator to instantiate
these policies, either per cell, per sector, per bearer or even per UE or per
type of service. The SMO or the Non-RT RIC collect RAN data on traffic, bearer,
cell, load, etc. from the E2 nodes and instruct the Near-RT RIC to enforce a
set of policies through the established parameters.
1.2.
QoE rApp and xApp
This rApp is assuming that specific services such as AR/VR
will require different QoE parameters that will need to be adapted in a semi
dynamic fashion. It proposes the use of AI/ML for prediction of traffic load
and QoE conditions to optimize the traffic profiles.
UE and network performance data transit from the RAN to the
SMO layer over the O1 interface, QoE AI/ML models are trained, process the data
and infer the state and predict its evolution over time, the rApp transmits QoE
policy directives to the Near-RT RIC via the Non-RT RIC.
1.3.
QoS based resource optimization rApp and xApp
QoS based resource optimization rApp is an implementation of
network slicing optimization for the RAN. Specifically, it enables the Non-RT
RIC to guide the Near-RT RIC in the allocation of Physical Resource Blocks to a
specific slice or sub slice, should the Slice Level Specification not be
satisfied by the static slice provisioning.
1.4.
Context-based dynamic handover management for
V2X rApp and xApp
Since mobile networks have been designed for mobile but
relatively low velocity users, the provision of high speed, reliable mobile
service along highways requires specific designs and configurations. As
vehicles become increasingly connected to the mobile network and might rely on
network infrastructure for a variety of uses, Vehicle to infrastructure (V2X)
use cases are starting to appear primarily as research and science projects. In
this case, the App is supposed to use AI/ML models to predict whether a UE is
part of a V2X category and its trajectory in order to facilitate cell handover
along its path.
1.5.
RAN Slice Assurance rApp and xApp
3GPP has
defined the concept of creating a connectivity product with specific attributes
(throughput, reliability, latency, energy consumption) applicable to specific
devices, geographies, enterprises… as slices. In an O-RAN context, the Non-RT
RIC and Near-RT RIC can provide optimization strategies for network slicing. In
both cases, the elements can monitor the performance of the slice and perform
large or small interval adjustments to stay close the slice’s Service Level
Agreement (SLA) targets.
Generally
speaking, these apps facilitate the allocation of resource according to slice
requirements and their dynamic optimization over time.
1.6.
Network
Slice Instance Resource Optimization rApp
The NSSI rApp aims to use AI/ML to model traffic patterns of
a cell through historical data analysis. The model is then used to predict
network load and conditions for specific slices and to dynamically and
proactively adjust resource allocation per slice.
1.7.
Massive
MIMO Optimization rApps and xApps
Massive MIMO (mMIMO) is a key technology to increase
performance in 5G. It uses complex algorithms to create signal beams which
minimize signal interference and provide narrow transmission channels. This
technology, called beamforming can be configured to provide variations in the
vertical and horizontal axis, azimuth and elevation resulting in beams of
different shapes and performance profiles. Beamforming and massive MIMO are a
characteristic of the Radio Unit, where the DU provides the necessary data for
the configuration and direction of the beams.
In many cases, when separate cells overlap a given
geography, for coverage or density with either multiple macro cells or macro
and small cells mix, the mMIMO beams are usually configured statically,
manually based on the cell situation. As traffic patterns, urban environment
and interference / reflection, change, it is not rare that the configured beams
lose efficiency over time.
In this instance, the rApp collects statistical and
measurement data of the RAN to inform a predictive model of traffic patterns.
This model, in turn informs a grid of beams that can be applied to a given
situation. This grid of beams is transmitted to the DU through the Near-RT RIC
and a corresponding xApp, responsible for assigning the specific PRB and beam
parameters to the RU. A variant of this implementation does not require grid of
beams or AI/ML, bit a list of statically configured beams that can be selected
based on specific threshold or RAN measurements.
Additional apps leveraging unique MIMO features such as
downlink transmit power, Multiple User MIMO and Single User MIMO allow, by
reading UE performance to adjust the transmit power or the beam parameters to
improve the user experience or the overall spectral efficiency.
1.8.
Network energy saving rApps and xApps
These apps are a collection of methods to optimize power
consumption in the open RAN domain.
Carrier and cell switch off/on rApp:
A simple mechanism to identify within a cell the
capacity needed and whether it is possible to reduce the power consumption by
switching off frequency layers (carriers) or the entire cell, should sufficient
coverage / capacity exist with other adjoining overlapping cells. AI/ML model
on the Non- RT RIC might assist in the selection and decision, as well as
provide a predictive model. The prediction in this case is key, as one cannot
simply switch off a carrier or a cell without gracefully hand over its traffic
to an adjoining carrier or cell before to reduce quality of experience negative
impact.
RF Channel reconfiguration rApp:
mMIMO is achieved by the combination of radiating elements to form
the beams. A mMIMO antenna array 64 64 transceivers and receivers (64T64R) can
be configured to reduce its configuration to 32, 16 or 8 T/R for instance,
resulting in a linear power reduction. An AI/ML model can be used to determine
the optimal antenna configuration based on immediate and predictive traffic
patterns.
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