Page 91 - AI for Good-Innovate for Impact

AI for Good-Innovate for Impact

and adapt to changing network conditions. It allows for customization of signal processing
algorithms and improves resource efficiency by consolidating functions onto a single platform.
Programmable baseband is essential for applications like Software-Defined Radio (SDR),
cognitive radio, and Network Function Virtualization (NFV), enabling dynamic spectrum access, 18 - HFCL
intelligent spectrum management, and virtualization of network functions.
For this use case programmable baseband will actually be responsible for providing the training
data of CPU occupancy to the AI prediction model.

LNA and PA Inputs: A low noise amplifier (LNA) is a critical component in communication
systems, amplifying weak signals while minimizing added noise, thus improving receiver
sensitivity. Conversely, a power amplifier (PA) boosts signal power to transmit data over long
distances, ensuring robust communication performance in wireless networks.

These building blocks are important as these are the part of multiple RF transmission chains
which carry the raw data transmitted or received by the antennas. Also, we are configuring
these blocks based on the prediction data received by the AI model PHY Connections: Each
LNA and PA unit is connected to the Programmable Baseband through physical connections,
represented as PHY connections. These connections facilitate the transmission of signals from
the antennas to the baseband processing unit.

Mod/Demod Processing: The received signals undergo modulation/demodulation (Mod/
Demod) processing within the Programmable Baseband. Modulation converts digital data into
analog signals suitable for transmission, while demodulation performs the reverse process.
They are an essential part of the RF chain and are controlled by our AI prediction model which
informs processors about their requirements based on the traffic prediction Processed Signals:
After modulation/demodulation processing, the signals are prepared for further processing
and transmission to other components.

Multicore Processor: A multicore processor is a CPU (central processing unit) design that
integrates multiple processing cores onto a single chip. This architecture allows for parallel
processing of tasks, improving overall system performance and efficiency by enabling
simultaneous execution of multiple instructions and tasks.

Data Reception: The Multicore Processor component receives the processed data from
the Programmable Baseband. This data includes the signals that have been modulated/
demodulated.

Communication Interfaces: The Multicore Processor utilizes various communication interfaces,
such as PCIe (Peripheral Component Interconnect Express), for data communication. These
interfaces enable communication with other components within the system.

Data Consolidation: Data from different sources, including the baseband processing unit and
the AI prediction model, are consolidated within the Multicore Processor. This consolidation
likely involves aggregating and synchronizing data for further processing or transmission.

AI Prediction Model:

Prediction Generation: The AI Prediction Model component is responsible for generating
predictions using artificial intelligence algorithms. These predictions could be related to
optimizing radio link configurations or enhancing network performance based on learned
patterns or predictive analytics.

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