Research

Agora

What is Agora?

Agora is a complete high-performance system for real-time massive-MIMO baseband processing that implements a queue-based master-worker model. A master thread is responsible for scheduling tasks and a pool of worker threads are responsible for executing tasks. The communication between the master thread worker threads are achieved through single-producer multi-consumer or multi-producer single-consumer shared memory FIFO queues.

Pros and cons of Agora:

• + Agora currently supports 64x16 Multi-User MIMO (MU-MIMO). In other words, the system supports 64 Remote Radio Unit (RRU) antennas and 16 concurrent User Equipment (UE) devices with 20 MHz bandwidth and 64QAM modulation, on a 36-core server with AVX512 support.
• + Agora is configurable in terms of numbers of RRU antennas and UEs, bandwidth, modulation orders, LDPC code rates.
• + Agora supports an emulated RRU and UEs with a high-performance packet generator.
• + Agora has been tested with real RRUs with up to 64 antennas and up to 8 UEs. The RRU and UE devices are available from Skylark Wireless.
• - Agora is a complex system and as such, it has a much steeper learning curve than RENEWLab and requires more advanced programming skills.

When to Use Agora?

Agora allows researchers to experiment with massive MIMO applications running in real-time and to further develop other baseband modules and evaluate their performance. The following are just a few examples of use cases enabled by Agora:

1. Channel Coding: Researchers can either evaluate the current LDPC enconder/decoder using different parameters or replace it with different error correcting coding mechanisms.
2. Massive MIMO channel estimation: Researchers can implement different techniques to improve channel estimation and evaluate their performance in real-time.
3. Rate Adaptation: Based on channel state information inferred from received pilots, the Agora system can vary the transmission rate to minimize losses and to avoid channel underutilization.
4. Precoding: By default, Agora implements zero-forcing precoding. Researchers can experiment with other precoding schemes such as conjugate beamforming and evaluate its performance in different scenarios.

The Agora code is open-source and can be found here.
See the wiki showing a design overview including a block diagram of the system.

RENEWLab

What is RENEWLab?

The best way to characterize RENEWLab is hardware-and-the-wireless channel-in-the-loop. You program what signals are transmitted by which antennas at what time, and which antennas will receive them. The signals can be constructed in Python or MATLAB, allowing you to use many existing libraries in these software frameworks.

Pros and cons of RENEWLab:

• + Flexible construction of signals and transmission patterns
• + Ability to use existing software libraries without any change
• + Shortest learning curve
• + Ability to bring hardware and actual wireless channels in your data
• + Full software control
• - Real-time processing often not possible, hence not suitable for Layer 2 or above research

When to Use RENEWLab?

RENEWLab is not only an important first step in many research projects but in fact, all you may need for many research ideas. Below we highlight some of the ways you can use RENEWLab for:

1. Channel characterization: RENEWLab has a sounder tool, which is a special function allowing channel measurements. Beyond just point-to-point link, one can develop channel models for self-interference (e.g. full-duplex radios), interference channels, study frequency reciprocity or study multipath effects in massive MIMO channels
2. Waveform evaluation: This is a big one hiding under a boring term. Most of the signaling research, crucial for radio development in the development of Physical Layer, can be extensively tested using RENEWLab. For example, 5G-NR waveforms and future variations, beamforming algorithms, beamforming to account for amplifier nonlinearity, impact of pilot design on channel estimation, gain control are just some of the ideas that can be extensively evaluated.
3. Datasets for machine learning: This is becoming a major research use case. Using RENEW’s ability to collect massive amounts of data programmatically, we envision countless uses in machine learning for wireless.

You can find all RENEWLab code here.