Periodic Reporting for period 4 - BEACON (Hybrid Digital-Analog Networking under Extreme Energy and Latency Constraints)
Reporting period: 2021-04-01 to 2022-09-30
The main goal of the BEACON project is to explore novel methods for the transmission of signals over noisy wireless channel, particularly under extreme low latency and low energy constraints. When we transmit an image from our phone to a friend, the two following fundamental operations take place: the image is first compressed into as few bits as possible at a certain quality level. This is typically done by image compression algorithms, such as JPEG or JPEG2000, which have been highly optimized over the years to be able to represent generic images with as few bits as possible. Then these bits are transferred to the channel transmission unit, which uses a particular modulation scheme and a channel code against noise in the channel. At the very basic level, this can be considered as adding redundant bits among the bits output by the source compressor, such that even if some the transmitted bits are lost over the channel, the receiver can still recover the compressed version of the image. Some of the well-known codes that provide robustness againt noise in communication channels are convolutional codes, Turbo codes, and LDPC codes. It also took researchers decades to develop and optimize these codes.
This two-step approach provides modularity to the system: we can separately design the source and channel coding components. For example, we can have a single channel coder in our phones, and that same unit can transmit bits coming from an image, video or an audio compressor, as it does not care where the bits come from. Its only goal is to get the bits to the receiver as reliably as possible. Thanks to its modularity and flexibility in adapting to the available bandwidth, digital communications has dominated existing communication systems. However, there is an increasing drive towards Internet-of-things (IoT) applications and machine-type communications, which have significantly different requirements than conventional communication systems. Many IoT applications have extremely low latency requirements, and most IoT devices are limited in energy and complexity, so they need to be highly energy efficient, and may not be able to employ the high-complexity algorithms. BEACON argues that we need to reconsider our communication system architecture for emerging IoT and other low-latency and low-energy applications.
Designing ultra-low latency and ultra-low energy communication protocols for IoT applications has numerous benefits. This would allow using wireless technology for many applications with such requirements that are now limited to wired connections. This would reduce both the installation and the operation costs. It would also lead to many novel applications from remote surgery to more efficient monitoring and control of medical devices, surveillance systems, etc.
The main objective of the BEACON project is to develop novel communication technologies that go beyond the conventional separate source and channel coding architecture in order to meet the extreme latency and energy efficiency requirements of emerging applications. In BEACON, we have not only studied the fundamental theoretical limits of such communication systems, but also developed practical coding and communication techniques, and designed and implemented these techniques through proof-of-concept demonstrators.
This two-step approach provides modularity to the system: we can separately design the source and channel coding components. For example, we can have a single channel coder in our phones, and that same unit can transmit bits coming from an image, video or an audio compressor, as it does not care where the bits come from. Its only goal is to get the bits to the receiver as reliably as possible. Thanks to its modularity and flexibility in adapting to the available bandwidth, digital communications has dominated existing communication systems. However, there is an increasing drive towards Internet-of-things (IoT) applications and machine-type communications, which have significantly different requirements than conventional communication systems. Many IoT applications have extremely low latency requirements, and most IoT devices are limited in energy and complexity, so they need to be highly energy efficient, and may not be able to employ the high-complexity algorithms. BEACON argues that we need to reconsider our communication system architecture for emerging IoT and other low-latency and low-energy applications.
Designing ultra-low latency and ultra-low energy communication protocols for IoT applications has numerous benefits. This would allow using wireless technology for many applications with such requirements that are now limited to wired connections. This would reduce both the installation and the operation costs. It would also lead to many novel applications from remote surgery to more efficient monitoring and control of medical devices, surveillance systems, etc.
The main objective of the BEACON project is to develop novel communication technologies that go beyond the conventional separate source and channel coding architecture in order to meet the extreme latency and energy efficiency requirements of emerging applications. In BEACON, we have not only studied the fundamental theoretical limits of such communication systems, but also developed practical coding and communication techniques, and designed and implemented these techniques through proof-of-concept demonstrators.
The work carried out within BEACON so far can be divided into three main thrusts. The first thrust focused on developing theoretical performance bounds. Such bounds serve as benchmarks for practical implementations. For example, if we know the capacity of a communication channel, this sets an upper bound on any communication system in terms of the reliable rate of communication. No system can transmit information beyond this rate in a reliable manner. In BEACON, we have developed such performance bounds for unconventional communication applications, mainly focusing on distributed learning.
The second thrust of the project focused on developing coding and communication schemes that can be used in practice. In particular, we have pioneered joint source-channel coding (JSCC) techniques based on data-driven deep learning tools. We have designed low-latency and low-power communication schemes, where both the transmitter and the receiver are modelled by deep neural networks. Our work in this area opened up the new research direction of semantic communications. In addition to many highly-cited publications, we have also filed patents.
Finally, in BEACON, we have also implemented some of the developed communication schemes on a software radio platform as a proof-of-concept. We have used this platform to demonstrate our ideas in action to potential industry partners.
The second thrust of the project focused on developing coding and communication schemes that can be used in practice. In particular, we have pioneered joint source-channel coding (JSCC) techniques based on data-driven deep learning tools. We have designed low-latency and low-power communication schemes, where both the transmitter and the receiver are modelled by deep neural networks. Our work in this area opened up the new research direction of semantic communications. In addition to many highly-cited publications, we have also filed patents.
Finally, in BEACON, we have also implemented some of the developed communication schemes on a software radio platform as a proof-of-concept. We have used this platform to demonstrate our ideas in action to potential industry partners.
We have made progress beyond the state-of-the-art in all three thrusts mentioned above. We have developed new performance bounds using tools from information theory and optimization tools for distributed communication and machine learning problems.
We have developed both performance bounds and practical caching and coded delivery schemes for efficient delivery of popular content over wireless edge networks.
We have developed novel coded computing paradigms for efficient and private distributed computing taking into account the presence of straggling servers.
Another significant achievement of the project is the design of a novel JSCC techniques for wireless image and video transmission. We have shown that our technology outperforms the combination of state-of-the-art compression and channel coding techniques, both of which are products of decades-long research and standardisation efforts. Our results are impressive as i) they achieve better performance, ii) they do not require accurate channel estimation and feedback, iii) they can easily adapt to different source and channel distributions and reconstruction metrics, and iv) they have much less complexity.
We have developed state-of-the-art results in distributed machine learning and inference problems over noisy communication channels. In particular, in an award-winning paper, we have introduced the concept of "over-the-air computation" to significantly accelerate distributed learning in wireless edge networks.
We have identified new security and privacy threats in distributed learning problems, and proposed novel information and coding theoretic techniques to mitigate such threats.
We have developed both performance bounds and practical caching and coded delivery schemes for efficient delivery of popular content over wireless edge networks.
We have developed novel coded computing paradigms for efficient and private distributed computing taking into account the presence of straggling servers.
Another significant achievement of the project is the design of a novel JSCC techniques for wireless image and video transmission. We have shown that our technology outperforms the combination of state-of-the-art compression and channel coding techniques, both of which are products of decades-long research and standardisation efforts. Our results are impressive as i) they achieve better performance, ii) they do not require accurate channel estimation and feedback, iii) they can easily adapt to different source and channel distributions and reconstruction metrics, and iv) they have much less complexity.
We have developed state-of-the-art results in distributed machine learning and inference problems over noisy communication channels. In particular, in an award-winning paper, we have introduced the concept of "over-the-air computation" to significantly accelerate distributed learning in wireless edge networks.
We have identified new security and privacy threats in distributed learning problems, and proposed novel information and coding theoretic techniques to mitigate such threats.