Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Deep Learning Enabled Semantic Communication Systems

In the past decades, communications primarily focus on how to accurately and effectively transmit symbols (measured by bits) from the transmitter to the receiver. Recently, various new applications appear, such as autonomous transportation, consumer robotics, environmental monitoring, and tele-health. The interconnection of these applications will generate a staggering amount of data in the order of zetta-bytes and require massive connectivity over limited spectrum resources but with lower latency, which poses critical challenges to conventional communication systems. Semantic communication has been proposed to overcome the challenges by extracting the meanings of data and filtering out the useless, irrelevant, and unessential information, which is expected to be robust to terrible channel environments and reduce the size of transmitted data. While semantic communications have been proposed decades ago, their applications to the wireless communication scenario remain limited. Deep learning (DL) based neural networks can effectively extract semantic information and can be optimized in an end-to-end (E2E) manner. The inborn characteristics of DL are suitable for semantic communications, which motivates us to exploit DL-enabled semantic communication. Inspired by the above, this thesis focus on exploring the semantic communication theory and designing semantic communication systems. First, a basic DL based semantic communication system, named DeepSC, is proposed for text transmission. In addition, DL based multi-user semantic communication systems are investigated for transmitting single-modal data and multimodal data, respectively, in which intelligent tasks are performed at the receiver directly. Moreover, a semantic communication system with a memory module, named Mem-DeepSC, is designed to support both memoryless and memory intelligent tasks. Finally, a lite distributed semantic communication system based on DL, named L-DeepSC, is proposed with low complexity, where the data transmission from the Internet-of-Things (IoT) devices to the cloud/edge works at the semantic level to improve transmission efficiency. The proposed various DeepSC systems can achieve less data transmission to reduce the transmission latency, lower complexity to fit capacity-constrained devices, higher robustness to multi-user interference and channel noise, and better performance to perform various intelligent tasks compared to the conventional communication systems