Related papers: Optimizing Multi-User Semantic Communication via T…

Semantic communication has gained attention as a key enabler for intelligent and context-aware communication. However, one of the key challenges of semantic communications is the need to tailor the resource allocation to meet the specific…

Lack of specialized data makes building a multi-domain neural machine translation tool challenging. Although emerging literature dealing with low resource languages starts to show promising results, most state-of-the-art models used…

Multilingual models have been widely used for cross-lingual transfer to low-resource languages. However, the performance on these languages is hindered by their underrepresentation in the pretraining data. To alleviate this problem, we…

Over the past year, the emergence of transfer learning with large-scale language models (LM) has led to dramatic performance improvements across a broad range of natural language understanding tasks. However, the size and memory footprint…

Semantic communication (SemComm) has emerged as a new communication paradigm. To enhance efficiency, multiple-input-multiple-output (MIMO) technology has been further integrated into SemComm systems. However, existing MIMO SemComm systems…

Semantic communication (SemCom) is an emerging technology that extracts useful meaning from data and sends only relevant semantic information. Thus, it has the great potential to improve the spectrum efficiency of conventional wireless…

Semantic communications is considered as a promising technology to increase the efficiency of next-generation communication systems, particularly targeting human-machine and machine-type communications. In contrast to the source-agnostic…

Semantic communication aims to convey meaning rather than bit-perfect reproduction, representing a paradigm shift from traditional communication. This paper investigates distribution learning in semantic communication where receivers must…

Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that…

Knowledge distillation is to transfer the knowledge from the data learned by the teacher network to the student network, so that the student has the advantage of less parameters and less calculations, and the accuracy is close to the…

The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication systems address the capability-deficiency issue mainly by…

In this letter, we propose a group-wise semantic splitting multiple access framework for multi-user semantic communication in downlink scenarios. The framework begins by applying a balanced clustering mechanism that groups users based on…

Degraded broadcast channels (DBC) are a typical multi-user communications scenario. There exist classic transmission methods, such as superposition coding with successive interference cancellation, to achieve the DBC capacity region.…

Knowledge distillation, a technique for model compression and performance enhancement, has gained significant traction in Neural Machine Translation (NMT). However, existing research primarily focuses on empirical applications, and there is…

Deep neural network architectures have attained remarkable improvements in scene understanding tasks. Utilizing an efficient model is one of the most important constraints for limited-resource devices. Recently, several compression methods…

Many recent breakthroughs in machine learning have been enabled by the pre-trained foundation models. By scaling up model parameters, training data, and computation resources, foundation models have significantly advanced the…

Semantic communication (SC) is recognized as a promising approach for enabling reliable communication with minimal data transfer while maintaining seamless connectivity for a group of wireless users. Unlocking the advantages of SC for…

Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex network topologies with multiple users, the enormous combinations of client data and channel state…

This paper investigates the secure resource allocation for a downlink integrated sensing and communication system with multiple legal users and potential eavesdroppers. In the considered model, the base station (BS) simultaneously transmits…

Today, wireless networks are becoming responsible for serving intelligent applications, such as extended reality and metaverse, holographic telepresence, autonomous transportation, and collaborative robots. Although current fifth-generation…