相关论文: Machine Learning for Large-Scale Optimization in 6…
Every new generation of mobile networks brings significant advances in two segments, enhancement of the network parameters within the legacy technologies and introduction of new technologies enabling new paradigms in designing the networks.…
The classic wireless communication channel modeling is performed using Deterministic and Stochastic channel methodologies. Machine learning (ML) emerges to revolutionize system design for 5G and beyond. ML techniques such as supervise…
Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G…
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging…
6G networks envision a pervasive service infrastructure spanning from centralized cloud to distributed edge and highly dynamic extreme-edge domains. This vision introduces significant challenges in orchestrating services over heterogeneous,…
In 6G wireless networks, multi-modal ML models can be leveraged to enable situation-aware network decisions in dynamic environments. However, trained ML models often fail to generalize under domain shifts when training and test data…
As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional…
Traditional wireless network design relies on optimization algorithms derived from domain-specific mathematical models, which are often inefficient and unsuitable for dynamic, real-time applications due to high complexity. Deep learning has…
In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in…
Extremely large-scale arrays (XL-arrays) and ultra-high frequencies are two key technologies for sixth-generation (6G) networks, offering higher system capacity and expanded bandwidth resources. To effectively combine these technologies, it…
The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly…
Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world. Key performance elevating factor from access to core network are softwareization, cloudification and…
Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G), particularly in the context of connecting the unconnected and ultraconnecting the connected.…
Wireless embedded edge devices are ubiquitous in our daily lives, enabling them to gather immense data via onboard sensors and mobile applications. This offers an amazing opportunity to train machine learning (ML) models in the realm of…
This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and…
Driven by the ever-increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth-generation (6G) mobile system enhanced by artificial intelligence (AI), has attracted substantial…
Ensuring a reliable communication in wireless networks strictly depends on the effective estimation of the link quality, which is particularly challenging when propagation environment for radio signals significantly varies. In such…
Deep learning based on artificial neural networks is a powerful machine learning method that, in the last few years, has been successfully used to realize tasks, e.g., image classification, speech recognition, translation of languages,…
Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and…
The recently commercialized fifth-generation (5G) wireless communication networks achieved many improvements, including air interface enhancement, spectrum expansion, and network intensification by several key technologies, such as massive…