41 research outputs found
The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches
The evolution of network virtualization and native artificial intelligence
(AI) paradigms have conceptualized the vision of future wireless networks as a
comprehensive entity operating in whole over a digital platform, with smart
interaction with the physical domain, paving the way for the blooming of the
Digital Twin (DT) concept. The recent interest in the DT networks is fueled by
the emergence of novel wireless technologies and use-cases, that exacerbate the
level of complexity to orchestrate the network and to manage its resources.
Driven by AI, the key principle of the DT is to create a virtual twin for the
physical entities and network dynamics, where the virtual twin will be
leveraged to generate synthetic data and offer an on-demand platform for AI
model training. Despite the common understanding that AI is the seed for DT, we
anticipate that the DT and AI will be enablers for each other, in a way that
overcome their limitations and complement each other benefits. In this article,
we dig into the fundamentals of DT, where we reveal the role of DT in unifying
model-driven and data-driven approaches, and explore the opportunities offered
by DT in order to achieve the optimistic vision of 6G networks. We further
unfold the essential role of the theoretical underpinnings in unlocking further
opportunities by AI, and hence, we unveil their pivotal impact on the
realization of reliable, efficient, and low-latency DT
On the Performance of Non-Orthogonal Multiple Access Systems with Imperfect Successive Interference Cancellation
Non-orthogonal multiple access (NOMA) technique has sparked a growing
research interest due to its ability to enhance the overall spectral efficiency
of wireless systems. In this paper, we investigate the pairwise error
probability (PEP) performance of conventional NOMA systems, where an exact
closed form expression for the PEP is derived for different users, to give some
insight about the reliability of the far and near users. Through the derivation
of PEP expressions, we demonstrate that the maximum achievable diversity order
is proportional to the user's order. The obtained error probability expressions
are used to formulate an optimization problem that minimizes the overall bit
error rate (BER) under power and error rate threshold constrains. The derived
analytical results, corroborated by Monte Carlo simulations, are presented to
show the diversity order and error rate performance of each individual user
Performance of reconfigurable intelligent surfaces in the presence of generalized Gaussian noise
In this letter, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted communications, under the assumption of generalized Gaussian noise (GGN), over Rayleigh fading channels. Specifically, we consider an RIS, equipped with N reflecting elements, and derive a novel closed-form expression for the symbol error rate (SER) of arbitrary modulation schemes. The usefulness of the derived new expression is that it can be used to capture the SER performance in the presence of special additive noise distributions such as Gamma, Laplacian, and Gaussian noise. These special cases are also considered and their associated asymptotic SER expressions are derived, and then employed to quantify the achievable diversity order of the system. The theoretical framework is corroborated by numerical results, which reveal that the shaping parameter of the GGN (α) has a negligible effect on the diversity order of RIS-assisted systems, particularly for large α values. Accordingly, the maximum achievable diversity order is determined by N
Performance of reconfigurable intelligent surfaces in the presence of generalized Gaussian noise
In this letter, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted communications, under the assumption of generalized Gaussian noise (GGN), over Rayleigh fading channels. Specifically, we consider an RIS, equipped with N reflecting elements, and derive a novel closed-form expression for the symbol error rate (SER) of arbitrary modulation schemes. The usefulness of the derived new expression is that it can be used to capture the SER performance in the presence of special additive noise distributions such as Gamma, Laplacian, and Gaussian noise. These special cases are also considered and their associated asymptotic SER expressions are derived, and then employed to quantify the achievable diversity order of the system. The theoretical framework is corroborated by numerical results, which reveal that the shaping parameter of the GGN (α) has a negligible effect on the diversity order of RIS-assisted systems, particularly for large α values. Accordingly, the maximum achievable diversity order is determined by N
A prospective look: key enabling technologies, applications and open research topics in 6G networks
The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is mainly driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks, which are expected to bring transformative changes to this premise. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. In particular, the present paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a comprehensive study of the 6G vision and outlining seven of its disruptive technologies, i.e., mmWave communications, terahertz communications, optical wireless communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss the associated requirements, key challenges, and open research problems. These discussions are thereafter used to open up the horizon for future research directions
Guest editorial: special issue on recent advances in security and privacy for 6G networks
No abstract available
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
The fifth generation (5G) mobile networks are envisaged to enable a plethora
of breakthrough advancements in wireless technologies, providing support of a
diverse set of services over a single platform. While the deployment of 5G
systems is scaling up globally, it is time to look ahead for beyond 5G systems.
This is driven by the emerging societal trends, calling for fully automated
systems and intelligent services supported by extended reality and haptics
communications. To accommodate the stringent requirements of their prospective
applications, which are data-driven and defined by extremely low-latency,
ultra-reliable, fast and seamless wireless connectivity, research initiatives
are currently focusing on a progressive roadmap towards the sixth generation
(6G) networks. In this article, we shed light on some of the major enabling
technologies for 6G, which are expected to revolutionize the fundamental
architectures of cellular networks and provide multiple homogeneous artificial
intelligence-empowered services, including distributed communications, control,
computing, sensing, and energy, from its core to its end nodes. Particularly,
this paper aims to answer several 6G framework related questions: What are the
driving forces for the development of 6G? How will the enabling technologies of
6G differ from those in 5G? What kind of applications and interactions will
they support which would not be supported by 5G? We address these questions by
presenting a profound study of the 6G vision and outlining five of its
disruptive technologies, i.e., terahertz communications, programmable
metasurfaces, drone-based communications, backscatter communications and
tactile internet, as well as their potential applications. Then, by leveraging
the state-of-the-art literature surveyed for each technology, we discuss their
requirements, key challenges, and open research problems
Large Language Models for Telecom: The Next Big Thing?
The evolution of generative artificial intelligence (GenAI) constitutes a
turning point in reshaping the future of technology in different aspects.
Wireless networks in particular, with the blooming of self-evolving networks,
represent a rich field for exploiting GenAI and reaping several benefits that
can fundamentally change the way how wireless networks are designed and
operated nowadays. To be specific, large language models (LLMs), a subfield of
GenAI, are envisioned to open up a new era of autonomous wireless networks, in
which a multimodal large model trained over various Telecom data, can be
fine-tuned to perform several downstream tasks, eliminating the need for
dedicated AI models for each task and paving the way for the realization of
artificial general intelligence (AGI)-empowered wireless networks. In this
article, we aim to unfold the opportunities that can be reaped from integrating
LLMs into the Telecom domain. In particular, we aim to put a forward-looking
vision on a new realm of possibilities and applications of LLMs in future
wireless networks, defining directions for designing, training, testing, and
deploying Telecom LLMs, and reveal insights on the associated theoretical and
practical challenges