1,870 research outputs found
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
Intelligent-Reflecting-Surface-Assisted UAV Communications for 6G Networks
In 6th-Generation (6G) mobile networks, Intelligent Reflective Surfaces
(IRSs) and Unmanned Aerial Vehicles (UAVs) have emerged as promising
technologies to address the coverage difficulties and resource constraints
faced by terrestrial networks. UAVs, with their mobility and low costs, offer
diverse connectivity options for mobile users and a novel deployment paradigm
for 6G networks. However, the limited battery capacity of UAVs, dynamic and
unpredictable channel environments, and communication resource constraints
result in poor performance of traditional UAV-based networks. IRSs can not only
reconstruct the wireless environment in a unique way, but also achieve wireless
network relay in a cost-effective manner. Hence, it receives significant
attention as a promising solution to solve the above challenges. In this
article, we conduct a comprehensive survey on IRS-assisted UAV communications
for 6G networks. First, primary issues, key technologies, and application
scenarios of IRS-assisted UAV communications for 6G networks are introduced.
Then, we put forward specific solutions to the issues of IRS-assisted UAV
communications. Finally, we discuss some open issues and future research
directions to guide researchers in related fields
Transition technologies towards 6G networks
[EN] The sixth generation (6G) mobile systems will create new markets, services, and industries making possible a plethora of new opportunities and solutions. Commercially successful rollouts will involve scaling enabling technologies, such as cloud radio access networks, virtualization, and artificial intelligence. This paper addresses the principal technologies in the transition towards next generation mobile networks. The convergence of 6G key-performance indicators along with evaluation methodologies and use cases are also addressed. Free-space optics, Terahertz systems, photonic integrated circuits, softwarization, massive multiple-input multiple-output signaling, and multi-core fibers, are among the technologies identified and discussed. Finally, some of these technologies are showcased in an experimental demonstration of a mobile fronthaul system based on millimeter 5G NR OFDM signaling compliant with 3GPP Rel. 15. The signals are generated by a bespoke 5G baseband unit and transmitted through both a 10 km prototype multi-core fiber and 4 m wireless V-band link using a pair of directional 60 GHz antennas with 10 degrees beamwidth. Results shown that the 5G and beyond fronthaul system can successfully transmit signals with both wide bandwidth (up to 800 MHz) and fully centralized signal processing. As a result, this system can support large capacity and accommodate several simultaneous users as a key candidate for next generation mobile networks. Thus, these technologies will be needed for fully integrated, heterogeneous solutions to benefit from hardware commoditization and softwarization. They will ensure the ultimate user experience, while also anticipating the quality-of-service demands that future applications and services will put on 6G networks.This work was partially funded by the blueSPACE and 5G-PHOS 5G-PPP phase 2 projects, which have received funding from the European Union's Horizon 2020 programme under Grant Agreements Number 762055 and 761989. D. PerezGalacho acknowledges the funding of the Spanish Science Ministry through the Juan de la Cierva programme.Raddo, TR.; Rommel, S.; Cimoli, B.; Vagionas, C.; PĂ©rez-Galacho, D.; Pikasis, E.; Grivas, E.... (2021). Transition technologies towards 6G networks. EURASIP Journal on Wireless Communications and Networking. 2021(1):1-22. https://doi.org/10.1186/s13638-021-01973-91222021
Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
Without any doubt, Machine Learning (ML) will be an important driver of
future communications due to its foreseen performance when applied to complex
problems. However, the application of ML to networking systems raises concerns
among network operators and other stakeholders, especially regarding
trustworthiness and reliability. In this paper, we devise the role of network
simulators for bridging the gap between ML and communications systems. In
particular, we present an architectural integration of simulators in ML-aware
networks for training, testing, and validating ML models before being applied
to the operative network. Moreover, we provide insights on the main challenges
resulting from this integration, and then give hints discussing how they can be
overcome. Finally, we illustrate the integration of network simulators into
ML-assisted communications through a proof-of-concept testbed implementation of
a residential Wi-Fi network
Advancements and Challenges in Energy-efficient 6G Mobile Communication Network
The arrival of 6G mobile communication networks is anticipated to revolutionize the technological landscape, bringing about profound innovations. This research paper explores the various technological advancements that will pave the way for the advent of 6G networks, with a particular focus on addressing energy consumption. It is widely recognized that energy efficiency plays a crucial role in the evolution of 6G networks. To enhance network performance, user experience, and resource management, the integration of Artificial Intelligence (AI) is expected to be a pivotal technology. AI-based solutions can effectively optimize energy usage and contribute to the overall efficiency of 6G networks. Furthermore, the incorporation of wireless communication systems, telecommunication, and the Internet of Things (IoT) will be integral to the infrastructure of 6G networks. The need for significant enhancements in 6G networks is also examined in this study. Ensuring the safety and protection of 6G networks from cyber threats becomes increasingly important due to the growing reliance on networked communication and the sensitive nature of transmitted information. Cutting-edge security methods such as homomorphic encryption and blockchain technology may be essential in this regard. Moreover, this research paper explores the impact of 6G networks on various domains and discusses the challenges that must be overcome to unlock the technology’s full potential. To ensure responsible adoption and usage of 6G networks, the development of new business models and regulatory frameworks may be necessary to support their implementation while addressing energy consumption concerns
Trends in Standardization Towards 6G
Mobile networks have always been an indispensable part of a fully connected digital society. The industry and academia have joined hands to develop technologies for the anticipated future wireless communication. The predicted Key Performance Indicators (KPIs) and use cases for the 6G networks have raised the bar high. 6G networks are developing to provide the required infrastructure for many new devices and services. The 6G networks are conceptualized to partially inherit 5G technologies and standards but they will open the ground for innovations. This study provides the vision and requirements for beyond 5G (B5G) networks and emphasizes our vision on the required standards to reach a fully functional and interoperable 6G era in general. We highlight various KPIs and enabling technologies for the B5G networks. In addition, standardization activities and initiatives concerning challenges in the se of spectrum are diuscussed in detail.This work was supported by FCT/MCTES through national funds and
when applicable cofounded EU funds under the project UIDB/50008/2020,
ORCIP (22141-01/SAICT/2016) and TeamUp5G. TeamUp5G has received
funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant
agreement No. 813391.info:eu-repo/semantics/publishedVersio
Communication-Assisted Sensing in 6G Networks
The exploration of coordination gain achieved through the synergy of sensing
and communication (S&C) functions plays a vital role in improving the
performance of integrated sensing and communication systems. This paper focuses
on the optimal waveform design for communication-assisted sensing (CAS) systems
within the context of 6G perceptive networks. In the CAS process, the base
station actively senses the targets through device-free wireless sensing and
simultaneously transmits the pertinent information to end-users. In our
research, we establish a CAS framework grounded in the principles of
rate-distortion theory and the source-channel separation theorem (SCT) in lossy
data transmission. This framework provides a comprehensive understanding of the
interplay between distortion, coding rate, and channel capacity. The purpose of
waveform design is to minimize the sensing distortion at the user end while
adhering to the SCT and power budget constraints. In the context of target
response matrix estimation, we propose two distinct waveform strategies: the
separated S&C and dual-functional waveform schemes. In the former strategy, we
develop a simple one-dimensional search algorithm, shedding light on a notable
power allocation tradeoff between the S&C waveform. In the latter scheme, we
conceive a heuristic mutual information optimization algorithm for the general
case, alongside a modified gradient projection algorithm tailored for the
scenarios with independent sensing sub-channels. Additionally, we identify the
presence of both subspace tradeoff and water-filling tradeoff. Finally, we
validate the effectiveness of the proposed algorithms through numerical
simulations
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