6G Radio Testbeds: Requirements, Trends, and Approaches

The proof of the pudding is in the eating - that is why 6G testbeds are essential in the progress towards the next generation of wireless networks. Theoretical research towards 6G wireless networks is proposing advanced technologies to serve new applications and drastically improve the energy performance of the network. Testbeds are indispensable to validate these new technologies under more realistic conditions. This paper clarifies the requirements for 6G radio testbeds, reveals trends, and introduces approaches towards their development

Why are glass-forming liquids non-Arrhenius?

A major mystery of glass-forming liquids is the non-Arrhenius temperature-dependence of the average relaxation time. This paper briefly reviews the classical phenomenological models for this phenomenon - the free-volume model and the entropy model - and critiques against these models. We then discuss a recent model [Dyre, Olsen, and Christensen, Phys. Rev. B 53, 2171 (1996)] according to which the activation energy for the average relaxation time is determined by the work done in shoving aside the surrounding liquid to create space needed for a flow event. In this model the non-Arrhenius temperature-dependence is a consequence of the fact that the instantaneous (infinite-frequency) shear modulus increases upon cooling.Comment: 18 pages, plain RevTex file, no figure

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communicatio

Z' mass limits and the naturalness of supersymmetry

The discovery of a 125 GeV Higgs boson and rising lower bounds on the masses of superpartners have lead to concerns that supersymmetric models are now fine tuned. Large stop masses, required for a 125 GeV Higgs, feed into the electroweak symmetry breaking conditions through renormalisation group equations forcing one to fine tune these parameters to obtain the correct electroweak vacuum expectation value. Nonetheless this fine tuning depends crucially on our assumptions about the supersymmetry breaking scale. At the same time $U(1)$ extensions provide the most compelling solution to the $\mu$-problem, which is also a naturalness issue, and allow the tree level Higgs mass to be raised substantially above $M_Z$. These very well motivated supersymmetric models predict a new $Z'$ boson which could be discovered at the LHC and the naturalness of the model requires that the $Z'$ boson mass should not be too far above the TeV scale. Moreover this fine tuning appears at the tree level, making it less dependent on assumptions about the supersymmetry breaking mechanism. Here we study this fine tuning for several $U(1)$ supersymmetric extensions of the Standard Model and compare it to the situation in the MSSM where the most direct tree level fine tuning can be probed through chargino mass limits. We show that future LHC $Z'$ searches are extremely important for challenging the most natural scenarios in these models.Comment: 58 pages, 5 figures; typos corrected, references added; matches version to be published in Phys. Rev.

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