5G spectrum sharing

In this paper an overview is given of the current status of 5G industry standards, spectrum allocation and use cases, followed by initial investigations of new opportunities for spectrum sharing in 5G using cognitive radio techniques, considering both licensed and unlicensed scenarios. A particular attention is given to sharing millimeter-wave frequencies, which are of prominent importance for 5G

Coordinated initial access in millimetre wave standalone networks

In this paper, a novel coordinated initial access (IA) scheme for clustered millimeter wave small cells (mmSCs) is proposed for the fifth generation mobile communication networks (5G). This solution is a method for efficient and fast initial access for ultra-dense millimeter wave standalone networks without presence of overlaid legacy networks operating on lower frequency. In contrast to the current full beam sweep scheme, where time consuming exhaustive searching is employed, the mmSCs within one cluster will perform the IA procedure in a coordinated manner based on the power delay profile (PDP) measurement reports shared with each other via the backhaul links and thereby avoiding the full beam sweep. The proposed scheme significantly reduces the initial access time, enhances the access robustness and reduces the cost and complexity of the mobile terminals

Demo abstract: a demonstration of automatic configuration of OpenFlow in wireless ad hoc networks

Using OpenFlow, a network can be controlled from one or more servers called controllers. In the demonstration, we show automatic configuration of OpenFlow in a wireless ad hoc network, deployed on a portable testbed, using MININET-WiFi (an emulator for software defined wireless networks). Automatic configuration is shown using a GUI (Graphical User Interface) which shows wireless nodes discovered by the controller. In addition, a video clip is streamed from one node to another and displayed in real time. The demonstration includes automatic configuration in the scenarios in which nodes move from one location to another

A Survey of Cognitive Radio Access to TV White Spaces

Cognitive radio is being intensively researched as the enabling technology for license-exempt access to the so-called TV White Spaces (TVWS), large portions of spectrum in the UHF/VHF bands which become available on a geographical basis after digital switchover. Both in the US, and more recently, in the UK the regulators have given conditional endorsement to this new mode of access. This paper reviews the state-of-the-art in technology, regulation, and standardisation of cognitive access to TVWS. It examines the spectrum opportunity and commercial use cases associated with this form of secondary access

Quantum Monte Carlo Analysis of Exchange and Correlation in the Strongly Inhomogeneous Electron Gas

We use variational quantum Monte Carlo to calculate the density-functional exchange-correlation hole n_{xc}, the exchange-correlation energy density e_{xc}, and the total exchange-correlation energy E_{xc}, of several electron gas systems in which strong density inhomogeneities are induced by a cosine-wave potential. We compare our results with the local density approximation and the generalized gradient approximation. It is found that the nonlocal contributions to e_{xc} contain an energetically significant component, the magnitude, shape, and sign of which are controlled by the Laplacian of the electron density.Comment: 4 pages, 3 figure

Deep learning based autoencoder for m-user wireless interference channel physical layer design

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this paper, we address the dynamic interference in a multi-user Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using n-psk or n-QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged