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We demonstrate a distributed and a centralized 4G/5G compliant approach to minimize signaling and latency related to user mobility in cellular networks. This is crucial due to the densification of networks and the additional signaling introduced by the new 5G service-based architecture. By exploiting standardized protocols, our solutions dynamically reorganize the association between nodes in radio access network (RAN) and the core. We validated the proposed approaches using real user mobility datasets. Our results show that both our distributed and centralized solutions significantly reduce the signaling between core and RAN compared to the traditional approach based on geographical proximity. As a result, both approaches significantly reduce the average handover procedure processing time. Moreover, by relying on locally available information, the distributed approach can quickly adapt to changes in the user movement patterns as they happen. [ABSTRACT FROM AUTHOR]

We present a novel way of considering in-network computing (INC), using ideas from statistical mechanics. We model the execution of a distributed computation with graphs called functional topologies, which allows us to provide a formal definition for degeneracy and redundancy in the context of INC. Degeneracy for INC is defined as the structural multiplicity of possible options available within the network to perform the same function with a given macroscopic property (e.g., delay). Two degenerate structures can partially overlap. Redundancy, on the other hand, does not allow overlapping between the functional graphs. We present an efficient algorithm to determine all these multiple options and compute both degeneracy and redundancy. Our results show that by exploiting the set of possible degenerate alternatives, we can significantly improve the successful computation rate of a symmetric function, while still being able to satisfy requirements such as delay or energy consumption. [ABSTRACT FROM AUTHOR]

Neutral host SCPs represent a key element of the 5G vision of ultra-dense mobile networks. However, current business models mostly focus on multi-year agreements for large venues, such as stadiums and hotel chains. These business agreements are regulated through binding SLAs, which tend to be too cumbersome and costly for smaller-scale SCPs. As a result, the neutral host model does not scale up to its full potential. In this article, we propose a framework to enable the participation of small- to medium-sized players in the cellular market as providers offering network resources to MNOs. To this purpose, we review the current and emerging spectrum and technology opportunities that SCPs can use for neutral host deployments. We also propose the use of blockchain-enabled smart contracts as a simple and cost-efficient alternative to traditional SLAs for small-scale SCPs. To demonstrate this, we describe a proof of concept implementation of an Ethereum-based smart contract platform for best effort service between an SCP and an MNO. Our simulations on potential smart contract-based deployments in city center Dublin show that the received signal strength in the considered area will increase by an average of 10 percent. [ABSTRACT FROM AUTHOR]

Licensed shared access (LSA) is a spectrum sharing mechanism where bandwidth is shared between a primary network, called incumbent, and a secondary mobile network. In this paper, we address dynamic spectrum management mechanisms for LSA systems. We propose a fair spectrum management algorithm for distributing incumbent's available spectrum among mobile networks. Then, we adapt the proposed algorithm to take mobile network operator's regulatory compliance aspect into account and penalize the misbehaving network operators in spectrum allocation. We extend our results to the scenario where more than one incumbent offer spectrum to the mobile operators in a service area and propose various protocols, which ensure long term fair spectrum allocation within the individual LSA networks. Finally, we numerically evaluate the performance of the proposed spectrum allocation algorithms and compare them using various performance metrics. For the single incumbent case, the numerical results show that the spectrum allocation is fair when the mobile operators follow the spectrum access regulations. We demonstrate the effect of our proposed penalty functions on spectrum allocation when the operators do not comply with the regulatory aspects. For the multi-incumbent scenario, the results show a tradeoff between efficient spectrum allocation and flexibility in spectrum access for our proposed algorithms. [ABSTRACT FROM AUTHOR]

Some of the new trends emerging in future wireless networks enable a vastly increased fluidity in accessing a wide range of resources, thus supporting flexible network composition and dynamic allocation of resources to VNOs. In this work we study a new resource allocation opportunity that is enabled by the cloud radio access network architecture. In particular, we investigate the relationship between cloud-based antennas and spectrum as two important resources in virtualized wireless networks. We analyze the interplay between spectrum and antennas in the context of an auction-based allocation mechanism through which VNOs can bid for a combination of the two types of resources. Our analysis shows that the complementarity and partial substitutability of the two resources significantly impact the results of the allocation of those resources and uncovers the possibility of divergent interests between the spectrum and the infrastructure providers. [ABSTRACT FROM PUBLISHER]

This paper proposes a two-stage process for assigning fungible orthogonal channel sets to multiple cognitive radios (CRs) for opportunistic spectrum access. Assigning orthogonal channel sets to the CRs eliminates the possibility of collision among them, and allows the CRs to focus on avoiding collisions with the primary user (PU). In particular, each CR uses a learning-based dynamic channel selection (DCS) algorithm to maximize the exploitation of the assigned channels. We propose a neural network that can accurately estimate the performance of the adopted learning-based DCS algorithm on a set of channels, using the duty cycle and the complexity of the PU's behavior on the channels. Our simulations on synthetic and real measurement data sets show that the proposed channel sets allocation algorithm, together with the neural network, significantly outperforms a method that selects channels with the lowest duty cycle. [ABSTRACT FROM PUBLISHER]

This paper suggests that the regulatory tool known as the Block Edge Mask (BEM), which is used to define the technical conditions governing a spectrum license, can be used as an instrument to jointly drive spectrum and energy efficiency in a network. The paper explores this concept and goes on to show how networks can generate waveforms which comply with the energy limits of the BEM and at the same time make best use of the spectral resources available to them. The focus of the paper is on orthogonal frequency division multiplexing (OFDM) waveforms. The spectrum- and energy-efficient BEM-compliant waveforms are generated using a process that optimally combines symbol shaping and subcarrier-power loading to create a waveform that minimizes the difference between the transmitted wave and BEM. Practical considerations relating to peak-to-average-power-ratio (PAPR) reduction and time and frequency synchronization issues are explored to ensure the BEM-compliant waveforms can be transmitted with minimal changes to the transceiver. It is envisaged that these techniques will be used by transmitters that change their frequency of operation and in doing so need to shape their transmitted waveforms to comply with the different BEMs governing the use of the different spectrum blocks they wish to access. [ABSTRACT FROM AUTHOR]

We explore the question of when learning improves the performance of opportunistic dynamic channel selection by characterizing the primary user (PU) activity using the concept of Lempel-Ziv complexity. We evaluate the effectiveness of a reinforcement learning algorithm by testing it with real spectrum occupancy data collected in the GSM, ISM, and DECT bands. Our results show that learning performance is highly correlated with the level of PU activity and the amount of structure in the use of spectrum. For low levels of PU activity and/or high complexity in its utilization of channels, reinforcement learning performs no better than simple random channel selection. We suggest that Lempel-Ziv complexity might be one of the features considered by a cognitive radio when deciding which channels to opportunistically explore. [ABSTRACT FROM AUTHOR]

The article focuses on the design and implementation of a visual control of a robotic system composed of a dexterous hand and video camera. The aim of the proposed system is to reproduce the movements of a human hand in order to learn complex manipulation tasks or to interact with the user. The method uses fingertips as features, extracted from gray level images with a black background. Each finger is considered as a planar manipulator. The hand posture is defined, reconstructing its joint angles. The coordinates of the four fingertips are employed to solve the inverse kinematics problem for joint angles.

All spectrum opportunities are not created equally. The diverse behavior of different primary users (PUs) in various spectrum bands affects a cognitive radio's ability to exploit spectrum holes. In a heavily used spectrum environment, the availability of unutilized, uncluttered, and easily accessible white space spectrum is unlikely. Rather, cognitive radios must explore the gray spectrum, those bands in which other users exist and in which the existence of exploitable spectrum holes is more difficult to discern and access. [ABSTRACT FROM PUBLISHER]