No abstract

In this paper we propose distributed load management in smart grid infrastructures to control the power demand at peak hours, by means of dynamic pricing strategies. The distributed solution that we propose is based on a network congestion game, which can be demonstrated to converge in a finite number of steps to a pure Nash equilibrium solution. We take advantage of the remarkable property of congestion games, according to which they are equivalent to potential games. We define a potential function characterized by a meaningful physical interpretation, so that we obtain the favorable result that the optimal local solution of each selfish consumer is also the solution of a global objective. We evaluate this approach for managing both the demand and the grid load and we show that load control can be effectively achieved implementing a distributed solution, which significantly reduce the signaling burden over the network.

No abstract

In this paper, we provide an analysis of selforganized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

No abstract

Licensed-Assisted Access (LAA) enabled LTE operators to access unlicensed spectrum while adhering to Listen-Before-Talk (LBT) requirements. LAA is based on enhancements over 4G LTE technology. Differently, 5G New Radio (NR) technology is being designed from the start to support operation in unlicensed bands through a technology referred to as NRbased access to unlicensed spectrum (NR-U). A large amount of unlicensed spectrum has been allocated in millimeter-wave (mmWave) bands, making it an attractive candidate for NR-U. However, the propagation characteristics in mmWave often require beam-based transmissions. Beam-based transmissions enhance spatial reuse, but also complicate interference management due to the dynamic nature of the directional antennas. Therefore, some major design principles need to be revisited in NR-U to address coexistence. This paper elaborates on the design challenges, opportunities, and solutions for NR-U by taking into account beam-based transmissions and the worldwide regulatory requirements. In particular, different problems and the potential solutions related to channel access procedures, frame structure, initial access procedures, HARQ procedures, and scheduling schemes are discussed.

Abstract-In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks (HetNets) with split control and data planes -a candidate architecture for meeting future capacity, quality of service and energy efficiency demands. In such architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, while the data BSs handle UE data. An implication of this split architecture is that, an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both the data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large scale minimize drive testing (MDT) reports data, and detects outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly detecting algorithms, i.e. k− nearest neighbor and local outlier factor based anomaly detector, within the control COD. On the other hand, for data cells COD, we propose a heuristic grey-prediction based approach, which can work with the small number of UEs in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity, by receiving a periodic update of the received signal reference power (RSRP) statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of residual error that is inherent to grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm which can be applied to both planes. Our COC solution utilizes an actor critic (AC) based reinforcement learning (RL) algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage, and also compensate for the detected outage in a reliable manner.

No abstract