Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC

[EN] One important issue that needs to be addressed in order to provide effective massive deployments of IoT devices is access control. In 5G cellular networks, the Access Class Barring (ACB) method aims at increasing the total successful access probability by delaying randomly access requests. This mechanism can be controlled through the barring rate, which can be easily adapted in networks where Human-to-Human (H2H) communications are prevalent. However, in scenarios with massive deployments such as those found in IoT applications, it is not evident how this parameter should be set, and how it should adapt to dynamic traffic conditions. We propose a double deep reinforcement learning mechanism to adapt the barring rate of ACB under dynamic conditions. The algorithm is trained with simultaneous H2H and Machine-to-Machine (M2M) traffic, but we perform a separate performance evaluation for each type of traffic. The results show that our proposed mechanism is able to reach a successful access rate of 100 % for both H2H and M2M UEs and reduce the mean number of preamble transmissions while slightly affecting the mean access delay, even for scenarios with very high load. Also, its performance remains stable under the variation of different parameters. (C) 2019 Elsevier B.V. All rights reserved.The research of D. Pacheco-Paramo was supported by Universidad Sergio Arboleda, P.t. Tecnologias para la inclusion social y la competitividad economica. 0.E.6. The research of L Tello-Oquendo was conducted under project CONV.2018-ING010. Universidad Nacional de Chimborazo. The research of V. Pla and J. Martinez-Bauset was supported by Grant PGC2018-094151-B-I00 (MCIU/AEI/FEDER,UE).Pacheco-Paramo, DF.; Tello-Oquendo, L.; Pla, V.; Martínez Bauset, J. (2019). Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC. Ad Hoc Networks. 94:1-14. https://doi.org/10.1016/j.adhoc.2019.101939S1149

Scheduling M2M traffic over LTE uplink of a dense small cell network

We present an approach to schedule Long Term Evolution (LTE) uplink (UL) Machine-to-Machine (M2M) traffic in a densely deployed heterogeneous network, over the street lights of a big boulevard for smart city applications. The small cells operate with frequency reuse 1, and inter-cell interference (ICI) is a critical issue to manage. We consider a 3rd Generation Partnership Project (3GPP) compliant scenario, where single-carrier frequency-division multiple access (SC-FDMA) is selected as the multiple access scheme, which requires that all resource blocks (RBs) allocated to a single user have to be contiguous in the frequency within each time slot. This adjacency constraint limits the flexibility of the frequency-domain packet scheduling (FDPS) and inter-cell interference coordination (ICIC), when trying to maximize the scheduling objectives, and this makes the problem NP-hard. We aim to solve a multi-objective optimization problem, to maximize the overall throughput, maximize the radio resource usage and minimize the ICI. This can be modelled through a mixed-integer linear programming (MILP) and solved through a heuristic implementable in the standards. We propose two models. The first one allocates resources based on the three optimization criteria, while the second model is more compact and is demonstrated through numerical evaluation in CPLEX, to be equivalent in the complexity, while it performs better and executes faster. We present simulation results in a 3GPP compliant network simulator, implementing the overall protocol stack, which support the effectiveness of our algorithm, for different M2M applications, with respect to the state-of-the-art approaches

A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communication and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions

Heterogeneous Networks for the IoT and Machine Type Communications

The Internet of Things promises to be a key-factor in the forthcoming industrial and social revolution. The Internet of Things concept rely on pervasive communications where ’things’ are ’always connected’. The focus of the thesis is on Heterogeneous Networks for Internet of Things and Machine Type Communications. Heterogeneous Networks are an enabling factor of paramount important in order to achieve the ’always connected’ paradigm. On the other hand, Machine Type Communications are deeply different from Human-to-Human communications both in terms of traffic patterns and requirements. This thesis investigate both concepts. In particular, here are studied short and long range solutions for Machine-to-machine applications. For this work a dual approach has been followed: for the short-range solutions analysis an experimental approach has been privileged; meanwhile for the long-range solutions analysis a theoretical and simulation approach has been preferred. In both case, a particular attention has been given to the feasibility of the solutions proposed, hence solutions based on products that already exist in the market have been privileged

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique