Lower Bounds on the Success Probability for ad hoc Networks with local FDMA scheduling

This paper studies the performance of ad hoc networks with local FDMA scheduling using stochastic point processes. In such networks, the Poisson assumption is not justified due to interdependencies between points introduced by scheduling. For this reason, an upper bound on the second reduced moment measure is derived. Using this result, two lower bounds on the success probability are given, based on the second order product density and a non-homogeneous Poisson approximation. The relative performance of local FDMA is compared to random channel access. It is shown that the relative outage probability reduction of local FDMA highly depends on the SIR threshold as well as on the ratio of transmission distance to orthogonalization distance. If these two quantities are small, the improvement is high; the number of channels has only a minor effect on the relative improvement

Interference Mitigation in Frequency Hopping Ad Hoc Networks

Radio systems today exhibit a degree of flexibility that was unheard of only a few years ago. Software-defined radio architectures have emerged that are able to service large swathes of spectrum, covering up to several GHz in the UHF bands. This dissertation investigates interference mitigation techniques in frequency hopping ad hoc networks that are capable of exploiting the frequency agility of software-defined radio platforms

Multiple Access Interference Mitigation Through Multi-Level Locally Orthogonal FH-CDMA

Multi-level locally orthogonal frequency hopping code division multiple access (MLLO-FH-CDMA) is introduced as a novel method to reduce self-interference in large scale FH-CDMA ad hoc networks. It is analyzed in a stochastic geometry scenario and verified with simulations. The performance gains of multi-level hopping are shown by comparing it to standard FH-CDMA channel access

Multiple access interference mitigation through multi-level locally orthogonal FH-CDMA

Abstract—Multi-level locally orthogonal frequency hopping code division multiple access (MLLO-FH-CDMA) is introduced as a novel method to reduce self-interference in large scale FH-CDMA ad hoc networks. It is analyzed in a stochastic geometry scenario and verified with simulations. The performance gains of multi-level hopping are shown by comparing it to standard FH-CDMA channel access. I

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial