Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems

Spectrum Resource Management and Interference Mitigation for D2D Communications with Awareness of BER Constraint in mmWave 5G Underlay Network

The work presented in this paper deals with the issue of massive demands for higher capacity. For that matter, we investigate the spectrum resource management in outdoor mmWave cell for the uplink of cellular and D2D communications. Indeed, we provide a first insight how to optimize the system performance in terms of achievable throughput while realizing a compromise between the large number of admitted devices and the generated interference constraint. We propose a mathematical formulation of the optimization objective which falls in the mixed integer-real optimization scheme. To overcome its complexity, we apply a heuristic algorithm and test its efficiency through simulation results with a particular regard to the BER impact in the QoS.Comment: Accepted in IEEE Symposium on Computers and Communications June, 201

Joint Caching and Resource Allocation in D2D-Assisted Wireless HetNet

5G networks are required to provide very fast and reliable communications while dealing with the increase of users traffic. In Heterogeneous Networks (HetNets) assisted with Device-to-Device (D2D) communication, traffic can be offloaded to Small Base Stations or to users to improve the network's successful data delivery rate. In this paper, we aim at maximizing the average number of files that are successfully delivered to users, by jointly optimizing caching placement and channel allocation in cache-enabled D2D-assisted HetNets. At first, an analytical upper-bound on the average content delivery delay is derived. Then, the joint optimization problem is formulated. The non-convexity of the problem is alleviated, and the optimal solution is determined. Due to the high time complexity of the obtained solution, a low-complex sub-optimal approach is proposed. Numerical results illustrate the efficacy of the proposed solutions and compare them to conventional approaches. Finally, by investigating the impact of key parameters, e.g. power, caching capacity, QoS requirements, etc., guidelines to design these networks are obtained.Comment: 24 pages, 5 figures, submitted to IEEE Transactions on Wireless Communications (12-Feb-2019

Intelligent Reflecting Surface Enhanced D2D Cooperative Computing

This paper investigates a device-to-device (D2D) cooperative computing system, where an user can offload part of its computation task to nearby idle users with the aid of an intelligent reflecting surface (IRS). We propose to minimize the total computing delay via jointly optimizing the computation task assignment, transmit power, bandwidth allocation, and phase beamforming of the IRS. To solve the formulated problem, we devise an alternating optimization algorithm with guaranteed convergence. In particular, the task assignment strategy is derived in closed-form expression, while the phase beamforming is optimized by exploiting the semi-definite relaxation (SDR) method. Numerical results demonstrate that the IRS enhanced D2D cooperative computing scheme can achieve a much lower computing delay as compared to the conventional D2D cooperative computing strategy

Distributed Resource Allocation in Device-to-Device Enhanced Cellular Networks

Cellular network performance can significantly benefit from direct device-to-device (D2D) communication, but interference from cochannel D2D communication limits the performance gain. In hybrid networks consisting of D2D and cellular links, finding the optimal interference management is challenging. In particular, we show that the problem of maximizing network throughput while guaranteeing predefined service levels to cellular users is non- convex and hence intractable. Instead, we adopt a distributed approach that is computationally extremely efficient, and requires minimal coordination, communication and cooperation among the nodes. The key algorithmic idea is a signaling mechanism that can be seen as a fictional pricing mechanism, that the base stations optimize and transmit to the D2D users, who then play a best response (i.e., selfishly) to this signal. Numerical results show that our algorithms converge quickly, have low overhead, and achieve a significant throughput gain, while maintaining the quality of cellular links at a predefined service level

V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks

Benefited from the widely deployed infrastructure, the LTE network has recently been considered as a promising candidate to support the vehicle-to-everything (V2X) services. However, with a massive number of devices accessing the V2X network in the future, the conventional OFDM-based LTE network faces the congestion issues due to its low efficiency of orthogonal access, resulting in significant access delay and posing a great challenge especially to safety-critical applications. The non-orthogonal multiple access (NOMA) technique has been well recognized as an effective solution for the future 5G cellular networks to provide broadband communications and massive connectivity. In this article, we investigate the applicability of NOMA in supporting cellular V2X services to achieve low latency and high reliability. Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed to tackle the technical hurdles in designing high spectral efficient scheduling and resource allocation schemes in the ultra dense topology. We then extend it to a more general V2X broadcasting system. Other NOMA-based extended V2X applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin

Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities

The ever-increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation-heavy Internet of things (IoT) applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. In specific, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone-cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure

Cloud Computing - Architecture and Applications

In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such computing framework for large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. This book presents some critical applications in cloud frameworks along with some innovation design of algorithms and architecture for deployment in cloud environment. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.Comment: Edited Volume published by Intech Publishers, Croatia, June 2017. 138 pages. ISBN 978-953-51-3244-8, Print ISBN 978-953-51-3243-1. Link: https://www.intechopen.com/books/cloud-computing-architecture-and-application

Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions

Besides enabling an enhanced mobile broadband, next generation of mobile networks (5G) are envisioned for the support of massive connectivity of heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large number of use-cases including smart cities, environment monitoring, smart vehicles, etc. Unfortunately, most IoTs have very limited computing and storage capabilities and need cloud services. Hence, connecting these devices through 5G systems requires huge spectrum resources in addition to handling the massive connectivity and improved security. This article discusses the challenges facing the support of IoTs through 5G systems. The focus is devoted to discussing physical layer limitations in terms of spectrum resources and radio access channel connectivity. We show how sparsity can be exploited for addressing these challenges especially in terms of enabling wideband spectrum management and handling the connectivity by exploiting device-to-device communications and edge-cloud. Moreover, we identify major open problems and research directions that need to be explored towards enabling the support of massive heterogeneous IoTs through 5G systems.Comment: Accepted for publication in IEEE Wireless Communications Magazin

Cooperation in 5G HetNets: Advanced Spectrum Access and D2D Assisted Communications

The evolution of conventional wireless communication networks to the fifth generation (5G) is driven by an explosive increase in the number of wireless mobile devices and services, as well as their demand for all-time and everywhere connectivity, high data rates, low latency, high energy-efficiency and improved quality of service. To address these challenges, 5G relies on key technologies, such as full duplex (FD), device-to-device (D2D) communications, and network densification. In this article, a heterogeneous networking architecture is envisioned, where cells of different sizes and radio access technologies coexist. Specifically, collaboration for spectrum access is explored for both FD- and cognitive-based approaches, and cooperation among devices is discussed in the context of the state-of-the-art D2D assisted communication paradigm. The presented cooperative framework is expected to advance the understandings of the critical technical issues towards dynamic spectrum management for 5G heterogeneous networks.Comment: to appear in IEEE Wireless Communication