Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer

Radio frequency (RF) energy harvesting and transfer techniques have recently become alternative methods to power the next generation of wireless networks. As this emerging technology enables proactive replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service (QoS) requirement. This article focuses on the resource allocation issues in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs, followed by a review of a variety of issues regarding resource allocation. Then, we present a case study of designing in the receiver operation policy, which is of paramount importance in the RF-EHNs. We focus on QoS support and service differentiation, which have not been addressed by previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ

NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)

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

Comparative Analysis of QoS-Aware Routing Protocols for Wireless Sensor Networks

The main ability of wireless sensor networks (WSNs) is communicating and sensing between nodes, which are deployed in a wide area with a large number of nodes. Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way, since their energy is limited. The limiting factors of the sensor nodes, such as their finite energy supplies and their moderate processing abilities, as well as the unreliable wireless medium restrict the performance of wireless sensor networks While contemporary best-effort routing approaches address unconstrained traffic, QoS routing is usually performed through resource reservation in a connection-oriented communication in order to meet the QoS requirements for each individual connection. This article surveys a sample of existing QoS-Aware Routing Protocols for Wireless Sensor Networks and highlights their key features, including merits and limitations. Keywords: Wireless sensor networks, Routing protocols, QoS-Aware Routing Protocols

QoS Framework for a Multi-stack based Heterogeneous Wireless Sensor Network

Wireless sensor nodes consist of a collection of sensor nodes with constrained resources in terms of processing power and battery energy. Wireless sensors networks are used increasingly in many industrial and consumer applications. Sensors detect events and send via multi hop routing to the sink node for processing the event. The routing path is established through proactive or reactive routing protocols. To improve the performance of the Wireless Sensor Networks, multi stack architecture is addressed. But the multi stack architecture has many problems with respect to life time, routing loop and QOS. In this work we propose a solution to address all these three problems of life time, routing loop and QOS in case of multi stack architecture