6,744 research outputs found
Cost-Aware Green Cellular Networks with Energy and Communication Cooperation
Energy cost of cellular networks is ever-increasing to match the surge of
wireless data traffic, and the saving of this cost is important to reduce the
operational expenditure (OPEX) of wireless operators in future. The recent
advancements of renewable energy integration and two-way energy flow in smart
grid provide potential new solutions to save the cost. However, they also
impose challenges, especially on how to use the stochastically and spatially
distributed renewable energy harvested at cellular base stations (BSs) to
reliably supply time- and space-varying wireless traffic over cellular
networks. To overcome these challenges, in this article we present three
approaches, namely, {\emph{energy cooperation, communication cooperation, and
joint energy and communication cooperation}}, in which different BSs
bidirectionally trade or share energy via the aggregator in smart grid, and/or
share wireless resources and shift loads with each other to reduce the total
energy cost.Comment: Submitted for possible publicatio
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)
Distributed Interference-Aware Energy-Efficient Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks
The introduction of device-to-device (D2D) into cellular networks poses many
new challenges in the resource allocation design due to the co-channel
interference caused by spectrum reuse and limited battery life of user
equipments (UEs). In this paper, we propose a distributed interference-aware
energy-efficient resource allocation algorithm to maximize each UE's energy
efficiency (EE) subject to its specific quality of service (QoS) and maximum
transmission power constraints. We model the resource allocation problem as a
noncooperative game, in which each player is self-interested and wants to
maximize its own EE. The formulated EE maximization problem is a non-convex
problem and is transformed into a convex optimization problem by exploiting the
properties of the nonlinear fractional programming. An iterative optimization
algorithm is proposed and verified through computer simulations.Comment: 6 pages, 3 figures, IEEE GLOBECOM 201
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