Optimal Resource Allocation for Data Offloading in Energy-Harvesting Small-Cell Networks

2017 ◽  
Author(s):  
Yutong Yan ◽  
Li Ping Qian ◽  
Yuan Wu ◽  
Weidang Lu
Keyword(s):  
Resource Allocation ◽  
Energy Harvesting ◽  
Small Cell ◽  
Optimal Resource Allocation ◽  
Data Offloading ◽  
Small Cell Networks ◽  
Optimal Resource ◽  
Cell Networks

scholarly journals Resource Allocation for SWIPT Systems with Nonlinear Energy Harvesting Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yifan Hu ◽  
Mingang Liu ◽  
Yizhi Feng
Keyword(s):  
Resource Allocation ◽  
Energy Harvesting ◽  
Time Slot ◽  
Achievable Rate ◽  
Time Sharing ◽  
Allocation Scheme ◽  
Optimal Resource Allocation ◽  
Optimal Resource ◽  
Resource Allocation Scheme ◽  
Nonlinear Energy

In this paper, we study the resource allocation for simultaneous wireless information and power transfer (SWIPT) systems with the nonlinear energy harvesting (EH) model. A simple optimal resource allocation scheme based on the time slot switching is proposed to maximize the average achievable rate for the SWIPT systems. The optimal resource allocation is formulated as a nonconvex optimization problem, which is the combination of a series of nonconvex problems due to the binary feature of the time slot-switching ratio. The optimal problem is then solved by using the time-sharing strong duality theorem and Lagrange dual method. It is found that with the proposed optimal resource allocation scheme, the receiver should perform EH in the region of medium signal-to-noise ratio (SNR), whereas switching to information decoding (ID) is performed when the SNR is larger or smaller. The proposed resource allocation scheme is compared with the traditional time switching (TS) resource allocation scheme for the SWIPT systems with the nonlinear EH model. Numerical results show that the proposed resource allocation scheme significantly improves the system performance in energy efficiency.

Distributed Dynamic Resource Allocation for OFDMA-Based Cognitive Small Cell Networks Using a Regret-Matching Game Approach

2015 ◽  
pp. 230-253 ◽  
Author(s):  
Wei-Sheng Lai ◽  
Tsung-Hui Chang ◽  
Ta-Sung Lee
Keyword(s):  
Resource Allocation ◽  
Small Cell ◽  
Small Cells ◽  
Allocation Strategy ◽  
Small Cell Networks ◽  
Distributed Resource Allocation ◽  
Cell Network ◽  
Resource Allocation Strategy ◽  
Matching Game ◽  
Cell Networks

Game theoretical approaches have been used to develop distributed resource allocation technologies for cognitive heterogeneous networks. In this chapter, we present a novel distributed resource allocation strategy for cognitive small cell networks based on orthogonal frequency-division multiple access. In particular, we consider a heterogeneous network consisting of macrocell networks overlaid with cognitive small cells that opportunistically access the available spectrum. We focus on a regret-matching game approach, aiming at maximizing the total throughput of the small cell network subject to cross-tier interference and quality of service (QoS) constraints. The regret-matching game approach exploits a regret procedure to learn the optimal resource allocation strategy from the regrets of the actions of cognitive users. Furthermore, the regret-matching game approach is extended to the joint resource allocation and user admission control problem. Numerical results are presented to demonstrate the effectiveness of the proposed regre-matching approaches.

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