Chunxiao Jiang

Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.

Interference-Limited Resource Optimization in Cognitive Femtocells With Fairness and Imperfect Spectrum Sensing

The use of cognitive-radio(CR)-enabled femtocell is regarded as a promising technique in wireless communications, and many studies have been reported on its resource allocation and interference management. However, fairness and spectrum sensing errors were ignored in most of the existing studies. In this paper, we propose a resource allocation scheme for orthogonal frequency division multiple access (OFDMA)-based cognitive femtocells. The target is to maximize the total capacity of all femtocell users (FUs) under given quality-of-service (QoS) and cotier/cross-tier interference constraints with imperfect channel sensing. To achieve the fairness among FUs, the minimum and maximum numbers of subchannels occupied by each user are considered. First, the subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing and applying cotier interference constraints, which is finally solved using a dual decomposition method. Based on the obtained solution, an iterative subchannel and power allocation algorithm is proposed. The effectiveness of the proposed algorithm in terms of capacity and fairness compared with the existing schemes is verified by simulations.

Information Security in Big Data: Privacy and Data Mining

The growing popularity and development of data mining technologies bring serious threat to the security of individual,s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Energy Efficient User Association and Power Allocation in Millimeter-Wave-Based Ultra Dense Networks With Energy Harvesting Base Stations

Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology are expected to increase both energy efficiency and spectral efficiency. In this paper, user association and power allocation in mmWave-based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. The joint user association and power optimization problem are modeled as a mixed-integer programming problem, which is then transformed into a convex optimization problem by relaxing the user association indicator and solved by Lagrangian dual decomposition. An iterative gradient user association and power allocation algorithm is proposed and shown to converge rapidly to an optimal point. The complexity of the proposed algorithm is analyzed and its effectiveness compared with existing methods is verified by simulations.

Energy-efficient non-cooperative cognitive radio networks: micro, meso, and macro views

Cognitive radio technology can significantly improve spectrum utilization efficiency via enabling secondary users to access licensed spectrum dynamically without harmful interference to primary users. Most existing works on cognitive radio networks were focused on enhancing spectrum efficiency using spectrum sensing, spectrum sharing, and network deployment schemes. However, the energy efficiency issue was largely ignored. Thus, the dilemma between high energy consumption by fast-growing wireless communications and fewer global energy resources has motivated research on energy-efficient cognitive radio networks for enhancing spectrum efficiency and energy efficiency simultaneously. In this article, we overview energy-efficient non-cooperative cognitive radio networks from the micro, meso, and macro perspectives, where the micro view means how to design energy-efficient spectrum sensing algorithms for each individual secondary user, the meso view means how to coordinate non-cooperative secondary users to share spectrum efficiently, and the macro view means how to deploy cognitive radio networks in an energy-efficient approach.

Signalling Cost Evaluation of Handover Management Schemes in LTE-Advanced Femtocell

Femtocell is a small access point using the wire broadband connections or wireless technologies to access the mobile operators network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for wireless communication system.Femtocell related handover cost reduction is one of the important targets in LTE-Advanced SON (Self-Organising Networks). In this paper, a handover optimization algorithm based on the UEs mobility state is proposed. An analytical model was presented for the handover signalling cost analysis. Numerical results are provided to compare the signalling cost of different handover management schemes. The comparison between the proposed algorithm and the traditional handover control algorithm shows that the algorithms proposed in this paper have a significant reduction in the signalling overhead.

Machine Learning Paradigms for Next-Generation Wireless Networks

Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

A Practical Semidynamic Clustering Scheme Using Affinity Propagation in Cooperative Picocells

Coordinated multipoint (CoMP) is corroborated to be an effective technology in mitigating cochannel interference (CCI) and enhancing system performance in picocell systems, which consist of a large number of pico base stations (BSs). In picocell systems, effective CoMP clustering schemes could provide significant gains of system performance such as throughput and cell-edge spectrum efficiency (SE). Moreover, an intrinsic problem of densely deployed networks is the cost of signaling overhead and data exchange between BSs in clusters. In this paper, a novel semidynamic clustering scheme based on affinity propagation for CoMP-Pico is presented to maximize user SE and throughput under the constraint of backhaul cost. Our proposed scheme consists of online and offline stages that can achieve good performance and low complexity. Simulation results show that the proposed scheme yields significant gains of SE and throughput and low running time compared with the existing clustering schemes.

Self-organization in disaster-resilient heterogeneous small cell networks

Heterogeneous small cell networks with overlay femtocells and macrocell is a promising solution for future heterogeneous wireless cellular communications. However, great resilience is needed in heterogeneous small cells in case of accidents, attacks, and natural disasters. In this article, we first describe the network architecture of DRHSCNs, where several self-organization inspired approaches are applied. Based on the proposed resilient heterogeneous small cell network architecture, self-configuring (power, physical cell ID, and neighbor cell list self-configuration) and self-optimizing (coverage and capacity optimization and mobility robustness optimization) techniques are investigated in the DRHSCN. Simulation results show that self-configuration and self-optimization can effectively improve the performance of the deployment and operation of small cell networks in disaster scenarios.

Signalling Overhead Evaluation of HeNB Mobility Enhanced Schemes in 3GPP LTE-Advanced

Home eNodeB (HeNB) is a low-power access point using the local broadband connections or a separate RF backhaul to access the mobile operators network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for users. Considering the potential frequent mobility between HeNB-HeNB and HeNBeNB, HeNB mobility enhancement is proposed as one of the most important work items in 3GPP LTE-Advanced. In this paper, four X2 interface based HeNB mobility enhanced architectures are explicitly discussed in terms of signalling overhead evaluation. The numerical results show that the direct-X2 based option 2 in HeNB-HeNB scenario and the X2-GW based option 3 in eNB-HeNB scenario have the best trade-off in signalling overhead and complexity respectively.