OFDMA毫微微小区双层网络中基于分组的资源分配

毫微微小区(Femtocell)网络能够增强室内覆盖,提高系统容量,但是在频谱共享的正交频分多址(OFDMA)毫微微小区网络中,毫微微小区之间的同层干扰以及毫微微小区与宏小区(Macrocell)之间的跨层干扰严重限制了系统的性能。针对这两种干扰,该文提出一种基于分组的资源分配算法。该算法包括两部分:一部分是宏基站先利用改进的匈牙利算法为宏小区用户分配信道,再用注水算法分配功率,保证宏小区用户的正常传输;另一部分是在避免干扰宏小区用户的基础上,先采用模拟退火算法对毫微微小区进行分组,再进行信道和功率分配,满足毫微微小区用户的数据速率需求,最大化频谱效率。仿真结果表明,该算法有效地抑制了这两种干扰,既能保证用户的数据速率需求,又能有效提升网络频谱效率。     

毫微微小区系统中的干扰分析与节能技术利用

毫微微小区（Femtocell）基站外观与Wi-Fi接入设备相似,发射功率小,适用于家庭及办公室环境。在分析Femtocell系统的资源复用模型和接入过程的基础上,文章针对宏小区（Macrocell）和Femtocell混合网络的干扰问题,通过对随机到达的用户合理分配时频资源,提出一种功率控制方案。该方案能达到系统节能、降低干扰和提高系统性能的目的。     

改进的基于合作博弈的资源分配和接入控制策略

为了提高频谱共享的有效性和公平性,提出了一个基于合作博弈的认知网络频谱共享 模型和接入控制策略。博弈规则为在满足各用户最小收益的前提下,认知用户进行相互合作 ,并最大化总体收益;接入控制策略以满足服务质量需求为约束条件,并引入惩罚函数,迫 使认知用户按照其实际需求竞争频谱资源。仿真结果表明：提出的合作博弈模型和接入策略 提高了认知用户的总体收益和公平性,改善了系统中认知用户的满意度。     

OFDM系统Femtocell网络下行联合资源分配

为进一步提高家庭基站(Femtocell)网络中频谱利用率并优化功率分配,在基于正交频分复用技术(OFDM)系统网络中,提出一种子载波联合优化的多用户资源分配算法,即以最大化频谱利用率作为目标函数,加入基站选择因子对家庭基站进行待机模式选择优化,再对用户的子载波资源进行公平分配,最后利用线性封顶注水算法对小区基站用户功率进行优化分配。仿真结果表明,多用户资源分配算法不仅使频谱和功率利用率都得到显著增长,而且提高了系统吞吐量和用户公平性。该家庭基站资源寻优模型有效地改善了频谱紧缺和功率浪费现状,降低了家庭基站之间的干扰。      

毫微微小区中一种基于分组的资源分配方法

为有效解决毫微微小区间( Femtocell)干扰,采用分布式方式对毫微微小区进行资源管理。首先,对毫微微接入点( FAPs)进行分组。基于Lingo数学建模的思想,提出了一种解决分组优化问题的算法。该算法在使用分支定界算法寻找最优解的同时,通过建立单纯形表剪去偏离最优解方向的分支;其次,每组选择一个簇头为本组内FAPs分配资源,为此,提出了新的子信道分配方法,该方法根据干扰指示矩阵修正子信道分配的情况。仿真结果表明：和其他算法相比,提出的算法不仅能找到分组优化问题的最优解,并且效率更高;另外,提出的资源分配算法不仅减小了用户间干扰,而且提高了户间速率公平。     

认知车载网络中基于QoS保障的频谱共享新策略

构建了基于认知无线电的新型车载网络,并提出基于安全业务服务质量(QoS)保障的两步式频谱共享策略:多认知小区间的频谱分配和单认知小区内的频谱共享。仿真结果表明,提出的GNBS(generalized nash bargaining solution)分配方案综合考虑了系统的公平性和总效用,可获得明显大于最大化最小方案的系统和速率,以及明显优于最大化和速率方案的多认知小区间的公平性;GNBS方案可以根据多小区间频谱需求的差异性来动态调节资源分配的比例,有效地实现了需求非对称的多小区间频谱资源的最优化配置;此外,单小区内的协作频谱共享机制,使得协作双方均获得了优于非协作方案的系统效用,有效地提高了系统吞吐量以及认知OBU间的公平性。     

异构Femtocell网络中基于用户公平性的干扰协调

为使含有家庭基站和宏基站的异构网络(HetNet)中各用户的服务质量需求都能满足并达到用户间的公平性,在功率限制条件下对宏用户中最小的用户数据速率最大化,将此最小用户速率提供给家庭基站用户,获得各家庭基站用户的目标信干噪比(SINR),再利用对分法调整家庭基站的发射功率.仿真结果表明,该方法能够保证最小的宏用户数据速率且提高异构网络用户间的公平性.     

基于非合作双干扰惩罚博弈的功率控制算法

为了提高认知用户的性能,文章提出了一种在认知CDMA网络中基于非合作双干扰惩罚博弈的功率控制算法（NPGP-DIP算法）,介绍了其系统模型,并对以下三点作了详细证明：该算法降低了保证系统正常通信的最小SINR要求,该算法存在纳什均衡点且是惟一的。仿真结果表明,该算法在满足对主用户最大干扰限制的条件下,提高了认知用户的性能,并且在一定程度上兼顾了系统的公平性。     

基于Q-Learning算法的毫微微小区功率控制算法

该文研究macro-femto异构蜂窝网络中移动用户的功率控制问题,首先建立了以最小接收信号信干噪比为约束条件,最大化毫微微小区的总能效为目标的优化模型;然后提出了基于Q-Learning算法的毫微微小区集中式功率控制(PCQL)算法,该算法基于强化学习,能在没有准确信道状态信息的情况下,实现对小区内所有用户终端的发射功率统一调整。仿真结果表明该算法能实现对用户终端的功率有效控制,提升系统能效。     

基于拍卖理论和补偿激励的频谱共享新算法

针对认知无线电系统中主次用户频谱共享问题,提出了一种基于拍卖理论和补偿激励的频谱共享新算法,该算法基于简化的VCG(vickrey-clarke-groves)拍卖模型,引入次用户间的合作关系,并运用货币补偿激励原则,建立了相应的合作型竞争的频谱博弈模型。该博弈模型不仅具有非合作博弈模型可自动实施的决策模式特点,还兼有寡头联合模型的公平性的特点。理论分析和仿真结果证明了所提出算法的正确性和公平性。     

Multi-objective Power Control Algorithm for Femtocell Networks

Future wireless networks are designed to cope with drastically increasing user demands. However, network resources reach the limits of their capacity to user requirements. Recently, femtocell has appeared as an effective solution to achieve larger coverage for indoor users while improving the cellular network capacity. In femtocell networks, the most important issue is to design an efficient and fair power control protocol, which can significantly influences the network performance. In this paper, a new multi-objective power control algorithm is developed based on the no-regret learning technique and intervention game model. The proposed control paradigm can provide the ability to practically respond to current system conditions and suitable for real network operations. Under a dynamically changing network environment, the proposed approach appropriately controls the power level to balance network performance between efficiency and fairness.     

Robust uplink power control for co-channel two-tier femtocell networks

This paper is concerned with uplink interference suppression problem in two-tier femtocell networks through power control. Specifically, we consider the Quality-of-Service (QoS) of the macrocell user and femtocell users in terms of their received Signal to Interference-plus-Noise Ratios (SINRs) at macrocell base station (MBS) and femtocell base stations (FBSs), and we also take femtocell users’ power efficiency into consideration by designing an objective function, which is a weighted sum of transmission power and squared SINR difference between femtocell user's maximum SINR and actual SINR. Due to the error of the SINR at MBS caused by distance errors, a robust uplink power control problem is formulated, and it is equivalent to a robust convex optimization problem with femtocell users’ SINR constraints. Then, the robust convex optimization problem is converted into a general convex optimization problem. Moreover, a distributed power control algorithm combined with admission control is presented to obtain femtocell users’ optimal power allocation. Numerical results show the convergence and effectiveness of the proposed uplink power control algorithm.     

Energy-Efficient Cooperative Transmission in Heterogeneous Wireless Networks with QoS Constraint

Heterogeneous wireless network is an effective scheme to improve both the spectrum efficiency and energy efficiency of cellular system. However, interference limits the performance of such heterogeneous wireless network seriously. Hence cooperative decision making, i.e., cooperative transmission is necessary. In this paper, we develop an energy-efficient scheme for cooperative transmission in heterogeneous wireless networks with QoS constraint. Since too much signaling is needed for absolutely centralized cooperation, which is infeasible in practical system, partly cooperation is considered in this paper. We first formulate the problem as a two-stage Stackelberg game. In the first stage, an energy-efficient transmission scheme for macrocell users (MUE) is proposed based on the interference state of the MUEs. In the second stage, after obtaining the SINR and target SINR of the MUEs, the femtocell base stations update their beamformers and optimize the transmission power individually through a non-cooperative game, the existence and uniqueness of the Nash equilibrium is proved. Then an iterative algorithm is proposed to obtain the Stackelberg equilibrium. Simulation results show the performance gain of the proposed scheme.     

Resource allocation scheme for orthogonal frequency division multiple access networks based on cooperative game theory

Cooperative game theory can be applied to orthogonal frequency division multiple access (OFDMA) networks for fair resource allocation. In this work, we consider a comprehensive cross‐layer framework including physical and medium access control layer requirements. We apply two cooperative games, nontransferable utility (NTU) game and transferable utility (TU) game, to provide fairness in OFDMA networks. In NTU game, fairness is achieved by defining appropriate objective function, whereas in TU game, fairness is provided by forming the appropriate network structure. For NTU game, we analyze the Nash bargaining solution as a solution of NTU game taking into account channel state information and queue state information. In a TU game, we show that coalition among subcarriers to jointly provide rate requirements leads to better performance in terms of power consumption. The subcarrier's payoff is determined according to the amount of payoff which that subcarrier brings to the coalition by its participation. We show that although NTU and TU games are modeled as rate adaptive and margin adaptive problems, respectively, both solutions provide a fair distribution of resources with minimum fairness index of 0.8. Copyright © 2012 John Wiley & Sons, Ltd.     

Dynamic subcarrier and power allocation based on cooperative game theory in symmetric cooperative OFDMA networks

In order to improve the efficiency and fairness of radio resource utilization,a scheme of dynamic cooperative subcarrier and power allocation based on Nash bargaining solution(NBS-DCSPA) is proposed in the uplink of a three-node symmetric cooperative orthogonal frequency division multiple access(OFDMA) system.In the proposed NBS-DCSPA scheme,resource allocation problem is formulated as a two-person subcarrier and power allocation bargaining game(SPABG) to maximize the system utility,under the constraints of each user’s maximal power and minimal rate,while considering the fairness between the two users.Firstly,the equivalent direct channel gain of the relay link is introduced to decide the transmission mode of each subcarrier.Then,all subcarriers can be dynamically allocated to the two users in terms of their selected transmission mode.After that,the adaptive power allocation scheme combined with dynamic subcarrier allocation is optimized according to NBS.Finally,computer simulation is conducted to show the efficiency and fairness performance of the proposed NBS-DCSPA scheme.     

Fair and Efficient Spectrum Resource Allocation and Admission Control for Multi-user and Multi-relay Cellular Networks

This paper studies the joint relay selection and spectrum allocation problem for multi-user and multi-relay cellular networks, and per-user fairness and system efficiency are both emphasized. First, we propose a new data-frame structure for relaying resource allocation. Considering each relay can support multiple users, a \(K\) -person Nash bargaining game is formulated to distribute the relaying resource among the users in a fair and efficient manner. To solve the Nash bargaining solution (NBS) of the game, an iterative algorithm is developed based on the dual decomposition method. Then, in view of the selection cooperation (SC) rule could help users achieve cooperation diversity with minimum network overhead, the SC rule is applied for the user-relay association which restricts relaying for a user to only one relay. By using the Langrangian relaxation and the Karush–Kuhn–Tucker condition, we prove that the NBS result of the proposed game just complies with the SC rule. Finally, to guarantee the minimum rate requirements of the users, an admission control scheme is proposed and is integrated with the proposed game. By comparing with other resource allocation schemes, the theoretical analysis and the simulation results testify the effectiveness of the proposed game scheme for efficient and fair relaying resource allocation.     

A preset threshold based cross-tier handover algorithm for uplink co-channel interference mitigation in two-tier femtocell networks

This paper considers the co-channel interference mitigation problem and proposes a preset threshold based cross-tier handover algorithm for uplink co-channel interference mitigation in two-tier femtocell networks. The proposed cross-tier handover algorithm introduces a preset threshold cross-tier handover policy, which takes into account both the time-to-stay (TTS) of a macrocell user equipment (MUE)/femtocell user equipment (FUE) in a femtocell/the macrocell, and the received signal to interference plus noise ratio (SINR) at a femtocell access point (FAP)/the macrocell base station (MBS) in making a cross-tier handover decision for an MUE/FUE. A cross-tier handover decision is made by comparing the TTS of an MUE/FUE in a femtocells/the macrocell and the SINR at a FAP/the MBS with a preset TTS threshold and different SINR thresholds. The objective of the preset threshold based cross-tier handover algorithm is to increase the received SINR at the MBS/FAPs and thus improve the network performance. The performance of the proposed cross-tier handover algorithm with the minimum power transmission and the optimal power transmission is analyzed, respectively. Numerical results show that the proposed preset threshold based cross-tier handover algorithm can significantly improve the network performance in terms of the outage probability, user sum rate, and network capacity.     

分层异构网络中基于斯坦克尔伯格博弈的资源分配算法

分层异构网络中家庭基站与宏基站之间往往存在干扰,如何分配资源以获得高谱率和高容量、保证用户性能一直是研究的重点。为了解决这个问题,本文提出了一种异构蜂窝网络中基于斯坦克尔伯格博弈的家庭基站与宏基站联合资源分配算法,算法首先基于图论的分簇算法对家庭基站和宏用户进行分簇和信道分配,以减少家庭基站之间的同层干扰和家庭基站层与宏蜂窝网络的跨层干扰;然后建立了联合家庭基站发射功率以及宏用户接入选择的斯坦克尔伯格博弈,推导出达到纳什均衡时的家庭基站发射功率的表达式,并据此为宏用户选择合适的接入策略。仿真结果表明,该算法能够有效地提高宏用户的信干噪比（SINR）,家庭用户的性能也得到改善。      

A game theoretic framework for bandwidth allocation and pricing inbroadband networks

In this paper, we present a game theoretic framework for bandwidth allocation for elastic services in high-speed networks. The framework is based on the idea of the Nash bargaining solution from cooperative game theory, which not only provides the rate settings of users that are Pareto optimal from the point of view of the whole system, but are also consistent with the fairness axioms of game theory. We first consider the centralized problem and then show that this procedure can be decentralized so that greedy optimization by users yields the system optimal bandwidth allocations. We propose a distributed algorithm for implementing the optimal and fair bandwidth allocation and provide conditions for its convergence. The paper concludes with the pricing of elastic connections based on users' bandwidth requirements and users' budget. We show that the above bargaining framework can be used to characterize a rate allocation and a pricing policy which takes into account users' budget in a fair way and such that the total network revenue is maximized