基于广义纳什议价解的CR网络子载波分配算法

在基于OFDMA的感知无线电网络中,提出了基于广义纳什议价解的动态子载波分配方案,通过在用户数目和业务需求不对等的两个从网络小区之间,实现了公平的子载波分配,仿真结果表明,方案明显优于基于最大最小公平算法的资源分配方案。     

认知无线电环境下MIMO—OFDM系统的无线资源管理

本文以IEEE802．22无线广域网标准为基础，研究了认知无线电环境下MIMO-OFDM系统中分布式无线资源管理问题．基于博弈理论分析，文中首先证明了在全局容量最大化的博弈情况下分布式多用户信道功率分配是Supermodular博弈，并给出了相应的分布式多用户信道功率分配算法，并进一步给出了在认知无线电环境下MIMO-OFDM多用户子载波功率分配问题的解决方法．仿真结果表明，本文算法通过有限的重复博弈，可以有效的提高网络容量。     

基于OFDM的协同认知网络跨层资源调度

协同通信与认知无线电是未来移动通信的重要技术。针对基于OFDM的无线协同认知网络中的资源分配问题,该文提出了一种有效的跨层资源调度方案。该调度方案在考虑对主用户所受干扰功率进行控制的前提下,以最大化认知用户超帧中总的传输速率为目标,在调度帧中对各传输帧两阶段的子载波进行最佳配对,并为配对子载波分配最优的发送功率。通过分步求解的方法,该调度方案形成的优化问题得到有效解决。仿真结果表明该资源调度方案使得认知用户传输速率有显著提升,方案的可行性与有效性得到了验证。     

基于能量收集驱动和信道感知的CRN

本文针对能量采集器供电的认知无线电网络进行了研究；首先,在采用离散时间马尔可夫链的建模基础上,通过分析认知用户在收集到的能量包和电池容量的限制下有效地利用能量,以最大化当前时隙的吞吐量；然后,找到多个信道的最优感知顺序和对应于该感知顺序下信道的最优传输能量集,得到使认知无线电网络在多个时隙的预期吞吐量最大化的递归表达式；最后提出了一种基于该递归表达式实现最大化预期吞吐量的算法；仿真实验结果表明,本文所提出的方案相比于其他方案,不仅提高了认知无线电网络的吞吐量,而且降低了主信道上的平均碰撞比。     

基于拉格朗日对偶的认知无线电网络最优资源分配算法

针对传统机会认知无线电网络容量有限的问题,提出了基于拉格朗日对偶的认知无线电网络最优资源分配算法。首先,将一个用户分配给每个子载波;然后,使用标准的凸优化方法确定每个子载波的对应功率,仅一个用户可获得功率正值;最后,利用拉格朗日对偶分解法同时分配CR网络中的子载波和功率,最大限度地提高系统的总容量。使用长期演进真实场景参数与空间信道传播模型评估了所提算法的有效性,仿真结果表明,相比次优资源分配算法,所提算法的总容量平均分别提高了9.3%,相比基于任意输入分布的最优资源分配算法,总容量提高了28.7%,并取得了较快的收敛速率,可以很好地用于解决无线电网络资源配置中的容量问题。     

基于容量最大化的多用户MIMO-OFDM子载波分配算法

针对多用户MIMO-OFDM通信网络,提出了一种基于系统和容量最大化的子载波分配算法。通过对MIMO-OFDM系统和容量公式进行分析,提取出子载波分配的判决参数,再利用匈牙利算法对子载波进行分配,同时分析了分配后系统平均容量的统计特性。为了兼顾公平,又提出了传输速率最小的用户传输速率最大化的子载波分配算法。仿真结果表明,所提容量最大化算法能使系统和容量达到全局最大值,且具有较低的复杂度。     

基于拍卖的认知无线电频谱分配研究

分析了认知无线电中基于拍卖的频谱分配的特点,给出了认知无线电系统中的频谱拍卖模型,并讨论了第一价格和第二价格密封拍卖机制在认知无线电频谱分配上的应用,研究了认知无线电中频谱拍卖问题的最优价格、保留底价等问题。最后还给出了简单情形下的仿真,结果表明,基于拍卖的频谱分配在吞吐量和丢包率等性能上较随机分配方案有明显改善。     

认知无线电频谱分配新算法研究

构造了一个基于合作博奕的非对称纳什协商效能函数,并通过2个认知用户间协商子载波的使用,实现了基于感知贡献加权的比例公平性频谱分配的新算法。仿真结果表明提出的方案不仅实现了频谱资源的公平有效分配,而且有利于频谱感知的最大化。     

基于二维空间的认知网络频谱分配方案

认知无线电(CR)是一种智能无线通信技术,通过从空间和时间上充分利用空闲的频谱资源,以提高频谱利用率.为解决基于OFDM的认知无线电系统中子载波及其功率分配方案中不公平和运算复杂的问题,利用认知无线电的基本思想,提出了将子载波分配和功率分配分为2个相继步骤的一种行之有效的方案.通过构建相应的系统模型及其仿真实验,验证了提出方法的优越性,既可以接近贪婪算法的系统容量,又能提高频谱分配的公平性和降低系统运算开销.     

基于合作中继的OFDM蜂窝网络的QoS感知资源调度算法

针对基于中继的OFDM蜂窝网络,该文考虑具有不同QoS要求的混合业务场景,引入合作传输机制,提出了一种基于合作中继的QoS感知资源调度算法,解决了合作中继节点选取,子载波分配以及功率控制等问题。以最大化系统效用为目标,在考虑QoS业务的速率要求与基站功率约束的同时,针对中继结构引入了中继节点的功率约束。为降低计算复杂度,将原非线性组合优化问题分解为子载波分配与功率控制两个子问题。仿真结果表明,该文所提算法在能量节约、系统效用,吞吐量等性能方面都有显著优势。     

Reinforcement-Learning-Based Double Auction Design for Dynamic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, an important issue is to share the detected available spectrum among different secondary users to improve the network performance. Although some work has been done for dynamic spectrum access, the learning capability of cognitive radio networks is largely ignored in the previous work. In this paper, we propose a reinforcement-learning-based double auction algorithm aiming to improve the performance of dynamic spectrum access in cognitive radio networks. The dynamic spectrum access process is modeled as a double auction game. Based on the spectrum access history information, both primary users and secondary users can estimate the impact on their future rewards and then adapt their spectrum access or release strategies effectively to compete for channel opportunities. Simulation results show that the proposed reinforcement-learning-based double auction algorithm can significantly improve secondary users’ performance in terms of packet loss, bidding efficiency and transmission rate or opportunity access.     

Spectrum auction with interference constraint for cognitive radio networks with multiple primary and secondary users

Extensive research in recent years has shown the benefits of cognitive radio technologies to improve the flexibility and efficiency of spectrum utilization. This new communication paradigm, however, requires a well-designed spectrum allocation mechanism. In this paper, we propose an auction framework for cognitive radio networks to allow unlicensed secondary users (SUs) to share the available spectrum of licensed primary users (PUs) fairly and efficiently, subject to the interference temperature constraint at each PU. To study the competition among SUs, we formulate a non-cooperative multiple-PU multiple-SU auction game and study the structure of the resulting equilibrium by solving a non-continuous two-dimensional optimization problem, including the existence, uniqueness of the equilibrium and the convergence to the equilibrium in the two auctions. A distributed algorithm is developed in which each SU updates its strategy based on local information to converge to the equilibrium. We also analyze the revenue allocation among PUs and propose an algorithm to set the prices under the guideline that the revenue of each PU should be proportional to its resource. We then extend the proposed auction framework to the more challenging scenario with free spectrum bands. We develop an algorithm based on the no-regret learning to reach a correlated equilibrium of the auction game. The proposed algorithm, which can be implemented distributedly based on local observation, is especially suited in decentralized adaptive learning environments as cognitive radio networks. Finally, through numerical experiments, we demonstrate the effectiveness of the proposed auction framework in achieving high efficiency and fairness in spectrum allocation.     

PCE-based routing and spectrum assignment in OFDM-based bandwidth-variable optical networks

Bandwidth-variable optical networks can not only provide bandwidth-flexible lightpaths,but also complete high capacity all-optical switching,and improve the spectrum efficiency.However,for the spectrum...     

Exploiting context, profiles, and policies in dynamic sub-carrier assignment algorithms for efficient radio resource management in OFDMA networks

Dynamic sub-carrier assignment (DSA) is considered as one of the most important aspects for achieving efficient spectrum utilization in orthogonal frequency division multiple access (OFDMA) based networks. Most of well-known DSA algorithms operate in a best effort manner, where the full set of sub-carriers is used in order to achieve the maximum possible quality of service level per user. However, in a real network environment, there are several management aspects to be considered such as context information (users, services, and radio environment conditions), user profiles, and network operator policies. In the context of OFDMA networks management, DSA algorithms should be extended incorporating such aspects in order to introduce fairness in the assignment of sub-carriers as well as to improve the overall system performance. In this paper, an efficient radio resource management scheme that incorporates a new DSA algorithm which exploits context profiles and policies information is presented. Results showcase the benefits that the proposed approach brings in terms of fairness on sub-carriers assignment and overall system performance.     

A game theoretic approach for energy‐efficient communications in multi‐hop cognitive radio networks

In multi‐hop cognitive radio networks, it is a challenge to improve the energy efficiency of the radio nodes. To address this challenge, in this paper, we propose a two‐level Stackelberg game model, where the primary users and the secondary users act as the leaders and the followers, respectively. Based on the game model, our proposed scheme not only considers the power allocation problem for secondary users but also takes into account the price of spectrum. First, we give the cognitive radio network model, and show how to set up the game theoretic model in multi‐hop cognitive radio networks. We then analyze this problem and show the existence and uniqueness of the Nash equilibrium point for the game. We also study the impact of the spectrum price of the primary users in the cognitive radio network and study how to select the best price for the primary users to maximize their own profit. Finally, we implement simulations to show the performance of our schemes. Our work gives an insight on how to improve the energy efficiency and allocate spectrum resources in multi‐hop cognitive radio networks. Copyright © 2016 John Wiley & Sons, Ltd.     

基于博弈策略的能效优化的子载波分配方法

本文引入显示当前子载波质量的信道状态矩阵,以系统总功耗、单个子载波上的功耗、总时延、干扰温度限和单个子载波上的次用户数等为约束条件,以能效为目标函数,建立多约束条件下的分式规划机制.设计演化博弈算子,为每个次用户建立效用函数,当每个次用户的效用函数达到最优时,演化博弈达到Nash均衡点,此时的策略组合认为是能效相对最优的资源分配状态.通过实验仿真对比,本文给出的EESA-EG（Energy Efficient Subcarrier Allocation withEvolutionary Game）算法的能效相对最优,且给出了相对最为合理的子载波分配方案,为信道状态更优的子载波分配了更多的子载波.     

OFDM快时变信道下的相位旋转调制技术

由于发射端和接收端振荡器之间的频率偏差以及无线信道的时变性,OFDM系统各个子载波之间的正交性将会被破坏,从而产生载波间干扰(ICI)。该文利用发送端的多天线分集,提出了一种新型的相位旋转调制技术。它通过在频域对不同发送天线上的发送信号进行不同角度的旋转,使接收端等效信道频域响应产生的载波间干扰最小,达到抑制载波间干扰的作用。实验证明,这种新型的相位旋转调制技术不会改变OFDM系统的发送功率,能够在不占用额外系统带宽的情况下有效地消除快时变信道环境下系统的载波间干扰,达到较好的误码率性能。     

填充式频谱共享中的功率分配算法分析

动态频谱共享技术允许认知用户接入未授权的频谱,可以有效地提高频谱资源的利用率。在分析了频谱共享的2种接入模式的基础上,提出了填充式频谱共享的系统模型。根据认知用户的效用函数,通过注水算法得到了用户间的功率迭代公式,进而求得了认知用户的功率和载波分配结果。仿真表明,该迭代算法在有效完成用户间功率与载波分配的同时能够在较短的时间内收敛。     

Auction–Stackelberg game framework for access permission in femtocell networks with multiple network operators

With the explosive growth of indoor data traffic in forthcoming fifth generation cellular networks, it is imperative for mobile network operators to improve network coverage and capacity. Femtocells are widely recognized as a promising technology to address these demands. As femtocells are sold or loaned by a mobile network operator (MNO) to its residential or enterprise customers, MNOs usually employ refunding scheme to compensate the femtocell holders (FHs) providing indoor access to other subscribers by configuring the femtocell to operate in open or hybrid access mode. Due to the selfishness nature, competition between network operators as well as femtocell holders makes it challenging for operators to select appropriate FHs for trading access resources. This inspires us to develop an effective refunding framework, with aim to improve overall network resource utilization, through promoting FHs to make reasonable access permission for well-matched macro users. In this paper, we develop a two-stage auction–Stackelberg game (ASGF) framework for access permission in femtocell networks, where MNO and mobile virtual network operator lease access resources from multiple FHs. We first design an auction mechanism to determine the winner femtocell that fulfils the access request of macro users. We next formulate the access permission problem between the winner femtocell and operators as a Stackelberg game, and theoretically prove the existence of unique equilibrium. As a higher system payoff can be gained by improving individual players’ payoff in the game, each player can choose the best response to others’ action by implementing access permission, while avoiding solving a complicated optimization problem. Numerical results validate the effectiveness of our proposed ASGF based refunding framework and the overall network efficiency can be improved significantly.     

On spectrum allocation in cognitive radio networks: a double auction-based methodology

Auction is often applied in cognitive radio networks due to its efficiency and fairness properties. An important issue in designing an auction mechanism is how to utilize the limited spectrum resource in an efficient manner. In order to achieve this goal, we propose a predictive double spectrum auction model in this paper. Our auction model first obtains the bidding range from statistical analysis, and then separates the interval into independent states and employees a Markovian prediction based algorithm to generate guidelines for the bidding range of primary and secondary users, respectively. Comparing with existing approaches, our proposed auction model is more efficient in spectrum utilization and satisfies the economic properties. Extensive simulation results show that our work achieves an utilization ratio up to 91 %.