认知无线电系统中频谱分配综述

认知无线电是一种新的智能无线电技术,它通过动态地接入频谱为用户提供高容量的服务,能极大地改善现有的低效的频谱利用率。文章主要对认知无线电中频谱分配进行了分析,其重点是对现有的频谱分配方式进行分类,分析它们各自性能,对基于图论、博弈论、定价拍卖和干扰温度等四种常见的频谱分配模型进行了阐述。     

基于博弈论的认知无线电频谱分配模型

基于博弈论的认知无线电频谱分配问题,是目前认知无线电领域的研究热点之一.文章首先阐述如何利用博弈论对认知无线电进行分析,提出算法设计需要解决的四个方面的基本问题.在此基础之上进一步提出了基于博弈论的认知无线电频谱分配问题模型,并重点对模型中效用函数的选择做了详细的探讨.     

认知无线电的相关技术研究

当前无线通信网络采用的固定频谱分配策略导致许多已分配的频段在大多数情况下未能被充分利用,认知无线电技术的出现为提高频谱利用率提供了一条新的思路,是当前通信技术研究的一个热点方向.本文论述了认知无线电相关的一些技术,包括频谱感知技术、基于超宽带的认知无线电技术、基于OFDM技术的认知无线电系统、认知无线电安全问题等,探讨了认知无线电未来发展值得关注的方向.     

基于强化学习的合作频谱分配算法

为了解决认知无线电网络中的频谱分配问题,提出了一种基于用户体验质量的合作强化学习频谱分配算法,将认知网络中的次用户模拟为强化学习中的智能体,并在次用户间引入合作机制,新加入用户可以吸收借鉴其他用户的强化学习经验,能够以更快的速度获得最佳的频谱分配方案;并且在频谱分配过程中引入了主用户和次用户之间的价格博弈因素,允许主用...     

基于并行分配算法的认知无线电频谱分配算法

针对传统频谱分配方式不合理造成频谱匮乏以及CSGC算法在频谱分配上消耗时间过长影响实时通信的问题,文中提出了一种基于并行分配算法的认知无线电频谱分配算法,其通过同时给各顶点进行上色,从而节省了时间开销。实验证明,并行算法不仅具有与CSGC算法同样的高频谱利用率,且其频谱分配时间开销更低,是一种理想的认知无线电频谱分配算法。     

一种认知无线电网络频谱分配策略

频谱分配技术是认知无线电的关键技术之一,为适应认知无线电系统的时变特性,频谱分配算法必须有较快的收敛速度。在干扰温度模型下,提出一种基于Kuhn-Munkras算法的认知无线电频谱分配策略。该策略利用Kuhn-Munkras算法可以实现最佳匹配并且收敛速度快的特性,根据不同的用户在不同信道上所产生的效益的差异性,实现认知用户和信道的最佳匹配。仿真表明,基于Kuhn-Munkras算法的频谱分配在性能上优于传统的配对算法和greedy算法。     

认知无线电频谱分配的博弈论方法

认知无线电中频谱分配问题目前受到了极大的关注。在问题的分析中,涉及了大量策略选择问题,因而可以利用博弈论的相关原理对其进行分析研究。文章介绍了认知无线电频谱分配的一些关键问题,分析了博弈论方法在认知无线电研究中的应用条件,并阐述了认知无线电频谱分配问题的博弈论框架,为今后的相关研究起到积极的促进作用。     

认知无线电系统中频谱分配技术的研究及应用

随着无线通信技术的快速发展,无线用户的数量急剧增加,频谱资源越来越紧张,如何提高频谱的利用率成为亟待解决的问题。认知无线电系统中的频谱分配技术,使这一问题得到了有效解决。文章简要介绍了认知无线电频谱分配的背景、研究现状,对认知无线电频谱分配的模型进行了分析,介绍了频谱分配发展过程中存在的问题,最后简要论述了频谱分配技术的一些应用。     

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.     

基于博弈论的认知无线电频谱分配技术探讨

在无线通信技术发展过程中,越来越频繁的使用频谱资源,几乎无剩余的无线电频谱空间,造成频谱资源缺乏的原因中,不合理分配频谱为主要原因,为解决这一问题,研究出了认知无线电技术,此种技术可对频谱使用情况主动监测,自身通信参数自适应式的改变,可有效的提高频率的利用效率.但由于应用频率时具有较强的灵活性,要求认知无线电技术可准确检测出频谱资源,并进行动态分配,基于此,本文在博弈论的基础上,探讨了认知无线电频谱分配技术.     

Auction-Based Throughput Maximization in Cognitive Radio Networks Under Interference Constraint

Cognitive radio is an emerging technique to improve the utilization of radio frequency spectrum in wireless communication networks. That is, spectrum efficiency can be increased significantly by giving opportunistic access of the frequency bands to a group of cognitive users to whom the band has not been licensed. In this paper, as a cross layer application (MAC and physical layers) of graph theory, we consider the problem of throughput maximization of spectrum allocation in cognitive radio networks under interference constraint. We propose a novel auction-based channel allocation mechanism which tries to maximize both total and primary users’ utilities while satisfying signal to interference ratio constraint on primary receivers so that transmitted packets will be successfully received, without controlling secondary user powers. For comparison we discuss a greedy algorithm as well, however, one that does not handle interference issue. In order to compare results of proposed and greedy algorithms, we propose net throughput by taking into account outage probability of primary receiver. Simulation results show that exposing higher SINR (outage) threshold not only decreases total gain and primary users’ utilities but also worsens channel distribution performance. On the other hand adding auction mechanism significantly increases total gain throughput and primary user’ s utility. Particularly, up to SINR threshold values of 20 dBs, auction provides outstanding performance and proposed algorithm has total throughput results close to those of the greedy one even though no interference constraint is applied in the greedy algorithm. Another noticeable point of simulation results is crossover of net throughputs of proposed and greedy algorithms at a SINR threshold level after which results of ABSA-UNIC and NASA-UNIC are much better. This clearly shows superiority of proposed mechanism.     

认知无线电系统中基于多标拍卖的信道分配机制

为在认知无线电系统中实现对频谱的有效管理,文中提出了一种基于多标拍卖的信道分配机制,并给出了三种分配规则:吞吐量最大规则、效用公平规则以及时间公平规则.最后通过仿真验证了三种分配规则下的系统性能,仿真结果说明,进行信道分配时利用吞吐量最大规则能使系统吞吐量达到最大,采用效用公平规则能达到最佳效用公平性.使用时间公平规则可保证用户在为传输文件而竞争频谱资源过程中的时间公平,且文件的平均传输时间最少、时延最小.     

基于拍卖的网络带宽分配方法的研究

基于拍卖的网络带宽分配方法的研究日益受到学术界的重视,主要集中在具有激励兼容性的Vickrey拍卖上.本文首先分析并比较了目前最具代表性的两种Vickrey拍卖方案(灵活市场模型和改进的第二价格拍卖),指出其在网络可扩展性、工程效率等方面的不足.然后提出了一种新的基于统一价格拍卖的资源分配算法,论证了其在网络这一特定环境中具有激励兼容性.该算法具有与网络状态无关的特性和更高的运行效率.最后,用NS-2仿真平台验证了该算法的有效性.     

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.     

An auction‐based MAC protocol for cognitive radio networks

Cognitive radio and dynamic spectrum sharing systems use innovative spectrum management techniques that allow different systems to share the same frequency band to utilize the radio spectrum in an efficient way. In this paper, we propose a novel cognitive media access control protocol for cognitive radio networks under the property‐rights model, in which secondary users are divided into several nonoverlapping groups, and each group uses the proposed auction algorithm to bid for required channels from the auctioneer appointed by primary users. Simulations indicate that our proposed media access control protocol can effectively utilize spectrum resources, achieve high system efficiency, and guarantee the fairness of channel allocation among groups. Copyright © 2011 John Wiley & Sons, Ltd.     

Quality of service assurance-based auction for spectrum sharing systems

Spectrum sharing has emerged as a promising solution to address the radio frequency spectrum bottleneck. The FCC has recently proposed a spectrum sharing framework that introduces a spectrum access system as the governing entity that manages access to spectrum for primary and secondary users to coexist. An important aspect of dynamic spectrum management is the pricing of spectrum from the perspective of both the primary and secondary users. Existing auction-based spectrum sharing models do not take into consideration an important aspect of successful secondary user operation: the duration of the available spectrum opportunity. In this paper we propose an auction-based spectrum sharing framework that statistically accounts for the accuracy of the estimates of spectrum availability duration. The proposed auctioning process allows both the PU and the SU to iteratively adjust their evaluation about the available spectrum opportunities over time and to achieve a price combination that maximizes their objectives. A method is proposed to statistically assure spectrum availability, supplementing the reporting method. Hidden Markov models and non-stationary hidden Markov models are used to estimate the activity of the spectrum, with simulation results showing high prediction accuracy.     

国际数字红利发展移动宽带业务的经济价值分析

广播电视经过模数转换后带来数字红利频段。分析了数字红利频段的传播特性和发射功率覆盖效率。使用700 MHz建设移动通信网络,在相同覆盖条件下,建设成本约为2.6 GHz频段的1/8。通过研究国外数字红利频段的规划,对比数字红利频段与其他频段的拍卖和收益情况,数字红利频段的拍卖价格为2.6 GHz等高频的30~40倍,带来的巨大经济收益也是广播电视业务的5~10倍。将数字红利频段应用于宽带移动业务将产生的巨大社会影响和经济收益,对我国数字红利频段的规划和使用起到参考作用。     

Economic Approaches for Cognitive Radio Networks: A Survey

Efficient resource allocation is one of the key concerns of implementing cognitive radio networks. Game theory has been extensively used to study the strategic interactions between primary and secondary users for effective resource allocation. The concept of spectrum trading has introduced a new direction for the coexistence of primary and secondary users through economic benefits to primary users. The use of price theory and market theory from economics has played a vital role to facilitate economic models for spectrum trading. So, it is important to understand the feasibility of using economic approaches as well as to realize the technical challenges associated with them for implementation of cognitive radio networks. With this motivation, we present an extensive summary of the related work that use economic approaches such as game theory and/or price theory/market theory to model the behavior of primary and secondary users for spectrum sharing and discuss the associated issues. We also propose some open directions for future research on economic aspects of spectrum sharing in cognitive radio networks.     

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.     

基于鲶鱼粒子群优化算法的认知无线电频谱分配

目前亟待解决如何获得认知无线电系统效益最大化问题,而求解最优频谱分配方法是一项关键技术,针对传统粒子群(PSO)算法收敛速度慢、易陷入局部最优解等缺陷,提出一种基于鲶鱼粒子群算法(CE-PSO)的认知无线电频谱分配方法。首先建立认知无线电频谱分配优化的数学模型,然后以用户取得的效益最大化为优化目标,引入"鲶鱼效应",保持粒子群的多样性,通过粒子间信息交流找到空闲频谱最优分配方案,最后采用仿真实验测试CE-PSO算法的有效性。结果表明,CE-PSO算法克服了PSO算法的缺陷,可以快速、准确地寻找到最优频谱分配方案,更好地实现系统效益的最大化,可以满足认知无线电系统的应用需求。