认知无线电中基于中继位置和主用户位置的功率分配

针对认知无线电网络,提出了一种基于组间协作的联合直接传输和协作传输的传输机制,在满足干扰约束和峰值功率约束的情况下,通过分析瑞利衰落环境下放大转发协作单中继系统的性能,研究了中继位置和主用户位置对于系统的最优功率分配和系统吞吐量的影响,并对最优功率分配方案和等功率分配方案的性能进行了比较。仿真结果表明,中继位置和主用户的位置对于系统性能起着关键的作用,最优功率分配方案取得相对于等功率分配方案更优的性能,采用最优功率分配方案时,中继节点位于源节点和目的节点连线的中点且靠近认知用户时的系统性能最优。     

改进蛙跳算法的多中继协作系统最优功率分配

协作通信与直接通信相比能够显著地提高系统性能,功率分配是协作通信中的一个关键问题。为了获得合理的协作中继通信系统功率分配方案,提出一种基于改进蛙跳算法的多中继节点功率分配方法。首先对功率分配问题进行分析,将其转换为一个非线性优化问题,然后将青蛙表示为源节点,中继节点的功率,以平均信噪比作为青蛙的食物,并通过青蛙的信息交流和协作找到最优的功率分配方案,最后采用仿真对比实验对本文算法性能进行测试。仿真结果表明,相对于其它功率分配方法,改进蛙跳算法有效地提高了系统的信道容量,降低了中断概率,以较低的复杂度提高了系统的性能。     

全双工中继协作下的认知MIMO系统吞吐量最大化研究

本文研究了全双工中继协作下的认知MIMO系统的平均吞吐量最大化问题。与传统的中继协作认知无线电系统不同的是,该系统模型中的双工中继节点既能协助认知用户源节点进行多天线频谱感知以提高频谱检测性能,也能解码转发认知用户源节点的发送信号以获得更大的系统吞吐量。为使系统平均吞吐量最大,首先,本文以认知用户能获得的最大平均频谱空洞被发现的概率为目标,对系统的帧结构进行优化以获得最佳的感知时间,接着对多个发送天线进行优化以选择出最佳的发送天线,并推导出了在总的发送功率和对主用户干扰受限条件下的认知用户源节点和双工中继节点的最佳功率分配方案。最后的仿真结果表明本文提出的系统模型和优化方案相比传统的双工等功率分配方案以及单工功率分配方案能够获得更大的系统平均吞吐量。      

AF协作分集中的功率优化分配方案

在慢衰落信道中,协作分集是一种新的空间分集方式。为提高协作通信系统的性能,提出以最小化系统中断概率为目标的功率分配方案。综合考虑所有路径的信道噪声功率,运用注水原理,提出在接收端最大比合并下的最佳功率分配方案。该方案计算简单,且仅需已知各个中继节点的平均信道状态信息,无需在传输中实时更新,因而不增加系统的额外开销。仿真结果表明,该方案与等功率分配方案相比,能显著降低系统的中断概率。     

AF协作分集中的功率优化分配方案

在慢衰落信道中,协作分集是一种新的空间分集方式.为提高协作通信系统的性能,提出以最小化系统中断概率为目标的功率分配方案.综合考虑所有路径的信道噪声功率,运用注水原理,提出在接收端最大比合并下的最佳功率分配方案.该方案计算简单,且仅需已知各个中继节点的平均信道状态信息,无需在传输中实时更新,因而不增加系统的额外开销.仿真结果表明,该方案与等功率分配方案相比,能显著降低系统的中断概率.     

认知无线网络中次用户的一种协作传输策略

在认知无线网络中同时存在着主用户和次用户,次用户的通信行为必须在不影响主用户数据传输的前提下进行。为了保证次用户的传输连续性并改善次用户Qos,研究人员提出了次用户间相互协作传输的策略,基于此该文提出了一种基于放大转发的双中继协作传输方案并对所提方案次用户的无冲突传输时间及误比特率进行了详细的理论分析。蒙特卡洛仿真结果表明,双中继协作传输策略能在单中继协作传输的基础上进一步增加次用户的无冲突传输时间并减小误比特率,双中继协作传输显著地提高了系统的传输性能。      

协作分集系统中基于注水算法的功率分配方案研究

为了提高协作分集系统的信道容量,该文在多中继节点协作背景下提出了基于注水算法的中继节点功率分配方案。首先,对空时矩阵的列序号提出了一种新的映射方案,该方案能够提升中继节点发射信号间的正交性。对于采用线性正交解码算法的GABBA码,列序号重新排布后能够降低误码率。然后,通过对接收信噪比与中继节点发射功率的分析,针对信道容量最大化问题提出了一种应用注水算法的两步求解方案。该文对瑞利平坦慢衰落信道下采用GABBA码的协作分集系统进行仿真,仿真结果表明,与中继节点发射功率均分方案相比,该文提出的功率分配方案能够提升系统抗误码性能,达到满分集增益NtNr。该文提出的方案在不同的仿真条件下对信道容量均有提升,并且信道容量与min{NtNr}log2M成正比。     

一种多源协作网络的分布式功率分配与中继选择算法

该文针对多源-多中继放大转发协作通信网络,以最小化系统总功率为目标,在保证系统满足一定中断概率的前提下,提出了一种分布式功率分配与中继选择算法.算法由源节点自主选择为其转发信息的中继节点,并引入定时器,通过竞争方式避免了分布式所导致的中继选择冲突.中继收到来自源节点的信号后,只需根据转发门限自主判断是否进行转发,从而完成传输.仿真结果表明该分布式算法能够有效降低传输所需要的总发射功率.并且与集中式控制所获得的最优中继选择与功率分配算法相比性能相近,但所提分布式算法显著降低了系统的控制开销.     

基于萤火虫算法的多中继功率分配

为了充分实现中继协作,降低多中继协作通信系统功率分配优化问题的计算复杂度,提出了基于萤火虫算法的多中继功率分配方案。在一定的总功率和节点功率约束下,以最大化平均信噪比为优化目标函数,建立了多中继协作系统的功率分配最优化模型。选取该目标函数作为萤火虫的适应度函数,用向量表示萤火虫的状态,该向量的维数为待分配源节点和中继节点的个数,通过萤火虫聚集得到种群中最好的萤火虫,即可获得渐进最优功率分配。仿真结果表明,与平均功率分配相比,基于萤火虫算法的功率分配方案能降低2.44%~6.17%的比特差错率,提高了系统性能。     

一种改进的中继节点选择方案

3GPP标准组织在2004年底启动了其长期演进(LTE)技术的标准化工作.上行传输方案采用带循环前缀的单载波频分多址接入(SC-FDMA)技术.对LTE上行链路进行仿真,实现无中继节点的直接传输.引入中继节点,通过传统最大路径损耗最小(LMP,least maximum pathloss)方案选取最优中继参与协作传输.重点分析并指出了传统LMP算法的缺点和不足,在此基础上提出了一种改进的中继节点选择算法,并结合考虑了源节点和中继节点的功率分配.从系统误码率和用户消耗功率两个方面对各传输方案进行了仿真分析.仿真结果表明LTE上行链路引入中继节点实现协作传输可明显提高系统的误码率性能.协作传输还节约了功率的使用,提高了用户的待机时间和功率效率.相比传统中继选择方案,改进后的LMP算法无论是从误码率还是功率消耗都得到明显改善.     

Optimal power allocation for cognitive radio networks with relay‐assisted directional transmission

In this paper, the problem of power allocation in a cooperative cognitive radio (CR) network is investigated. An optimal power allocation is proposed to maximize efficiency of the secondary network in which secondary users transmit simultaneously over a spectral band assigned to the primary users. The CR network employs directional relays to improve efficiency of the communication links and minimize interference introduced to the primary users. Unlike the conventional cooperative relay‐assisted network, the directional relays are grouped in clusters. This clustering technique along with directional transmission can significantly reduce interference to the primary links and improve the system performance. Two algorithms are also developed on the basis of the penalty method to determine unknown transmission powers. Some corroborant numerical examples are provided to illustrate quick convergence behavior of the proposed algorithms and great superiority of employing clustered directional relays in cooperative CR networks. Copyright © 2013 John Wiley & Sons, Ltd.     

Access Control Engine with Dynamic Priority Resource Allocation for Cognitive Radio Networks

An access control engine with dynamic priority resource allocation (ACE-DPRA) is proposed for unlicensed users to utilize free spectrum of wireless communication systems. A cognitive radio (CR) network with sensing and learning abilities is essential for unlicensed users to achieve ACE-DPRA. Three algorithms are included in ACE-DPRA to improve the spectral efficiency. While requesting to set up connection, unlicensed CR users generate excessive interferences to licensed users. The proposed ACE-DPRA with an admission control scheme allows the connection of unlicensed CR users without degrading the communication quality of licensed users. The priority algorithm for utilizing the unused spectrum is designed according to the location information of unlicensed users. A transmitted power control method is achieved by a fuzzy-learning mechanism. The spectral efficiency of wireless communication systems can be increased after adopting the proposed ACE-DPRA algorithm. Simulation results show that licensed users keep the advantages of high transmission data rate, low interference power, and low average outage probability after the connection of unlicensed CR users.     

Cognitive Engine with Dynamic Priority Resource Allocation for Wireless Networks

A cognitive engine with dynamic priority resource allocation (CE-DPRA) is proposed for wireless networks by utilizing a maximum likelihood estimation method. The receiving signal strength (RSS) from an unlicensed cognitive radio (CR) user can be measured through estimating the unknown position of a licensed mobile user. The priority algorithm for access control enables the selection of a proper CR user waiting for transmission. Both data rate and spectral efficiency can be increased after adapting CE-DPRA. Also the power constraint method can avoid excessive interference caused by signal transmitting from CR users so as to improve the communication quality of mobile users. Simulation results show that the proposed CE-DPRA achieves the performance of high transmission data rate, less interference power, and low average outage probability.     

Resource Allo cation in High Energy-E? cient Co op erative Sp ectrum Sharing Communication Networks

High energy-e?cient wireless communica-tion has become hot due to global low-carbon economy. The systems total transmitting power can be saved by resource allocation in cooperative spectrum sharing net-works. In cooperative spectrum sharing, the secondary user relays the primary users tra?c, as a return, the sec-ondary user is admitted to access the licensed spectrum for its own data transmission. An optimal power and time al-location are presented to minimize the overall energy con-sum ption with the users service quality guaranteed in co-operative spectrum sharing. We formulated it as a convex problem and achieve its optimal power and time allocation in closed form. We analyze the performance of two different transmission protocols for the single relay channel.     

多天线系统中多中继传输的时间分配和功率分配优化方法

本文考虑单小区内的两个中继使用解码转发的方法为两个用户提供下行数据的场景,基站和中继配置多根天线,用户配置单天线。两中继使用相同的频率资源同时为两个用户提供服务。为减小两中继同时向两用户传输时产生的干扰,中继到用户的传输采用协作干扰避免的策略;相应的基站到中继的传输采用多用户空分复用的传输策略。本文在时间分配和功率分配两个方面对上述两跳传输过程进行了优化,提出了两跳传输最优的时间分配策略。由于功率分配最优化问题难以求解,本文提出了一种匹配链路容量的次优功率分配方法,并对其进行简化以降低复杂度。通过仿真可以看出,简化的功率分配方法与匹配链路容量的功率分配性能很接近;所提出的时间分配和功率分配方案可以获得有效的性能提升。      

Dynamic resource allocation algorithm in multi-user cooperative OFDMA systems: considering QoS and fairness constraints

Wireless transmission systems are constrained by several parameters such as the available spectrum bandwidth, mobile battery energy, transmission channel impairments and users’ minimum quality-of-service. In this paper, a new strategy is investigated that aims at improving the allocation of resources in a dual hop OFDMA cooperative network consisting in multi source–destination pairs and multiple decode-and-forward relays. First, the joint optimization of three types of resources: power, sub-channel and relay nodes, is formulated as a problem of subchannel-relay assignment and power allocation, with the objective of minimizing overall transmission power under the bit-error-rate and data rate constraints. However, the optimal solution to the optimization problem is computationally complex to obtain and may be unfair. Assuming knowledge of the instantaneous channel gains for all links in the entire network, an iterative three-step resource allocation algorithm with low complexity is proposed. In order to guarantee the fairness of users, several fairness criteria are also proposed to provide attractive trade-offs between network performance (i.e. overall transmission power, average network lifetime and average outage probability) and fairness to all users. Numerical studies are conducted to evaluate the performance of the proposed algorithm in two practical scenarios. Simulation results show that the proposed allocation algorithm achieves an efficient trade-off between network performance and fairness among users.     

On co-channel and adjacent channel interference mitigation in cognitive radio networks

Cognitive radio (CR) is an emerging wireless communications paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR user themselves. In this paper, we consider the problem of interference mitigation via channel assignment and power allocation for CR users. A cross-layer optimization framework for minimizing both co-channel and adjacent channel interference is developed; the latter has been shown to have considerable impact in practical systems. Cooperative spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation–linearization technique (RLT) based centralized algorithm, as well as a distributed greedy algorithm that uses local information for near-optimal solutions. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.     

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

In cognitive radio (CR) networks, secondary users should effectively use unused licensed spectrums, unless they cause any harmful interference to the primary users. Therefore, spectrum sensing and channel resource allocation are the 2 main functionalities of CR networks, which play important roles in the performance of a CR system. To maximize the CR system utility, we propose a joint out‐of‐band spectrum sensing and operating channel allocation scheme based on genetic algorithm for frequency hopping–based CR networks. In this paper, to effectively sense the primary signal on hopping channels at each hopping slot time, a set of member nodes sense the next hopping channel, which is called out‐of‐band sensing. To achieve collision‐free cooperative sensing reporting, the next channel detection notification mechanism is presented. Using genetic algorithm, the optimum sensing and data transmission schedules are derived. It selects a sensing node set that participate the spectrum sensing for the next expected hopping channel during the current channel hopping time and another set of nodes that take opportunity for transmitting data on the current hopping channel. The optimum channel allocation is performed in accordance with each node's individual traffic demand. Simulation results show that the proposed scheme can achieve reliable spectrum sensing and efficient channel allocation.     

A low‐complexity resource allocation algorithm in OFDMA‐based cooperative cognitive radio networks

In this paper, we propose a low‐complexity resource allocation algorithm for the orthogonal frequency division multiplexing cooperative cognitive radio networks, where multiple primary users (PUs) and multiple secondary users (SUs) coexist. Firstly, we introduce a new concept of ‘efficiency capacity’ to represent the channel conditions of SUs by considering both of the interference caused by the PUs and the channel gains of the SUs with the assist of the relays. Secondly, we allocate the relay, subcarrier and transmission power jointly under the constraint of limiting interference caused to the PUs. Simulation results show that the proposed algorithm can achieve a high data rate with a relative low power level. Copyright © 2014 John Wiley & Sons, Ltd.     

Joint Power Allocation and Interference Mitigation Techniques for Cooperative Spread Spectrum Systems with Multiple Relays

This paper presents joint power allocation and interference mitigation techniques for the downlink of spread spectrum systems which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to an individual power constraint and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problem efficiently, we develop joint adaptive power allocation and interference suppression algorithms that can be implemented in a distributed fashion. The proposed stochastic gradient (SG) and recursive least squares (RLS) algorithms mitigate the interference by adjusting the power levels across the relays and estimating the parameters of the linear receiver. SG and RLS channel estimation algorithms are also derived to determine the coefficients of the channels across the base station, the relays and the destination terminal. The results of simulations show that the proposed techniques obtain significant gains in performance and capacity over non-cooperative systems and cooperative schemes with equal power allocation.