A stochastic game framework for joint frequency and power allocation in dynamic decentralized cognitive radio networks

Cognitive radio networks (CRNs) have been recognized as a promising solution to improve the radio spectrum utilization. This article investigates a novel issue of joint frequency and power allocation in decentralized CRNs with dynamic or time-varying spectrum resources. We firstly model the interactions between decentralized cognitive radio links as a stochastic game and then proposed a strategy learning algorithm which effectively integrates multi-agent frequency strategy learning and power pricing. The convergence of the proposed algorithm to Nash equilibrium is proofed theoretically. Simulation results demonstrate that the throughput performance of the proposed algorithm is very close to that of the centralized optimal learning algorithm, while the proposed algorithm could be implemented distributively and reduce information exchanges significantly.     

Ergodic capacity and outage probability optimization for secondary user in cognitive radio networks under interference outage constraint

This paper considers a cognitive radio network where a secondary user (SU) coexists with a primary user (PU). The interference outage constraint is applied to protect the primary transmission. The power allocation problem to jointly maximize the ergodic capacity and minimize the outage probability of the SU, subject to the average transmit power constraint and the interference outage constraint, is studied. Suppose that the perfect knowledge of the instantaneous channel state information (CSI) of the interference link between the SU transmitter and the PU receiver is available at the SU, the optimal power allocation strategy is then proposed. Additionally, to manage more practical situations, we further assume only the interference link channel distribution is known and derive the corresponding optimal power allocation strategy. Extensive simulation results are given to verify the effectiveness of the proposed strategies. It is shown that the proposed strategies achieve high ergodic capacity and low outage probability simultaneously, whereas optimizing the ergodic capacity (or outage probability) only leads to much higher outage probability (or lower ergodic capacity). It is also shown that the SU performance is not degraded due to partial knowledge of the interference link CSI if tight transmit power constraint is applied.     

Dynamic cooperator selection in cognitive radio networks

The primary objective of cooperation in cognitive radio (CR) networks is to increase the spectrum access efficiency and improve the network performance. However, Byzantine adversaries or unintentional erroneous conduct in cooperation can lead to destructive behavior of CR users that can decrease their own and others’ performances. This work presents a dynamic solution for cooperation reliability in conditions with constraints typical for a CR network. Specifically, in CR networks, the information on the success of cooperation can be limited only to cases with interference; when malicious, cooperators can be completely non-correlated and can alter behavior; and the set of available cooperators can dynamically change in time. In order to face these challenges, each CR user autonomously decides with whom to cooperate by learning cooperators behavior with a reinforcement learning (RL) algorithm. This RL algorithm determines the suitability of the available cooperators, and selects the most appropriate ones to cooperate with the objective to increase the efficiency of spectrum access in CR networks. The simulation results demonstrate the learning capabilities of the proposed solution and especially its reliable behavior under highly unreliable conditions.     

分布式认知无线电网络中的主动频谱切换

主要研究分布式认知无线电网络中的频谱切换,并提出一种主动的频谱切换方案。认知用户通过感知结果与历史信息建立授权用户的活动模型,预测频谱的使用情况。在不对授权用户产生干扰的前提下,提前安排频谱切换。同时,采用基于蚁群任务分工算法的频谱选择方法,使得认知用户综合考虑通信参数,实现认知用户的按需切换,保证不间断的传输。仿真表明,提出的方案能够提前执行切换,并在较短的时间内完成业务量的传输,同时具备较高的灵活性,适用于分布式认知无线电网络。     

Optimal resource allocation solutions for heterogeneous cognitive radio networks

Cognitive radio networks (CRN) are currently gaining immense recognition as the most-likely next-generation wireless communication paradigm, because of their enticing promise of mitigating the spectrum scarcity and/or underutilisation challenge. Indisputably, for this promise to ever materialise, CRN must of necessity devise appropriate mechanisms to judiciously allocate their rather scarce or limited resources (spectrum and others) among their numerous users. ‘Resource allocation (RA) in CRN', which essentially describes mechanisms that can effectively and optimally carry out such allocation, so as to achieve the utmost for the network, has therefore recently become an important research focus. However, in most research works on RA in CRN, a highly significant factor that describes a more realistic and practical consideration of CRN has been ignored (or only partially explored), i.e., the aspect of the heterogeneity of CRN. To address this important aspect, in this paper, RA models that incorporate the most essential concepts of heterogeneity, as applicable to CRN, are developed and the imports of such inclusion in the overall networking are investigated. Furthermore, to fully explore the relevance and implications of the various heterogeneous classifications to the RA formulations, weights are attached to the different classes and their effects on the network performance are studied. In solving the developed complex RA problems for heterogeneous CRN, a solution approach that examines and exploits the structure of the problem in achieving a less-complex reformulation, is extensively employed. This approach, as the results presented show, makes it possible to obtain optimal solutions to the rather difficult RA problems of heterogeneous CRN.     

Wireless distributed computing for cyclostationary feature detection

Recently, wireless distributed computing (WDC) concept has emerged promising manifolds improvements to current wireless technologies. Despite the various expected benefits of this concept, significant drawbacks were addressed in the open literature. One of WDC key challenges is the impact of wireless channel quality on the load of distributed computations. Therefore, this research investigates the wireless channel impact on WDC performance when the latter is applied to spectrum sensing in cognitive radio (CR) technology. However, a trade-off is found between accuracy and computational complexity in spectrum sensing approaches. Increasing these approaches accuracy is accompanied by an increase in computational complexity. This results in greater power consumption and processing time. A novel WDC scheme for cyclostationary feature detection spectrum sensing approach is proposed in this paper and thoroughly investigated. The benefits of the proposed scheme are firstly presented. Then, the impact of the wireless channel of the proposed scheme is addressed considering two scenarios. In the first scenario, workload matrices are distributed over the wireless channel. Then, a fusion center combines these matrices in order to make a decision. Meanwhile, in the second scenario, local decisions are made by CRs, then, only a binary flag is sent to the fusion center.     

Dynamic spectrum sharing in cognitive radio networks using truthful mechanisms and virtual currency

In cognitive radio networks, there are scenarios where secondary users (SUs) utilize opportunistically the spectrum originally allocated to primary users (PUs). The spectrum resources available to SUs fluctuates over time due to PUs activity, SUs mobility and competition between SUs. In order to utilize these resources efficiently spectrum sharing techniques need to be implemented. In this paper we present an approach based on game-theoretical mechanism design for dynamic spectrum sharing. Each time a channel is not been used by any PU, it is allocated to SUs by a central spectrum manager based on the valuations of the channel reported by all SUs willing to use it. When an SU detects a free channel, it estimates its capacity according to local information and sends the valuation of it to the spectrum manager. The manager calculates a conflict-free allocation by implementing a truthful mechanism. The SUs have to pay for the allocation an amount which depends on the set of valuations. The objective is not to trade with the spectrum, but to share it according to certain criteria. For this, a virtual currency is defined and therefore monetary payments are not necessary. The spectrum manager records the credit of each SU and redistributes the payments to them after each spectrum allocation. The mechanism restricts the chances of each SU to be granted the channel depending on its credit availability. This credit restriction provides an incentive to SUs to behave as benefit maximizers. If the mechanism is truthful, their best strategy is to communicate the true valuation of the channel to the manager, what makes possible to implement the desired spectrum sharing criteria. We propose and evaluate an implementation of this idea by using two simple mechanisms which are proved to be truthful, and that are tractable and approximately efficient. We show the flexibility of these approach by illustrating how these mechanisms can be modified to achieve different sharing objectives which are trade-offs between efficiency and fairness. We also investigate how the credit restriction and redistribution affects the truthfulness of these mechanisms.     

Resource allocation in heterogeneous cooperative cognitive radio networks

In cognitive radio networks (CRNs), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary users. Attempts to mitigate the limiting effects of this constraint, thus achieving higher productivity, are a current research focus, and in this work cooperative diversity is investigated as a promising solution. Cooperative diversity has the capability to achieve diversity gain for wireless networks. In the work, therefore, the possibility of and mechanism for achieving greater utility for the CRN when cooperative diversity is incorporated are studied. To accomplish this, a resource allocation model is developed and analyzed for the heterogeneous, cooperative CRN. In the model, during cooperation, a best relay is selected to assist the secondary users that have poor channel conditions. Overall, the cooperation makes it feasible for virtually all the secondary users to improve their transmission rates while still causing minimal harm to the primary users. The results show a marked improvement in the resource allocation performance of the CRN when cooperation is used in contrast to when the CRN operates only by direct communication.     

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.     

Optimal power allocation for cognitive radio under interference outage constraint

Cognitive radio is able to share the spectrum with primary licensed user, which greatly improves the spectrum efficiency. We study the optimal power allocation for cognitive radio to maximize its ergodic capacity under interference outage constraint. An optimal power allocation scheme for the secondary user with complete channel state information is proposed and its approximation is presented in closed form in Rayleigh fading channels. When the complete channel state information is not available, a more practical transmitter-side joint access ratio and transmit power constraint is proposed. The new constraint guarantees the same impact on interference outage probability at primary user receiver. Both the optimal power allocation and transmit rate under the new constraint are presented in closed form. Simulation results evaluate the performance of proposed power allocation schemes and verify our analysis.     

认知自组网分布式频谱感知最优协作算法

认知无线电技术使得自组织网络节点能够充分利用空闲频谱资源,提高了传输性能。通过协作频谱感知,可有效解决由于无线信道存在阴影、噪声和衰落等情况导致的单节点感知准确性偏低。为了解决梯度算法随着协作节点数量增大后计算复杂度变高,文中提出部分梯度算法ψ-GBCS,该模型通过基于SNR的动态阈值保证了感知准确性,同时通过最佳协作节点数提高了感知效率。仿真结果表明,该模型下,综合评估系统效率和性能的J函数值提高37%,能耗降低50%,有效保证大规模认知自组网频谱感知的鲁棒性,降低了对主用户的干扰及设备功耗。     

Optimal spectrum utilisation in cognitive radio networks based on processor sharing techniques

Cognitive radio networks have achieved higher efficiency in terms of spectrum usage; however they do not readily solve any competition for access among secondary users. Optimisation is applied to an underlay network to obtain the optimal solution for at least two secondary users operating simultaneously on the same channel. Performance measures are used as the target for optimisation. However, the objective function is difficult to obtain in closed form. For the performance measures, queueing theory, particularly weighted processor sharing techniques are employed to model the system dynamics and behaviour. Transmission power and the interference temperature limit are used to allocate weights to the secondary users. Queue length and waiting time functions obtained from the queuing models are used for optimisation. After establishing that the objective function can be considered to be pseudo‐convex, convex programming is then deployed to obtain the optimised solution. The results suggest that there is indeed an improvement in network performance after optimisation. The immediate benefits of such a system are firstly improved spectrum utilisation through adding multiple secondary users and secondly, through optimisation, higher performance that can be achieved by the secondary users.     

Reactive routing for mobile cognitive radio ad hoc networks

Although more than a decade has passed from the proposal of the Cognitive Radio paradigm, in these years the research has mainly focused on physical and medium access issues, and few recent works focused on the problem of routing in cognitive networks. This paper addresses such a problem by evaluating the feasibility of reactive routing for mobile cognitive radio ad hoc networks. More specifically, we design a reactive routing protocol for the considered scenario able to achieve three goals: (i) to avoid interferences to primary users during both route formation and data forwarding; (ii) to perform a joint path and channel selection at each forwarder; (iii) to take advantage of the availability of multiple channels to improve the overall performance. Two different versions of the same protocol, referred to as Cognitive Ad-hoc On-demand Distance Vector (CAODV), are presented. The first version exploits inter-route spectrum diversity, while the second one exploits intra-route spectrum diversity. An exhaustive performance analysis of both the versions of the proposed protocol in different environments and network conditions has been carried out via numerical simulations. The results state the suitability of the proposed protocol for small mobile cognitive radio ad hoc networks.     

认知无线网络中基于联合波束形成和功率分配的低复杂度SINR均衡算法

认知无线电作为提高频谱使用效率的有效手段成为当前通信领域的研究热点。本文研究了单输入多输出的多用户认知无线网络,在主用户的干扰功率约束和认知用户的发射功率约束下,针对认知用户的信干噪比均衡问题,提出了一种低复杂度的联合波束形成和功率分配算法。与已有算法相比,该算法可以在保证最优的均衡信干噪比水平的同时,大幅度降低矩阵求逆和矩阵特征值分解的次数,特别是当处于通信状态的主用户和认知用户数目很多时。本文理论证明了所提算法的收敛性和最优性,并进行了详细的复杂度分析。理论分析和仿真结果均表明在多用户的认知无线网络中,本文所提算法可以将已有算法的平均运算复杂度降低50%以上。     

Channel allocation and reallocation for cognitive radio networks

In this paper, a new channel allocation and re‐location scheme is proposed for cognitive radio users to efficiently utilize available spectrums. We also present a multiple‐dimension Markov analytical chain to evaluate the performance of this scheme. Both analytical results and simulation results demonstrate that the new scheme can enhance the radio system performance significantly in terms of blocking probability, dropping probability, and throughput of second users. The proposed scheme can work as a non‐server‐based channel allocation, which has practical values in real engineering design. Copyright ©2011 John Wiley & Sons, Ltd.     

Spectrum-energy-efficient sensing with novel frame structure in cognitive radio networks

Cooperative spectrum sensing (CSS) is a promising technology in spectrum sensing with an admirable performance. In this paper, we define a utility function which jointly considers the spectrum-efficiency and the energy-efficiency. In a single-user sensing scenario, by maximizing the utility function, a rigorous analytical expression for the optimal threshold of the energy detector is derived. In CSS, the general frame structure is inefficient since the time consumed by reporting contributes little to the sensing performance. In this paper, we propose a novel CSS frame structure, in which one secondary user's (SU's) reporting time is also used for other SUs’ sensing. For time varying channels, collecting the sensing results at different time points is expected to achieve a time diversity gain for a SU, then the novel multi-minislot CSS scheme is proposed. In CSS, the optimal randomized rule and the optimal final decision threshold are derived. Simulation results show a significant improvement of the utility by using the proposed multi-minislot CSS scheme. It is also shown that there exists an optimal number of cooperating SUs that maximizes the utility, and the optimal number decreases as the price of the sensing energy increases.     

分布式认知无线电网络多路径路由协议研究综述

为了深入研究分布式认知无线电网络的多路径路由问题,阐述了分布式认知无线电网络基本特征以及多路径路由设计面临的问题与挑战。分布式认知无线电网络多路径路由采用多路径并行传输,可有效降低传输时延、增加网络吞吐量与传输可靠性、实现网络负载均衡。根据路由优化目标不同,从吞吐量、带宽、干扰、时延、负载均衡和路由发现六方面对近年来多路径路由协议的主要研究成果进行了分类,然后逐类对多路径路由协议进行了分析和讨论,最后分别从分布式认知无线电网络基本特征的适应性与多路径路由协议特征两个方面进行了比较,并展望了分布式认知无线电网络多路径路由协议需进一步研究的方向。     

认知无线网络中基于主用户行为的联合路由和信道分配算法

针对认知无线网络中主用户行为将导致频谱瞬时变化而影响路由稳定性的问题,提出了一种基于主用户行为的路由和信道联合分配算法。该算法通过采用呼叫模型对主用户行为建模,并根据动态源路由协议的路由寻找机制,在目的节点等待多个路由请求分组后选择受主用户行为影响最小的路由,然后沿着所选定路径的反方向传送路由回复分组并完成信道分配。理论分析证明了算法中的链路平均持续时间期望与主用户活动概率成反比且具有与网络节点数成正比的计算复杂度。仿真结果表明,该算法具有比Gymkhana路由方案更高的分组投递率和更低的平均分组时延。     

Graph-based criteria for spectrum-aware clustering in cognitive radio networks

Cognitive radios (CRs) can exploit vacancies in licensed frequency bands to self-organize in opportunistic spectrum networks. Such networks, henceforth referred to as cognitive radio networks (CRNs), operate over a dynamic bandwidth in both time and space. This inherently leads to the partition of the network into clusters depending on the spatial variation of the primary radio network (PRN) activity. In this article, we analytically evaluate the performance of a new class of clustering criteria designed for CRNs, which explicitly take into account the spatial variations of spectrum opportunities. We jointly represent the network topology and spectrum availability using bipartite graphs. This representation reduces the problem of spectrum-aware cluster formation to a biclique construction problem. We investigate several criteria for constructing clusters for the CRN environment, and characterize their performance under different spectrum sensing and PR activity models. In particular, we evaluate the expected cluster size and number of common idle channels within each cluster, as a function of the spectrum and topology variability. We verify our analytical results via extensive simulations.