基于图论的认知无线网络频谱动态分配

在认知无线电网络中,图论与量子遗传算法相结合的频谱分配策略能够提高频谱利用率,但存在早熟和收敛精度不够等缺点。为了解决该问题而实现算法的优化,对图着色理论的频谱分配模型进行数学建模,并针对该模型提出了改进的量子遗传算法。首先,通过使用小生境技术初始化种群,使种群分布更加广泛、算法的收敛度更高;其次,根据进化代数对量子旋转角进行实时动态调整,对染色体进行阈值变异,防止个体陷入早熟,跳出局部解;然后,对干扰约束条件进行重新设计,有效地避免盲目性,提高了网络的公平性和网络效益。仿真结果表明,所提算法有效地提高了频谱利用率,极大地增强了网络系统的性能。     

认知无线电动态频谱分配新算法

动态频谱分配技术是认知无线电的一项关键技术,本文针对以OFDM为传输技术的认知无线电系统,提出了固定速率认知用户和变速率认知用户并存情况下的动态频谱分配新算法。本文算法以最小化每比特发射功率为目标,运用最小最大准则与分步求解的方法,对系统目标函数予以简化,在最大公平意义上对认知小区的空闲频谱资源进行动态分配。仿真结果表明,该算法具有较好的公平性,在固定速率用户功率最小化需求和变速率用户吞吐量最大化的需求之间取得了比较好的平衡,适用于两种需求用户并存环境下的认知无线电系统。      

认知无线电网络中频谱分配算法

 随着新型无线业务的不断发展,频谱供需矛盾日益明显.认知无线电网络被认为是实现动态频谱共享、缓解频谱供需矛盾的重要途径,近年来相关研究受到了广泛关注.本文对认知无线电网络中的频谱分配研究进展进行了分析.论文首先介绍了认知无线电网络的技术背景,分析了认知无线电网络中频谱分配的关键问题和算法设计目标.在此基础上总结了主流频谱分配模型的设计思想与技术特点,并详细描述了各模型经典分配算法的实现机制.最后,对频谱分配研究趋势进行了展望.     

Dynamic Spectrum Allocation with Maximum Efficiency and Fairness in Interactive Cognitive Radio Networks

In Interactive Cognitive Radio Networks (ICRNs), secondary usage of spectrum is of competition and cooperation, and radio frequency environment is time-varying. In order to realize real-time secondary spectrum usage, coalition-competition architecture for the ICRNs is introduced in this paper. In the coalition-competition architecture, the ICRNs are divided into many coalitions according to geographical locations, utilization degree of spectrum, frequency range and transmission power level. There exists competition among different coalitions. In the same coalition with competition and cooperation, a model based on cooperative differential games is proposed to solve dynamic spectrum allocation, and cooperative equilibrium solution to the model is given and analyzed in this paper. From an overall perspective, the relationships between available spectrum percentage and the spectrum access rate are studied. How to form a coalition, and the mechanism to allocate total spectrum among secondary users considering Pareto optimality and individual rationality in the coalition are analyzed. The simulation results show the dynamic spectrum allocation model is fair and efficient, and it reflects realistically time-varying radio frequency environment. Cooperative differential games are particularly helpful for the spectrum management in the time-varying radio environment.     

认知无线网络中基于用户需求和频谱聚合的动态频谱分配

认知无线电技术可以感知周围无线环境择机利用频谱空洞,从而达到提高频谱效率的目的.在认知无线电网络中,现有的频谱分配算法大部分是基于连续频谱分配并且没有考虑用户的需求.但是连续频谱分配会生成许多小于用户需求的频谱片段,这些频谱片段不能被充分利用,从而造成频谱浪费.本文基于图论提出了一种联合考虑用户需求和非连续频谱聚合的频谱分配算法.在该算法中,非连续的频谱片段被聚合以尽可能多地满足用户总需求,充分利用了小的频谱片段,避免了频谱浪费,从而提高频谱效率.     

Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.

认知无线电系统中利用模糊逻辑的频谱分配

为了较全面地分析频谱分配问题,突出系统的效益和公平性能,提出了一种利用模糊逻辑的认知无线电频谱分配方案.首先给出模糊逻辑系统(FLS)的概念,然后利用FLS综合分析感知空闲频谱和次用户的特性,最后结合系统吞吐量最大、公平性和随机分配3种准则将空闲频谱分配给次用户使用.仿真结果证实了方案的可行性.     

认知无线网络中基于模糊逻辑和Q学习的集中式动态频谱分配(英文)

A novel centralized approach for Dynamic Spectrum Allocation (DSA) in the Cognitive Radio (CR) network is presented in this paper. Instead of giving the solution in terms of formulas modeling network environment such as linear programming or convex optimization, the new approach obtains the capability of iteratively on-line learning environment performance by using Reinforcement Learning (RL) algorithm after observing the variability and uncertainty of the heterogeneous wireless networks. Appropriate decisi...     

认知无线电系统中的自适应动态频谱分配方法

该文提出了一种认知无线电系统中多小区间的动态频谱分配方法。该方法提出了对检测到的空闲频谱的空闲时长的预测机制,在此基础上,提出了基于频谱可靠性的自适应频谱分配算法。该算法在提高系统稳定性的基础上,最大化频谱资源的连续性;不仅实现了认知系统中频谱切换率的降低,同时保证了各小区所分配频谱的连续性,避免系统开销的增加及频谱管理的复杂化。此外,该方法采用图论着色理论模型实现了各小区之间干扰的抑制。仿真结果验证了该文方法的正确性。     

Joint Spectrum Allocation and Relay Selection in Cellular Cognitive Radio Networks

Cognitive Radio Network (CRN) has been proposed in recent years to solve the spectrum scarcity problem by exploiting the existence of spectrum holes. One of the important issues in the cellular CRNs is how to efficiently allocate primary user (PU) spectrum inside a CRN cell without causing harmful interference to PUs. In this paper, we present a cross-layer framework which jointly considers spectrum allocation and relay selection with the objective of maximizing the minimum traffic demand of secondary users (SUs) in a CRN cell. Specifically, we consider (1) CRN tries to utilize PU spectrum even when the CRN cell is not completely outside the protection region of the PU cell, and (2) cooperative relay is used in cellular CRNs to improve the utilization of PU spectrum. We formulate this cross-layer design problem as a Mixed Integer Linear Programming (MILP) and propose a low complexity heuristic algorithm to solve it. Compared to a simple channel allocation scheme, the numerical results show a significant improvement by using our proposed method and the performance is close to the optimal solution. We further consider the spectrum allocation among several CRN cells with the objective of maximizing the overall minimum throughput of all cells while ensuring each individual cell’s minimum throughput requirement. A low complexity algorithm is proposed to achieve the objective with satisfactory performance.     

A Parallel Repeated Auction for Spectrum Allocation in Distributed Cognitive Radio Networks

Cognitive radio (CR) is applied to solve spectrum scarcity. Although the auction theory and learning algorithm have been discussed in previous works, their combination is not yet researched in the distributed CR networks, where secondary users (SUs) can occupy several channels simultaneously by assuming that one channel can be accessed by at most one SU. A parallel repeated auction scheme is proposed to solve resource allocation in multi-user multi-channel distributed spectrum-overlay CR networks. A novel bid scheme in the light of the first-price sealed auction is designed to balance the system utility and allocation fairness. The proposed auction scheme can be developed based on a learning algorithm and be applied to the scenarios where the cooperation among SUs is unavailable. Under the assumption of limited entry budget, SUs can directly decide whether or not to participate in spectrum auction by comparing the possible bid with access threshold which can be applied into situations that SUs have different transmit power. Theoretical analysis and simulation results show that, compared with original myopic scheme and original genie-aided scheme, the proposed auction scheme can obtain a considerable improvement in efficiency and fairness, especially with adequate available resources.     

Optimal Transmission Strategies for Dynamic Spectrum Access in Cognitive Radio Networks

Cognitive radio offers a promising technology to mitigate spectrum shortage in wireless communications. It enables secondary users (SUs) to opportunistically access low-occupancy primary spectral bands as long as their negative effect on the primary user (PU) access is constrained. This PU protection requirement is particularly challenging for multiple SUs over a wide geographical area. In this paper, we study the fundamental performance limit on the throughput of cognitive radio networks under the PU packet collision constraint. With perfect sensing, we develop an optimum spectrum access strategy under generic PU traffic patterns. Without perfect sensing, we quantify the impact of missed detection and false alarm, and propose a modified threshold-based spectrum access strategy that achieves close-to-optimal performance. Moreover, we develop and evaluate a distributed access scheme that enables multiple SUs to collectively protect the PU while adapting to behavioral changes in PU usage patterns. Our results provide useful insight on the trade-off between the protection of the primary user and the throughput performance of cognitive radios.     

Sensing Confidence Level-Based Joint Spectrum and Power Allocation in Cognitive Radio Networks

Cognitive radio (CR) has been extensively investigated in the past decade to tackle the contradiction between wireless spectrum shortage and underutilization. In this paper, we present a unified analytical framework to design PHY-layer spectrum sensing and MAC-layer resource scheduling jointly for CR networks. A key parameter, named sensing confidence level (SCL), is introduced to characterize the presence of imperfect sensing, and bridge the designs between kinds of spectrum sensing schemes and resource allocation algorithms. The SCL-based joint design of spectrum and power allocation is formulated as a mixed integer non-linear optimization problem and Lagrange duality theory is introduced to make the problem tractable. The proposed joint design framework in this paper provides a baseline for comparing different spectrum sensing schemes plus bandwidth and power allocation algorithms. Numerical results demonstrate the effectiveness of the proposed framework.     

Designing a MAC Algorithm for Equitable Spectrum Allocation in Cognitive Radio Wireless Networks

Spectrum sharing is one of the most important stages in cognitive radio wireless networks, responsible for the opportunistic allocation of free channels to unlicensed users (SUs) to be utilized in data transmission. One of the critical issues at this stage, is related to the absence of a module capable of allocating the available resources fairly to all network users. In this sense, the paper develops a media access control protocol (MAC) for cognitive networks based on infrastructure called CRUD-MAC, which allows to take advantage of channel access in a more equitable and efficient way; for this purpose two algorithms we designed within the MAC standard (using ANFIS and FAHP) for the ranking or classification of SUs by score when assigned channels based on network usage historical metrics, so that nodes with better ranking have priority in the allocation. Validation of the proposals was made by comparing the performance of CRUD-MAC with ANFIS, FAHP, and a channel assignment algorithm, not including ranking. The results show that the system is more efficient from the standpoint of fair allocation of resources.     

Dynamic Spectrum Sharing for OFDMA-Based Multi-hop Cognitive Radio Networks with Priority

In this paper, we propose a resource assignment scheme of a secondary user (SU) for a multi-hop cognitive radio network. In multi-hop networks, since each link has different SNR because of their different distance between stations and multipath fading, the link of the smallest SNR is the bottleneck. For overcoming this problem, it is proposed to give a priority to each link based on the link SNR and to assign the resource blocks (RBs) in ordering of instantaneous SNR on the link. This link priority information is shared among nodes through a control channel. Then, we assign the selected SU RB to each SU node according to the SNR ordering with distributed manner. We confirm the effectiveness of the proposed SU RB assignment by using computer simulation.     

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 Sensing Framework for Cognitive Radio Networks

Spectrum sensing feature of cognitive radio devices represents a cornerstone characteristic facilitating real-time and accurate spectrum occupancy measurements in cognitive radio networks. It practically enables the cognitive radio devices to detect vacant spectrum holes and use them for their communication purposes. There are numerous spectrum sensing methods proposed in the literature ranging from local based ones to cooperative strategies among several devices increasing the confidence level of the detected spectrum. This paper gives a general spectrum sensing framework for cognitive radio networks, classifies and explores different spectrum sensing techniques and approaches and shows practical examples, from authors’ own experience, of realized spectrum sensing engines and strategies along with some obtained results.     

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.     

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。