非理想信道检测的认知无线电网络中能量感知频谱接入

Due to the problem of spectrum underutilization and energy inefficiency in wireless communications, the research on energy efficient Cognitive Radio Networks (CRNs) has received significant attention in both industry and academia. In this paper, we consider the problem of optimal spectrum selection and transmission parameters design with the objective of minimizing energy consumption in CRNs. Since the system state cannot be directly observed due to miss detections and estimation errors, we formulate the optimal spectrum access problem as a Partially Observable Markov Decision Process (POMDP). In particular, the proposed scheme selects the optimal spectrum, modulation and coding scheme, transmission power, and link layer frame size in each time slot according to the belief state, which captures all the history information of past actions and observations. The optimal policy can be acquired by solving POMDP problem with linear programming based algorithm. Simulation results show that significant energy savings can be achieved by the proposed scheme.     

Performance Enhancement of Dynamic Spectrum Access via Channel Reservation for Cognitive Radio Networks

Wireless Personal Communications - In cognitive radio networks models, quality of service (QoS) of primary users (PUs) must be assured. Dynamic spectrum access is a paradigm by which a radio system...     

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.

非实时业务下认知网络的频谱共享

根据某种特定业务的QoS要求制定相应的频谱共享策略,可以在这种情况下提高系统的频谱利用率,增加用户平均收益。对非实时业务下的认知网络的频谱共享策略进行了研究,考虑各非授权用户业务量和信道质量对系统频谱效率的影响,提出了一种非实时业务专用的NRT-dedicate频谱共享算法。仿真结果表明,在非实时业务情况下,该算法可以获得比通用业务的频谱共享算法的频谱利用率。     

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.     

Cooperative Spectrum Prediction in Multi-PU Multi-SU Cognitive Radio Networks

Spectrum sensing is considered as the cornerstone of cognitive radio networks (CRNs). However, sensing the wide-band spectrum results in delays and resource wasting. Spectrum prediction, also known as channel status prediction, has been proposed as a promising approach to overcome these shortcomings. Prediction of the channel occupancy, when feasible, provides adequate means for an SU to determine, with a high probability, when to evacuate a channel it currently occupies in anticipation of the PU’s return. Spectrum prediction has great potential to reduce interference with PU activities and significantly enhance spectral efficiency. In this paper, we propose a novel, coalitional game theory based approach to investigate cooperative spectrum prediction in multi-PU multi-SU CRNs. In this approach, cooperative groups, also referred to as coalitions, are formed through a proposed coalition formation algorithm. The novelty of this work, in comparison to existing cooperative sensing approaches, stems from its focus on the more challenging case of multi-PU CRNs and the use of an efficient coalition formation algorithm, centered on the concept of core, to ensure stability. Theoretical analysis is conducted on the upper bound of the coalition size and the stability of the formed coalition structure. A through simulation study is performed to assess the effectiveness of the proposed approach. The simulation results indicate that cooperative spectrum prediction leads to more accurate prediction decisions, in comparison with local spectrum prediction individually performed by SUs. To the best of our knowledge, this work is the first to use coalitional game theory to study cooperative spectrum prediction in CRNs, involving multiple PUs.     

认知无线网络中基于混合频谱切换的最优目标信道选择算法

在主动频谱切换中,预先确定目标信道存在高失效风险,从而增加切换时延的问题。该文提出一种基于混合主被动频谱切换的最优目标信道选择算法。该算法结合主动频谱切换和被动频谱切换的优点,引入最小累积切换时延评价标准,综合考虑次用户的多次中断、主用户的到达率和非理想频谱检测对次用户累积切换时延的影响,并详细分析和推导了非理想检测下次用户的累积切换时延。仿真表明,相较于主动和被动频谱切换中目标信道选择策略,在信道状态变化频繁或业务量较大等极端情况下,该算法的性能优势尤为明显。     

Analysis of Cognitive Radio Spectrum Access with Optimal Channel Reservation

A Markov chain analysis for spectrum access in licensed bands for cognitive radios is presented and forced termination probability, blocking probability and traffic throughput are derived. In addition, a channel reservation scheme for cognitive radio spectrum handoff is proposed. This scheme allows the tradeoff between forced termination and blocking according to QoS requirements. Numerical results show that the proposed scheme can greatly reduce forced termination probability at a slight increase in blocking probability     

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.     

OFDMA感知无线电网络中一种基于拍卖博弈的频谱共享新算法

在基于OFDMA的感知无线电网络中,为了最大化从网络的吞吐量,考虑了空间频率复用,通过干扰判据和邻接矩阵描述感知链路在子载波上的冲突,将最大化从网络和速率的问题建模为基于VCG机制的多个赢家的子载波拍卖博弈。通过逐一求解单个子载波上的二进制整数规划问题获得最优解。仿真结果表明,所提出的子载波拍卖算法有效地提高了感知无线电网络中资源的利用效率。     

认知无线电中基于感知门限的频谱预测研究

频谱感知为未获得频谱授权的次用户提供频谱空穴,并监测主用户以防止对其造成干扰。然而频谱感知会带来时间和能量消耗,采用准确、主动的频谱预测技术可以改善频谱感知过程。文中通过研究感知门限对平均感知时间和频谱利用率的影响,提出了基于感知门限的频谱预测-感知机制,并利用频谱预测结果按照空闲概率从大到小的顺序进行感知。仿真结果表明,文中所提出的机制能够减少感知时间并提高频谱利用率。     

Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks

Wireless Personal Communications - A major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks....     

基于业务区分的频谱接入策略分析

在认知无线网络中,如何保证认知用户的传输质量,满足其QoS要求是值得关注的问题。本文首先根据认知用户的QoS要求,将其分成两种等级,实时用户和非实时用户,并分别被建模成M/G/1排队模型,降低了对应的系统时延均值。然后根据系统传输时间均值,讨论了多信道接入策略,通过经典的遗传算法可得最优接入策略,考虑到遗传算法的高复杂度,实际应用中采用反比例接入算法求得次优解,实验数据表明反比例接入算法可以取得与最优接入策略相近的结果且能极大地降低计算时间。     

Neighbor Detection-Based Spectrum Sensing Algorithm in Distributed Cognitive Radio Networks

Security issues of spectrum sensing have drawn a lot of attentions in Cognitive radio networks (CRNs). Malicious users can m islead the network to m ake wrong decision about the states of channels by tampering spectrum sensing data. To defense against Spectrum sens-ing data falsification (SSDF) attack, we propose a neighbor detection-based spectrum sensing algorithm in distributed CRNs, which can detect attackers with the help of neigh-bors during spectrum sensing to improve the accuracy of decision making. The proposed scheme can also guarantee the connectivity of the network. Simulation results illus-trate that the proposed scheme can defense against SSDF attacks effectively and reach the unified information of spectrum sensing data.     

认知无线电网络中基于强化学习的智能信道选择算法

认知无线电系统不仅要具有自适应性,更应具备一定的智能性。该文将强化学习理论引入到认知无线电系统中,用于解决次用户在频谱感知过程中的信道选择问题,提出了一种基于强化学习的信道选择算法。该算法在未知主用户占用规律和动态特性的前提下,仅通过不断与环境进行交互学习,便能够引导次用户选择“较好”信道优先进行感知,使次用户吞吐量得到提高。仿真结果表明,相对于现有信道选择算法,所提算法可有效提高次用户的吞吐量,并且在主用户使用规律发生变化时,能够自动实现二次收敛,可作为认知无线电系统迈向智能化的一种尝试。      

带有接入阈值和超时隙的认知无线网络频谱分配策略

为了提高认知用户的响应性能,并降低传输中断的概率,通过限制进入系统的认知用户数据包数量,并令授权用户以超时隙为单位优先使用信道,提出一种带有接入阈值和超时隙的集中式认知无线网络频谱分配策略。结合时隙序号及系统中认知用户数据包的数量建立2维离散时间Markov链模型,给出认知用户数据包的平均延迟、吞吐量及信道切换率等性能指标的表达式。建立系统收益函数,针对不同的超时隙大小给出接入阈值的优化设置方案,并利用优化结果,进行系统实验。实验结果显示：合理的接入阈值和超时隙大小可以有效提高认知用户的传输质量。     

认知无线网络中一种基于蚁群优化的频谱分配算法

针对认知无线电中的频谱分配问题,该文提出一种基于蚁群优化的频谱分配方法。该方法在授权用户和认知用户共存的认知网络模型中,通过蚁群访问各个认知节点,并释放信息素,从而实现概率型的全局搜索的并行频谱分配算法。与传统的频谱分配方式比较,该算法能够进行增强型学习积累,快速收敛到最优路径,从而获得了最优的平均信道效益。文中对该算法进行了分析和说明,并通过仿真证明了算法的有效性和稳定性。