Reinforcement Learning Based Dynamic Spectrum Access in Cognitive Internet of Vehicles

Cognitive Internet of Vehicles (CIoV) can improve spectrum utilization by accessing the spec-trum licensed to primary user (PU) under the premise of not disturb...     

Cognitive Wireless Mesh Networks with Dynamic Spectrum Access

Wireless Mesh Networks (WMNs) are envisaged to extend Internet access and other networking services in personal, local, campus, and metropolitan areas. Mesh routers (MR) form the connectivity backbone while performing the dual tasks of packet forwarding as well as providing network access to the mesh clients. However, the performance of such networks is limited by traffic congestion, as only limited bandwidth is available for supporting the large number of nodes in close proximity. This problem can be alleviated by the cognitive radio paradigm that aims at devising spectrum sensing and management techniques, thereby allowing radios to intelligently locate and use frequencies other than those in the 2.4 GHz ISM band. These promising technologies are integrated in our proposed Cognitive Mesh NETwork (COMNET) algorithmic framework, thus realizing an intelligent frequency-shifting self-managed mesh network. The contribution of this paper is threefold: (1) A new approach for spectrum sensing is devised without any change to the working of existing de facto mesh protocols. (2) An analytical model is proposed that allows MRs to estimate the power in a given channel and location due to neighboring wireless LAN traffic, thus creating a virtual map in space and frequency domains. (3) These models are used to formulate the task of channel assignment within the mesh network as an optimization problem, which is solved in a decentralized manner. Our analytical models are validated through simulation study, and results reveal the benefits of load sharing by adopting unused frequencies for WMN traffic.     

一种基于认知无线电的动态频谱接入MAC方案

在传统的无线通信系统中,频谱的分配是固定的。但是由于通信过程的突发性,这些频谱的使用率很低。另一方面,随着无线通信和多媒体的高速发展和广泛应用,无线频谱资源日趋紧张。如何提高频谱利用率已经成为迫切需要解决的问题。一种可行的思路是把这些授权频谱向未授权用户开放,未授权用户采用动态频谱接入技术,在不对授权用户造成干扰的前提下使用频谱。本文以认知无线电技术(Cognitive Radio,CR)为基础,提出了一种基于CR的动态频谱接入MAC方案(CR-Ad Hoc-MAC)。该方案允许未授权用户自适应地选取可用带宽,实现了动态频谱接入,有效地提高了频谱利用率。     

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.     

Low-Complexity Adaptive Transmission for Cognitive Radios in Dynamic Spectrum Access Networks

Cognitive radios that are employed in a network with dynamic frequency assignments must operate efficiently in the presence of uncertainties and variations in the propagation characteristics of the network's communication links. A low-complexity adaptive transmission protocol is described and evaluated for use in cognitive radio networks whose links have unknown and possibly time-varying propagation losses as a result of such phenomena as slow fading or variations in shadowing. The cognitive radios are required to derive only simple statistics in the receivers in order to provide the information that is needed by our protocol; no estimates or measurements of received power or channel gain are used. The protocol's primary mechanism for responding to changes in propagation loss is to adjust the modulation and coding. Because of disruptions that can be caused by higher levels of interference to other radios in the network, the transmitter power is increased only if the most powerful combination of coding and modulation is inadequate. We employ finite-state Markov models for slowly varying channels, and we demonstrate that for such channels our protocol performs nearly as well as an ideal protocol that is told the exact value of the propagation loss for each packet transmission. Thus, the additional complexity that is required to enable cognitive radios to obtain precise channel-gain estimates is not justified and would lead to only negligible improvement in throughput. The throughput of our adaptive transmission protocol is compared with an upper bound that is derived from information theory for a hypothetical ideal protocol that is given perfect channel-state information, and some preliminary results on learning the adaptation decision intervals are included.     

基于PCN的水下认知网络动态频谱接入算法

水下网络可用频谱范围比较窄,且部分被水下生物占用,导致了水下传感器网络可用的频谱资源更为稀缺.针对上述问题,提出一种基于累积干扰预测(Predicted Cumulative Noise,PCN)的水下认知网络动态频谱接入算法.该算法把水下生物作为认知网络的主节点,水下传感器节点作为次节点;通过建立水下生物业务行为的马尔科夫模型预测累积干扰,次节点根据预测结果,采用合作的方式动态地接入授权频谱.仿真结果表明,该算法能够保护水下生物正常通信的同时,实现最优化的频谱共享,网络容量增益达到6.3dB.     

Algorithms for Dynamic Spectrum Access With Learning for Cognitive Radio

We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm.      

认知网络管理在物联网中的应用

The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new opportunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the“Internet of Things” (IoT). Since a huge number of heterogeneous resources are brought into IoT, one of the main challenges is how to efficiently manage the increasing complexity of IoT in a scalable, flexible, and autonomic way. Furthermore, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Internet. In this paper, we propose a new IoT network management architecture based on cognitive network management technology and Service-Oriented Architecture to provide effective and efficient network management of IoT.     

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.     

认知无线电中一种新的频谱接入方法

提出一种新的可实行性频谱接入算法.利用爬坡方法求解目标函数,使得计算频谱接入参数可行.根据对主用户接收机端的干扰温度估计方法不同,提出了合作式频谱接入算法和非合作式频谱接入算法,并对两种方法进行了系统仿真和比较.     

动态频谱接入综述

动态频谱户在授权的频谱带宽中获得丰富频谱空隙。DSA技术可以缓解频谱短缺问题并且提高频谱利用率。文中讨论了DSA所面临的挑战,旨在揭示其未来的走向。介绍了前沿的频谱感知和频谱分享。调查了阻碍DSA成为主要的商业部署的挑战。为了应对这些挑战,建立了一个新的DSA模型,在这种模式下,授权的用户可以在DSA中相互合作,而且可以获得更加灵活的频谱共享。     

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...     

物联网与UHF RFID频谱资源

本文从物联网的概念出发,谈到物联网对RFID的关联,介绍了各国UHFRFID频谱资源安排情况,指出现实可供频谱资源与物联网发展需求的矛盾。分析了现行RFID空中接口频谱资源不足与频谱资源浪费的现实的矛盾,提出出路在于研究新的技术体制。     

无线物联网中的频谱感知模型研究

In the last few years, the number of devices operating in wireless Internet of Things (IoT) has experienced tremendous growth. On the other hand, the growth results in spectrum scarcity. Cognitive Radio (CR) systems have been proposed to efficiently exploit the spectra that have been assigned but are underutilized. In this paper, a spectrum sensing model based on Markov chain is proposed to predict the spectrum hole for CR in wireless IoT. Theoretical analysis and simulation results have been evaluated that a Markov model with two-state or four-state works well enough in wireless IoT whereas a model with more states is not necessary for it is complex.     

Optimal and Suboptimal Access and Transmission Polices for Dynamic Spectrum Access over Fading Channels in Cognitive Radio Networks

We propose a cross-layer optimal access and transmission framework for dynamic spectrum access to maximize expected long-term average throughput under power and collision constraints by a dynamic programming method namely Constrained Markov decision process （CMDP）. The optimal policy for CMDP is capable of guiding transmitter to choose an available channel and transmission rate at the beginning of each frame for its long-term goals according to current channel sensing results and prior channel fading information. The complexity of finding the optimal policy by Linear programming （LP） approach increases exponentially with the number of channels and fading levels, which incurs so-called curse of dimensionality. Therefore we propose two complexity- reduced suboptimal policies, namely, policy separation and heuristic algorithms. Finally, we compare the performances of policies by numerical results.     

Cognitive Radio Dynamic Access Techniques

The ever growing demand for wireless services has placed enormous burden on valuable resources such as spectral bandwidth. This has resulted in a major rethinking in resource allocation policies culminating in an explosion of research activity in the field of Cognitive Radio (CR) towards optimum resource usage. In this tutorial paper the physical layer design and transmission techniques for CR in the context of efficient spectrum utilization are discussed. Spectrum sensing as the key element of CR awareness is described. Orthogonal frequency division multiplexing (OFDM) as a spectrally efficient modulation scheme is discussed and the rationale for its use in the CR system is explained. Spectrum pooling for efficient use of spectrum is studied and the role of adaptive OFDM techniques in this method is highlighted. Wavelet basis function as a replacement of Fourier transform in OFDM is evaluated. MIMO system as an added value to the CR performance is described. Adaptive Waveform and beamforming as alternative techniques in CR are reviewed.

基于频谱预测的认知无线接入研究

本文提出基于认知无线电的语音类业务,来扩大认知无线电的应用范围。并且针对认知无线电中,频谱变化时,会强制中断语音类业务,降低认知无线电服务质量,引入一种基于测量的不断修正的频谱资源预测模型。通过频谱资源预测模型,在发生强制中断时或者前,提前切换到其他在下一时刻发生强制中断率低的信道。