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.      

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。     

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.     

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.     

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.     

Spread Spectrum Access in Two-Hop CATV Data Networks

A technique for data transmission over a CATV network, structured in a two-hop configuration, is presented. The technique combines the use of random access over the outer hop (distribution branches) and spread spectrum over the inner hop (main trunk) to maximize the CATV network throughput. A model is constructed for the spread spectrum access strategy. The throughput-delay relationship is obtained as a function of several design parameters. Numerical results are presented and discussed based on the assumptions and limitations of the model.     

Applications of Machine Learning to Cognitive Radio Networks

Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.     

Decision Based Model for Real-Time IoT Analysis Using Big Data and Machine Learning

Use of internet of things (IoT) in different fields including smart cities, health care, manufacturing, and surveillance is growing rapidly, which results in massive amount of data generated by IoT devices. Real-time processing of large-scale data streams is one of the main challenges of IoT systems. Analyzing IoT data can help in providing better services, predicting trends and timely decision making for industries. The systematic structure of IoT data follows the pattern of big data. In this paper, a novel approach is proposed in which big data tools are used to perform real-time stream processing and analysis on IoT data. We have also applied Spark’s built-in support of the machine learning library in order to make real-time predictions. The efficiency of the proposed system is evaluated by conducting experiments and reporting results on the case scenario of IoT based weather station.

     

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。     

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...     

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     

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.     

Performance Analysis of Cognitive Radio Spectrum Access with Different Primary User Access Schemes

The cognitive access process based on different access schemes of the primary user in the spectrum sharing system of cognitive radio is studied. In the cognitive system with finite user population, the primary users access the licensed channels through random access, real-reservation access, pseudo-reservation access and non-random access. The cognitive users access the licensed channels without interfering the primary users. Their access process is modeled using the three-dimensional continuous Markov model. The performance analysis is presented with variation of the number of cognitive users. It is known from the simulated results that the handoff probability, the blocking probability and the forced termination probability of the cognitive users increase with the increase of the number of cognitive users. It is also found that the random access scheme has the highest handoff probability. And the real-reservation access scheme has the highest blocking probability and forced termination probability. In addition, the non-random access scheme has the maximum channel utilization.     

Energy‐Efficient Power Allocation for Cognitive Radio Networks with Joint Overlay and Underlay Spectrum Access Mechanism

Traditional designs of cognitive radio (CR) focus on maximizing system throughput. In this paper, we study the joint overlay and underlay power allocation problem for orthogonal frequency‐division multiple access–based CR. Instead of maximizing system throughput, we aim to maximize system energy efficiency (EE), measured by a “bit per Joule” metric, while maintaining the minimal rate requirement of a given CR system, under the total power constraint of a secondary user and interference constraints of primary users. The formulated energy‐efficient power allocation (EEPA) problem is nonconvex; to make it solvable, we first transform the original problem into a convex optimization problem via fractional programming, and then the Lagrange dual decomposition method is used to solve the equivalent convex optimization problem. Finally, an optimal EEPA allocation scheme is proposed. Numerical results show that the proposed method can achieve better EE performance.