Dynamic multichannel access with imperfect channel state detection

Abstract—A restless multi-armed bandit problem that arises in multichannel opportunistic communications is considered, where channels are modeled as independent and identical Gilbert–Elliot channels and channel state detection is subject to errors. A simple structure of the myopic policy is established under a certain condition on the false alarm probability of the channel state detector. It is shown that myopic actions can be obtained by maintaining a simple channel ordering without knowing the underlying Markovian model. The optimality of the myopic policy is proved for the case of two channels and conjectured for general cases. Lower and upper bounds on the performance of the myopic policy are obtained in closed-form, which characterize the scaling behavior of the achievable throughput of the multichannel opportunistic system. The approximation factor of the myopic policy is also analyzed to bound its worst-case performance loss with respect to the optimal performance. Index Terms—Cognitive radio, dynamic multichannel access, myopic policy, restless multi-armed bandit

On Optimality of Myopic Policy for Restless Multi-armed Bandit Problem with Non i.i.d. Arms and Imperfect Detection

We consider the channel access problem in a multi-channel opportunistic communication system with imperfect channel sensing, where the state of each channel evolves as a non independent and identically distributed Markov process. This problem can be cast into a restless multi-armed bandit (RMAB) problem that is intractable for its exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In particular, we develop three axioms characterizing a family of generic and practically important functions termed as $g$-regular functions which includes a wide spectrum of utility functions in engineering. By pursuing a mathematical analysis based on the axioms, we establish a set of closed-form structural conditions for the optimality of myopic policy.Comment: Second version, 16 page

On Optimality of Myopic Sensing Policy with Imperfect Sensing in Multi-channel Opportunistic Access

We consider the channel access problem under imperfect sensing of channel state in a multi-channel opportunistic communication system, where the state of each channel evolves as an independent and identically distributed Markov process. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem that is of fundamental importance in decision theory. It is well-known that solving the RMAB problem is PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In this paper, we perform an analytical study on the optimality of the myopic policy under imperfect sensing for the considered RMAB problem. Specifically, for a family of generic and practically important utility functions, we establish the closed-form conditions under which the myopic policy is guaranteed to be optimal even under imperfect sensing. Despite our focus on the opportunistic channel access, the obtained results are generic in nature and are widely applicable in a wide range of engineering domains.Comment: 21 pages regular pape

Convex Optimization Resource Allocation Algorithm in Cogniti＇^e Heterogeneous Networks under Imperfect Spectrum Sensing

当前认知异构网络中无线频谱日益紧缺,而传统固定频谱分配模式日益成为限制无线通信性能的重要瓶颈,在非理想频谱感知情况下资源分配的问题尤为突出。为实现非理想频谱感知情况下无线资源的高效分配,提出一种基于认知异构网络的凸优化资源分配算法。该算法首先构建了基于主用户活跃度的用户到达模型,以精确描述认知网络中主用户的频谱使用状态,为认知用户分配资源提供依据;并通过认知异构网络干扰分析构建非理想频谱感知条件下的干扰容限条件,最后通过凸优化算法实现对认知网络中频谱资源的优化分配。仿真结果表明,在非理想频谱感知条件下,该算法能够有效降低系统平均时延,提升认知异构网络的传输速率和系统吞吐量。The cognitive radio spectrum in heterogeneous networks become increasingly scarce, and thetraditional fixed spectrum allocation model has become an important bottleneck of wireless communication. Theproblem of the allocation of resources under imperfect spectrum sensing is particularly prominent. To realizeeficient allocation of wireless resources under imperfect spectrum sensing,our paper presents a convex optimization resource allocation algorithm in Cognitive Heterogeneous Networks. Firstly,it constructs the arriving model basedon the primary users activity,and accurately describes the spectrum usage of primary users,provides a basis for cognitive users to allocate resources. Through cognitive heterogeneous network interference analysis,w e reason out of the interference tolerance limits under imperfect spectrum sense, and finally proposes a convex optimizationalgorithm to achieve an optimal allocation of spectrum resources. Simulation results show thatunder tlie imperfectspectrum sensing,this method can effectively reduce the average delay of system,and improve transmission speedand the overall throughput of the cognitive heterogeneous network.福建师范大学闽南科技学院教学改革项目（12014002）和福建师范大学闽南科技学院本科教学工程培育项目（PYXM-2015-01）资

An Efficient Subcarrier and Power Allocation Scheme for OFDM based Cognitive Radio Networks Considering Channel Sensing Errors

Cognitive radio plays a major role in today’s wireless communications and solves the spectrum scarcity problem by efficiently utilizing the vacant spectrum. Most CR systems employ OFDM as a modulation technique because of its flexibility in allocating spectrum resources. Allocation of vacant spectrum to the secondary users introduces interference to the primary users. In this paper, subcarrier and power allocation for OFDM based cognitive radio network for joint overlay and underlay spectrum access mechanism (JOUSAM) with channel sensing error is proposed. For such a CR systems, the transmission rate is maximized for a given power budget, while keeping the interference level of the primary user below a certain threshold. The numerical results show that the proposed scheme achieves higher transmission rate when compared to system without considering sensing error