Spectrum Selection Scheme Research Articles

Determining a bandpass filter for interfering fringe spectrum extraction using clustering analysis

We propose a clustering-analysis approach that addresses the bandpass filter (BPF) detection problem in the analysis of the peak position of the interference envelope in vertical-scanning wideband interferometry. Our approach exploits the fact that the fringe signal formed by a wideband light source is a superposition of deterministic fringe signals and noise. Consequently, two clusters can be specified in advance for use in the k-means clustering analysis. We describe our measurement procedure and its development using a two-means method-based spectrum selection scheme based on the constituent characteristics of interference fringes. The details of our approach are introduced within the framework of femtosecond optical frequency comb-based interferometry. Our experimental results demonstrate the superiority of the proposed algorithm over the conventional heuristic threshold-based BPF estimation for envelope peak determination.

Energy-efficiency opportunistic spectrum allocation in cognitive wireless sensor network

The developments in wireless sensor network (WSN) that enriches with the unique capabilities of cognitive radio technique are giving impetus to the evolution of Cognitive Wireless Sensor Network (CWSN). In a CWSN, wireless sensor nodes can opportunistically transmit on vacant licensed frequencies and operate under a strict interference avoidance policy with the other licensed users. However, typical constraints of energy conservation from battery-driven design, local spectrum availability, reachability with other sensor nodes, and large-scale network architecture with complex topology are factors that maintain an acceptable network performance in the design of CWSN. In addition, the distributed nature of sensor networks also forces each sensor node to act cooperatively for a goal of maximizing the performance of overall network. The desirable features of CWSN make Multi-agent Reinforcement Learning (RL) technique an attractive choice. In this paper, we propose a reinforcement learning-based transmission power and spectrum selection scheme that allows individual sensors to adapt and learn from their past choices and those of their neighbors. Our proposed scheme is multi-agent distributed and is adaptive to both the end-to-end source to sink data requirements and the level of residual energy contained within the sensors in the network. Results show significant improvement in network lifetime when compared with greedy-based resource allocation schemes.

Cognitive channel selection and scheduling for multi-channel dynamic spectrum access networks considering QoS levels

Dynamic spectrum access (DSA) networks are composed of unprivileged users, called secondary users (SUs), that utilize the spectrum opportunities produced by the absence of co-located privileged primary users (PUs) via their cognitive capabilities. Providing a certain level of quality of service (QoS) to these users is a very challenging problem whilst providing protection to licensed PUs and contenting with nearby SUs. Even though centralized solutions may lead to better solutions in terms of network efficiency, the dynamic nature of DSA networks make the distributed solution approaches more attractive. In this paper, we present a fast, distributed, PU temporal-activity-estimation-aided spectrum assignment scheme for a multi-channel DSA system, including several multi-interface capable SUs with traffic demands at differentiated QoS levels. We first developed the proposed cognitive channel selection method, considering a simplified network having single channel and investigated its performance. Second, considering multi-channel environment, along with contending multi-SUs each of which can utilize multiple channels using their multi-interface property, we adopted the designed algorithm and coupled the proposed spectrum selection scheme with a distributed spectrum sharing mechanism that we devised to increase the overall network utility further. We modeled our scheme along with the network model using MATLAB® and evaluated its performance via several simulations. The extensive simulations validate the effectiveness of the proposed channel assignment scheme in terms of the Figure of Merit we defined, composed of the weighted sum of the throughput ratio values for packets of different QoS levels that successfully reached at the destination. The results also show the superior performance of the proposed scheme with respect to a native scheme that utilizes every spectrum opportunity in favor of the most demanding traffic flow.

Quadratic Greedy Algorithm for Multichannel Access in Cognitive Radio Network

Spectrum handoff is one of the main issues in cognitive radio networks. Spectrum handoff occurs when the owner of the spectrum or the primary user reclaims a channel already used opportunistically by a secondary user. Such handoff becomes more frequent especially in case of multichannel access since that in a wider band, a PU is likely to reclaim a part of the band more quickly. This process has a negative impact on the performance of SU transmission in terms of delay and link maintenance. In this paper, the problem of minimizing the secondary user handoff process because of primary user activity is underlined. An analytical formulation based on knapsack problem is established and a new spectrum selection scheme is proposed. The main idea is to maximize jointly the holding time and the achievable throughput of the selected channels. The performance of the proposed access model is investigated through numerical simulations.

Channel prediction-based channel allocation scheme for multichannel cognitive radio networks

Cognitive radio (CR) has been proposed to solve the spectrum utilization problem by dynamically exploiting the unused spectrum. In CR networks, a spectrum selection scheme is an important process to efficiently exploit the spectrum holes, and an efficient channel allocation scheme must be designed to minimize interference to the primary network as well as to achieve better spectrum utilization. In this paper, we propose a multichannel selection algorithm that uses spectrum hole prediction to limit the interference to the primary network and to exploit channel characteristics in order to enhance channel utilization. The proposed scheme considers both the interference length and the channel capacity to limit the interference to primary users and to enhance system performance. By using the proposed scheme, channel utilization is improved whereas the system limits the collision rate of the CR packets.

인지 무선 네트워크를 위한 채널 스케줄링기법

In Cognitive Radio network, spectrum selection scheme is one of a important part to manage idle spectrums efficiently. However, in CR networks, they have to adopt time-varying channel availability to minimize the interference to primary users (PU), and be able to man