Spectrum Occupancy Information Research Articles

Intelligent Spectrum Anti-Jamming With Cognitive Software-Defined Architecture

Malicious jamming in congested, contested, and constrained wireless environments has stimulated the demand of jamming-resistant communications. Nevertheless, classical anti-jamming techniques suffer from not only an extremely low data rate but also the risk of being interfered by the recently developed high-power jammers. As a promising solution, dynamic spectrum access (DSA) with software-defined platforms and cognitive functionality has been conceptualized, while systematic design remains an open issue. To this end, in this article, we propose a cognitive software-defined network (CSDN) architecture. Based on this, spectrum semantic extraction is developed to remove irrelevant spectrum features, such that learning a frequency preconfiguration policy (for DSA) can directly utilize the spectrum occupancy information. In order to learn the policy online, we propose a novel kernel-based distributed actor-critic algorithm, where the kernel method and online feature admission are exploited to make policy learning more adaptive. Numerical results show that semantics-driven reinforcement learning (RL) makes policy learning more efficient and accurate. Moreover, the proposed RL outperforms various baseline algorithms in terms of the strength in adapting to dynamic jamming patterns as well as the convergence speed of policy learning. Finally, a testbed is prototyped to validate the CSDN-based intelligent spectrum anti-jamming communication system.

Research on the scheme of radio cooperative spectrum sensing for offshore ships based on trust degree in Ad Hoc network mode

Radio communication is an important part of maritime communication. As the number of ships in the offshore area continues to increase, spectrum resources appear to be particularly tight. In this work, a trust-based cooperative spectrum sensing scheme in Ad Hoc network is proposed to improve the accuracy and reliability of maritime radio communications. This scheme first divides the ship nodes into different subnets according to the Ad Hoc network protocol. And after the malicious ship nodes are selected based on actual conditions, a trust-based collaborative spectrum sensing fusion method will be given, that is, a smaller trust weight is given to malicious nodes, the method will be more trustworthy to receive the spectrum occupancy information provided by ship nodes with a higher degree of trust. Simulations have been performed to illustrate the high detection probability of the proposed spectrum sensing scheme. Therefore, it is feasible and beneficial to apply the proposed spectrum sensing scheme to improve the trustworthiness of maritime radio communication.

S-transformation based integrated approach for spectrum estimation, storage, and sensing in cognitive radio

Cognitive Radio (CR) uses the principle of dynamic spectrum allocation to improve the utilization of spectrum bands. The estimation of missing data is essential for maintaining an uninterrupted quality of service in the CR. However, the existing methods are not suitable for interpolating missing data in high frequency signals. The storage of spectrum occupancy information is crucial for learning the spectrum usage and prediction. The existing techniques for wideband spectrum sensing suffer from poor edge detection capabilities. This paper proposes an S-Transformation (ST) based approach to solve these problems. For missing samples, the proposed method improves the accuracy of estimation. The ST can also be used to store the spectrum occupancy information. The simulation results show that the proposed scheme outperforms others by improving the accuracy of edge detection. Further, the simple implementation of the ST in the frequency domain is an advantage for the real time application.

Spectrum Occupancy Prediction for Realistic Traffic Scenarios: Time Series versus Learning-Based Models

Spectrum occupancy information is necessary in a cognitive radio network (CRN) as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access (DSA). However, in a CRN, it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior. In this context, the spectrum occupancy prediction proves to be very useful in enhancing the quality of experience of the secondary user. This paper investigates the practical prowess of various time-series modeling approaches and the machine learning (ML) techniques for predicting spectrum occupancy, based on a spectrum measurement campaign conducted in Jaipur, Rajasthan, India. Moreover, the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data. Nevertheless, prediction through ML-based recurrent neural network proves to perform reasonably well, thereby providing an accurate future spectrum occupancy information for DSA.

Spectrum Occupancy Information in Support of Adaptive Spectrum Sensing for Cognitive Radio

Cognitive radio (CR) is an emerging solution to spectrum scarcity currently faced worldwide as CR can change its operating parameters like frequency and bandwidth according to the radio environment and provides efficient spectrum utilisation. Many measurement campaigns conducted around the world showed that the licensed spectrum is not efficiently utilized which led to the need of implementation of dynamic spectrum access (DSA). Statistical information about current frequency usage and its use for developing new algorithms and protocols for CR network is the research area that still requires much more effort. In this paper we discuss the comparison of measurement results obtained from two measurement campaigns conducted in Pune at same location but with different total bandwidths considered. Further to implement CR the best suited channels are obtained from the data collected by using adaptive sensing methods, reference, duty cycle, periodicity and Markov method. The results of spectrum sensing methods show that spectrum occupancy information play a vital role in sensing best suited channels for cognitive radio.

Geolocation-aware resource management in cloud computing-based cognitive radio networks

With the rapid development of cognitive radios, spectrum efficiency in cognitive radio networks (CRN) has increased by secondary users (SU) accessing the licensed spectrum dynamically and opportunistically without creating harmful interference to primary users. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity. In this paper, we propose geolocation-aware radio resource management algorithm for CRN where distributed storage and computing resource in cloud computing platform and geolocation of secondary users are leveraged to store spectrum occupancy information of heterogeneous wireless networks and facilitates the access of spectrum opportunities for secondary users (SU). The proposed algorithm leverages the geolocation of secondary users and idle licensed bands to facilitate efficient allocation of radio resources to SU. Furthermore, the secondary users who provide high benefit are admitted while satisfying the quality of service (QoS) requirement of secondary users in terms of data rate and service time. We also propose a scalable mapping method using storm, a real-time distributed processing model in cloud computing platform to dynamically partition the geographical area according to the SU density. Simulation results are presented to demonstrate the performance of the proposed geolocation-aware radio resource management algorithm.

Radio environment map as enabler for practical cognitive radio networks

Latest regulations on TV white space communications and emerging trends of spectrum access through geolocation databases relax the regulatory constraints on Cognitive Radios (CRs). Geolocation databases are designed to store information related to incumbents, and CRs are envisioned to consult this database before spectrum access. Spectrum occupancy and related environment information can be constructed using these geolocation databases. In that regard, Radio Environment Map (REM) is a promising tool that provides a practical means for the realization of cognitive radio networks (CRNs). It constructs a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. REMs contribute to cognition engines by building long-term knowledge via processing spectrum measurements collected from sensors to estimate the state of locations where there is no measurement data. In addition, REM utilizes feedback from the CRs and can apply various learning tools. The vision is to design CRNs such that CRs, though being simple devices without advanced cognitive functionalities, can become cognitive via REMs and operate in an efficient manner. In this paper, an overview of the REM concept is presented in various dimensions ranging from its architecture and stored information to REM construction techniques as well as REM quality metrics.

Distributed Exploitation of Spectrum and Channel State Information for Channel Reservation and Selection in Interweave Cognitive Radio Networks

A channel-and-sensing-aware channel access (CSCA) policy is proposed for multi-channel interweave cognitive radio systems, where multiple secondary users (SUs) are competing for transmission to a common access point. The proposed CSCA policy consists of two key elements: (i) a decentralized channel selection policy that allows each SU to utilize knowledge of both the spectrum occupancy information and its local channel state information to make local channel access decisions and (ii) a channel reservation policy that allows each SU to compete for use of its selected channel by emitting short reservation packets at the beginning of each frame. In the reservation period, a channel-aware splitting algorithm is utilized to resolve collision among SUs that are competing for the same channel. The splitting procedure is optimized with respect to SUs' channel selection policy and ensures that the SU with the best channel quality prevails when collision is resolved. The CSCA policy is derived with the goal of maximizing the SU's throughput subject to a constraint on the probability of collision with primary users (PUs). To satisfy the collision probability constraint, a minimum channel gain threshold is set on each channel to limit the probability that the channel is accessed by SUs. An iterative algorithm is proposed to optimize the parameters in the channel selection and reservation policies, and a low-complexity policy is devised for use in systems with large numbers of channels. The proposed CSCA policies allow SUs to exploit optimally the tradeoff between spectrum availability and channel quality.

Distance Difference Test for Detection of Malicious User in Cognitive Radio

A number of wireless applications have been growing over the last decade. Most of the frequency spectrum has already been licensed by government agencies, such as Federal Communications Commission (FCC). Therefore, there exists an apparent spectrum scarcity for new wireless applications and services. Cognitive radio(CR) can efficiently utilize the unused spectrum for secondary usage without interfering a primary licensed user. In cooperative environment, a primary licensed user can share spectrum occupancy information with a secondary user to enable dynamic spectrum access. However, a secondary user needs to verify accuracy of the spectrum occupancy information and it comes from the legitimate primary users. Without the verification, a malicious user can falsify the spectrum occupancy information. This can result in Interference to the primary users and Primary User Emulation attack(PUE) which can minimize spectrum occupancy for secondary user. In this paper, we propose to develop an efficient technique to verify the source of the spectrum occupancy information to be from the legitimate primary user thereby maximizing the spectrum utilization efficiency and minimizing any interference to the primary licensed users.

CRABSS: CalRAdio‐Based advanced Spectrum Scanner for cognitive networks

AbstractThe first step required by the process of cognition is the intelligent observation of the environment that surrounds the actors of such a process. We present the CalRAdio‐Based advanced Spectrum Scanner (CRABSS), an open platform developed to monitor the ISM 2.4–2.499 GHz band and reveal opportunities for a better utilization of the available spectrum resources. CRABSS is built through a modular approach by integrating the development platform CalRadio 1 with the Unified Link Layer API (ULLA) framework. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it takes advantage of the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs. Copyright © 2010 John Wiley & Sons, Ltd.

Performance of dynamic spectrum access based on spectrum occupancy statistics

At present multiband wireless devices are able to select their working frequency only to a limited extent because of the strict, current spectrum regulation. Dynamic spectrum access is a promising approach that might solve this inefficiency. The authors focus on spectrum sensing, one of the main tasks involved. First, three strategies are compared to efficiently sense the current spectrum based on the spectrum occupancy information statistics. In contrast to the simulation-based studies, the authors evaluate the performance of those strategies on real spectrum occupancy data gathered during an extensive measurement campaign. The authors show that the usage of historical information considerably improves the spectrum sensing process and also that the modelling of the periodic behaviour of the licensed signals leads to negligible performance enhancements because only very few periods shorter than several minutes can be found within 20 MHz–6 GHz. Secondly, the authors unveil the fundamental tradeoff between the required bandwidth for the transmission and the total bandwidth that has to be sensed in order to guarantee that the required bandwidth is available. All the results are provided in terms of outage probability that can be viewed as an approximation of the packet loss rate.