Dynamic Spectrum Access Technology Research Articles

Spatiotemporal attention aided graph convolution networks for dynamic spectrum prediction

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.

Dynamic multiple access based on deep reinforcement learning for Internet of Things

With the development of wireless communication technology, the rational use of spectrum resources utilizing efficient multiple access technology has become a research hotspot. The channel access strategy in dynamic spectrum access technology will directly affect the system’s spectrum utilization. Deep reinforcement learning is proposed to train users in the network to learn optimal channel access strategy to achieve reasonable allocation of spectrum resources on the channel. At the same time, the multiple access scheme adopted by users who have access to the channel will also affect the throughput of the system. Traditional multiple access schemes include Orthogonal Multiple Access scheme and Non-orthogonal Multiple Access (NOMA) scheme. In a dynamic environment, the multiple access scheme suitable for users on the channel is also dynamic. Therefore, DRL is introduced to help users learn the multiple access scheme selection strategy to maximize user throughput. Finally, we propose a dynamic multiple access algorithm based on deep reinforcement learning, which optimizes the channel access strategy and scheme selection strategy respectively, and improves the performance. The simulation results show that the proposed scheme is superior to other schemes in various scenarios, especially, the average throughput of the proposed scheme is about 0.8 Mbps and 0.5 Mbps higher than that of the FDMA scheme and NOMA scheme, respectively.

SIMULASI DISTRIBUTED BINARY POWER ALLOCATION PADA SISTEM RADIO KOGNITIF

Cognitive radio is a technology for wireless communication networks that employ the upcoming Dynamic Spectrum Access ( DSA ) . DSA technology allows users who do not have a license called the secondary spectrum users to use the licensed spectrum that primary users while not in use. Application of DSA by secondary users to improve the spectrum utilization efficiency without disrupting or causing interference to the primary user. Later developed into a multi - user cognitive radio or cognitive radio networks use frequency spectrum together with a primary user ( PU ).
 Methods switch 'off' the user who gives the biggest interference to PU is the best method with the greatest increase in performance, both the capacity and the number of active SU while the combination of methods switch 'off' the user who raised the largest overall interference with the method switches 'off' user given biggest interference to the PU is an alternative method that shows the consistency of performance improvement in the number of potential users over 10 and able to dominate the entire method over another is begun saturation and decreased capacity.
 Abstracts are made in two languages, English and Bahasa Indonesia. Abstract more about background, purpose, up to, the results of research, and manai research. Abstract contains up to 250 words, single write spaces with italics (Italics) for English abstracts. Below the abstract are listed keywords consisting of six words, where the first word is again the forward. Abstract in Indonesian can be a translation of an English translation. Tiff editor for abstract syncing for reasons of abstract content

Dynamic spectrum access for Internet-of-Things with hierarchical federated deep reinforcement learning

In Internet-of-Things (IoT), blooming IoT terminals along with growing demand for broadband services, such as video stream and virtual reality (VR) applications, have led the enhancement of spectrum efficiency to the research focus in IoT. The technology of dynamic spectrum access or sharing is commonly utilized to address this challenge. With the development of artificial intelligence (AI), there has been an increasing amount of research on dynamic spectrum access based on machine learning models, such as Q-learning, deep reinforcement learning and federated learning. When using federated learning to secure user privacy in dynamic spectrum access, several key challenges such as the lack of personalized model selection and parameter updates, and the difficulty of meeting the needs of different devices with a single global model are still under-investigated. In this paper, the heterogeneity of IoT users is considered and a hierarchical and personalized federated deep reinforcement learning approach is proposed to fulfill dynamic spectrum access. First, IoT users are trained locally by using deep reinforcement learning to obtain optimal rewards and spectrum access rate. Second, considering that users have their personalized characteristics, a client-edge-cloud federated learning framework is proposed to accelerate the model convergence and reduce communication overhead between cloud servers and clients. Accordingly, user parameters are divided into personalized and basic parameters. Basic parameters are individually uploaded for global model training, meanwhile personalized parameters are retained to guarantee the applicability of the model. Lastly, the proposed framework is used for dynamic spectrum access, thereby improving the efficiency of spectrum access and decrease the bandwidth occupied by exploiting the global optimization capabilities for federated learning. Only basic parameters are uploaded to substantially protect user’s privacy. Simulation results show that the proposed framework can improve user communication performances and the accuracy of spectrum access. The average convergency time for IoT users can be reduced by about 40% compared with common federated-learning-oriented dynamic spectrum access.

Dynamic spectrum access based on deep reinforcement learning for multiple access in cognitive radio

With the increasing shortage of spectrum resources, dynamic spectrum access (DSA) technology is proposed to maximize the spectrum resources utilization. Traditional DSA solutions can no longer meet the requirements of high throughput and low interference in large-scale access scenarios of cognitive radio (CR). Therefore, in this paper, we propose a DSA scheme based on deep reinforcement learning (DRL) combined with multiple access methods to maximize the system throughput. In the DSA network, the access strategy adopted by the secondary user (SU) will directly affect the performance of the entire system, so we introduce DRL to help the SU learn the best access strategy in a dynamic environment. The trained SU can intelligently access the appropriate channel to avoid interference to the primary user (PU) and other SUs. By combining deep Q-network (DQN) into two multiple access methods: Frequency Division Multiple Access (FDMA) and Non-orthogonal Multiple Access (NOMA), DQN-based FDMA scheme and DQN-based NOMA scheme are designed, respectively, which can find the best DSA strategy to avoid collisions with PU or other SU and improve system throughput. Simulation results show that the DQN-based NOMA scheme has better performance than the DQN-based FDMA scheme.

A Trust-Centric Privacy-Preserving Blockchain for Dynamic Spectrum Management in IoT Networks

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.

Computation rate maximization for IRS-enhanced dynamic spectrum access assisted MEC system

Intelligent reflecting surface (IRS)-enhanced dynamic spectrum access (DSA) is a promising technology to enhance the performance of the mobile edge computing (MEC) system. In this paper, we consider the integration of the IRS enhanced DSA technology to a MEC system, and study the pertinent joint optimization of the phase shift coefficients of the IRS, the transmission powers, the central processing unit (CPU) frequencies, as well as the task offloading time allocations of the secondary users (SUs) to maximize the average computation bits of the SUs. Due to the non-convexity, the formulated problem is difficult to solve. In order to tackle this difficulty, we decompose the optimization problem into tractable subproblems and propose an alternating optimization algorithm to optimize the optimization variables in an iterative fashion. Numerical results are provided to show the effectiveness and the correctness of the proposed algorithm.

Vehicular Channel in Urban Environments at 23 GHz for Flexible Access Common Spectrum Application

With the development of the vehicular network, new radio technologies have been in the spotlight for maximizing the utilization of the limited radio spectrum resource while accommodating the increasing amount of services and applications in the wireless mobile networks. New spectrum policies based on dynamic spectrum access technology such as flexible access common spectrum (FACS) have been adopted by the Korea Communications Commission (KCC). 23 GHz bands have been allocated to FACS bands by the KCC, which is expected extensively for vehicular communications. The comprehensive knowledge on the radio channel is essential to effectively support the design, simulation, and development of such radio technologies. In this paper, the characteristics of 23 GHz vehicle-to-infrastructure (V2I) channels are simulated and extracted for the urban environment in Seoul. The path loss, shadow factor, Ricean K-factor, root-mean-square (RMS) delay spread, and angular spreads are characterized from the calibrated ray-tracing simulation results, and it can help researchers have a better understanding of the propagation channel for designing vehicular radio technologies and a communication system in a similar environment.

Periodic multimedia spectrum sensing method based on high-order anti-jamming mechanism in cognitive wireless networks

The cognitive radio network provides high bandwidth for mobile users to reconstruct wireless architecture and dynamic spectrum access technology. The spectrum allocation is the key to cognitive radio spectrum resources for the relative scarcity of radio spectrum resources. The spectrum allocation algorithm must have faster convergence speed to adapt to the time-varying characteristics of cognitive radio networks. In this paper, a spectrum sensing method is proposed based on high-order anti-jamming mechanism for cognitive radio spectrum allocation. Firstly, the combination of cognitive anti-jamming spectrum sensing, channel estimation, learning comprehension, time-hopping frequency hopping, spectrum access control and other technologies can improve the anti-jamming ability. Secondly, cognitive radio can effectively improve the utilization of spectrum resources according to the different benefits of different users on different channels, achieve the best matching between cognitive users and channels and flexible spectrum allocation. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.

Price Competition in Spectrum Markets: How Accurate is the Continuous Prices Approximation?

Dynamic spectrum access technology enables two types of users to operate on a channel–primary users and secondary users, which can use the channel when it is not in use by the primaries. We consider a scenario in which multiple primaries own bandwidth in a large region which is divided into smaller locations. A primary that has unused bandwidth in a time slot would like to lease it out to secondaries in return for a fee. This results in price competition among the primaries. In prior work, this price competition has only been studied under the approximation, made for analytical tractability, that the price of each primary takes values from a continuous set. However, in practice, the set of available prices is discrete. In this paper, we investigate the fundamental question of how the behavior of the players involved in the price competition changes when this continuity assumption is removed. Our analysis reveals several important differences between the games with continuous and discrete price sets. However, we show that as the number of available prices becomes large, the strategies of the primaries under every symmetric NE converge to the unique NE strategy of the game with continuous price sets.

Attack-Aware Defense Strategy: A Robust Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks

Cognitive radio (CR) is a key enabling technology of dynamic spectrum access to achieve better utilization of frequency spectrum. Cooperative spectrum sensing (CSS) exploits the spatial diversity between CR users to improve the accuracy of spectrum sensing. But, in practice, the reliability of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In this type of attack, some malicious CR sensors intentionally report falsified local sensing results to a fusion center and disrupt the global decision-making process. In this study, we present a smart and robust defense scheme called attack-aware CSS (ACSS). The idea is based on the estimation of attack strength where the attack strength corresponds to the attack population. The attack strength is defined as the ratio of the number of malicious sensors to the total number of sensors which is equal to the probability that a specific sensor is malicious. Assuming the malicious sensor behavior or attack strategy, the proposed approach estimates attack strength and uses the Bayesian hypothesis test to improve the collaborative sensing performance. Computer simulation results illustrate the effectiveness of the proposed ACSS approach against SSDF attacks, especially for CR networks located in hostile environments.

The impact of spectrum policies on the secondary spectrum market: A system dynamics approach

There has been a growing significance for Dynamic Spectrum Access (DSA) technology as a method to relieve the spectrum shortage problem and improve the efficiency of spectrum usage. For DSA technology to provide social and economic benefits, however, the dynamics of the secondary spectrum market must also co-evolve with the technology. Using the system dynamics (SD) methodology, we developed a secondary spectrum market model. The objective of this model is to integrate the SD approach, scenario and simulation analysis into a holistic dynamic consideration of the secondary spectrum market. Drawing on the model, we examined how the secondary spectrum market could evolve in the future and investigated how spectrum policies could influence spectrum utilization in the secondary spectrum market.

Assessment of Dynamic Spectrum Allocation Technique in Heterogeneous Network

Mobile devices are becoming the priority of access to a growing trend of online services. As services use higher quality images & video, an increase of wireless network capacity is required. In this case, spectrum is a way to go. Even though capacity is important, there are other factors as well, for example, coverage, flexibility and resilience. Dynamic spectrum access technology allows higher transmission power according to location & safe sharing with licensed users (LU). Dynamic spectrum allocation (DSA) technique enhances the spectrum efficiency for the users in Heterogeneous Network. This paper explains about the findings that are observed by two different researches that are related to our research title. The first paper is about the basic OFDM structure using GNU Radio software and implemented using USRP hardware. The second research is about the implementation of Dynamic Resource Allocation for LTE using GNU Radio. The first research explained about the advantages and disadvantages of OFDM configuration. The second research explained more about the implementation of Dynamic Resource Allocation in the uplink and downlink configuration, and are tested using three algorithms; Max-sum, max-min and maxproduct. All the results are obtained from GNU Radio. However, the results are not implemented using USRP because of the short amount of time. Based on these two researches, we identified the advantages and disadvantages of the proposed designs and develop our own design to mitigate the cross-tier interference in multi-tiers HetNets.

A Cross-Layer Based Location-Aware Forwarding Using Distributed TDMA MAC for Ad-Hoc Cognitive Radio Networks

Cognitive Radio has been the key technology for dynamic spectrum access in wireless networks. Cognitive Radio seems to be a promising solution to the radio spectrum overcrowding problem as well. Cognitive Radio Network allows opportunistic usage of the spectrum holes by enabling the coexistence of licensed (primary) users and unlicensed (secondary) users. The existing wireless legacy network protocols limit the productive utilization of spectrum holes by the secondary users. The contribution of this paper is a cross-layer based location-aware forwarding protocol, using a distributed TDMA MAC, for ad-hoc Cognitive Radio Networks. The distributed TDMA based MAC protocol constructs TDMA schedules by exchanging three rounds of control messages with collision avoidance among secondary users as a fundamental goal. Exchange of control messages takes place over a common control channel in a distributed fashion. The MAC protocol also uses the service of network layer, which classifies the available channels and selects the best ranked channel and passes this information to the MAC sub-layer for effective communication. The proposed location-aware forwarding protocol constructs request zone for making decisions regarding data packet forwarding with highest packet delivery ratio, by confining the flooding of packets, as a key objective. Simulation results show the efficacy of the proposed cross-layer scheme.

Suppression of Mutual Interference in Noncontiguous Orthogonal Frequency Division Multiplexing Based Cognitive Radio Systems

Orthogonal frequency division multiplexing (OFDM) is a favourable technology for dynamic spectrum access (DSA) due to the flexibility in spectrum shaping. In spite of that, high sidelobes of OFDM subcarriers bring in considerable interference to the nearby users, particularly in OFDM based cognitive radio (CR) networks, where the secondary users (SUs) are capable of accessing the spectrum opportunistically. In this paper, two new techniques for the suppression of high sidelobes are proposed. The proposed techniques composed of an optimization scheme are followed by generalized sidelobe canceller. The proposed techniques can be considered as a two-level suppression technique in the sense that in the first level the sidelobe is reduced by using cancellation carriers (CCs), whose amplitudes are determined using genetic algorithm (GA) and differential evolution (DE), while in the second level further reduction of sidelobe is achieved using generalized sidelobe canceller (GSC). Simulation results show the power spectral density (PSD) performance of the proposed techniques in comparison with already existing techniques, demonstrating that the proposed techniques minimize the out-of-band radiation (OOBR) significantly, thus qualifying for more effective spectrum sharing.

Spectrum-Availability Based Routing for Cognitive Sensor Networks

With the occurrence of Internet of Things (IoT) era, the proliferation of sensors coupled with the increasing usage of wireless spectrums especially the ISM band makes it difficult to deploy real-life IoT. Currently, the cognitive radio technology enables sensors transmit data packets over the licensed spectrum bands as well as the free ISM bands. The dynamic spectrum access technology enables secondary users (SUs) access wireless channel bands that are originally licensed to primary users. Due to the high dynamic of spectrum availability, it is challenging to design an efficient routing approach for SUs in cognitive sensor networks. We estimate the spectrum availability and spectrum quality from the view of both the global statistical spectrum usage and the local instant spectrum status, and then introduce novel routing metrics to consider the estimation. In our novel routing metrics, one retransmission is allowed to restrict the number of rerouting and then increase the routing performance. Then, the related two routing algorithms according to the proposed routing metrics are designed. Finally, our routing algorithms in extensive simulations are implemented to evaluate the routing performance, and we find that the proposed algorithms achieve a significant performance improvement compared with the reference algorithm.

Adoption of dynamic spectrum access technologies: a system dynamics approach

The introduction of dynamic spectrum access (DSA) technologies in mobile markets faces technical, economic and regulatory challenges. This paper defines industry openness and spectrum centralization as the two key factors that affect the adoption of DSA technologies. The adoption process is analyzed employing a comprehensive System Dynamics model that considers the network and substitution effects. Two possible scenarios, namely operator-centric and user-centric adoption of DSA technologies are explored in the model. The analysis indicates that operator-centric DSA technologies may be adopted in most countries where spectrum is centralized, while end-user centric DSA technologies may be adopted in countries with decentralized spectrum regime and in niche emerging services. The study highlights the role of standards-based design and concludes by citing case studies that show the practicality of this analysis and associated policy prescriptions.

A Comprehensive Analysis of Spectrum Handoff Under Different Distribution Models for Cognitive Radio Networks

The static frequency allocation in wireless communication became a major concern for efficient spectrum utilization. Also due to spatio-temporal variation, some of the frequency channels are not utilized efficiently. The prologue of open spectrum and dynamic spectrum access (DSA) methodology provides the secondary (unlicensed) users, supported by cognitive radios (CRs) to opportunistically utilize the unused spectrum bands. When a primary user returns to the engaged spectrum band, secondary users should release it immediately through proper handing off to another spectrum band available to the network satisfying the quality of service (QoS) of both the network. Hence the focus of the new spectrum management policies is DSA technology based on CR. Spectrum mobility is considered to be the subsequent big challenge in CR technology. Spectrum mobility is associated with spectrum handoff which is directly associated with link maintenance and QoS. In this paper, we scrupulously investigate and analyze the probability of spectrum handoff under diverse realistic primary and secondary user traffic models. We have established a state of the art standard generalized form of probability of spectrum handoff without switching delay considering secondary user call duration and residual time of availability of spectrum holes as measurement metrics for diverse distribution functions designed for tele-traffic analysis. We thoroughly investigate different distribution models for the residual time under both zero switching delay and finite switching delay conditions. The switching delay (tr) between spectrum holes comprises of spectrum sensing time and transition delay for realizing all the related parameters. A comprehensive simulation results are presented to validate the generalized theory established in this paper.

Dynamic Spectrum Access Algorithm Based on Game Theory in Cognitive Radio Networks

As an enabling technology for dynamic spectrum access (DSA), cognitive radio (CR) is widely regarded as one of the most promising technologies for future the fifth generation (5G) wireless communications. Although there have been significant prior researches to combat interference on primary users (PUs), the problem of mitigating mutual interference between secondary users (SUs), -which is tightly coupled with SU's spectrum leasing- is still not understood well. This paper proposes a DSA algorithm based on game theory, which jointly performs spectrum leasing and interference mitigation among SUs. The problem is modeled as an oligopolistic competition using Stackelberg model. We have carefully studied the SU's spectrum utilization behavior with respect to various criteria of the proposed game theoretic model. Simulation results shows that, Compared with Cournot game model, the proposed scheme enables SUs to efficiently utilize the licensed spectrum shared with PUs in a dynamic environment while maximizing the spectrum utilization.

High-throughput transmission-quality-aware broadcast routing in cognitive radio networks

Cognitive radio is an enabling technology of dynamic spectrum access (DSA) networking. In DSA, unlicensed secondary users can coexist with primary licensed users and can share the radio spectrum opportunistically. Broadcasting is an important networking primitive that is useful for many CRN applications such as control information dissemination, warning notification, etc. Unfortunately, the sporadic channels availability degrades the performance of broadcast routing. The quality of a broadcast transmission on a particular channel depends on the channel quality of all the receivers for the same transmitter. Current broadcast routing protocols lack transmission quality awareness. In this paper, we develop a transmission quality-aware broadcasting framework, comprising algorithm for transmission quality-aware broadcast routing in multi-radio dynamic-spectrum-access CRNs, and formulate a transmission quality metric wherein we consider a receiver-centric view rather than a transmission-centric view. We perform a detailed simulation performance evaluation of our proposed framework using OMNeT++. The proposed broadcast routing algorithm is validated by comparing results with state-of-the-art routing algorithms. Analysis of the results shows average performance gains of approximately 40 % in throughput and packet delivery ratio.