Dynamic Spectrum Access Approach Research Articles

Blockchain for Dynamic Spectrum Access and Network Slicing: A Review

Dynamic spectrum access (DSA) and network slicing are some of the principal concepts to realize the emerging applications in Beyond 5th Generation (B5G) and 6th Generation (6G) networks. The frequency spectrum remains scarce and underutilized, while the performance requirements in terms of data throughput and latency of the network tenants have diversified. DSA allows for the efficient utilization of spectrum resources, while network slicing aims to serve network users with highly distinctive service needs. Lack of incentivization, sharing of spectrum resources among multiple operators, and lack of trust between operators are some of the challenges faced by centralized DSA approaches. Similarly, secure network slice orchestration, slice-isolation, secure access to network resources, privacy of user sensitive data, assuring the provision of Service Level Agreements (SLAs), are some of the challenges in existing network slicing techniques. Blockchain due to its innate capabilities can be a promising technology to solve the key issues pertaining to current DSA and network slicing approaches. Blockchain through smart contracts, provides traceability of network resources, auditability and accountability of network operators and service providers. Smart contracts facilitate the automation of resource sharing and network slice orchestration, while ensuring that SLAs are met, and network operators are compensated. This paper first provides a comprehensive overview of the DSA concept, the existing DSA techniques, and the challenges posed by these techniques. This is followed by a detailed description of network slicing, its key elements and architecture, various network slicing parameters, existing network slicing techniques and their challenges. Then an in- depth review on blockchain, its working principle, factors that affect blockchain implementation, and a comparison of various open-source blockchain platforms that support smart contracts is presented. This discussion is summarized by presenting the state-of-the-art in the blockchain-enabled DSA and network slicing, challenges and trade-offs of these techniques, and the gaps and future directions in this research area. Finally, we conclude by providing some future research directions.

Heuristic Greedy Method for Spectrum Sensing in Cognitive Radio Network

In recent years, radio frequency spectrum in wireless communication is not effectively utilized. To utilize the spectrum effectively, an optimistic technology called “Cognitive Radio network” used. It is the best preferable next generation wireless networks. Using DSA (Dynamic Spectrum Access) approaches, it shares the spectrum effectively between the primary and secondary users. It allows the secondary users to use the spectrum by dynamic spectrum sharing algorithms. When the primary users and secondary users are using same frequency band and transmitting simultaneously, there is a spectrum underlay problem in the network. A novel heuristic greedy algorithm proposed for improving the performance parameters of cognitive radio network using co-operative spectrum sensing.

Spectrum Sensing in Cognitive Radio using Frequency Domain

An efficient bandwidth allocation and dynamic bandwidth access away from its previous limits is referred as cognitive radio (CR).The limited spectrum with inefficient usage requires the advances of dynamic spectrum access approach, where the secondary users are authorized to utilize the unused temporary licensed spectrum. For this reason it is essential to analyze the absence/presence of primary users for spectrum usage. So spectrum sensing is the main requirement and developed to sense the absence/ presence of a licensed user. This paper shows the design model of energy detection based spectrum sensing in frequency domain utilizing Binary Symmetric Channel (BSC) ,Additive white real Gaussian channel (AWGN), Rayleigh fading channel users for 16-Quadrature Amplitude Modulation(QAM) which is utilized for the wide band sensing applications at low Signal to noise Ratio(SNR) level to reduce the false error identification. The spectrum sensing techniques has least computational complexity. Simulink model for the energy detection based spectrum sensing using frequency domain in MATLAB 2014a.

Declining of Sensing Errors in Cognitive Radio by Threshold Determination of Energy Spotting Method

The conventional Cognitive Radio Network is anticipates the fundamental solution to problem of spectrum scarcity in the forthcoming wireless and cellular communication networks, furthermore CR networking senses vacant spectrum bands in opportunistic method, and though, increases the effectiveness of spectrum usage. The CRN (Cognitive radio Network) provides the wireless connectivity and integration through the medium of dynamic spectrum access approaches and heterogeneous wireless designs and architectures. The enabling techniques and utilities of the CRNs have received the dynamic research and the industry interest. In this specific paper, we proposed an solution to decrease the error rate by enhancing the performance of detection which occur in time of spectrum sensing mechanism.

Analysis of the Saturation Throughput in an IEEE 802.11 Network Using Cognitive Radio

We present a Markov chain model to estimate the saturation throughput of an IEEE 802.11 network in the basic access mode using cognitive radio. IEEE 802.11 networks are very popular and likely more spectrum is needed for their future deployment and applications. On the other hand the cognitive radio idea has been proposed to deal with the spectrum shortage problem. It is thus reasonable to assume that future IEEE 802.11 network may operate in the context of cognitive radio. One paradigm is that IEEE 802.11 networks will act as secondary users sharing the spectrum of a primary use using the dynamic spectrum access approach. In this scenario it will be important to study how the physical layer of cognitive radio may impact on the IEEE 802.11 network as a whole. Based on a Markov chain model, we investigate the impact of the primary user’s arrivals on the IEEE 802.11 network’s saturation throughput. We validate our presented model with ns2 simulations. As shown by our numerical results, our model results are very close to the ns2 simulation results.

Analysis of the Saturation Throughput in an IEEE 802.11 Network Using Cognitive Radio

We present a Markov chain model to estimate the saturation throughput of an IEEE 802.11 network in the basic access mode using cognitive radio. IEEE 802.11 networks are very popular and likely more spectrum is needed for their future deployment and applications. On the other hand the cognitive radio idea has been proposed to deal with the spectrum shortage problem. It is thus reasonable to assume that future IEEE 802.11 network may operate in the context of cognitive radio. One paradigm is that IEEE 802.11 networks will act as secondary users sharing the spectrum of a primary use using the dynamic spectrum access approach. In this scenario it will be important to study how the physical layer of cognitive radio may impact on the IEEE 802.11 network as a whole. Based on a Markov chain model, we investigate the impact of the primary user’s arrivals on the IEEE 802.11 network’s saturation throughput. We validate our presented model with ns2 simulations. As shown by our numerical results, our model results are very close to the ns2 simulation results.

Robust Large-Scale Spectrum Auctions against False-Name Bids

Auction is a promising approach for dynamic spectrum access in cognitive radio networks. Existing auction mechanisms are mainly strategy-proof to stimulate bidders to reveal their valuations of spectrum truthfully. However, they can suffer significantly from a new cheating pattern, named false-name bids, where a bidder can manipulate the auction by submitting bids under multiple fictitious names. We show such false-name bid cheating is easy to make but difficult to detect in dynamic spectrum auctions. To address this issue, we propose ALETHEIA, a novel flexible, false-name-proof auction framework for large-scale dynamic spectrum access. ALETHEIA not only guarantees strategy-proofness but also resists false-name bids. Moreover, ALETHEIA enables spectrum reuse across a large number of bidders, to improve spectrum utilization. Following that, we extend ALETHEIA to its general version that supports more practical and flexible auction, where bidders accept the spectrum allocation under their partial satisfactions. Theoretical analysis and simulation results show that ALETHEIA achieves both high spectrum redistribution efficiency and auction efficiency.

Intelligent dynamic spectrum access using hybrid genetic operators

Abstract This paper presents a novel hybrid dynamic spectrum access approach, combining classical and stochastic flavors being augmented with new genetic operators, for multi-channel single-radio cognitive radio networks. Existing classical and stochastic approaches exhibit different advantages and disadvantages depending on network architecture. Our proposed approach exploits a delicate balance between the two different approaches for extracting advantages from both of them while limiting their disadvantages. Additionally, in our proposed approach, we boost up extent of the exploitation through designing new genetic operators. Furthermore, we provide a thorough performance evaluation of our approach using a widely used discrete event simulator called ns-2 . Here, we also simulate several existing approaches that are based on graph theory, game theory, heuristic, genetic algorithm, agent-based learning, and online learning. Simulation results demonstrate significant performance improvement using our proposed intelligent dynamic spectrum access approach over these state-of-the-art ones based on various standard metrics.

Real-time spectrum sensing using software defined radio platforms

As the lack of frequency resources became a critical problem in recent years, solutions had to be found in order to use the available spectrum in a more efficient way. Cognitive radio (CR) technology is a possible answer to this, by proposing a dynamic spectrum access approach, which allows unlicensed users to access unused licensed frequency bands. In order to detect the presence of licensed users, any CR equipment has to perform a spectrum sensing process. This paper presents a practical solution for building this essential part of a frequency agile device. The implementation of the proposed real-time spectrum sensing solution is based on commercial software defined radio platforms. Hardware and software details regarding the described prototype are given, together with aspects related to the optimal configuration of the used platforms for such an application. Moreover, the performance that can be obtained using the proposed solution is evaluated through measurements performed in several different scenarios.

Joint Pricing and Load Balancing for Cognitive Spectrum Access: Non-Cooperation Versus Cooperation

In the dynamic spectrum access (DSA), pricing is an efficient approach providing economic incentives for operators, whereas load balancing yields congestion-avoidance incentives for secondary users (SUs). Despite complexities of 1) the couplings among pricing, load balancing, and SUs' spectrum access decision, and 2) the heterogeneity of primary users' traffic and SUs classes/types, we tackle the joint load balancing and pricing problem to maximize operators' revenue in two cognitive radio markets: monopoly and duopoly. For the monopoly market, we first show there exists a unique SUs' equilibrium arrival rate to the monopolist's channels. We then show that the joint problem can be solved efficiently by exploiting its convex structure. For the duopoly market, we first characterize a unique SUs' equilibrium arrival rate to two operators employing different DSA approaches. When two operators are noncooperative, we show that there exists a unique Nash equilibrium for each operator's revenue. When they are cooperative, we show that the social revenue optimization can achieve a unique optimal solution. Using the Nash bargaining framework, we also present a sharing contract that determines the optimal fraction of the social revenue for each operator. In both markets, we propose two algorithms that can find the largest SU class supportable by the operators.

Spectrum Leasing in Cognitive Radio Networks: A Survey

Cognitive Radio (CR) is a dynamic spectrum access approach, in which unlicensed users (or secondary users, SUs) exploit the underutilized channels (or white spaces) owned by the licensed users (or primary users, PUs). Traditionally, SUs are oblivious to PUs, and therefore the acquisition of white spaces is not guaranteed. Hence, a SU must vacate its channel whenever a PU reappears on it in an unpredictable manner, which may affect the SUs' network performance. Spectrum leasing has been proposed to tackle the aforementioned problem through negotiation between the PU and SU networks, which allows the SUs to acquire white spaces for a guaranteed period of time. Through spectrum leasing, the PUs and SUs enhance their network performances, and additionally PUs maximize their respective monetary gains. Numerous research efforts have been made to investigate the CR, whereas the research into spectrum leasing remains at its infancy. In this paper, we present a comprehensive review on spectrum leasing schemes in CR networks by highlighting some pioneering approaches and discuss the gains, functionalities, characteristics, and challenges of spectrum leasing schemes along with the performance enhancement in CR networks. Additionally, we discuss various open issues in order to spark new interests in this research area.

Cross-layer design of partial spectrum sharing for two licensed networks using cognitive radios

To utilize spectrum resources more efficiently, dynamic spectrum access attempts to allocate the spectrum to users in an intelligent manner. Uncoordinated sharing with cognitive radio CR users is a promising approach for dynamic spectrum access. In the uncoordinated sharing model, CR is an enabling technology that allows the unlicensed or secondary users to opportunistically access the licensed spectrum bands belonging to the so-called primary users, without any modifications or updates for the licensed systems. However, because of the limited resources for making spectrum observations, spectrum sensing for CR is bound to have errors and will degrade the grade-of-service performance of both primary and secondary users. In this paper, we first propose a new partial spectrum sharing policy, which achieves efficient spectrum sharing between two licensed networks. Then, a Markov chain model is devised to analyze the proposed policy considering the effects of sensing errors. We also construct a cross-layer design framework, in which the parameters of spectrum sharing policy at the multiple-access control layer and the spectrum sensing parameters at the physical layer are simultaneously coordinated to maximize the overall throughput of the networks, while satisfying the grade-of-service constraints of the users. Numerical results show that the proposed spectrum sharing policy and the cross-layer design strategy achieve a much higher overall throughput for the two networks. Copyright © 2013 John Wiley & Sons, Ltd.

Cross-Layer QoS Provisioning in Cognitive Radio Networks

This letter investigates the quality of service provisioning in infrastructure-based secondary cognitive radio networks operating in compliance with the hierarchical spectrum overlay approach for dynamic spectrum access. A cross-layer analytical model is developed and a novel cross-layer approach for quality of service provisioning of the secondary users is proposed and validated by extensive simulation experiments.

Spatio-temporal spectrum modeling: Taxonomy and economic evaluation of context acquisition

Much of the research in dynamic spectrum access (DSA) has focused on opportunistic access in the temporal domain. While this has been quite useful in establishing the technical feasibility of DSA systems, it has missed large sections of the overall DSA problem space. This paper argues that the spatio-temporal operating context of specific environments matters to the selection of the appropriate technology for learning context information. It identifies twelve potential operating environments and compares four context awareness approaches (on-board sensing, databases, sensor network, and cooperative sharing) for these environments. In order for operators, regulators, and users to be interested in deploying DSA based networks, the expected costs should be in proportion to what the users are realistically willing to pay for services. Consequently, it is important to conduct cost analysis for different DSA approaches in parallel with the technical research. Regulators and major stakeholders should pay attention to the operating environment of DSA systems when determining which approaches to context learning to encourage.The incremental capital costs; over a basic software radio; have been compared for the four context awareness approaches after an estimation analysis for each cost component. Since DSA is still a relatively new research field, there is a lot of uncertainty associated with incremental cost analyses. As a result, the cost analysis is parameterized to allow for explicit reasoning about the bounds of cost components.

Efficient QoS support for secondary users in cognitive radio systems [Dynamic Spectrum Management

The fundamental condition for the legal approval of dynamic spectrum access approaches is the protection of the primary user. However, for dynamic spectrum access to become an attractive service reality, it is crucial to also ensure some quality of service support for the secondary user communication. In this article we discuss sensing-based opportunistic spectrum access approaches, in which primary user protection is achieved by a properly organized sensing process and secondary user communication reconfiguration. While the required reliability of the sensing process can be expressed in terms of rarely enough overlooking the primary user, we assume that the proper QoS for the secondary user is given by maintaining - with a given confidence level - a minimum bandwidth availability for the secondary user in spite of primary user dynamics. In this article we present an overview of approaches that might be used to achieve these objectives. In addition, we point out that both the sensing process and the secondary link maintenance (necessary to keep the required bandwidth in spite of reconfiguration due to detected primary users) require significant spectrum overhead. We identify and elaborate a fundamental trade-off in using these overheads in either sensing or link maintenance, and present examples of its optimization.

A scalable collusion-resistant multi-winner cognitive spectrum auction game

Dynamic spectrum access (DSA), enabled by cognitive radio technologies, has become a promising approach to improve efficiency in spectrum utilization, and the spectrum auction is one important DSA approach, in which secondary users lease some unused bands from primary users. However, spectrum auctions are different from existing auctions studied by economists, because spectrum resources are interference-limited rather than quantity-limited, and it is possible to award one band to multiple secondary users with negligible mutual interference. To accommodate this special feature in wireless communications, in this paper, we present a novel multi-winner spectrum auction game not existing in auction literature. As secondary users may be selfish in nature and tend to be dishonest in pursuit of higher profits, we develop effective mechanisms to suppress their dishonest/collusive behaviors when secondary users distort their valuations about spectrum resources and interference relationships. Moreover, in order to make the proposed game scalable when the size of problem grows, the semi-definite programming (SDP) relaxation is applied to reduce the complexity significantly. Finally, simulation results are presented to evaluate the proposed auction mechanisms, and demonstrate the complexity reduction as well.

Primary-prioritized Markov approach for dynamic spectrum allocation

Dynamic spectrum access has become a promising approach to fully utilize the scarce spectrum resources. In a dynamically changing spectrum environment, it is very important to consider the statistics of different users' spectrum access so as to achieve more efficient spectrum allocation. In this paper, we propose a primary-prioritized Markov approach for dynamic spectrum access through modeling the interactions between the primary and the secondary users as continuous-time Markov chains (CTMC). Based on the CTMC models, to compensate the throughput degradation due to the interference among secondary users, we derive the optimal access probabilities for the secondary users, by which the spectrum access of the secondary users is optimally coordinated, and the spectrum dynamics are clearly captured. Therefore, a good tradeoff can be achieved between the spectrum efficiency and fairness. The simulation results show that the proposed primary-prioritized dynamic spectrum access approach under proportional fairness criterion achieves much higher throughput than the CSMA-based random access approaches and the approach achieving max-min fairness. Moreover, it provides fair spectrum sharing among secondary users with only small performance degradation compared to the approach maximizing the overall average throughput.

Primary user behavior in cellular networks and implications for dynamic spectrum access

Dynamic spectrum access approaches, which propose to opportunistically use underutilized portions of licensed wireless spectrum such as cellular bands, are increasingly being seen as a way to alleviate spectrum scarcity. However, before DSA approaches can be enabled, it is important that we understand the dynamics of spectrum usage in licensed bands. Our focus in this article is the cellular band. Using a unique dataset collected inside a cellular network operator, we analyze the usage in cellular bands and discuss the implications of our results on enabling DSA in these bands. One of the key aspects of our dataset is its scale-it consists of data collected over three weeks at hundreds of base stations. We dissect this data along different dimensions to characterize if and when spectrum is available, develop models of primary usage, and understand the implications of these results on DSA techniques such as sensing.