Access In Cognitive Radio Networks Research Articles

Secure MIMO communication in energy harvesting-assisted NOMA Cognitive Radio Network with jamming under hardware impairment

Energy harvesting (EH)-assisted non-orthogonal multiple access (NOMA) cognitive radio (CR) networks allow simultaneous transmission of multiple secondary user signals on primary frequency bands with harvested energy, enhancing spectral, spectrum utilization, and energy efficiencies. Although multiple antennas are used for efficient energy transfer and signal transceiving, multiple-input multiple-output (MIMO) communication in these networks is facing reliability/security performance degradation due to nonlinear EH, hardware impairment (HWi), and wire-tapping. The paper aims to numerically evaluate the security and reliability of MIMO communication in EH-assisted NOMA CR networks with jamming (MehNOwJ) under such effects. The results indicate that MehNOwJ prevents full outage and achieves optimum performance with proper parameter selection of preset spectral efficiency, power saturation threshold, EH duration, number of antennas of jammer. In addition, the performance improves with an accreting quantity of antennas but experiences saturation. Moreover, MehNOwJ drastically outperforms alternative approaches (MIMO communication in EH-assisted orthogonal multiple access CR network with jamming and MIMO communication in EH-assisted NOMA CR network without jamming), offering insights into the benefits of combining NOMA and jamming techniques.

Spectral–Temporal Model for Opportunistic Spectrum Access in Cognitive Radio Networks

Spectral–Temporal Model for Opportunistic Spectrum Access in Cognitive Radio Networks

Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks

AbstractThe internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level.

Fuzzy Tree Schematic for Spectrum Handoff Reduction in Cognitive Radio Networks

Spectrum is a limited resource, so it must be used effectively. The fixed spectrum allocation method currently in operation may not sustain the growth in the network access users, which continues to increase over time. Many studies have shown that the spectrum deficiency problem can be solved with cognitive radio network (CRN) technology. CRN allows flexible spectrum utilization. Spectrum handoff is one of the fundamental prerequisites for dynamic spectrum access in cognitive radio networks. The ping-pong effect is one of the problems caused by the frequent occurrence of unnecessary handoff spectrum. So, in CRN, the right handoff decision is needed to avoid the ping-pong effect. The proposed fuzzy system carries the concept of a fuzzy tree that uses two fuzzy inference systems (FIS), which work in two stages, namely FIS-1 and FIS-2. In the early stages, the FIS-1 controls the secondary users (SU) power and prevents interference with the primary users (PU). FIS-2 is used to execute precise spectrum handoff decisions. The test results show that the amount of handoff spectrum has decreased by up to 43 % compared to other methods, which are only 60 %. This results in a decrease of 16.67% from the previous method. Thus, the ping-pong effect can be minimized with the right handoff decisions.

A novel game theoretic approach for market-driven dynamic spectrum access in cognitive radio networks

A novel game theoretic approach for market-driven dynamic spectrum access in cognitive radio networks

A hybrid channel transmission method for effective spectrum access in cognitive radio networks

A hybrid channel transmission method for effective spectrum access in cognitive radio networks

A Hybrid Channel Transmission Method for Effective Spectrum Access in Cognitive Radio Networks

A Hybrid Channel Transmission Method for Effective Spectrum Access in Cognitive Radio Networks

Cooperative Multi-Agent Reinforcement-Learning-Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks

With the development of wireless communication and Internet of Things (IoT), there are massive wireless devices that need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this article, we investigate the distributed DSA problem for multiusers in a typical multichannel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we propose a centralized off-line training and distributed online execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of a cognitive radio network in a distributed fashion without information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. The experimental results show that the proposed CoMARL-DSA algorithm outperforms the state-of-the-art deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning for spectrum access (DQSA) in terms of successful access rate and collision rate by at least 14% and 12%, respectively.

Evaluation of the performance of a collaborative proposal of multiple access in cognitive radio networks

Cognitive radio networks (CRN) allow for an increase in spectral efficiency and performance of today's wireless networks. Currently, multiple proposals exist in the area of spectral decision-making and mobility; however, very few evaluate the impact of collaboration between secondary users and the performance of spectrum access by many secondary users. Unlike existing works, this article provides a comprehensive quantitative analysis of the performance of CRN taking into account access to the spectrum simultaneously by multiple users and decision making based on collaboration through the exchange of information between nearby secondary users. This proposal is developed through the implementation of four modules: Input Module, Multi-user Module, Collaborative module and Decision-making module, where the results are evaluated comparatively through the handoff rate generated with two multicriteria techniques: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Criteria Optimization and Compromise Solution (VIKOR). The evaluation is carried out taking into account three levels of collaboration, three multi-user access scenarios, and two multi-criteria techniques for a total of 18 simulation scenarios. The results obtained show the importance of implementing collaboration strategies, as for multi-user access, the number of handoffs increases as the number of serial users increases. TOPSIS presented the best results in 76 % of the analyzed cases where VIKOR generated a smaller number of handoffs; TOPSIS maintained good performance with differences not exceeding 90 handoffs.

Cognitive Radio Networks Based on Users' Reputation Scheme

Cognitive Radio (CR) can be defined as a technology that allows users to change the transmission parameters as required to increase the spectrum efficiency. Because of this mechanism, some threats emerge. Two major threats are found in CR. The first is the Primary User Emulation Attack (PUEA), where the attacker is able to transmit at a forbidden time slot effectively, emulating the signals of the primary user. This makes all the system users believe that the spectrum is occupied by a good primary user. The second threat is known as the spectrum sensing data falsification attack (SSDF). In this case, the attackers send false observation information, intentionally or unintentionally, to the fusion center (FC), causing it to make the wrong decision. In this study, the scheme presented was based on a users' reputation for secure spectrum access in cognitive radio networks. Each Secondary User (SU) performs local sensing and then forwards the sensing results to the main fusion center FC. The FC makes the final decision about the presence of the primary user based on the proposed approach. The schemes substantially reduce the effect of cognitive users with low reputation values while improving the impact of cognitive users with the high reputation values on the final decision. It has been verified that the proposed approach can improve the sensing performance under the impact of a different number of reliable and unreliable users in a CR network. Results based on simulation show that the proposed scheme outperforms the traditional majority scheme despite a high number of malicious users.

Decentralized Opportunistic Channel Access in CRNs Using Big-Data Driven Learning Algorithm

Opportunistic channel access in cognitive radio networks (CRNs) under an unknown environment is gradually receiving a great deal of attention. This article studies the basic problem of decentralized secondary users (SUs) performing multiple channel sensing and access in CRN, when sensing is not perfect. The channel availability information is unknown and must be estimated and learned through big-data samples from the wireless channels by SUs. Both the independent identical distribution (i.i.d.) channel model and the Markov channel model are considered. In the i.i.d. model, the availability of each channel is modeled as an i.i.d. process, while in the Markov model, the availability of each channel is set as a Markov chain with an unknown probability transition matrix. If multiple SUs access to the identical channel, collision will occur and none of SUs gets a reward. Learning loss, which is also referred to as regret, is thus inevitable. To handle with the sampling data on large scale, we formulate the channel sensing and access process as a multi-armed bandit problem (MABP), based on which big-data driven online algorithms are proposed. The theoretical analysis and simulations prove that the regret of our algorithms is both logarithmic in finite time and asymptotically.

Multiple Access in Cognitive Radio Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Due to the increasingly complicated communication scenarios and network architectures as well as growing traffic demands for high speed connectivity, dynamic spectrum allocation in fifth generation (5G) networks becomes insufficient to guarantee the satisfaction of main network requirements in terms of spectrum efficiency (SE), scalability, delay, and energy efficiency (EE). Enormous multiple access schemes and cognitive radio (CR) network scenarios come to fulfill these requirements and enhance network functionalities. With multiple access schemes, users are able to transmit their data streams simultaneously under maximum capacity constraints. On the other hand, vacant spectrum holes are exploited in an opportunistic manner via CR and software defined radio. In order to exploit these spectrum holes as well as meeting different network requirements, several multiple access techniques have been presented that have been initiated through the adoption of orthogonal multiple access (OMA) scheme. Additionally, non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) are presented to achieve a promising multiplexing gain as well as to address the inefficient spectrum utilization incurred with OMA schemes. However, such multiplexing gain is limited as it depend on the channel conditions. Accordingly, a generalized multiple access scheme has been presented recently, namely rate splitting multiple access (RSMA), to further enhance the SE. In this paper, we provide a comprehensive study regarding the key multiple access schemes presented for CRNs to further enhance the use of spectral resources, and additionally highlights the key implementation challenges and the enabling techniques addressed to overcome it. We have given a special attention to the enhances provided by RSMA as compared with OMA, SDMA, and NOMA techniques. Finally, some open issues are spotted to shed lights on the need for further studies and future research efforts.

Securing hybrid channel access cognitive radio networks with energy harvesting

Physical layer security protects the transmitted data by trying to prevent the information from reaching the adversary. Adding artificial noise (AN) to the transmitted data helps to achieve that goal. In this paper, AN technique is used to protect the transmission of the secondary user (SU) from an eavesdropping attack in an energy harvesting cognitive radio network with hybrid channel access. At the beginning of each time slot, the SU is faced with multiple alternatives concerning the energy harvesting/transmission mode. The uncertainty in the PU’s activity adds another challenge to the decision of the SU. The mixed observable Markov decision process (MOMDP) is selected to model SU’s decision process in order to handle these challenges. The optimal policy is solved using the point-based value iteration algorithm (PBVI) to mitigate the time complexity of the basic value iteration algorithm. Moreover, the fraction of power allocated to AN (AN ratio) is optimized and an adaptive policy is introduced. The results show the superiority of the proposed model compared to other models in literature in terms of secrecy capacity and throughput. The effect of multiple system parameters on the model performance is studied and the optimal AN ratio is identified for different system scenarios. Moreover, an insight of the resultant policy is provided and the proposed adaptive model is proved to be superior compared to the fixed AN ratio models.

A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks

ABSTRACT Due to exponential growth in demand for radio spectrum for wireless communication networking, the radio spectrum has become over-crowded. The fixed spectrum allocation policy of the radio spectrum leads to inefficient utilisation of the available spectrum, which diverted the attention of researchers towards different intelligent techniques to access the spectrum dynamically and efficiently. The concept of Cognitive Radio (CR) has been considered as a promising technology to solve the problem of spectrum scarcity through the utilisation of various unutilised spectrum bands. In a future network deployment, multiple radio access networks may coexist having different characteristics. Hence, it becomes a challenge for CR networks to select the optimal network out of available networks. For efficient realisation, CRs requires intelligent spectrum management techniques for Dynamic Spectrum Management (DSM). Till now, there does not exist a literature survey that addresses the spectrum management with machine learning techniques in an intelligent manner. Hence, this paper presents the detailed classification and comprehensive survey of various machine learning techniques for intelligent spectrum management with their paradigms of optimisation for cognitive radio networks. The paper also provides new directions and open issues for the research community to work further in CR networks.

An Optimal Policy for Hybrid Channel Access in Cognitive Radio Networks With Energy Harvesting

In this aticle, an optimal policy is presented to maximize the secondary user's (SU's) throughput in an energy harvesting cognitive radio network with multiple SUs and single primary user (PU). The SU can access the PU's channel in a hybrid interweave/underlay channel access mode using time division multiple access (TDMA) technique. This hybrid access mode combines the benefits of interweave channel access mode of accessing the PU's channel opportunistically with high transmission power to gain high throughput and that of the underlay channel access mode of transmission at any time regardless of the PU's activity with controlled power. Moreover, energy harvesting from ambient sources promotes the SU's device to be portable and self sustainable. To deal with the random environment, the system is modeled as a mixed (full/partial) observable Markov decision process (MOMDP). Moreover, to reduce the solution complexity, the point-based value iteration (PBVI) algorithm is used to solve the proposed model. The simulation results show the superiority of the proposed model compared to other related models in terms of throughput. Based on an insight on the optimal policy of the proposed model and the time complexity comparison with the related models, a couple of suboptimal policies are proposed, namely, a myopic policy, an interweave only policy, and a combined-policy approach.

A Novel Clustering Algorithm Based on Information Geometry for Cooperative Spectrum Sensing

Spectrum sensing is used to detect whether primary user are using authorized spectrums, which can be regarded as a key and core issue for opportunistic spectrum access in cognitive radio networks. In traditional information theory and clustering algorithm-based spectrum sensing methods, they need to evaluate noise environment for constructing a reference point. However, the reference point is fixed, which is unreasonable in dynamic cognitive radio environment. Moreover, these methods convert signal feature from manifold onto Euclidean space, which will cause to overall performance degradation, since sensing information losses. To address this problem, an information geometry (IG)-based K-means clustering algorithm, namely IGK, is developed, it clusters samples on manifold instead of Euclidean space with an unsupervised way to train a classifier for spectrum sensing. Specifically, secondary users observe and collect data from a selected authorized spectrum, which needs to be detected, and send these sensing data to a fusion center (FC). Then, the FC transforms these data into samples on the manifold to obtained a classifier by using the proposed IGK algorithm. According to the trained classifier, we can get related result of the authorized spectrum. Finally, in simulation section, the effectiveness of the proposed scheme is verified under different conditions.

Analysis of Spectrum Sensing and Spectrum Access in Cognitive Radio Networks with Heterogeneous Traffic and p-Retry Buffering

It is well known that cognitive radio (CR) techniques have great potential to deal with the problem of radio spectrum scarcity. Spectrum sensing technique plays a critical role in enabling unlicensed secondary users (SUs) to utilize spectrum holes in cognitive radio networks (CRNs). However, a licensed primary user (PU) can be appropriately protected by simultaneously performing spectrum sensing and data transmission i.e., by using full-duplex (FD) mode. In this paper, we have proposed and analyzed a strategy which includes spectrum sensing and spectrum access mechanisms both. We consider heterogeneous traffic of real-time and non real-time SUs based on their different delay tolerance characteristics. We address the issue of false alarm rate (FAR) associated with FD sensing. Spectrum handoff and call buffering strategies with <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -Retry policy are employed jointly so that SUs that would otherwise be blocked or forcibly dropped could be buffered and possibly served later. To evaluate the performance of the proposed strategy, five-dimensional continuous time Markov chain (CTMC) model is developed and the queueing-theoretic approach is utilized. Numerical results demonstrate the influence of spectrum sensing errors on the performance of such CRNs. Results also reveal that the provision of buffers under retrial policy increases the overall network resource utilization while decreasing blocking and dropping probabilities.

Orthogonal codes-based dynamic spectrum access in cognitive radio networks

Dynamic spectrum access (DSA) in cognitive radio (CR) networks became a challenging research area recently. In CR technology, the DSA between primary users (PUs) and secondary users (SUs) simultaneously can be achieved without degrading the performance of the PUs by SUs interference. This can be achieved by donating incentive power to the PUs in order to compensate the interference caused by the SUs. Consequently, PUs allow SUs to share the spectrum. In this paper, orthogonal codes-based dynamic spectrum access (OC-DSA) technique is proposed. OC-DSA technique employs orthogonality between PUs and SUs transmitted data symbols in addition to the incentive power donation. Compared to other techniques, the proposed technique uses a simple encoder at the SU network for the same PU network infrastructure. By applying orthogonal codes, the interference caused by SUs is canceled and hence the donated power to incentivize the PUs is reduced. Also, the SU packet rate is increased significantly. The simulation results show that the proposed technique provides effective improvements over other existing techniques in the signal strength and the bit error rate performance of both the PU network and the SU network at the receiver side. Moreover, the proposed technique requires less donated power to incentivize the PU and has higher packet rate.

Analysis of the On-Demand Spectrum Access Architecture for CBRS Cognitive Radio Networks

An on-demand spectrum access cognitive radio network offers spectrum services to users, so that users can dynamically set up application-oriented virtual topologies to support user applications. In this paper, we develop a mathematical model for the on-demand spectrum access architecture for cognitive radio networks based on the citizens broadband radio service (CBRS). The model can be used to estimate the blocking probability of spectrum demands from priority access license users, the network capacity, and the number of free spectrum bands available for lower priority general authorized access users. The performance evaluation indicates that the results from the mathematical model match simulation results well, which validates the accuracy of our model.

Analysis of Spectrum Handoff delay using Finite Queuing model in Cognitive Radio Networks

Spectrum handoff is an inevitable phenomenon to exploit dynamic spectrum access in cognitive radio networks (CRNs) for better spectrum utilisation. When a licensed or primary user reclaims its operating channel, the cognitive user has to initiate a suitable handoff procedure for successful completion of its ongoing transmission. This paper analyses the prioritised proactive spectrum handoff decision for a finite capacity cognitive radio network using preemptive resume priority (PRP) M/G/1/K queuing model. Cumulative handoff delay (CHD) and total service time (TST) are taken to investigate the performance of the handoff strategies. We establish an analytical model of cumulative handoff delay and total service time of proactive switching spectrum handoff for the proposed finite length queuing network. The impact of queue length on performance measuring metrics such as CHD and TST in terms of arrival rate of primary users and mobility factor of spectrum holes are presented extensively.