Multi-channel Cognitive Radio Networks Research Articles

Advanced Recursive Best-First Search (RBFS) based Routing Protocol for Multi-hop and Multi-Channel Cognitive Wireless Mesh Networks

Cognitive Wireless Mesh Network (CWMN) is an opportunistic network in which radio channels can be assigned according to their availability to establish connections among nodes. After establishing a radio connection among nodes, it is necessary to find an optimal route from the source node to the destination node in the network. If there remain more channels among nodes, the minimum weighted channel should be taken into account to establish expected routes. The graph theoretic approach fails to model the multi-channel cognitive radio networks due to abrupt failure in finding new successful routes as it can’t figure multi-channel networks. In this paper, a multi-edged graph model is being proposed to overcome the problems of cognitive radio networks, such as flooding problems, channel accessing problems etc. A new channel accessing algorithm has been introduced, and optimal routes have been selected using a heuristic algorithm named RBFS. Simulation results are compared with DJKSTRA based routing protocols.

LSTM-enabled prediction-based channel switching scheduling for multi-channel cognitive radio networks

To protect primary users (PUs) from harmful interference and to maintain seamless communication, secondary users (SUs) need to trigger channel switching in cognitive radio networks (CRNs). However, due to frequent channel switching, SUs lose considerable energy. In order to perform channel switching efficiently in multi-channel CRNs (MC-CRNs), an optimal channel switching schedule is desirable, which can assist SUs in minimizing the number of switching and the switching energy overhead. In this work, we propose a machine learning based prediction-driven channel-switching scheduling scheme for MC-CRNs. The proposed scheme optimizes the utility of SUs by balancing the trade-off between spectral efficiency and channel switching overhead. Initially, a Long-Short Term Memory (LSTM) network-based channel prediction scheme is presented using which individual SUs locally predict PU occupancy in future time slots based on their spectrum sensing history. Thereafter, a cooperative channel prediction framework is introduced using which SUs perform cooperation of local prediction results to enhance the prediction results. Using the cooperative prediction results, a channel-switching scheduling problem is formulated, which captures the trade-off between spectral efficiency and channel-switching overhead. A proof is carried out to establish that the formulated scheduling problem is NP-Hard. To solve the formulated problem in polynomial time, a greedy-based heuristic solution is proposed for scheduling the SUs into different slots of channels to achieve a sub-optimal solution. A simulation-based analysis has been carried out to demonstrate our proposed scheme’s efficacy compared to a conventional exhaustive search and fixed channel switching schemes.

Energy-Efficient Opportunistic Spectrum Access in Multichannel Cognitive Radio Networks

We study an opportunistic spectrum access problem in multichannel Cognitive Radio Networks in which the secondary user (SU) has an energy cost when switching across channels. Channel switching from one RF chain to another also involves a reconfiguration delay that results in a shorter duration of transmission opportunity for the SU. For an imperfect sensing, we formulate the spectrum access problem in the framework of Partially Observable Markov Decision Process from which we obtain an average energy optimal policy. Also, we propose Efficacy - Efficient channel access with switching policy, for channel sensing and access based on the structure of the optimal policy. We compare the optimum policy with Efficacy, and show that Efficacy has a near optimal average energy performance.

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.

A Multichannel Allocation Strategy Based on Preemption Threshold and Preemption Probability in Cognitive Radio Networks

In traditional multichannel cognitive radio networks (CRNs), users are split into two different priorities. Because of the low priority of secondary users (SUs), SU packets’ transmissions are easily interrupted by primary users (PUs). In this paper, two control parameters, called preemption threshold H and preemption probability q, are used to regulate the preemption behavior of PU packets to improve the transmission performance of SU packets. When all channels in the system are occupied, the preemption behavior of PU packets will be adjusted according to the amount of SU packets that are transmitting in the system. If the amount is larger than H, the recently arrived PU packet either preempts a channel with probability q or leaves the system with probability 1 − q . The central controller manages the system’s channel usage right and determines a series of access behaviors of user packets. Considering the possible imperfect sensing, a discrete-time queueing model is developed with the proposed preemption control mechanism. Then we obtain some performance index expressions of PU and SU packets founded on the system’s state transition matrix and make the corresponding performance figures through numerical experiment. Finally, we construct a system utility function and determine the optimal preemption threshold and preemption probability through the seagull optimization algorithm (SOA). Experimental data show that the proposed mechanism by setting preemption threshold and preemption probability can significantly reduce SU packets’ outage rate and improve SU packets’ throughput rate.

Throughput Optimal Opportunistic Channel Switching in Cognitive Radio Networks

In this letter, we consider a problem of throughput optimum channel switching for channel access by a secondary user in multichannel Cognitive Radio Networks in which there is a delay involved in switching from one channel to another. We consider an imperfect sensing model that takes into account false alarm and detection probabilities. We show that the problem can be posed in the framework of Partially Observable Markov Decision Process (POMDP), and obtain an average throughput optimal policy for which we provide structural results. Also, we propose a low complex algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASTUTE</i> for channel access based on one stage reward. We compare the performance of the optimum policy with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASTUTE</i> . Our results show that the average throughput performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASTUTE</i> is very close to that of the optimal solution.

Utility driven cooperative spectrum sensing scheduling for heterogeneous multi-channel cognitive radio networks

Owing to the spectrum scarcity and energy constrained devices in wireless networks arises the demand for an efficient spectrum sensing technique which improves both sensing performance and energy efficiency for cognitive radio networks. This paper proposes a cooperative spectrum sensing scheduling (CSSS) scheme for heterogeneous multi-channel cognitive radio networks with the objective of finding an efficient sensing schedule to enhance network utility while keeping the energy depletion at a lower level. We start with formulating the CSSS problem as an optimization problem, which captures both the energy-performance and performance opportunity trade-offs. We prove that the formulated CSSS problem is non-deterministic polynomial hard (NP-hard). To tackle the higher computational complexity of the formulated problem, we propose a greedy-based heuristic solution, which produces a sub-optimal result in polynomial time. To reduce energy consumption during spectrum sensing, we make secondary users to adaptively decide on the sensing duration based on the received signal-to-noise ratio (SNR), where higher SNR leads to lower sensing duration and vice-versa. For enhancing network throughput, SUs sense multiple channels in the order of their suitability for data transmission to explore as many numbers of channels as possible within the permitted maximum sensing time. We consider erroneous nature of reporting channel to make the cooperative decision robust against errors during reporting. Simulation based results show the effectiveness of the proposed scheme in terms of utility, energy overhead, and the number of channels explored compared to similar schemes from literature.

Interference Management and Resource Allocation in Multi-Channel Ad Hoc Cognitive Radio Network

Interference Management and Resource Allocation in Multi-Channel Ad Hoc Cognitive Radio Network

A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks

A cognitive radio network (CRN) is a novel solution that promises to solve the spectrum scarcity problem and enhance spectrum utilization. However, unsecured CRN can easily be manipulated in order to attack legacy users on the communication channel. As a result, the network's performance significantly degrades. Therefore, communication channel security is an important issue that needs to be addressed in a CRN. In this work, we focus on improving the security of multi-channel communication in a CRN, while various jammers try to access channels of interest to prevent SUs from using them. By using game-theoretic concepts and by defining states, actions, and players' rewards, we propose game-based schemes that find the best channel for the secondary users (SUs) in order to avoid jammer's attacks on communication channels. Accordingly, the problem is finding the optimal channel to maximize the long-term reward of the SU where communication channels are not used by the primary users (PUs) and are not jammed by attackers. In addition, the idea of transfer learning might be applied to the problem under consideration, and thus, a transfer Game-Actor-Critic (TGACT) scheme is proposed, which uses the transferred knowledge in a double-game period to accelerate the learning process and provide performance improvement in channel selection. Finally, the performance of the proposed schemes is simulated with different configurations. The simulation results show that the proposed schemes are quite resistant to jammer attacks, and achieve better performance compared to other channel selection schemes.

MIMO Based Cognitive Radio Network with Efficient Spectrum Utilization

This paper presents an energy-efficient power control scheme for Multi-Input–Multi-Output (MIMO) based multi-channel cognitive radio networks (CRNs) with adaptive mode selection protocol. Cognitive radio device operates in the temporal domain to utilize spectrum holes and MIMO system operates in the spatial domain. Thus CRN in collaboration with MIMO can produce a significant improvisation for spectrum utilization, and therefore, the integration of MIMO and CRN has received widespread research interest. To address the challenges of the collaboration of MIMO and CRN, we propose a MIMO based CRN network in this paper, in which basic CRN is modified with the QPSK transmission technique having four antenna elements per primary users (PU) and secondary users (SU). Similarly, MIMO significantly improves channel capacity particularly in SU transmission under low SNR condition, but issues like several antenna elements and specific antenna selection issues increase the computational complexity and energy consumption. The proposed network protocol strives to improve communication stability and rate of throughput via selecting the MIMO operating parameters such as multiplexing, diversity, or hybrid multiplexing-diversity gains. Specifically, we designed transmission power distribution along with an antenna selection scheme for enhancing the energy efficiency of CRN’s under the influence of the maximum interference caused by the SU to PU, utmost power transmission from SU, and the least rate of transmission through the SU link. Finally, the simulation results are compared with basic CRN alone using the BPSK scheme to evaluate and validate the proposed scheme.

Noise uncertainty effect on multi-channel cognitive radio networks

Achieving high throughput is the most important goal of cognitive radio networks. The main process in cognitive radio is spectrum sensing that targets getting vacant channels. There are many sensing methods like matched filter, feature detection, interference temperature and energy detection which is employed in the proposed system; however, energy detection suffers from noise uncertainty. In this paper a study of throughput under noise fluctuation effect is introduced. The work in this paper proposes multi-channel system; the overall multi-channel throughput is studied under noise fluctuation effect. In addition, the proficiency of the network has been examined under different number of channels and sensing time with noise uncertainty.

Performance analysis and optimization research of multi-channel cognitive radio networks with a dynamic channel vacation scheme

&lt;p style='text-indent:20px;'&gt;In order to resolve the issues of channel scarcity and low channel utilization rates in cognitive radio networks (CRNs), some researchers have proposed the idea of "secondary utilization" for licensed channels. In "secondary utilization", secondary users (SUs) opportunistically take advantage of unused licensed channels, thus guaranteeing the transmission performance and quality of service (QoS) of the system. Based on the channel vacation scheme, we analyze a preemptive priority queueing system with multiple synchronization working vacations. Under this discipline, we build a three-dimensional Markov process for this queueing model. Through the analysis of performance measures, we obtain the average queueing length for the two types of users, the mean busy period and the channel utility. By analyzing several numerical experiments, we demonstrate the effect of the parameters on the performance measures. Finally, in order to optimize the system individually and socially, we establish utility functions and provide some optimization results for PUs and SUs.&lt;/p&gt;

Joint Channel Assignment and Adaptive Mode Selection in MIMO-Based Cognitive Radio Networks

In this paper, we develop a joint channel assignment and adaptive Multi-Input–Multi-Output (MIMO) mode selection protocol for MIMO-based multi-channel cognitive radio networks. The proposed protocol utilizes the dynamic access capabilities of cognitive radio devices along with the multiplexing and/or diversity gains offered by MIMO systems in order to improve network throughput and communication reliability. The proposed protocol attempts at enhancing system performance by selecting the best MIMO operating modes (multiplexing, diversity, or hybrid multiplexing-diversity mode) for each idle channel according to the surrounding RF environment, and then selects the channel that provides the highest throughput. The network performance of our protocol (the overall network throughput and MIMO efficiency) is investigated under various traffic and network conditions through simulations. Compared to diversity-mode channel assignment, multiplexing-mode channel assignment, and adaptive mode selection with random channel selection protocols, our proposed protocol reveals significant performance improvement in network throughput under different traffic and network conditions.

Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

In cognitive radio networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utilization. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. The result of channel access is stochastic with unknown distribution, and statistically different for each user. In deciding the channel for access, a user needs to either explore a channel to learn its statistics, or exploit the channel with the highest expected reward based on the information collected so far. Further, a channel should be accessed exclusively by one user at a time to avoid collision. Using multi-armed bandit framework, we develop two rate-optimal algorithms with low computational complexities of <inline-formula><tex-math notation="LaTeX">$O(N)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$O(NK)$</tex-math></inline-formula> , respectively, where <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> denotes the number of users and <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> denotes the number of channels. Further, we extend the results and develop an algorithm that is amenable to implement in a distributed fashion.

Joint Resource Allocation and Transmission Mode Selection Using a POMDP-Based Hybrid Half-Duplex/Full-Duplex Scheme for Secrecy Rate Maximization in Multi-Channel Cognitive Radio Networks

Physical layer wireless communications are more and more essential, and are vulnerable to malicious users owing to the nature of broadcast channels. Herein, we consider a centralized multi-channel cognitive radio network in the presence of eavesdroppers (EVEs). In the network, the secondary base station (SBS) shares currently free primary channels to simultaneously communicate with secondary users (SUs), while passive eavesdroppers attempt to overhear data in the secondary communications. Each limited-battery SU is equipped with two antennas (one for transmitting signals, and other for receiving signals) and is powered by a solar energy harvester. Meanwhile, the SBS equipped with multiple antennas can operate in full-duplex (FD) transmission mode (simultaneously transmit and receive signals) or in half-duplex (HD) transmission mode (transmit and receive signals in turn during each half of a time slot) with the SUs. In this paper, we propose a novel scheme to maximize the secondary system security of the multi-channel cognitive system in the presence of multiple passive EVEs, in which the EVEs are able to overhear the data of the SBS-SU transmissions on all the primary channels. The problem of decision making is formulated as the framework of a partially observable Markov decision process (POMDP), and an optimal solution is achieved by adopting value iteration-based dynamic programming. Specifically, in each time slot, the SBS allocates optimal channel and optimal action (i.e. either stay silent or employ HD/FD transmission modes with optimal transmission power) for each SU in order to obtain maximum long-term secrecy rate for the secondary system.

Spectrum sensing in single channel and multi-channel cognitive radio networks

&lt;p&gt;Sensing the existence or absence of primary user is the major chore of cognitive radio networks. Nevertheless, Spectrum sensing is the core process of cognitive radio and with target to find idle channels.Various detection techniques exist, however, energy detection is considered as the most used detector because of its lower computational cost. In this paper, we proposed a study of throughput for a cognitive radio system. We had two scenarios, in the first scenario; a study of throughput against probability of false alarm was done; where, only one channel is sensed, to maximize the individual channel throughput. In the second scenario, multi-channel is sensed to maximize the overall system capacity. In addition, different number of channels is considered with different sensing times and at different throughput costs.The performance of the network has been investigated in terms of maximum throughput for optimal number of CR channels. &lt;/p&gt;

A Hybrid Spectrum Access Strategy with Channel Bonding and Classified Secondary User Mechanism in Multichannel Cognitive Radio Networks.

Cognitive radio networks (CRNs) can improve spectrum utilization by allowing secondary users (SUs) to dynamically access channels unoccupied by primary users (PUs). The spectrum access strategy, as a point to enhance user performance, has received much attention. In this paper, we propose a hybrid access mode for network users in multichannel CRNs. For meeting different SU demands, SUs are classified as SU1s and SU2s. We further introduce a channel bonding scheme for high-priority (PU and SU1) user packets to enhance transmission efficiency. At the same time, we propose a hybrid spectrum access strategy for SU2 packets to improve their transmission stability. By establishing a Markov chain model, some important SU2 packets’ performance measures are derived. Furthermore, we display the comparison of hybrid, overlay and underlay modes by numerical results to analyze the advantages of different modes.

TPO-MAC: Traffic-Priority-based Opportunistic MAC Protocol for Multi-Channel Cognitive Radio Networks

Opportunistic medium access control (MAC) protocol design is a challenging issue for multi-channel cognitive radio networks (CRN). This paper provides a novel traffic-priority-based opportunistic MAC (TPO-MAC) protocol in multi-channel CRN. An important mechanism in TPO-MAC is that whether the primary users (PUs) can access the licensed channels immediately depends on their traffic priority, which alleviates the collision between secondary users (SUs) and PUs. In order to investigate the performance of TPO-MAC, a Markov chain model is further proposed to derive the saturation throughput of TPO-MAC. We validate our model by extensive simulations and compare the performance of TPO-MAC with the results from other literature. The results show that TPO-MAC performs better than the existing opportunistic MAC protocols.

Spectrum management with simultaneous power‐controlled assignment decisions in cognitive radio networks

SummarySpectrum sharing protocols for cognitive radio networks (CRNs) are often designed based on an exclusive channel occupancy policy. This policy does not allow concurrent cognitive radio (CR) transmissions to take place over the same channel. Unfortunately, this can significantly affect spectrum utilization and network performance. Allowing several secondary users to simultaneously share the same channel naturally improves spectrum utilization and avoids unnecessary blocking of CR transmissions. The key challenge in enabling efficient operation of multichannel CRNs is how to perform efficient power‐controlled media‐access‐control (MAC) protocols. These protocols include both power control and channel assignment. In this paper, we propose a power‐controlled MAC protocol for CRNs based on interference‐channel occupancy model with the objective of maximizing the number of served CR transmissions. It uses a novel power‐controlled channel assignment mechanism that allows several concurrent interference‐limited transmissions to simultaneously proceed over each idle channel in the same neighborhood. We present two variants of the power‐controlled channel assignment mechanism. The first proposed variant is suitable for CRNs with fixed data packet size, whereas the other is suitable for dynamic CRNs with variable packet size. The second variant of our protocol employs a users' scheduling procedure that attempts to fully utilize the available time‐frequency blocks. Simulation results indicate that employing transmission power control along with appropriate channel assignment and users' scheduling in CRNs can significantly improve the spectrum utilization and the overall network performance.

Cooperative Spectrum Sensing in Multichannel Cognitive Radio Networks With Energy Harvesting

This paper investigates cooperative spectrum sensing in multi-channel cognitive radio networks (CRNs) with energy harvesting. Our goal is two-fold: first, to determine the optimal sensing parameters for effective management of the limited energy budget in order to maximize the achievable throughput, and second, to exploit the benefits of a practical CRN towards improving the performance of the energy constrained CRN. Two different scenarios are considered. In the first, the secondary user (SU) is assigned a single radio frequency (RF) harvesting source, while in the second, the SU is assigned multiple RF harvesting sources and can opportunistically harvest from any of the sources. For these scenarios, the problem is formulated as a stochastic optimal control system with infinite and continuous state and action spaces. This is known to be computationally intractable and becomes even more complicated in a two-dimensional problem such as considered. In order to reduce the computational complexity, a myopic optimization approach is taken, and the problem is formulated into a mixed integer nonlinear problem (MINLP) to determine the channel assignment, the sensing duration, the distribution of the sensing duration associated with the assigned channels and the detection threshold under the constraint of energy causality and primary user (PU) protection. A near-optimal solution is obtained to the MINLP based on the alternating convex optimization technique. The simulation results obtained show that the considered work can improve the amount of energy harvested and, by extension, the active probability of the SUs by exploiting the multi-channel benefits of practical CRN for enhanced throughput.