Problem In Cognitive Radio Networks Research Articles

Multi‐objective spectrum assignment in heterogeneous cognitive radio networks for internet of things

SummaryInternet of Things (IoT) applications have massively increased in the last few years. Future‐generation IoTs are going to be massive in number, heterogeneous in nature, and extremely demanding in terms of computation and communication requirements. Therefore, effective techniques are needed to optimize the use of scarce communication resources. This work proposes a novel multi‐objective framework to investigate the channel assignment problem in cognitive radio network (CRN)‐based IoT applications. The multi‐objective optimization framework comprises three objective functions. The first objective aims to maximize the overall system throughput fairness, the second objective aims to maximize the overall system residual energy, and the third objective aims to increase the user satisfaction level by maximizing the overall priority index. The problem of channel assignment is modeled and solved using Mixed Integer Linear Programming (MILP) and ‐constraint technique. This technique generates a set of Pareto efficient solutions, and an optimal solution among these solutions is selected using a fuzzy logic decision mechanism. Moreover, power level selection is incorporated by solving the problem multiple times for different power levels and obtaining a preferred solution for each power level. The simulations are conducted to unveil an interesting trade‐off between the conflicting system design objectives, that is, the overall throughput and the overall residual energy while incorporating the significance of primary user (PU) activity when preforming channel assignment in CRN.

Design Problems in Cognitive Radio Networks for Spectrum Sensing

Cognitive radio is regarded as an innovative approach aimed at enhancing the efficient utilization of the valuable natural resource, namely the electromagnetic spectrum for radio communication. Based on principles of SDR, cognitive radio is wireless communication system with intelligence to interpret its environment. It uses "understanding-by-building" approach to learn about its surroundings and adjust to statistical variations in input signals.Providing rock-solid connectivity whenever and wherever it's needed.aiding in effective use of radio spectrum.Spectrum sensing methods are discussed in this abstract. Spectrum sensing requires energy, so keep that in mind. Therefore, our focus centers on energy-efficient spectrum sensing in order to conserve energy during transmissions, taking into account relevant factors. In this context, an efficient model will be proposed for cognitive radio, encompassing both analytical and simulation aspects.

An efficient coprime integer sets based rendezvous algorithm for distributed cognitive radio networks

AbstractMultichannel blind rendezvous problem is a fundamental problem in cognitive radio networks (CRNs). It refers to how to ensure that any two secondary users can meet on a common available channel at the same time. In this paper, an efficient channel hopping algorithm, called CISR (Coprime Integer Sets based Rendezvous), is designed for asymmetric role model (i.e. users can be divided into two types of roles, say, sender/leader and receiver/follower) in distributed CRNs. CISR can guarantee the rendezvous of the leader and follower and lowers the upper bound of both the maximum time to rendezvous and expected time to rendezvous by carefully constructing two coprime integer sets. The simulation results show that CISR has the near‐optimal performance.

AN IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SPECTRUM ALLOCATION IN COGNITIVE RADIO NETWORKS

The seriousness of the spectrum scarcity has increased dramatically due to the rapid increase of wireless services. The key enabling technology that can be viewed as a novel approach for utilizing the spectrum more efficiently is known as Cognitive Radio. Therefore, assigning the spectrum opportunistically to the unlicensed users without interfering with the licensed users, concurrently with maximizing the spectrum utilization is addressed as a major challenge problem in cognitive radio networks. In this paper, an improved metaheuristic optimization algorithm has been proposed to solve this problem that contingent on a graph coloring model. The proposed approach is a hybrid algorithm composed of a Particle Swarm Optimization algorithm with Random Neighborhood Search. The key objective function is maximizing the spectrum utilization in the cognitive radio networks with the subjected constraints. MATLAB R2021a was used for conducting the simulation. The proposed hybrid algorithm improved the system utilization by 1.23% compared to Particle Swarm Optimization algorithm, 5.57% compared to Random Neighborhood Search, 7.9% compared to Color Sensitive Graph Coloring algorithm, and 27.33% compared to Greedy algorithm. Moreover, the system performance was evaluated with various deployment scenarios of the primary users, secondary users, and channels for investigating the impact of varying these parameters on the system performance.

Riemannian Mean Shift-Based Data Fusion Scheme for Multi-Antenna Cooperative Spectrum Sensing

In this article, the multi-antenna cooperative spectrum sensing problem in cognitive radio networks is investigated over Riemannian manifold. At the beginning, a signal matrix is constructed by using the sensing signals from secondary users (SUs) and the corresponding covariance matrix is calculated. Subsequently, the covariance matrices are transmitted to the fusion center and mapped to points on the manifold. In order to reduce the impact of aberrant SUs, a data fusion scheme based on Riemannian mean shift algorithm is developed. After data fusion, the representative points are obtained to train a classifier. In order to realize clustering directly over Riemannian manifold, a novel Riemannian distance based particle swarm optimization (RDPSO) algorithm, is proposed to train a classifier, which is employed to determine the state of primary user (PU). Finally, in simulation part, the validity of the proposed scheme is verified under different scenarios.

On reliability in the performance analysis of cognitive radio networks

Satisfying the quality of service (QoS) requirements of users in the form of channel availability and service retainability within the resource limited environment has been a major problem in cognitive radio networks. In this connection, several research studies have been carried out in the literature to improve the QoS of users by proposing dynamic channel reservation algorithms. However, the studies have a number of limitations in their conceptual and mathematical modeling of channel availability and service retainability, which render their performance evaluation unreliable. In this paper, we address these limitations for leading to more realistic, reliable, and practically valid modeling. For conceptual modeling, we use connection availability in place of channel availability, motivated by the fact that the latter does not necessarily lead to a successful establishment of connection and, thus, is not a suitable performance indicator. For instance, obtaining a channel for transmission is of no avail if the intended receiver is inaccessible. Similarly, we consider service retainability with accessibility/inaccessibility of the intended receiver incorporated. For mathematical modeling, we use CTMC and the resultant closed from expressions to include all the required states of channel availability yet unsuccessful connection establishment. Additionally, we derive closed form equations for channel availability and service retainability that are in exact conformance with the CTMC model. Results confirm that considering the impact of receiver’s accessibility leads to performance difference for the channel availability and service retainability presented in the sate-of-the-art.

Performance Analysis of Cognitive Radio Networks With Burst Dynamics

The impact of the traffic characteristics of secondary users (SUs) on the performance of cognitive radio networks (CRNs) should be understood for designing operation rules. This paper focuses on multi-type burst services and network congestion problem in CRNs and evaluates the QoS of SUs based on multiple cross-layer considerations. A two-state Markov-modulate Bernoulli process (MMBP-2) is adopted to model packet flows of SUs with different burst degrees in CRNs. We propose a two-dimensional discrete queuing model to consider spectrum access, burstiness of traffic, network congestion, channel environment, user activity and finite buffer. We construct an iterative algorithm to compute the steady-state distribution of the proposed queuing model and determine the performance metrics like the throughput, the average delay, the average queue length and the total packet loss probability. Numerical analysis evaluates the QoS of SUs under different burst environments.

A Reinforcement Learning based evolutionary multi-objective optimization algorithm for spectrum allocation in Cognitive Radio networks

To cope up with drastically increasing demand for radio resources lead to raise a challenge to the wireless community. The limited radio spectrum and fixed spectrum allocation strategy have become a bottleneck for various wireless communication. Cognitive Radio (CR) technology along with potential benefits of machine learning has attracted substantial research interest especially in the context of spectrum management. However, a variety of performance attributes as objectives draw attention during the technological preparations for spectrum management such as higher spectral efficiency, lower latency, higher network capacity, and better energy efficiency as these objectives are often conflicting with each other. Hence, this paper addresses the spectrum allocation problem concerning network capacity and spectrum efficiency as conflicting objectives and model the scenario as a multi-objective optimization problem in CR networks. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) which combines the feature of evolutionary algorithm and machine learning called Non-dominated Sorting Genetic Algorithm based on Reinforcement Learning (NSGA-RL) is proposed which incorporates a self-tuning parameter approach to handle multiple conflicting objectives. The numerical findings validate the effectiveness of the proposed algorithm through the Pareto optimal set and obtain optimal solution efficiently to satisfy various requirements of spectrum allocation in CR networks.

Resource allocation algorithm for downlink MIMO‐OFDMA based cognitive radio networks in spectrum underlay scenario

In this study, the sum throughput maximisation problem for cognitive radio networks (CRNs) based on the multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) structure has been investigated. Hence, the intention is to maximise the downlink sum throughput of the CRN in a way that the sum power of the cognitive base station (CBS) remains below the given power limit and the induced interference on each subcarrier guarantees the interference threshold level predefined by primary users. Here, due to combinatorial constraints, the complex problem should be resolved. Hence, the core purpose of this study is to prepare a novel resource assignment algorithm that tries to fulfil these combinatorial constraints simultaneously. Therefore, this study makes progress towards solving the problem theoretically based on convex optimisation framework. An optimal solution has been implemented by proposing the iterative algorithm in which an optimal level of Lagrangian coefficients is obtained. Then, because of the optimal algorithm intricacy, the two low complicated algorithms are further suggested based on the solutions of two simplified versions of the original problem. Numerical results demonstrate that the suggested algorithms improve sum throughput considerably in comparison with classical algorithm. The proposed simplified algorithms converge to the optimal solutions' performance while their complexities are desirable for practical implementation.

Spectrum efficiency in CRNs using hybrid dynamic channel reservation and enhanced dynamic spectrum access

Blocking of new arriving services and dropping of ongoing services are inherent problems in Cognitive Radio Networks (CRNs), which need to be addressed to enhance spectrum efficiency. In particular, Secondary Users (SUs) undergo service degradation in the face of Primary Users (PUs)’ arrivals. In this paper, we present a scheme called Efficient Spectrum Utilization (ESU) that reduces the dropping and blocking probabilities of existing and new services, respectively, to make efficient use of the available spectrum. The scheme divides the available spectrum into reserved and non-reserved bands. The reserved band is dynamically allocated a number of channels from the non-reserved band in order to accommodate those services which face interruptions while operating in the non-reserved band. The scheme renders dynamic access to the available spectrum and facilitates priority-based channel allocation and termination. SUs are divided into low and high priority levels depending on their Quality of Service (QoS) requirements. SUs with low priority level are granted direct access to both the bands to enhance channel utilization. SUs operating in the reserved band with high priority levels are granted uninterruptible status to ensure a certain level of service provisioning to SUs. The proposed ESU scheme is modeled using Continuous Time Markov Chain (CTMC) and mathematical expressions are derived for several QoS parameters. Performance of the proposed scheme is evaluated under various network conditions. Results demonstrate that ESU reasonably improves spectrum efficiency under channel failure in CRNs.

Low-Latency Data Aggregation Scheduling for Cognitive Radio Networks With Non-Predetermined Structure

Data aggregation is a fundamental yet popular operation in wireless networks where the sink needs to obtain the combined information of the whole network. However, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in cognitive radio networks. Few studies have addressed this issue and most previous aggregation methods all assume that a fixed-structure based aggregation tree is constructed in advance, which may result in the selection of a node with limited spectrum opportunities as the parent by many nodes and by extension results in a large latency. Thus, the MLAS problem in cognitive radio networks (MLAS-CR) without the above limitation is investigated in this paper. First, the MLAS-CR problem with primary social behaviors where the activity of primary users can be predicted is studied. To make full use of the limited spectrum opportunities, we integrate the construction of the aggregation tree, and the computation of a conflict-free schedule simultaneously, without any predetermined structures. Second, the MLAS-CR problem without the above assumption is also investigated. To reduce the latency, a two-way aggregation scheduling method is proposed to adaptively choose the parent with only current channel information. To further reduce the latency, we also introduce a new data aggregation mode for CRN, i.e., Data Aggregation Scheduling in The Dark, to utilize the spectrum opportunities of scheduled nodes. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

HBLP: A Hybrid Underlay-Interweave Mode CRN for the Future 5G-Based Internet of Things

Enhancing spectrum utilization efficiency (SUE) to accommodate the multitude of 5G-based IoT devices within the available scarce spectrum has been a pivotal point of research in the current decade. Equipped with interweave and underlay modes, cognitive radio networks (CRNs) are envisioned to be the most promising technology for SUE enhancement. Since the 5G-based IoT is swiftly transforming into a heterogeneous network, hybrid underlay-interweave mode CRNs appear to be the optimal key for SUE enhancement. Besides enhancing SUE, ensuring fairness among secondary users (SUs) to equitably utilize the network’s resources in the wake of primary users’ (PUs) service emergence has been a critical problem in CRNs. Considering the importance of SUE enhancement, in this paper, we investigate two problems. Firstly, we precisely analyze, in a novel way, the performance of hybrid underlay-interweave mode of CRNs for SUE enhancement from the standpoint of SUs. For this purpose, we propose a hybrid underlay-interweave mode enabled CRN scheme and apply it on a legacy CRN. We model the proposed scheme with continuous time Markov chain and derive closed form expressions for various SUE-related quality of service parameters. The analysis is conducted under dynamic reservation of channels for interrupted users, considering the effects of multi-levels network traffic loads and varying channel failure rates. Secondly, we propose a multi-attribute based fairness driven algorithm for determination and interruption of SUs’ services to ensure fairness among services in the network’s resources utilization. We evaluate our proposed scheme under both perfect and imperfect spectrum sensing scenarios. The obtained results demonstrate that, as compared to the state-of-the-art, the proposed scheme significantly enhances SUE while the proposed algorithm achieves a noticeable fairness among SUs’ services.

Performance Evaluation of Relay-Aided CR-NOMA for Beyond 5G Communications

Non-orthogonal multiple access (NOMA) is considered as one of the most promising technologies to handle the issue of spectrum scarcity in beyond fifth-generation (5G) networks. Integrating NOMA in cognitive radio (CR) network is expected to usher a new era of reliable, seamless, and massive connectivity. In this regard, this work explores the relay selection problem in CR networks when operating in the spectrum sharing model. Specifically, the secondary users (SUs) in the CR-NOMA network opportunistically access the licensed spectrum resources to boost the number of accessible SUs sharing the limited and dynamic spectrum resources. Moreover, to improve the performance of far users, partial relay selection architecture is exploited at full-duplex (FD) and half duplex (HD) relays for both uplink and downlink communications. For in-depth performance evaluation, we provide closed-form expressions of the outage probabilities of the users in FD and HD relay-aided CR-NOMA networks. In addition to this, the analytical expressions of asymptotic outage probabilities and ergodic capacity are also provided which unveils the critical factors affecting the performance of CR-NOMA networks. To validate the derived expressions, extensive simulations are performed that demonstrate the accuracy of analytical expressions for FD and HD relay-aided CR-NOMA networks.

A Novel Jaya-BAT Algorithm Based Power Consumption Minimization in Cognitive Radio Network

The aim of this paper is to propose a new hybrid optimization technique, namely Jaya-BAT algorithm (JBA) and to demonstrate its application for constrained power consumption minimization in cognitive radio network considering Class B power amplifier. JBA is motivated by recently developed Jaya algorithm (JA) having good exploration ability and nature inspired BAT algorithm (BA) with good exploitation feature. In JBA, both JA and BA help each other to get away from local optimum solution and converge towards best optimal solution. The proposed algorithm when applied to different benchmark functions shows enhanced performance in comparison to other state-of-the-art metaheuristic techniques available in literature. Reconfiguration of transmission parameters for cognitive radio (CR) user supporting data transmission mode is carried out with a purpose of minimizing the power consumption while supporting different QoS requirements. The solutions show that the constrained optimization by cognitive decision module using JBA provides better results as compared to BA and JA based optimization techniques. It proves the potential of JBA as an efficient technique to be used for power consumption minimization problem in CR networks.

Approximating Common Control Channel Problem in Cognitive Radio Networks

Cognitive radio networks (CRNs) involve an extensive exchange of control messages to coordinate spectrum sensing, medium access, routing, etc. Static control channel allocation uses preassigned common control channel (CCC) contrary to the opportunistic access paradigm. We propose a k-hop clustering scheme for the optimal CCC assignment problem (k-CCCP) for CRNs. We prove that the k-CCCP problem is NP-hard (nondeterministic polynomial-time hard) by reduction using minimum dominating set problem. We also found that the k-CCCP problem is approximable hard. We propose a ln Δ approximation algorithm for the k-CCCP problem, where Δ is the largest cardinality among all khop neighborhood in a CRN. To account for primary user dynamics, we propose a distributed ranking algorithm for the k-CCCP problem with message complexity O(nδk), where n, δ, and k are number of nodes, the largest node degree, and hop parameter, respectively. To address the negative effect of multihop clustering, we also propose a mechanism to find the optimal k value in multihop clustering. For performance comparison, extensive simulations are performed and the results show that our proposed scheme outperforms with the competitive schemes.

Multiple Radios for Fast Rendezvous in Heterogeneous Cognitive Radio Networks

In cognitive radio networks (CRNs), if two unlicensed secondary users (SUs) want to communicate with each other, they need to rendezvous with each other on the same channel at the same time. Rendezvous is the first key step for SUs to be able to communicate with each other. Channel hopping (CH) is a representative technique to solve the rendezvous problem in CRNs. SUs equipped with multiple radios can significantly reduce the time-to-rendezvous (TTR) for several existing CH algorithms while the additional cost is low. However, several drawbacks exist in the existing CH algorithms based on multiple radios. One of the main drawbacks is that they cannot be well applied in heterogeneous CRNs. The reason is that the number of radios for different SUs is implicitly assumed same or must be more than one in the existing CH algorithms based on multiple radios, which is unrealistic for heterogeneous CRNs. In heterogeneous CRNs, SUs may be equipped with different numbers of radios including one radio. To mainly address the above issue, hybrid radios rendezvous (HRR) algorithm is proposed in this paper. Moreover, the upper bounds of maximum TTR (MTTR) for the HRR algorithm are derived by a theoretical analysis. Furthermore, extensive simulations are performed to evaluate the expected TTR (ETTR), the MTTR, and the channel qualities of the rendezvous channels for the HRR algorithm. Simulation results show that rendezvous can be guaranteed by the HRR algorithm in heterogenous CRNs. Besides, the qualities of the rendezvous channels can be improved by the HRR algorithm. In addition, our algorithms can achieve rendezvous faster than several existing algorithms.

Generalized Real-Valued Weighted Covariance-Based Detection Methods for Cognitive Radio Networks With Correlated Multiple Antennas

This paper is concerned with the spectrum sensing problem for cognitive radio networks with correlated receiving multiple antennas in the time-varying fading channel. We first consider the scenario that all the antennas have the same noise variance and present a generalized real-valued weighted-covariance-based detection (GRWCD) method. In particular, we derive the distribution of the GRWCD statistic under the null hypothesis, which allows us to develop the theoretical decision threshold for a given false alarm probability. Besides, we derive the distribution of the GRWCD statistic under the alternative hypothesis, which enables us to provide a mathematical expression for the detection probability as well as the theoretical receiver operating characteristic. Meanwhile, we consider a more general scenario of unequal per-antenna noise variances and present a modified GRWCD method as well as the theoretical expressions of the decision threshold. The simulation results are provided to verify the accuracy of the derived results and show that the proposed two methods are capable of providing performance improvement over several advanced methods in the literature.

Efficient joint power and admission control in underlay cognitive networks using Benders’ decomposition method

In this paper, the joint power and admission control (JPAC) problem in cognitive radio networks is studied. This problem is decomposed into two subproblems, by utilizing the Benders’ decomposition theory, which are efficiently addressed. Specifically, the former is a simple linear optimization problem which a closed-form expression for its optimal solution is derived, and the latter is solved via an iterative distributed power control algorithm. Furthermore, we use the outcomes of the decomposed two subproblems to propose a sequential searching JPAC algorithm with a novel removal metric whereas minimal number of SUs are sequentially removed. In an infeasible system, where the minimum target signal-to-interference-plus-noise-ratios (SINRs) of all primary and secondary users are not simultaneously reachable, our proposed JPAC algorithm guarantees protecting all primary users while the maximal number of SUs are admitted and supported with their target SINRs. Not only our proposed algorithm does converge to an equilibrium, but also outperforms existing algorithms in terms of average outage ratio and average aggregate power, as demonstrated through the extensive simulations.

Approximating the largest connected topology in cognitive radio networks

Connection between two secondary users (SUs) in cognitive radio networks (CRNs) is not only determined by their transmission power and distance, it also depends on the availability of a common channel for both SUs to open it for communication. In CRN, each SU is equipped with a number of antennas, denoted as β, which is the maximum number of channels that an SU can open simultaneously, known as antenna budget constraint. As each SU has a limit on the maximum number of channels it can open simultaneously, so network may not be connectable. But, it is desirable to connect the largest subset of SUs while minimizing the interference introduced due the nearby transmissions among SUs on the same channel, this problem is called the largest-connected minimum-interference topology control (LMTC) problem in CRNs. In this paper, we model the network of SUs as a potential graph PG=(V(PG),E(PG)), where V(PG) is set of SUs and E(PG) is set of potential edges. First, we show that the LMTC problem is NP-hard then we propose an approximation algorithm to address LMTC problem with min (m/log n, n.β/2log n) ratio, where n and m are the number of nodes and edges in potential graph respectively. We also propose a distributed algorithm called distributed-LMTC with message complexity O(n2), to address the LMTC problem. To address this NP-hard problem, we combine both topology control and channel assignment phase. In topology control phase, a network subgraph is derived with satisfying antenna budget constraints. In channel assignment phase, we assign channel to link to minimize interference. Simulation results show that the constructed topology can achieve higher connectivity and throughput than other competitive topology control algorithms.

Impact of Heterogeneous Fading Channels in Power Limited Cognitive Radio Networks

The tradeoff between decreasing the interference to primary user (PU) and increasing secondary users’ (SUs’) achievable throughput is an important problem in cognitive radio networks. Heterogeneous fading channels from PU to multiple SUs, PU’s traffic distribution, limited SU’s power and multiple SUs’ access contention impact both these two conflicting objectives. In this paper, we study the joint impact of these four factors on the tradeoff. More specifically, we consider that the channels from PU to SUs are exposed to non-identically independent free space path losses, PU’s traffic randomly arrives and departs from the channel, every SU’s average power consumption is limited, while multiple SUs contend to transmit. We first model the impact of these factors on SUs’ spectrum sensing and data transmission. Then, we formulate the tradeoff aiming at maximizing SUs’ aggregated throughput under two constraints: 1) interference probability to PU and 2) SUs’ average power consumption. To solve the optimization problem, we design a novel cluster based particle swarm optimization (C-PSO) algorithm. By iteratively updating the particles in a cluster based on the comparison of their fitnesses, the cluster converges to the optimal solution rapidly. Simulation results validate the feasibility of the C-PSO algorithm and the outperformance of our proposal compared against related contributions which consider the homogeneous fading channel. They also show how the optimal solution varies with path losses and PU’s traffic distribution.