Optimization For Cognitive Radio Networks Research Articles

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

Modelling and optimization for cognitive radio networks with preemption backoff mechanism

In cognitive radio networks (CRNs), secondary users (SUs) are permitted to use the available spectrum in opportunity ways to optimize spectrum utilization. As legal spectrum owners, primary users (PUs) can always interrupt SUs’ transmissions when they have transmission needs. To increase SUs’ transmission continuity while guaranteeing PUs’ transmission quality, we propose a preemption backoff mechanism (PBM) with backoff threshold m and backoff parameter f. Under the centralized spectrum sharing structure, we construct a discrete-time queueing model by developing a 3-dimensional Markov-chain (3DMC). Through the model analysis, the system’s steady-state probability distribution is obtained. To assess the comprehensive performance of our proposed mechanism, we deduce various important expressions of performance indices and get the corresponding performance figures. At last, we design a cost function by compromising various performance indices and find the optimum parameter combination under the lowest system cost through the Transient Search Optimization (TSO) algorithm. The numerical and simulation results illustrate our proposed PBM can improve SUs’ transmission continuity. For example, when m=1 and f=0.05, under the parameter settings given by the experiments, the proposed PBM can reduce SU packets’ outage rate and improve throughput by about 60% at most.

A Game Theory Based Hybrid NOMA for Efficient Resource Optimization in Cognitive Radio Networks

The deployment of Secondary Networks (SNs) gives an energy and cost-saving outcome for service providers. The existing research on SNs is gleaned from capacity enhancement while reducing interference instead of energy-efficient system, which is wilful for green communication. This paper proposes a game theory-based joint energy-efficient optimization of resources in Cognitive Radio Networks (CRNs) consisting SNs and Primary Networks (PNs) through Hybrid Non-Orthogonal Multiple Access (HNOMA). HNOMA provides freedom for users clustering to utilize either NOMA or OMA. The proposed clustering (priority and non-priority) and PA schemes are jointly optimized to enhance the system sum rate. At the same time, clustering and EGPA are used to break SUs into distinct coalitions and allocate efficient powers. EGPA operates in a distributed manner (without communicating with other BSs), where each BS selfishly updates its PA strategies to maximize its utility. The existence, stability, and uniqueness criteria for the proposed schemes are derived and proved. Simulations are used to corroborate analytical outcomes and uphold the success of proposed schemes in terms of optimum enhancing system energy and spectral efficiency over existing ones.

Soft fusion‐based cooperative spectrum sensing using particle swarm optimization for cognitive radio networks in cyber‐physical systems

SummaryAs a multi‐dimensional complex system, Cyber physical systems (CPS) integrates computing, network, and physical environment. How to effectively fuse the local detection results is the key to improve the sensing performance in CPS. In order to improve the spectrum sensing performance of cognitive radio, a soft fusion‐based cooperative spectrum sensing using particle swarm optimization (PSO) for cognitive radio networks is proposed. To find the optimal weighting coefficient of soft fusion, the traditional PSO algorithm is investigated and improved. To further reduce the local convergence of PSO and accelerate the convergence speed of the algorithm, the immune algorithm is introduced and chaotic sequence mechanism is applied to ensure the diversity of the particles. Moreover, the inertia weight is adjusted adaptively according to the convergence of particles so as to maintain the tradeoff between the search ability and the convergence of particles. Simulation results show that the proposed algorithm can achieve fast convergence in CPS. Compared with other typical methods, it can obtain better sensing performance under different signal‐to‐noise ratio environments and the constraint of false alarm probability.

Multi-objective optimization of cognitive radio networks

New generation networks, based on Cognitive Radio technology, allow dynamic allocation of the spectrum, alleviating spectrum scarcity. These networks also have a resilient potential for dynamic operation for energy saving. In this paper, we present a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism. Three Key Performance Indicators (KPIs) were optimized: spectrum usage, power consumption, and exposure. For a realistic suburban scenario in Ghent city, Belgium, we determine the optimal trade-off between the KPIs. Compared to a traditional Cognitive Radio network design, our optimization algorithm for the cloud-based architecture reduced the network power consumption by 27.5%, the average global exposure by 34.3%, and spectrum usage by 34.5% at the same time. Even for the worst-case optimization (worst achieved result of a single KPI), our solution performs better than the traditional architecture by 4.8% in terms of network power consumption, 7.3% in terms of spectrum usage, and 4.3% in terms of global exposure.

Dynamic Interference Optimization in Cognitive Radio Networks for Rural and Suburban Areas

In this paper, we investigate the coexistence of cognitive radio networks on TV white spaces for rural and suburban connectivity. Although experimental models and laboratory measurements defined the maximum interference threshold for TV white space technologies for general use cases, our research found that in real wireless rural and suburban scenarios, severe interference to the broadcasting services might occur. This is particularly relevant when the traffic load of the telecom base stations (BSs) exceeds 80% of their maximum capacity. We propose a dynamic management algorithm for minimizing the interference, based on a centralized access control architecture for cognitive radio wireless networks. In an experimental emulation for assessing the impact of cognitive radio interference on the broadcasting service’s QoE, our method reduced the perceived video distortion by the broadcasting users by at least 50% and 27.5% in a rural and suburban scenario, respectively, while the spectrum usage is increased by just 8%.

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

SummaryThe vacant licensed spectrum can be employed by the secondary users in cognitive radio networks. Nevertheless, this process of identification is frequently agreed with shadowing, multipath fading and receiver uncertainty problems. The primary contributions of the spectrum sensing techniques adopted in this work mainly concentrate on energy detection for spectrum sensing as well as to recognize the optimal spectral estimation according to the multitaper spectral estimation. The algorithm proposed in this work is based on bivariate Lévy‐stable bat algorithm (BLSBA), energy detector (ED), and with the help of K out of M fusion rule; it is analysed for the single user as well as cooperative multiple users. In the BLSBA algorithm, a modified search equation with more helpful information from the search experiences is brought‐in to create an optimal energy solution and bivariate Lévy‐stable random walk is associated with BLSBA to remove the trapping process into local optima. The energy detection is defined numerically from this optimal detection. At last, a multi‐taper spectral estimator (MSE) is proposed to cognitive radio detection for a huge network. The simulation results in both cases are computed and checked with the help of a BLSBA optimizer. For an individual secondary user scenario, the advancement of MSE to the ED is broadly described. Experimental result indicates that the objective false alarm likelihood is minimized and the demanded signal‐to‐noise ratio is accomplished to the extent.

Effective capacity optimization for cognitive radio networks under primary QoS provisioning

Cognitive radios have emerged as a key enabler for opportunistic spectrum access, in order to tackle the wireless spectrum scarcity and under utilization problems over the past two decades. In this paper, we aim to enhance the secondary user (SU) performance while maintaining the desired average packet delay for the primary user (PU). In particular, we investigate the trade-off between delay-constrained primary and secondary users in cog- nitive radio systems. In the first part of this work, we use the hard-sensing scheme to make a decision on the PU activity and maximize the SU effective capacity subject to an average PU delay constraint. Second, we propose a soft-sensing scheme by dividing the PU energy interval where the PU is decided to be idle into multiple decision. We also maximize the SU effective capacity subject to an average primary user delay constraint; then, we present three modifications for the proposed soft-sensing scheme to allow for low complexity implementation that is comparable to the complexity of the hard-sensing scheme, but with better performance. The numerical results reveal interesting insights comparing our soft sensing to the hard-sensing models in terms of the optimal performance obtained from our optimization solution compared to the unconstrained PU delay baseline system studied earlier in the literature. For instance, the hard sensing system in Akin and Gursoy (IEEE Trans Wirel Commun 9(11):3354–3364, 2010) and Abdel-Malek et al. (CrownCom 156:30–42, 2015) yields a SU effective capacity of only 50 % of the ideal, perfect sensing system. On the other hand, we show that the soft sensing system yields almost 87 % of the perfect sensing performance (at a primary user arrival rate of $$\lambda _p = 0.1$$), which further increases for a larger number of decision sub-intervals.

Performance analysis and optimization for cognitive radio networks with a finite primary user buffer and a probability returning scheme

In this paper, in order to reduce possible packet loss of the primary users (PUs) in cognitive radio networks, we assume there is a buffer with a finite capacity for the PU packets. At the same time, focusing on the packet interruptions of the secondary users (SUs), we introduce a probability returning scheme for the interrupted SU packets. In order to evaluate the influence of the finite buffer setting and the probability returning scheme to the system performance, we construct and analyze a discrete-time Markov chain model. Accordingly, we determine the expressions of some important performance measures of the PU packets and the SU packets. Then, we show numerical results to evaluate how the buffer setting of the PU packets and the returning probability influence the system performance. Moreover, we optimize the system access actions of the SU packets. We determine their individually and the socially optimal strategies by considering different buffer settings for PU packets and different returning probabilities for SU packets. Finally, a pricing policy by introducing an admission fee is also provided to coincide the two optimal strategies.

Throughput analysis and optimization of cognitive radio networks using incremental relaying

In this paper, we derive the throughput and Bit Error Probability of incremental relaying for cognitive radio networks. Relaying in the secondary network is performed only when the SNR of the direct link is lower than a predefined threshold $$\beta $$ . Also, all relays must verify interference constraints: the generated interference to primary receiver must be below a chosen threshold (T) so that the primary throughput is larger than a predefined value. The analysis is valid for different relay selection techniques: Opportunistic amplify and forward, opportunistic decode and forward, partial and reactive AF relay selection. We also consider multihop relaying with single and multiple branches. For multiple branches, two scenarios are considered: all branches are maximum ratio combined or the best branch is activated. We also take into account the interference from primary to secondary nodes. The parameter $$\beta $$ of incremental relaying is also numerically optimized to achieve the highest secondary throughput.

Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks

The cognitive radio network (CR) is a primary and promising technology to distribute the spectrum assignment to an unlicensed user (secondary users) which is not utilized by the licensed user (primary user).The cognitive radio network frames a reactive security policy to enhance the energy monitoring while using the CR network primary channels. The CR network has a good amount of energy capacity using battery resource and accesses the data communication via the time-slotted channel. The data communication with moderate energy-level utilization during transmission is a great challenge in CR network security monitoring, since intruders may often attack the network in reducing the energy level of the PU or SU. The framework used to secure the communication is using the discrete-time partially observed Markov decision process. This system proposes a modern data communication-secured scheme using private key encryption with the sensing results, and eclat algorithm has been proposed for energy detection and Byzantine attack prediction. The data communication is secured using the AES algorithm at the CR network, and the simulation provides the best effort-efficient energy usage and security.

Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users

In this paper, we consider a cognitive radio network with multiple secondary users (SUs). The SU packets in the system can be divided into two categories: SU1 packets and SU2 packets, where SU1 packets have transmission priority over SU2 packets. Considering the absolute priority of the primary users (PUs), the PU packets have the highest priority in the system to transmit. In order to guarantee the Quality of Service (QoS) of the network users, as well as reduce the average delay of the SU2 packets, we propose an adjustable access control scheme for the SU2 packets. A newly arriving SU2 packet can access the system with an access probability related to the total number of packets in the system. A variable factor is also introduced to adjust the access probability dynamically. Based on the working principle of the adjustable access control scheme, we build a discrete-time queueing model with a finite waiting room and an adjustable joining rate. With a steady-state analysis of the queueing model, using a three-dimensional Markov chain, we derive some performance measures, such as the total channel utilization, the interruption rate, the throughput, and the average delay of the SU2 packets. Moreover, we show the influence of the adjustment factor on different system performance measures by using numerical results. Finally, considering the trade-off between the throughput and the average delay of the SU2 packets with respect to the adjustment factor, we build a net benefit function and show an optimal algorithm to optimize the adjustment factor.

Truthful Double Auction for Joint Internet of Energy and Profit Optimization in Cognitive Radio Networks

With the development of cognitive radio networks in recent years, spectrum utilization has been enhanced, as secondary users can lease under-utilized spectrum from the spectrum owners. Spectrum is allocated through auctions in wireless communication networks. The auction can provide benefits for both primary users and secondary users. Existing auction mechanisms for spectrum are mainly based on interference graphs and consider the heterogeneity of spectrums only to a minimal degree. The economic efficiency of the auction is usually neglected due to the focus on improving spectrum utilization. In this paper, we consider a signal-to-interference-plus-noise ratio ( $\mathsf {SINR}$ ) constrained interference model; this model is more realistic as users can simultaneously communicate as long as their requirements $\mathsf {SINR}\text{s}$ are satisfied. We propose a truthful profit maximization double auction mechanism to improve the benefit of networks with low energy. At the same time, security concerns are guaranteed because buyers and sellers make their true critical decision, i.e., they cannot improve their utility by misreporting their asks and bids. Moreover, our proposed novel auction mechanism is individually rational and budget-balanced. The experiments demonstrate that our auction mechanism efficiently increases the number of winners and improves the auctioneer’s profit.

Performance analysis of cognitive radio networks for secondary users with slotted central control

This paper deals with analysis, performance evaluation and optimization of cognitive radio networks with central controller. The main principle of this technology is that secondary users are enabled to make opportunistic use of the spectrum part, which is actually unused by the primary users. The considered network enables heterogeneous slotted structure for the channel, in which the secondary user’s packets are transmitted on a slot basis, while the primary user’s packets are forwarded in super-slots, i.e. in fixed length slot-blocks. This heterogeneous slotted channel structure enables more flexible operation leading to more realistic system model of cognitive radio network. We model the cognitive radio networks by preemptive priority queueing model with two classes of customers. We solve the model by applying Markov chain technique and derive the steady-state distributions of the number of primary user’s packets and secondary user’s packets in the system. We provide the formulas for several performance measures including the interruption rate, loss rate, throughput, and average latency of secondary users. After validating the analysis by simulation the influence of the secondary user’s buffer capacity on various system performance measures is investigated. In the last part of the paper we address the question of optimal design of secondary user’s buffer capacity.

Performance analysis and optimization of cognitive radio networks with retransmission control

In usual cognitive radio networks with buffer settings, secondary user packets that are interrupted by primary user packets will return to the SU buffer for later retransmission. But this may increase the average delay of the SU packets. In this paper, we propose a novel spectrum sharing strategy (SSS) to dynamically control the retransmission of the SU packets by introducing a returning threshold and a returning probability. This will simultaneously guarantee the Quality of Service for the SUs. In this SSS, when the transmission of an SU packet is interrupted, if the number of SU packets already in SU buffer reaches a returning threshold that is set in advance as a system parameter, an interrupted SU packet is admitted to return to the SU buffer with a dynamic returning probability. This returning probability is inversely proportional to the total number of packets in the system. Based on the SSS with retransmission control proposed in this paper, we build a discrete-time preemptive priority queueing model to comply with digital nature of modern networks. Accordingly, by presenting and analyzing a two-dimensional discrete-time Markov chain that represents the system state transition, we obtain the steady-state distribution of the system and then provide the formulas for several system performance measures. Moreover, with numerical results, we show the influence of the returning threshold on different performance measures. Finally, by building a net benefit function, we optimize the system performance for the returning threshold to balance different system performance measures.

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

Throughput Optimization in Cognitive Radio Networks Ensembling Physical Layer Measurement

Wireless networks are developed under the fashion of wider spectrum utilization (e.g., cognitive radio) and multi-hop communication (e.g., wireless mesh networks). In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks (CRNs). The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS (receiving signal strength) among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers: in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized (1 − e)-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme.

Cross-layer optimization with MIPv6-based multiple mobile routers for cognitive networks

To allow secondary and primary concurrent transmissions achieving the increment of optimal mobile network performance in heterogeneous cognitive networks, this paper proposes a cross-layer optimization framework with a distributed cooperative MIPv6-based multiple mobile routers using adaptive opportunistic route optimization entitled Mobile IPv6 Route Optimization for Cognitive radio networks (MIROC) protocol. A gripping slant of MIROC protocol is the benefit to develop mobile cognitive routers based on MIPv6. The proposed protocol is elaborated in details from angular RO and multiangular routing which can be utilized in practical network deployment. The performance in M/M/n and M/M/? Queue and evaluation of cognitive mobile Network with multiple mobile routers using stochastic process in Markov chain model are analyzed.

Wide-Band Sensing and Optimization for Cognitive Radio Networks With Noise Variance Uncertainty

This paper considers wide-band spectrum sensing and optimization for cognitive radio (CR) networks with noise variance uncertainty. It is assumed that the considered wide-band contains one or more white sub-bands. Under this assumption, we consider throughput maximization of the CR network while appropriately protecting the primary network. We address this problem as follows. First, we propose novel ratio based test statistics for detecting the edges of each sub-band. Second, we employ simple energy comparison approach to choose one reference white sub-band. Third, we propose novel generalized energy detector (GED) for examining each of the remaining sub-bands by exploiting the noise information of the reference white sub-band. Finally, we optimize the sensing time (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> ) to maximize the CR network throughput using the detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to noise ratio (SNR) wall and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED) is quantified analytically. We show that the optimal To depends on the noise variance information. In particular, with 10TV bands, SNR = -20 dB and 2s frame duration, we found that the optimal T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> is 28.5 ms (50.6 ms) with perfect (imperfect) noise variance scenario.