Cognitive Radio Systems Research Articles

Estimation of Distribution of Primary Channel Periods Based on Imperfect Spectrum Sensing in Cognitive Radio

Spectrum occupancy modeling plays an important role in improving the performance of Cognitive Radio (CR) systems, because most of the methods to improve Dynamic Spectrum Access (DSA) performance need to make assumptions about the channel periods’ distribution. However, Secondary Users (SUs) cannot directly observe the accurate channel periods’ distribution due to the sensing errors caused by noise, which will lead to inaccurate analysis of transmission efficiency or throughput. In this paper, we analyze the influence of different types of sensing errors on the observed channel periods, and establish the relationship between the Probability Mass Function (PMF) of idle periods under Imperfect Spectrum Sensing (ISS) and the results under Perfect Spectrum Sensing (PSS). In addition, we derive a closed-form expression for estimating the PMF of channel periods from the sensing results under ISS. Simulation results show that the proposed estimation method is more accurate than the existing methods, and is not affected by the sensing period, channel mean period and sensing error probability.

AI-Managed Cognitive Radio Digitizers

Embedding Artificial Intelligence (AI) in integrated circuits is one of the technology pillars of the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">digital transformation</i> . Nowadays, the vast majority of electronic devices benefits from digital signal processing to implement more and more functionalities, which can be further enhanced by the action of AI algorithms and artefacts. Moreover, as the analog/digital interfaces are moving closer and closer to the point where the information is either acquired or transmitted, the so-called AI-managed data converters are becoming key building blocks in an increasingly number of interconnected <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyberphysical</i> systems–made up of both software and hardware components. Software Defined Radio (SDR) and Cognitive Radio (CR) systems intended for 5G/6G communications are good examples which can benefit from an early digitization managed by AI engines. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this context, this paper presents an overview of circuits and systems techniques for AI-managed analog/digital interfaces with application in SDR/CR mobile telecom systems. Some design trends and challenges are discussed, going from new communications and computing paradigms for AIoT devices and networks, to digital-based/scaling-friendly analog circuit techniques for an efficient digitization. The state of the art on Analog-to-Digital Converters (ADCs) is surveyed, putting emphasis on highly-programmable Sigma-Delta Modulators (<inline-formula><tex-math notation="LaTeX">$\Sigma\Delta$</tex-math></inline-formula>Ms) as one of the best ADC candidates for SDR/CR transceivers. Some chip examples are shown to illustrate their potential application in AI-enhanced CR end-devices.

Joint Design of Improved Spectrum and Energy Efficiency With Backscatter NOMA for IoT

To develop emerging transmission techniques for potential applications of Internet of Things (IoT), the system performance analysis of a cognitive radio (CR)-enabled ambient backscatter (AmBC) system will be studied in this paper with functionality of non-orthogonal multiple access (NOMA). In the proposed scheme, a base station communicates with two destinations via a designated backscatter device. It is assumed that the relay node is fitted with two different interfaces and can simultaneously collect/decode and backscatter the received source signals. Such transmission mechanism benefits to design various applications in IoT as well as wireless systems with improved performance. To exhibit system performance metrics, the outage probability and the ergodic capacity of the recipient nodes are derived analytically. Furthermore, it is shown that employing AmBC NOMA together with CR for secondary communication can significantly improve overall network performance in terms of the achievable throughput in delay-limited and delay-tolerant modes and outage probability. Numerical results show that: <i>1) The proposed system can improve the spectrum efficiency by employing both CR and NOMA techniques; 2) Compared with the orthogonal multiple access (OMA)-aided AmBC systems, the considered CR AmBC system relying on NOMA can obtain better reliability in the whole range of SNR; 3) There are error floors for the outage probability in the high SNR regime due to required target rates; 4) There exists a trade-off between system performance of IoT devices and power allocation coefficients associated with NOMA; 5) We find energy efficiency factor as evident of further improvement in such system</i>.

Tunable Filtenna With DGS Loaded Resonators for a Cognitive Radio System Based on an SDR Transceiver

This paper introduces the design of a filtenna for cognitive radio systems. Four varactor diodes, which are inserted in the bandpass filter, are used to achieve the desired tunability. The proposed bandpass filter is integrated with a wideband monopole antenna. The bandpass filter is achieved by using two coupled defected ground structure (DGS) resonators and two coupled microstrip lines ended with two stubs. The monopole antenna is designed to operate in a frequency band from 1.3 GHz to 3 GHz. The four varactor diodes are used to tune the antenna resonance frequency from 2.7 GHz to 2 GHz when the capacitance increases from 1.55 pF to 2.67 pF. The performance of the proposed filtenna is validated by simulation and measurements from a fabricated prototype. Finally, the proposed filtenna is tested using the Software Defined Radio (SDR) platform bladeRF. This transceiver can transmit and receive in a wide frequency range of 0.3 GHz up to 3.8 GHz, which makes it suitable for testing the proposed reconfigurable filtenna.

Outage and Error-Rate Analysis of NOMA-Inspired Multicarrier Underlay Cognitive Radio System

In this paper, the performance of a multicarrier underlay cognitive radio system is analyzed considering that the successive interference cancellation (SIC) method is used for detection. In the proposed system model, a multicarrier-based secondary user (SU) uses all the subcarriers of the primary user (PU) in underlay mode, guaranteeing the quality of service (QoS) to the primary system. The power allocation strategy is modified accordingly. With a fixed PU transmit power, power is allocated to the SU subcarriers to maximize its throughput while ensuring minimum throughput to PU. The system is analyzed for its performance considering an L-tap multipath Rayleigh fading channel. Analytical expression for outage probability at SU is derived in closed-form. Also, analytical expressions and closed-form approximations for average symbol error rates (SER) per subcarrier of PU and SU are obtained. The derived expressions are validated with the Monte-Carlo (MC) simulations. Further, the proposed scheme was implemented in an orthogonal frequency division multiplexing (OFDM)-based system with different detection schemes used at the receiver. SERs were compared through extensive simulations and it was noticed that SIC with channel estimation based on minimum-mean-squared-error (MMSE) often performed better than that based on least squares (LS) estimation. Observations reveal that the nominal error rates were achieved on the optimal choice of the parameters. The proposed system ensures sufficient throughput to SU, meanwhile eliminating the need for opportunistic sensing.

Improved Multifunctional MIMO Cognitive Radio System for Integrated Interweave-Underlay Operations

This article presents the design of an integrated system involving multiple-input–multiple-output (MIMO) and cognitive radio (CR) technologies for sub-6 GHz applications. Two types of CR techniques, i.e., interweave and underlay CR, have been discussed in this article. The design model has a four-port multifunctional MIMO antenna system involving lumped elements for three selectable operation choices. Based on the switching conditions, the MIMO antenna system can operate as a wideband antenna (for sensing purpose), or narrowband frequency tunable antenna (for communicating purpose in interweave CR), or a wideband antenna with frequency tunable band notch (for communicating purpose in underlay CR). The sensing frequency range is 2.45–5.3 GHz. The interweave CR communicating antenna frequency tuning range is 3.6–4.2 GHz. To cover the entire desired spectrum range, i.e., from 2.5 to 4.2 GHz, another four-port narrowband MIMO antenna system with resonance frequency tuning from 2.5–3.6 is designed on the other/backside of the substrate. In the underlay CR communicating antenna, the stopband frequency tuning range is 3.1–3.85 GHz. The complete structure is having dimensions of 110 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 70 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1.52 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The measurements are performed, and good matching is found between simulated and measured results.

Estimation of Primary Channel Mean Period Based on State Transition Probability in Cognitive Radio

Primary channel mean period plays an important role in improving the performance of Dynamic Spectrum Access (DSA), because many algorithms to improve the performance of Cognitive Radio (CR) need to use the channel mean period as a prior knowledge. Secondary Users (SUs) can obtain statistics of the primary channel by spectrum sensing. However, SUs’ estimation of the mean period is inaccurate due to errors in the spectrum sensing in the real world, which will lead to performance degradation of CR systems. In this paper, we use a two-state Markov chain to model channel states, and use state transition probability to analyze the influence of sensing errors on the mean period. At the same time, we derive the estimation formula of the mean period of the original channel. Simulation results confirm that the proposed estimation method is superior to the existing estimation methods, and can accurately estimate the original period even with high sensing error probability.

Secure Communication for Cognitive Networks Based on MIMO and Spreading LDPC Codes

The existing cognitive radio (CR) systems lack the ability to deal with the attack of a pretended primary user (PPU), which can palsy cognitive network. To solve the problem, in this paper, a secure cognitive radio (CR) system is proposed. The proposed systems combines MIMO and spread spectrum techniques as well as low density parity check (LDPC) codes to cancel the electronic interference of PPU. The simulation results in Rayleigh flat-fading channel show that, comparing with MIMO cognitive system without the proposed method, the proposed approach can effectively cancel electronic interference from enemy and channel noises interference, and obtain about 9dB gain.

An adaptive learning algorithm for spectrum sensing based on direction of arrival estimation in cognitive radio systems

&lt;p class="Abstract"&gt;In cognitive radio systems, estimating primary user direction of arrival (DOA) measurement is one of the key issues. In order to increase the probability detection multiple sensor antennas are used and they are analysed by using subspace-based technique. In this work, we considered wideband spectrum with sub channels and here each sub channel facilitated with a sensor for the estimation of DOA. In practical spectrum sensing process interference component also encounters in the sensing process. To avoid this interference level at output of receiver, we used an adaptive learning algorithm known as Normalised Least Absolute Mean Deviation (NLAMD) algorithm. Further to achieve better performance a bias compensator function is applied in weight coefficient updating process. Using this hybrid realization, the vacant spectrum can be sensed based on DOA estimation and number of vacant locations in each channel can be identified using maximum likelihood approach. In order to test at the diversified conditions different threshold parameters 0.1, 0.5, 1 are analysed.&lt;/p&gt;

Online Primary User Emulation Attacks in Cognitive Radio Networks Using Thompson Sampling

Spectrum sensing is one of the main components of the cognitive radio (CR) system, based on which secondary users (SUs) can get access to spectrum holes available. On the other hand, a malicious adversary can also attack the primary user (PU) system and the legitimate CR system via spectrum sensing, which can lead to serious security issue for both systems. In this article, we study an online attacking strategy, referred to as PU emulation attacker (PEA), which transmits forged PU signals over available channels to deteriorate the spectrum sensing performance of the SUs. We propose an online learning based attacking scheme for both the single attacker and the multiple-attacker cases, and analyze the regret upper bound of the proposed algorithm. The proposed PEA strategy can work in both stationary and non-stationary CR networks where the statistical characteristics of channels and the access strategy of SUs change over time. Numerical results show that it is more efficient than others in two different performance metrics: successful accesses of SUs and effective attacks of PEAs. Our proposal raises an interesting open question on how to develop CR networks with security guarantee.

An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime.

With the recent explosion in the number of wireless communication technologies, the frequency spectrum has become a scarce resource. The need of the hour is an efficient method to utilize the existing spectrum and Cognitive Radio is one such technology that can mitigate the spectrum scarcity. In a cognitive radio system, the unlicensed secondary user accesses the spectrum allotted to licensed primary users when it lies vacant. To implement dynamic or opportunistic access of spectrum, secondary users perform spectrum sensing, which is a quintessential part of a Cognitive radio. From the Cognitive user’s point of view, lesser error probability means an increased likelihood of channel reuse when it is vacant, and a higher detection probability signifies better protection to the licensed users. In both cases the decision threshold plays a pivotal role in determining the fate of the unused spectrum. In this paper, we study the difficulty of selecting an appropriate threshold to minimize the error probability in an uncertain low SNR regime. The sensing failure issue is analyzed, and an optimal threshold is computed that yields minimum error rate. An adaptive double threshold concept has been proposed to make the detection robust and a closed-form equation for optimal threshold has been derived to minimize the error. The novel findings through simulation results exhibit improvement in Probability of detection and reduction in probability of error at low SNR in the presence of noise uncertainty factor.

Development of a Mathematical Model for Multi-user Coded-Cooperation Based Cognitive Radio System and Its Outage Probability Analysis

In this paper, mathematical system model for a single and multi-user coded-cooperation-based cognitive radio system is developed. In this model, both source and relay are communicating to a single destination with the help of each other. Further, all possible multi-user scenarios are developed and their end-to-end outage probability (Pout) is calculated for the underlay mode of cognitive radio. The performance of the system is analyzed in the form of channel gain and interference temperature constraint for the Rayleigh fading channel. The proposed system concludes that the coded cooperation with cognitive radio outperforms the available techniques in the form of bandwidth, diversity, spectrum utilization efficiency and thus improving the quality of communication. Furthermore, the theoretical analysis of the outage probability for both system models is validated by asymptotic analysis. The proposed system can be set as a standard for all those cognitive radio applications which require better spectrum efficiency even if there is a scarcity of multiple physical antennas.

Threshold optimisation with Bayesian approach in covariance absolute value detection method

ABSTRACT This study investigate statistical properties of Bayes cost, it proposes new group-based detection models for Cognitive Radios (CR) systems using this statistic. In commonly used spectrum sensing applications, multi-user or multi-antenna detection scenario with different optimisation methods are recommended to increase the detection under high SNRs. In this study, the optimum threshold for Covariance Absolute Value detection methods is determined using the Bayesian Cost. Thus, the detection scenario is given for the proposed method and the results are compared with the traditional Covariance Absolute Value method. Simulations based on random communication signals are presented to verify the methods.

A highly efficient approach for performance enhancement of multiple antenna elements based spectrum sensing techniques using side lobe level reduction

Spectrum sensing (SS) is substantial demand for enabling cognitive radio (CR) systems. In recent years, digital beamforming (DBF) is exploited for performance enhancement of multiple antenna elements or uniform linear array (ULA) based spectrum sensing techniques. Generally, a ULA suffers from its high side lobe level (SLL) and has a relatively low realized array gain. Subsequently, the ULA-based CR receiver is compelled to provide low detection performance. Furthermore, the efforts to increase the realized array gain are still not sufficient to dramatically increase the detection performance, because the problem of high SLL remains a challenging issue. In this paper, a highly efficient approach is introduced for performance enhancement of ULA-based SS techniques using side lobe level reduction (SLLR) beamforming that is applied to the receiving ULA. Worthy, a large SLLR combined with maintaining the same half power beamwidth (HPBW) as the original array, results in a synthesized array with a significantly improved gain. Thence, both the received signal-to-noise ratio (SNR) and signal-to-interference plus noise ratio (SINR) of the combined received signal are significantly enhanced at the CR receiver, which would considerably increase the detection capability of the proposed system. The proposed approach is investigated by integrating the SLLR-based antenna array with the generalized likelihood ratio test/direction of arrival (GLRT/DOA)-based SS technique that is denoted as GD technique. In contrast to the ULA, the synthesized array's excitation coefficients are non-uniform, necessitating special signal processing to incorporate it in the received signal model and derive closed-form expressions for the probability of detection, probability of false alarm, and decision threshold, all of which are already reported in this work. Several simulation scenarios are performed to validate the supremacy of the proposed SS approach compared to the traditional single antenna element based techniques and other state of the art beamforming based techniques.

A two‐fold optimization framework using hybrid B2 algorithm for resource allocation in long‐term evolution based cognitive radio networks system

SummaryCognitive radio networks (CRNs) have allowed efficient spectrum sharing while also presenting new challenges to the conventional problems of resource allocation and wireless network interference management. The aim is to provide wireless connectivity to secondary stations on a best‐effort basis while preserving the primary station's service quality. In order to achieve this objective, we have developed a model using hybrid B2 optimization. This method is used for resource allocation in a CRN based on the long‐term evolution (LTE) standard platform of the Third Generation Partnership Project (3GPP). The hybrid B2 optimization algorithm is formed by integrating the bacterial foraging and black widow optimization algorithm. Initially, to optimize the level of service for primary customers, spectrum resources are allocated to primary stations using our proposed model. Later, the spare capacity of the primary channels is then spread among the secondary stations in order to increase the secondary users' service efficiency. The proposed work is twofold where it handles both the low traffic and high traffic scenarios. To reduce the prediction error in a high traffic and bursty environment, an adaptive weighted least square support vector machine (AWLSSVM) is presented. Furthermore, the proposed model is compared with other existing models to evaluate the system's effectiveness.

Cooperative Sensing and Allocation Algorithm of Cognitive Radio Spectrum Based on Artificial Intelligence

In recent years, with the rapid development of artificial intelligence technology, people’s demand for wireless spectrum resources is increasing, which poses a huge challenge to the originally tight and limited wireless spectrum resources. On the other hand, the traditional fixed spectrum cooperative sensing and allocation algorithms result in extremely low spectrum utilization for a considerable part of the licensed spectrum. The purpose of this paper is to study the cooperative sensing and allocation algorithm of cognitive RS (radio spectrum) based on artificial intelligence. This dissertation focuses on cooperative perception and cognitive radio systems, respectively, from the aspects of cooperative perception of user fairness, maximization of system energy efficiency, and user detection when user access is busy. Firstly, a joint optimization model of fairness cooperative spectrum sensing and allocation is established to compensate the sensing overhead of cooperative users to ensure its fairness; then, define and analyze the energy efficiency of the cognitive system, and establish a joint optimization model of cooperative spectrum sensing and allocation based on artificial intelligence to maximize energy efficiency, and optimize wireless sensing and allocation parameters while ensuring maximum system energy efficiency. The experimental results show that when = 0.7, the algorithm proposed in this study has reached 100% of the RS perception performance, while the traditional algorithm only has 93%. The algorithm proposed in this paper has greater advantages in perception and distribution performance.

Evaluation of Primary User Power Impact for Joint Optimization of Energy Efficiency in Cognitive Radio Networks

Energy efficiency (EE) is of great concern in cognitive radio networks since the throughput and energy consumption of secondary users (SUs) vary with the sensing time. However, the conditions of the detection probability and false alarm probability should be respected to better protect primary users (PUs) and to improve the sensing performance of SUs. Additionally, the PUs’ minimum averaged power provision should also be regarded as a key problem of interactive linking to SUs. Therefore, an integrated design between the PU and SUs is desired for the coordination of the whole cognitive radio system, especially regarding the satisfaction of EE and performance metrics. This study formulates sensing constraints in a unified way and calculates the minimum SNR of SUs, based on which the essential PU power provision is computed. Furthermore, EE is proved as a decreasing function with the PU’s active ratio, where the maximum EE is obtained corresponding to the minimum QoS requirements of the sensing process. Hence, a bisection-based method is proposed to maximize EE, which is considered as a concave function of SUs’ sensing time and has only one unique optimum. EE’s optimization was analyzed under different fusion rules for diverse SNR conditions. The optimum was also studied with sensing performance targets for various cases of PU power provision.

Quality of service optimization in orthogonal frequency division multiplexing‐based cognitive radio systems based on shuffled frog leaping algorithm

AbstractIn this article, we propose to use shuffled frog leaping algorithm (SFLA) in order to provide the quality of service (QoS) requested by the secondary user (SU) in orthogonal frequency division multiplexing‐based cognitive radio (CR) systems. The performance of the SFLA was evaluated through three transmission modes (emergency, multimedia, and low battery). SFLA provided the best possible configuration in a multicarrier context and thus proved its effectiveness to satisfy the needed QoS for the SU in CR systems. The obtained simulation results showed that SFLA offers better performance compared with genetic algorithms.

Broadband Instantaneous Multi-Frequency Measurement Based on a Fourier Domain Mode-Locked Laser

Broadband instantaneous multi-frequency measurement based on frequency-to-time mapping using a Fourier domain mode-locked (FDML) laser source is proposed and experimentally demonstrated. An electrically controlled silicon microdisk resonator (MDR) with an ultra-narrow linewidth of 60 pm (~7.5 GHz) functioning as a fast tunable optical filter is used to implement the FDML to generate a linearly chirped optical waveform (LCOW) with a wide frequency-sweeping range of 0.5 nm. The LCOW is then mixed at a modulator with a microwave signal with its frequency to be measured, detected at a low-speed photodetector (PD) and sent to a narrow bandpass filter (BPF). When the difference between the instantaneous frequency of the LCOW and that of the signal to be measured is equal to the center frequency of the BPF, a short-duration temporal signal is produced, and the time location of the temporal signal represents the frequency of the signal to be measured. A proof-of-concept experiment is carried out. Both single and multi-tone microwave frequency measurements are experimentally demonstrated. The measurement range is as large as 20 GHz with a measurement resolution of 200 MHz and an accuracy better than ±100 MHz. The proposed method showcases a new method for instantaneous frequency measurement (IFM) with high performance in terms of multi-frequency identification, real-time measurement, and high measurement speed compared with traditional approaches, which is attractive for applications in modern radar, electronic warfare, communication, and cognitive radio systems.

Design of an Novel Spectrum Sensing Scheme Based on Long Short-Term Memory and Experimental Validation

Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and derive certain detection performance. To deal with this uncertainty, learning based approaches are being adopted and more recently deep learning based tools have become popular. Here, we propose an approach of spectrum sensing which is based on long short term memory (LSTM) which is a critical element of deep learning networks (DLN). Use of LSTM facilitates implicit feature learning from spectrum data. The DLN is trained using several features and the performance of the proposed sensing technique is validated with the help of an empirical testbed setup using Adalm Pluto. The testbed is trained to acquire the primary signal of a real world radio broadcast taking place using FM. Experimental data show that even at low signal to noise ratio, our approach performs well in terms of detection and classification accuracies, as compared to current spectrum sensing methods.