Cognitive Radio Receiver Research Articles

Analysis of NR and LTE Target Signal Structure Based on Neural Network Approach and Deep Learning Methods

Purpose: the rapid development and consolidation of broadband wireless access networks of the fourth 4G LTE and fifth 5G NR generations determine the need to find ways to reuse frequencies. A well-known solution to this problem is the concept of cognitive radio. The use of artificial intelligence in radio networks in general and the use of a neural network approach with deep learning for recognizing signals of LTE and NR standards in particular have serious potential for the practical implementation of frequency resource reuse according to the concept of cognitive development. The aim of the work: is to analyze models, methods and algorithms that allow recognizing signals of the NR and LTE standards based on recording samples of radio signals obtained using pre-recorded NR and LTE signals in MatLab software. In this paper, the procedures for scanning and recognizing sections of the spectrum are considered. The operations of training a semantic segmentation network for the identification of these signals in a broadband spectrogram and the use of a trained network for subsequent spectrum sensing and recognition of signals of the NR and LTE standards are investigated. Methods: the method used to test the concept of cognitive radio is spectrum sensing, which results in information about the occupancy of frequency bands in a given location by primary users, and the identification of spectrum sections available for use by secondary users without interfering with the work of primary users in real time. The novelty of the work is the use of a neural network approach in the analysis of the target NR and LTE signal. Results: the model, trained on the basis of a neural network approach and deep learning methods, is able to distinguish between signals of the NR and LTE standards. Theoretical / Practical relevance: the software implementation of spectral sensing is implemented using an extension package in the MatLab environment and allows for experimental testing of a cognitive radio receiver when performing procedures for analyzing the structure of the target signal of the NR and LTE standards based on a neural network approach and deep learning methods.

Enhancing spectrum utilization and primary user detection in cognitive radio networks through greedy cooperative spectrum sensing

The utilization of the spectrum is optimized through which primary users of modern wireless communication technologies might obtain a higher chance of detection. The research aims to study how the NI-USRP hardware platform can be used to set up greedy cooperative spectrum sensing for cognitive radio networks. Research primarily deals with energy detection and eigenvalue-based detection approaches, both of which are highly recognized for their capacity to sense the spectrum without having prior knowledge of the primary user signals. In the hardware arrangement, there is one transmitter and two cognitive radio receivers. LABVIEW makes it simple to deploy and maximizes the detection probability across a large sample. Here, it was demonstrated that cooperative spectrum sensing is superior to non-cooperative spectrum sensing, which results in a reduction in the risk of errors occurring during detection. The research discovered that the OR combination rule has a higher detection probability than the AND rule at the same time. The research emphasizes the significance of expanding cooperative spectrum sensing to improve overall detection capabilities. SNRs that are more than 10 dB allow the energy detector to operate, and the eigenvalue detector continues to work when the SNR drops to –9 dB.

Eigenvalue-Based Spectrum Sensing Under Correlated Noise for Multi-dimensional Cognitive Radio Receiver

Eigenvalue-Based Spectrum Sensing Under Correlated Noise for Multi-dimensional Cognitive Radio Receiver

Attentive Siamese Networks for Automatic Modulation Classification Based on Multitiming Constellation Diagrams.

Automatic modulation classification (AMC) is an essential part in a cognitive radio receiver. Benefited from the discriminative constellation characteristics among most modulations, AMC methods based on constellation diagrams usually achieve pleasant performance. However, in noncooperation communication systems, constellation diagrams expressing modulations explicitly are difficult to obtain via blind symbol timing synchronization, especially in complicated wireless channels. Therefore, this article proposes a novel constellation diagram-based AMC architecture called attentive Siamese networks (ASNs) by considering multitiming constellation diagrams (MCDs) and selecting the proper symbol timings at the feature level, which is a more robust way than the conventional signal-level symbol timing synchronization. In detail, convolutional neural networks sharing the same parameters first extract deep feature vectors for MCDs. Then, an attention inference module weights all the deep feature vectors. Finally, AMC is realized based on the weighted feature vectors. Moreover, the ASN architecture can be trained end-to-end. Comparing with the state-of-the-art methods that take diverse representations of received baseband signals as input, experimental results based on the RadioML 2018.01A dataset and non-Gaussian noise dataset demonstrate that ASN achieves a remarkable improvement, whose classification accuracy goes over 99% when the signal-to-noise ratio (SNR) > 10 dB.

USRP RIO-Based Testbed for Real-Time Blind Digital Modulation Recognition in MIMO Systems

Modulation recognition is one of the key elements in the cognitive radio (CR) technology. Chiefly, automatic modulation recognition (AMR) is a challenging task in such systems. It refers to blindly identifying the modulation type by a CR receiver which is highly recommended for military as well as civil applications, e.g. security, threat analysis. In this letter, a comprehensive testbed is provided. It aims at assessing the AMR performance in near real-world multiple-input multiple-output (MIMO) communication scenario, as a first step for industrial integration and commercialization. Our testbed is based on the Software-Defined Radio (SDR) technology where the radio reconfigurability feature is warranted by integrating the NI-2954R Universal Software Radio Peripheral (USRP) reconfigurable I/O (RIO) boards. The different processing modules of the designed testbed have been implemented within a scalable and flexible architecture to realize real-time recognition. It also serves as an efficient tool to bridge the gap between theory and practice. Empirical results showed that the proposed testbed yields high recognition accuracy and strong robustness.

Elite-CAM: An Elite Channel Allocation and Mapping for Policy Engine over Cognitive Radio Technology in 5G.

The spectrum allocation in any auctioned wireless service primarily depends upon the necessity and the usage of licensed primary users (PUs) of a certain band of frequencies. These frequencies are utilized by the PUs as per their needs and requirements. When the allocated spectrum is not being utilized in the full efficient manner, the unused spectrum is treated by the PUs as white space without believing much in the concept of spectrum scarcity. There are techniques invented and incorporated by many researchers, such as cognitive radio technology, which involves software-defined radio with reconfigurable antennas tuned to particular frequencies at different times. Cognitive radio (CR) technology realizes the logic of the utility factor of the PUs and the requirements of the secondary users (SU) who are in queue to utilize the unused spectrum, which is the white space. The CR technology is enriched with different frequency allocation engines and with different strategies in different parts of the world, complying with the regulatory standards of the FCC and ITU. Based on the frequency allocation made globally, the existing CR technology understands the nuances of static and dynamic spectrum allocation and also embraces the intelligence in time allocation by scheduling the SUs whenever the PUs are not using the spectrum, and when the PUs pitch in the SUs have to leave the band without time. This paper identifies a few of the research gaps existing in the earlier literature. The behavioral aspects of the PUs and SUs have been analyzed for a period of 90 days with some specific spectrum ranges of usage in India. The communal habits of utilizing the spectrum, not utilizing the spectrum as white space, different time zones, the requisites of the SUs, the necessity of the applications, and the improvement of the utility factor of the entire spectrum have been considered along with static and dynamic spectrum usage, the development of the spectrum policy engine aligned with cooperative and opportunistic spectrum sensing, and access techniques indulging in artificial intelligence (AI). This will lead to fine-tuning the PU and SU channel mapping without being hindered by predefined policies. We identify the cognitive radio transmitter and receiver parameters, and resort to the same in a proposed channel adaption algorithm. We also analyze the white spaces offered by spectrum ranges of VHF, GSM-900, and GSM-1800 by a real-time survey with a spectrum analyzer. The identified parameters and white spaces are mapped with the help of a swotting algorithm. A sample policy has been stated for ISM band 2.4 GHz where such policies can be excited in a policy server. The policy engine is suggested to be configured over the 5G CORE spectrum management function.

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.

Simulation and comparison of single and differential ended CG-CS LNA for Cognitive Radio

In this paper, comparison of common gate-common source single ended and differential ended Low Noise Amplifier (LNA) for Cognitive Radio (CR) receiver is presented. LNA for CR receiver architectures operating over a frequency range of 50–900[Formula: see text]MHz is designed for narrow band applications. In this work, Noise figure (NF), Third-Order Intercept Point (IIP3), voltage gain and scattering parameters (SP) have been analyzed. The voltage gain is 10.16866[Formula: see text]dB for single ended and 29.21371[Formula: see text]dB for differential ended, NF is 1.012[Formula: see text]dB for single ended and 3.8[Formula: see text]dB for differential ended. Power gain (S21) obtained is 15.67481[Formula: see text]dB for single ended and 14.89[Formula: see text]dB for differential ended. Compression point is [Formula: see text][Formula: see text]dB for single ended and [Formula: see text][Formula: see text]dB for differential ended, Third-Order Intercept Point (IIP3) is 1.97705 for single ended and 5.01608[Formula: see text]dB for differential ended amplifier, respectively. The design has been implemented using CMOS technology with cadence virtuoso design environment/automation tools.

Multi-Dimensional Small-Scale Cooperative Spectrum Sensing Approach for Cognitive Radio Receivers

Cognitive radio (CR) is one of the most important emerging technologies that have been introduced to meet the heavy data traffic of future wireless networks. Effective implementation of CR networks strongly requires devolution of efficient spectrum sensing (SS) techniques. Recently many SS techniques based on the utilization of multiple antenna elements (MAE) and matched filtering (MF) at the CR receiver are emerged due to their high detection performance. In this paper, a multi-dimensional small-scale cooperative SS approach based on the use of MAE, MF, and DOA estimation is proposed. The multi-dimensional detection is performed by identifying both the primary user (PU) signal and its DOA, which corresponds to the temporal domain and spatial domain, respectively. The use of MAE provides many versions of the received PU signal, which considered as the backbone of many signal processing operations such as DOA estimation of PU signals. DOA estimation is based on calculating time difference of arrival (TDOA) between the output correlation signals coming from fast convolution blocks. The received PU signal versions are also used as inputs for separate MF detectors to obtain various decisions taking the advantage of the spatial diversity at the CR receiver antennas. Moreover, the estimated DOA of the PU signal is used to make a combination of the signal versions emanating from each antenna branch, using the delay and sum combiner to produce a strong signal with high signal to noise ratio (SNR). This strong signal is also used as input to a separate MF to produce an accurate decision. Finally, the principle of cooperation is achieved by combining these various decisions to obtain a reliable decision. Several simulation scenarios are carried out to verify the superiority of the proposed SS approach compared to the traditional MAE based SS technique under extremely low SNR regimes and high interference. Moreover, the simulations and theoretical closed-form expressions are highly matched together.

KL distance function over GLRT for a multi-antenna-based cognitive radio

ABSTRACT Cognitive Radio (CR) has been identified as a promising technology for recycling underutilized frequency bands. Proper detection of frequency voids a.k.a. spectrum sensing is the fundamental necessity to accomplish the objective. In this article, spectrum sensing for a multi-antenna based CR receiver has been considered. Initially, the performance of a Generalized Likelihood Receiver (GLR) has been demonstrated in conventional approach. Here a test statistic is formulated for binary hypothesis testing and respective false alarm and detection probabilities are calculated. Later, the same analysis has been carried out by alternative Kullback-Leibler (KL) distance function. It is shown that the proposed alternative method provides better detection probability for the same false alarm probability especially to identify weak primary signals. Numerical results are provided in support of the proposition.

Enhancing localization accuracy of collaborative cognitive radio users by internal noise mitigation

Location information of mobile primary users is one of the essential requirements for an underlay cognitive radio user to utilize the licensed spectrum efficiently. The performance of various location-based applications such as global navigation satellite system, device to device communication in dense urban 5G network also depends on the localization accuracy. In this paper, a collaborative localization scheme based on received signal strength has been proposed. The weighted centroid localization algorithm has been applied in the proposed network scenario to compute location coordinates of the mobile primary user. Since the channel noise effects are random and unavoidable, this paper has focused on the mitigation of the internal noise by designing a suitable reconfigurable FIR filter after the demodulator stage of a cognitive radio receiver circuit to improve precision of signal measurement during primary user localization. The localization error rate has come down to (1.3–1.62) % after internal noise mitigation. The enhancement in the localization accuracy improves the overall spectrum utilization efficiency and reduces the miss detection and false detection probabilities in the proposed underlay network.

A Power-Efficient Spectrum-Sensing Scheme Using 1-Bit Quantizer and Modified Filter Banks

Spectrum sensing is an efficient way to determine the spectrum availabilities over the frequency range of interest, aiding in improving the spectrum utilization in the cognitive radio (CR) systems. Conventional Nyquist multiband sensing entails higher computational capability for sampling, quantization, and subsequent processing, lending the approach infeasible for applications with limited power budgets. In this brief, a power-efficient spectrum-sensing technique is proposed, which explores an accuracy–complexity tradeoff. The presented spectrum-sensing architecture is based on 1-bit quantization at the CR receiver and implements it in hardware by a resource- and power-efficient approach, using a finite-impulse-response (FIR) filter-bank channelizer. The proposed scheme allows the complex operators like multipliers and quantizers to be replaced by the inverter logic and high-speed comparators, reducing the hardware complexity and power consumption. We validate the proposed scheme on a field-programmable gate-array (FPGA) emulator for an aeronautical $L$ -band digital aeronautical communication system (LDACS) application, and our results show that the proposed scheme achieves substantial resource reduction with at most 5% degradation in the detection accuracy in this case.

A 1.0-8.3 GHz Cochlea-Based Real-Time Spectrum Analyzer With Δ-Σ-Modulated Digital Outputs

The biological inner ear, or cochlea, is a sophisticated signal processing system that performs spectrum analysis over an ultra-broadband frequency range of ~20 Hz to 20 kHz with exquisite sensitivity and high energy efficiency. Electronic cochlear models, which mimic the exponentially-tapered structure of the biological inner ear using bidirectional transmission lines or filter cascades, act as fast and hardware-efficient spectrum analyzers at both audio and radio frequencies. This paper describes a cochlea-based digitally-programmable single-chip radio frequency (RF) spectrum analyzer in 65 nm CMOS. This “RF cochlea” chip includes a transmission-line active cochlear model with 50 parallel exponentially-spaced stages that analyzes the radio spectrum from 1.0-8.3 GHz. The outputs of all stages are encoded in parallel as delta-sigma ( $\Delta - \Sigma $ ) modulated digital signals for real-time demodulation and analysis by a digital back-end processor. The chip consumes 418 mW and typically generates ~1 GS/s of total data at an ENOB of 5–6 bits. An artificial intelligence (AI)-driven single-channel cognitive radio (CR) receiver based on the RF cochlea has also been implemented and tested.

Intelligent multicast routing for multimedia over cognitive radio networks: a probabilistic approach

Cognitive radio (CR) technology has been demonstrated as one of the key technologies that can provide the needed spectrum bands for supporting the emerging spectrum-hungry multimedia applications and services in next-generation wireless networks. Multicast routing technique plays a significant role in most of wireless networks that require multimedia data dissemination to a group of destinations through single-hop or multi-hop communication. Performing multimedia multicasting over CR networks can significantly improve the quality of multimedia transmissions by effectively exploiting the available spectrum, reducing network traffic and minimizing communication cost. An important challenge in this domain is how to perform a multi-cast transmissions over multiple hops in a dynamically varying CR environment while maintaining high-quality received video streaming to all multi-case CR receivers without affecting the performance of legacy primary radio networks (PRNs). In this paper, we investigate the problem of multicast multimedia streaming in multi-hop CR networks (CRNs). Specifically, we propose an intelligent multicast routing protocol for multi-hop ad hoc CRNs that can effectively support multimedia streaming. The proposed protocol consists of path selection and channel assignment phases for the different multi-cast receivers. It is based on the shortest path tree (SPT) that implements the expected transmission count metric (ETX). The channel selection is based on the ETX, which is a function of the probability of success (POS) over the different channels that depends on the channel-quality and availability. Simulation results verify the significant improvement achieved by the proposed protocol compared to other existing multicast routing protocols under different network conditions.

Performance Analysis of Cooperative Underlay Spectrum Sharing System in Co-channel Interference Limited Environment

In this paper, performance analysis of an asynchronous distributed space time block coded system with optimum combining at the cognitive radio receiver (CR-Rx) has been done in a cooperative underlay cognitive Internet of Things network under the influence of multiple co-channel primary users (PUs) interferers. The transmit power strategy being employed at the CR-Tx considers the PIP (peak interference power) at the PU-Rx and peak transmit power constraints at the CR-Tx, respectively. We have derived the probability density function of the signal-to-interference ratio (SIR) at the destination for the proposed system. The system is numerically and mathematically analyzed in terms of average post processed SIR (APPSIR), ergodic capacity ($${\text{C}}_{\text{erg}}$$), average bit error rate (ABER) and outage probability. All the numerical outcomes are in corroboration with its simulation counterpart.

A Highly Efficient Spectrum Sensing Approach Based on Antenna Arrays Beamforming

In cognitive radio (CR), the sensitivity of the spectrum sensing (SS) algorithms depends mainly on several factors such as the receiver sensitivity, the antenna gain, the antenna efficiency and the SS algorithm itself. Researchers depend mainly on a single antenna element and sometimes on a uniform linear antenna (ULA) arrays to increase the system sensitivity. But actually the ULA suffers from the large side lobe level which in turn reduces the antenna gain. Furthermore, the separation between elements in the ULA is almost chosen to be half wavelength at the operating frequency which results in a dense array of close elements. If this separation increases slightly in order to reduce the mutual coupling between elements, the side lobes will increase and thus the realized gain is reduced more and more and as a consequence, the SNR will be reduced also which has a direct effect on the sensitivity of the system. In this paper, a low complexity and highly efficient SS technique based on antenna arrays beamforming and the generalized likelihood ratio test (GLRT) denoted as BF/GD is introduced. The proposed beamforming approach allows the utilization of the limited number of ULA elements and their corresponding RF chains to synthesize the desired radiation patterns of larger size antenna arrays for maximum gain realization. The desired pattern is realized by optimizing the excitation coefficients and inter-element spacing of the array. Consequently, the enhancement in the realized array gain will lead to a direct increase in the received signal-to-noise ratio (SNR) which significantly improves the sensitivity and the SS capability of the CR receiver without the need for extra hardware.

Quickest physical‐layer MGD anomaly detection for jamming attacks in centralized modulated wideband converter‐based ROC curve

SummaryIn wireless communication systems, physical‐layer security menaces have evolved from jammers. Jammers, due to their furtive nature, make wireless communication systems vulnerable. The novelty in this work is to combine centralized modulated wideband converter, which is a networking system developed from the modulated wideband converter–based sub‐Nyquist sampling theory with a multivariate Gaussian distribution (MGD) anomaly detector‐based receiver operating characteristic curve that plot the detection rate (DR) versus false alarm rate (FAR) at various threshold values. We supposed the presence of a group of jammers in the spectrum corrupted with the primary source signal and noise. The received primary signal at each cognitive radio (CR) receiver is converted in to a digital signal using an analog‐to‐information converter. Each CR receiver give minimum number of samples denoted N1. All these compressed samples from every CR receiver are collected in the form of matrix called compressed sampling matrix, which is considered directly as the input of the MGD detector. The intelligent MGD detector proposed in the level of fusion center is based on the characteristics of the MGD. The numerical results show that this new system of combination detects faster anomalies perfectly in the presence of jammers in the spectrum in real‐time scenarios. Performance evaluation is performed in terms of DR versus FAR at different detection threshold values, under the presence of attacks in the system. By employing well‐known machine learning algorithms called MGD, the performance of this new proposed system shows good.

A novel proactive handoff scheme with CR receiver based target channel selection for cognitive radio network

Spectrum handoff in cognitive radio networks (CRN) enables the cognitive radio (CR) users to guarantee the desired QoS of the primary users. The handoff schemes grant an efficient exploitation of the available spectrum holes in the network. In the presented work, a novel proactive spectrum handoff scheme is made for ad hoc network scenario. Here, a network coordination schemes for unlicensed users is incorporated first. In our proposed framework, unlicensed users coordinate with each other using a Common Control Channel (CCC). Primary user free channel lists (PCL’s) are shared among CR users before actual data transmission to achieve better CR node coordination as well as to share information of nearby scenario. The PCL history stored at each CR transmitter (CRTx) node is used to make channel switching policies and to let unlicensed users vacate a channel before a licensed user utilizes it to avoid unwanted interference. Moreover, the node channel matrix created for the scenario helps to improve the performance further. The improvement is achieved through taking PCL history of CRRx into count. So, a novel proactive approach is developed here which uses the PCL history of both CRTx and CRRx. Simulation results show that proposed novel proactive spectrum handoff outperforms the reactive spectrum handoff approach in terms of higher throughput and fewer collisions to licensed users. Furthermore, a distributed channel selection can achieve higher packet delivery rate compared with existing channel selection schemes.

Improved Cognitive Radio Receivers Using Timing Mismatch of Primary and Secondary Users

We consider an asynchronous cognitive radio framework, where the primary user (spectrum license holder) and the secondary user are, naturally, not aligned in their timing. We show that applying our optimal receiver design results in improving the performance compared to the case of synchronous cognitive radio. We optimize the rates subject to the constraint to keep interference imposed on the primary user below a certain level. We also derive the constraint for the receiver of the secondary user to perform interference cancellation. Simulation results show that our proposed oversampling secondary user receiver boosts the received signal. As a result, the secondary user’s transmission power can be decreased without negatively affecting the quality of the received signal. Not only does our method reduce the interference to the primary user but also it saves power at the secondary user.

Optimal Spectrum Sensor Assignment in Multi-channel Multi-user Cognitive Radio Networks

Accurate detection of spectrum holes is the most important and critical task in any cognitive radio (CR) communication system. When a single spectrum sensor is assigned to detect a specific primary channel, then the detection may be unreliable because of noise, random multipath fading and shadowing. Also, even when the primary channel is invisible at the CR transmitter, it may be visible at the CR receiver (the hidden primary channel problem). With a single sensor per channel, a high and consistently uniform level of sensitivity is required for reliable detection. These problems are solved by deploying multiple heterogeneous sensors at distributed locations. The proposed spectrum hole detection method uses cooperative sensing, where the challenge is to properly assign sensors to different primary channels in order to achieve the best reliability, a minimum error rate and high efficiency. Existing methods use particle swarm optimization, the ant colony system, the binary firefly algorithm, genetic algorithms and non-linear mixed integer programming. These methods are complex and require substantial pre-processing. The aim of this paper is to provide a simpler solution by using simpler binary integer programming for optimal assignment. Optimal assignment minimizes the probability of interference which is a non-linear function of decision variables. We present an approach used to linearize the objective function. Since multiple spectrum sensors are used, the optimal constrained assignment minimizes the maximum of interferences. While performing the optimization, the proposed method also takes care of the topological layout concerned with channel accessibility. The proposed algorithm is easily scalable and flexible enough to adapt to different practical scenarios.