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This paper has collectively exploited cognitive radio (CR), non-orthogonal multiple access (NOMA) and multiple-input-multiple-output (MIMO) communication systems in order to propose a spectral-and interference-efficient framework which is named as MIMO-based CR–NOMA communication system. The proposed framework is analysed for the uplink and downlink scenarios. Further, the closed-form expressions for throughput at each user due to the number of transmitting-and receiving-antennas are derived numerically for the downlink-and uplink-scenarios. In addition to this, the total/sum throughput for different frameworks such as CR–NOMA, CR–MIMO and MIMO-based CR–NOMA systems is derived for both the downlink and uplink scenarios. Moreover, in order to satisfy the interference constraints at the primary user due to cell-edge/far-user [cognitive user (CU)-4] transmission in the uplink scenario, a new metric known as interference efficiency is derived. Furthermore, the proposed frameworks are simulated for downlink and uplink scenarios and the relationship between throughput of cell-centred/near-user (CU-1) and CU-4 are presented. Moreover, the total throughput for different frameworks and interference efficiency is also illustrated with the simulation results. The simulation results reveal that the proposed MIMO-based CR–NOMA system outperforms the existing MIMO–NOMA, CR–NOMA and CR–OMA systems in terms of the CUs' individual throughput, total throughput and interference efficiency of the system.

Communication industry demands the frameworks, which allow significant improvement in energy efficiency with spectral efficiency, however, these two do not always coincide and may even conflict sometimes in cognitive radio networks. In addition to this, the distributed network structures are preferred over centralised structures due to ease of deployment as well as less information exchange between the cognitive users which reduce the network-overhead. Therefore, in this study, the authors have proposed a framework to enhance the spectral as well as energy efficiency by exploiting the novel frame structure in the distributed cognitive radio networks. A self-scheduled multichannel-medium access control (SMC-MAC) protocol has been proposed in which the contention and sharing intervals are exploited for the efficient spectrum and energy utilisation. Moreover, the closed-form expressions of the spectral efficiency, power consumption and energy efficiency for the proposed SMC-MAC protocols are derived. Furthermore, the relation between energy and spectral efficiency for the proposed framework is also derived. The numerically simulated results are presented to validate the proposed work and Monte-Carlo simulations are performed for the random events in order to support the numerically simulated results.

The spectrum sensing is a key process of the cognitive radio technology in which the cognitive users identify the unutilized/underutilized primary users (PUs)/licensed users spectrum for its efficient utilization. The sensing performance of cognitive radio (CR) is generally measured in terms of false-alarm probability ( $$ P_{f} $$ ) and detection probability ( $$ P_{d} $$ ). IEEE 802.22 wireless regional area network is one of the typical cognitive radio standards to access unused licensed frequencies of TV band and according to this standard, the false-alarm probability of CR should be ≤ 0.1 and the detection probability must be ≥ 0.9. Further, the detection and false-alarm probabilities are greatly affected by the selected threshold value in the spectrum sensing approach and selection of threshold is a crucial step to yield the status (presence/absence) of PU. In most of the available literatures, the threshold is decided by fixing one parameter ( $$ P_{f} $$ or $$ P_{d} $$ ) and optimizing the other parameter ( $$ P_{d} $$ or $$ P_{f} $$ ). Moreover, at low SNR, while achieving one of the targeted sensing parameter, there is significant degradation in the other sensing parameter. Therefore, in this paper, we are motivated to decide the optimal threshold at low SNR (signal-to-noise ratio) in such a way where we can jointly achieve both sensing matrices ( $$ P_{f} $$ ≤ 0.1 and $$ P_{d} \ge 0.9 $$ ) and provided better sensing performance in comparison to that of the traditional constant false-alarm rate and constant detection rate (CDR) threshold selection approaches. Further, we have illustrated that at low SNR, the proposed optimal threshold selection approach has provided better throughput as compare to that of the threshold selected by traditional CDR approach. The proposed approach has improved throughput approximately 24.63% when compared with CDR at chosen SNR.

This paper introduces the concept of imperfect spectrum monitoring for realistic wireless communication environments with high-traffic cognitive radio networks (HTCRN). The effect of imperfect spectrum monitoring in the HTCRN is analyzed in terms of the probability of data-loss (in the form of packets) and interference at primary user (PU). Further, two algorithms are proposed to compute the data-loss and interference-at-PU considering different scenarios of the traffic-intensity and spectrum monitoring error. Moreover, the closed-form expressions for the ratio of achieved-throughput to the data-loss, power-wastage, interference-efficiency as well as energy-efficiency are derived, and numerically simulated results are presented to support the proposed work. Furthermore, the randomness in emergence of PU is considered and the Monte-Carlo simulations are exploited to validate the numerically simulated results.

This paper focuses on potential issues related to the random selection of a sensing channel that occurs after the prediction phase in a Cognitive Radio Network (CRN). A novel approach (Approach-1) for improved selection is proposed, which relies on the probabilities of channels by which they are predicted idle. Further, closed-form expressions are derived for the throughput of Cognitive Users (CUs) in the conventional and proposed approaches. In addition to this, a fundamental approach for computing the prediction probabilities is also proposed. Moreover, a new challenging issue named “sense and stuck” was observed in the conventional approach. The proposed approach is validated by comparing the results achieved with the results of the conventional approach. However, to achieve the prediction probabilities, the pre-channel-state-information is a prerequisite, but it may be unavailable for particular scenarios; therefore, a modified selection method is introduced to avoid the sense and stuck problem. An algorithm to evaluate the throughput using the random, improved, and modified selection methods is presented with its space and time complexities. Furthermore, for additional improvement in the throughput of the CU, a new frame structure is introduced, in which the spectrum prediction and sensing periods are exploited for simultaneous transmission of data via the underlay spectrum access technique (Approach-2). The simulated results of Approach-2 are compared with our pre-obtained results of Approach-1, which confirm significant improvement in the throughput. Keywords: Cognitive radio, Interweave, Prediction, Spectrum accessing, Throughput, Underlay

The spectrum-monitoring is a prominent technique to detect the reappearance of the primary user (PU) during the cognitive users' (CUs) data transmission. However, the imperfect spectrum monitoring (SM) causes a delay in the identification of PU that introduces interference at PU as well as data-loss. The cooperation among CUs for SM is a proficient solution to diminish these impairments of imperfection. Thus, in this study, a scenario of the heterogeneous cognitive radio network (CRN) is presented in which the CUs perform cooperation among them for SM and have analysed the impact of cooperation on several performance metrics such as the data-loss, interference efficiency and energy efficiency. An algorithm is presented for the evaluation of data-loss for different circumstances of the traffic intensity of PU and probability of SM error in the proposed heterogeneous CRN. In addition to this, the closed-form expressions for the cooperative and non-cooperative SM of these metrics are derived. Furthermore, the simulation results are illustrated for different state-of-affairs of the probability of SM error and traffic intensity.

The imperfect spectrum monitoring (SM) is a major obstacle to detect the emergence of primary user (PU) quickly during the cognitive users’ (CUs’) data transmission which results data-loss and introduces the interference at PU. The cooperation in CUs for SM is an effective solution to improve its performance. Therefore, in this paper, a scenario, where CUs can cooperate with each other for SM is presented and have analyzed the effect of cooperation on various performance metrics namely, the data-loss, interference efficiency, and energy efficiency. An algorithm is illustrated for the computation of data-loss under various conditions of the traffic intensity of PU and probability of SM error. Moreover, the closed-form expressions of these metrics are derived for the cooperative and non-cooperative SM. Further, the simulation results are presented for various scenarios of traffic intensity, probability of SM error and channel gain between the CUs’ transmitter to PU receiver. Furthermore, the Monte-Carlo simulation results are exploited to consider the random nature of the PUs’ traffic intensity as well as to support the numerically simulated results.

The spectrum mobility during data transmission is an integral part of the cognitive radio network (CRN) which is conventionally two types for instance reactive and proactive. In the reactive approach, the cognitive user (CU) switches its communication after the emergence of the primary user (PU), where the detection of emergence of PU relies either on spectrum sensing and/or monitoring. Due to certain limitations of the reactive approach such as: (1) loss at least one packet on the emergence of PU and (2) resource (bandwidth) wastage if the periodic sensing is used for mobility, the researchers have introduced the concept of proactive spectrum mobility. In this approach, the emergence of PU is predicted on the bases of pre-available spectrum information, and switching is performed before true emergence of the PU, in order to avoid even the single packet loss. However, the imperfect spectrum prediction is a major milestone for the proactive spectrum mobility. Recently, due to introduction of the spectrum monitoring simultaneous to the data transmission, the reactive approach has come into lime-light again, however, it suffers from the single packet loss and imperfect spectrum monitoring issues. Therefore in this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of cognitive radio network (CRN). In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of PU during the data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed approaches as well as for the prediction and monitoring approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction and monitoring approach. In a special case, when the prediction error is zero, the graphs of all metric values overlies the spectrum monitoring approach, which further validates the proposed approach.

In this letter, we have exploited a novel hybrid-cum-improved spectrum access technique for significant improvement in the throughput and data-loss rate. This proposed technique comprises of hybrid spectrum access and improved frame structure strategies of the cognitive radio communication system. Also, the closed-form expressions for throughput and data-loss of these approaches are derived. Moreover, the proposed approaches are validated numerically as well as with reported literature.

In this paper, the hybrid spectrum access and prediction techniques are exploited simultaneously in the high-traffic cognitive radio communication system, in order to enhance the throughput and overcome the problem of waiting states. The hybrid spectrum access is responsible for throughput enhancement by escaping the waiting states whereas the spectrum prediction alleviates the sensing errors in the high-traffic communication environment. The closed-form expression for the throughput of cognitive user (CU) communication is derived and validated the proposed approach with the reported literature. Moreover, a new framework is proposed to conquer the sharing issues of conventional and proposed approaches. In addition to this, the performance metrics of proposed framework such as the data-loss, energy-loss of the CU and interference at the PU have been analyzed.

In this paper, we have exploited the key technologies of the next generation communication systems which are cognitive radio (CR), non-orthogonal multiple access (NOMA) and multiple-input-multiple-output (MIMO) and have proposed a framework of simultaneous use of these technologies for the spectral efficient design, named as MIMO based CR-NOMA communication systems. The proposed communication system is defined for the specific case that is MIMO with 2 × 2 antennas and NOMA serving two users. Moreover, the data rate at each antenna of users in derived numerically and further, the sum-data rate for the cognitive users (CUs) is computed. The relationship between sum-data rates of two NOMA users with two antennas each, is described with simulation results.

In the area of Internet-of-Things, devices are supposed to collect or exchange the data by using limited number of resources in which spectrum utilization is one of the most essential factor. This paper presents the overview, applications, characteristics of Internet-of-Things & Cognitive radio as well as the combination of both for better spectrum utilization. In today's scenario, IoT is becoming the hot topic in communication world because everyday devices are being combined with internet connectivity. Numbers of devices are connecting in this network so a lot of spectrum is needed. Since the spectrum is finite resource therefore we introduced the idea of Cognitive radio networks (CRNs) in Internet-of-Things for providing the better and efficient way of utilizing spectrum in communication world.

In this paper, we have analyzed the effects of cooperative spectrum sensing and threshold selection over the performance of cognitive radio network (CRN) using energy detector in the fading environment. Firstly, we have compared the receiver operating characteristics (ROC) curve of both the cooperative and non-cooperative scenario for different fading environments. Further, we have illustrated the probability density function (PDF) of binary hypothesis for proposed CRN system, and analyzed the necessity of dynamic selection of threshold over that of the fixed threshold selection. In addition to this, we have compared the spectrum sensing performance and throughput of proposed CRN for fixed and dynamic threshold selection approaches on the spectrum sensing error at low and high signal-to-noise (SNR) ratio. Moreover, we have improved the sensing performance of proposed CRN by employing cooperative spectrum sensing with dynamic threshold selection scheme. Further, the percentage improvement in detection probability, throughput and total error probability of the proposed system model has been presented.

The orthogonal space–time block codes (OSTBCs) increase the diversity order of a multiple-input multiple-output (MIMO) system at a reasonable cost of complexity. However, the precoding technique in which complete channel knowledge is available at the base transceiver station has come into existence with OSTBC for improving the system performance. Moreover, a composite generalized-K channel model has become the key for analyzing both the effects of multipath fading and shadowing. Therefore, this chapter has exploited the precoded OSTBC to improve the bit error rate (BER) performance of MIMO system over a generalized-K fading channel.

In this paper, we have presented different threshold computation schemes for spectrum sensing using Fixed threshold (FT), Dynamic threshold (DT), Neyman-Pearson (NP) and Bayesian detection (BD) approach for energy detection technique. Further, we have analyzed the effect of different threshold selection approaches on the spectrum sensing performance parameters. We have also analyzed the effect of prior probability of occupancy of channel over different sensing performance parameters in BD approach.

This paper introduces the concept of imperfect spectrum monitoring for realistic high-traffic cognitive radio networks (HTCRN). The effect of imperfect spectrum monitoring in the HTCRN is analyzed in term of probability of the data loss (in the form of packets) and interference at primary user (PU). In addition to this, two algorithms to compute the data-loss and interference at PU, considering various scenarios of the traffic intensity and spectrum monitoring error, are proposed. Moreover, the closed-form expression for the ratio of the achieved-throughput to the data-loss is derived. Furthermore, the simulation results are presented in order to validate the proposed work.

Spectrum sensing is a key step in cognitive radio communication systems. The probability of detection and probability of false-alarm are two crucial performance parameters in spectrum sensing. In this paper, we have presented the effect of selection of fixed and dynamic threshold scheme of energy detection technique on various spectrum sensing performance parameters such as probability of detection, probability of false-alarm, and probability of error, considering the constant noise variance in the channel. In addition to this, we have also computed the minimum value of time-bandwidth product when the noise variance is constant or variable in the channel.

The aim of this manuscript is to propose a secure handoff scheme for cognitive radio network by ensuring a secure communication among cognitive users (CU). A coordinator cognitive user(CCU) is selected based on users trust values/factor (TV/TF) by periodically computing and updating these values of each CU depending upon their characteristics. The CCU is the controlling unit to allow the unused band/channel during spectrum mobility/handoff by the CU as well as upon entry of new CU in the cognitive radio network (CRN). The proposed mechanism is validated by simulating in the Matlab environment against network throughput and to know the status of the handoff CU either it is trusted or untrusted.

In this paper, we have exploited the novel hybrid-cum-improved spectrum access strategy to significant improvement in the throughput and data-loss rate. This proposed strategy is comprise of hybrid spectrum access and improved frame structure strategies for the cognitive radio communication system. In addition to this, the mathematical expressions of throughput for these approaches are also presented. Moreover, the proposed approaches are validated numerically as well as with reported literature.

In this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of the cognitive radio network. In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of the PU during data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed and prediction approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction approach.

Compressive sampling emerged as a very useful random protocol and has become an active research area for almost a decade. Compressive sampling allows us to sample a signal below Shannon Nyquist rate and assures its successful reconstruction with some limitations on signal, that is, signal should be sparse in some domain. In this paper, we have used compressive sampling for an arbitrary one-dimensional signal and two-dimensional image signal compression and successfully reconstructed them by solving L1-norm optimization problems. We also have showed that compressive sampling can be implemented if a signal is sparse and incoherent through simulations. Further, we have analyzed the effect of noise on the recovery.

Summary Due to the explosive growth of wireless services with high-speed and better quality-of-services, the demand of spectrum is increasing proportionally. In addition to this, the fixed spectrum allocation scheme of the desired radio frequency bands also creates the spectrum scarcity problem. However, the possible solution of these problems is the cognitive radio (CR) communication technology, which is an emerging area of the wireless communication and relies on the dynamic spectrum access (DSA)/opportunistic spectrum access (OSA) as well as spectrum sharing (SS) techniques. The DSA/OSA allows CR users to access the spectrum opportunistically, when the primary/licensed user (PU) is idle, however the SS allows the CR user to utilize the spectrum in the presence of PU with certain power constraints. The power constraints are applied on the cognitive user/secondary user (SU) transmitter to keep PU's communication impervious. In the CR technology, the user needs to sense its environment and adapt communication parameters on the basis of environmental conditions, which are always random in nature. Therefore, the implementation of cognitive radio with imperfect channel state information is an emerging issue that needs to be analysed. In this technical review, we have presented the potential growth in the area of power allocation strategies in CR communication with perfect and imperfect channel state information. Further, various approaches of the optimal power allocation to cognitive users are presented. Finally, the potential issues of power management in the cognitive radio regime are exploited.