Cognitive radio network with secrecy and interference constraints

In this paper, we investigate the physical-layer security of a secure communication in single-input multiple-output (SIMO) cognitive radio networks (CRNs) in the presence of two eavesdroppers. In particular, both primary user (PU) and secondary user (SU) share the same spectrum, but they face with different eavesdroppers who are equipped with multiple antennas. In order to protect the PU communication from the interference of the SU and the risks of eavesdropping, the SU must have a reasonable adaptive transmission power which is set on the basis of channel state information, interference and security constraints of the PU. Accordingly, an upper bound and lower bound for the SU transmission power are derived. Furthermore, a power allocation policy, which is calculated on the convex combination of the upper and lower bound of the SU transmission power, is proposed. On this basis, we investigate the impact of the PU transmission power and channel mean gains on the security and system performance of the SU. Closed-form expressions for the outage probability, probability of non-zero secrecy capacity, and secrecy outage probability are obtained. Interestingly, our results show that the strong channel mean gain of the PU transmitter to the PU’s eavesdropper in the primary network can enhance the SU performance.     

Performance analysis of overlay cognitive NOMA network with imperfect SIC and imperfect CSI

In this paper, we investigate a multiple users cooperative overlay cognitive radio non-orthogonal multiple access (CR-NOMA) network in the presence of imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). In the context of cellular network, cell-center cognitive secondary users act as relays to assist transmission from the primary user (PU) transmitter to the cell-edge PU receiver via NOMA. According to the received signals between the primary transmitter and multiple cognitive secondary center users, the best cell-center cognitive SU with the maximum signal to noise ratio (SNR) is selected to transmit the PU’s signals and its own signal to cell-edge users through NOMA principle. Then, the PU cell-edge user combine the signals received from direct transmission in the first phase and relay transmission from the best cell-center cognitive SU in the second phase by selection combining (SC). To measure the performance of the system quantitatively, we derive the end-to-end outage probability and capacity for the primary and secondary networks by taking the imperfect SIC and CSI into consideration. Finally, the performance analysis is validated by the simulations, and show that serious interference caused by imperfect SIC and (or) imperfect CSI reduce the system performance.     

Optimal sensing energy and transmission interval in hybrid overlay–underlay cognitive radio networks with energy harvesting

In this work, we present a new concept called “transmission interval” in a hybrid overlay/underlay cognitive radio network. A transmission interval consists of a sequence of time slots during which the secondary user (SU) transmits its data using the optimal mode based on its current state. After the transmission interval ends, the SU has to choose between staying idle for a single time slot to save energy for future possible transmission, transmitting using the underlay mode without sensing to optimize the usage of the limited amount of available energy, or sensing the channel and transmitting using either overlay or underlay mode depending on the primary user (PU) state. The energy harvesting technology is also considered in the presence of multiple PUs and multiple SUs. For the SU network, a sequential decision problem is formulated using the mixed observable Markov decision process to determine the optimal sensing energy and the optimal transmission interval length that maximize the SU network throughput and minimize both the consumed energy and the interference to the PUs. Numerical results show that applying the transmission interval concept increases the SU network throughput and decreases the interference to the PUs compared to conventional models. Moreover, adding the action of underlay transmission without sensing increases the SU network throughput.     

Analysis of degrees of freedom under mixture Gaussian model in cognitive radio systems

We propose a mixture-Gaussian model for a cognitive radio channel to analyze the interplay between the interference in the system and the degrees-of-freedom (DOF), i.e., the average number of channel uses per transmission frame, used by the secondary user (SU) for communications in the long run. In contrast to the conventional studies, we assume that the SU receiver (SU-RX) does not precisely know whether the primary-user (PU) transmitter is on or off. Due to this assumption the resulting interference channel is mixture-Gaussian. Our objective is to find the optimal sensing threshold and sensing time for the signal detector used by the SU transmitter (SU-TX). Our formulation of the optimization problem reflects the trade-off between SU-TX’s DOF for communications and that for detection. Both the DOFs affect PU’s interference to SU, and SU’s interference to PU. The latter interference causes PU performance degradation, which is kept within tolerable range as a constraint. As a further contribution, we define interference regimes for SU performance on the basis of PU transmission power level. We also address the scenario when PU receiver uses the nearest neighbor decoding while wrongly anticipating that the channel is Gaussian. Finally, we demonstrate that even if SU-TX’s signal detector performs suboptimally, SU can still achieve the optimal detector’s performance in the high interference regime by adjusting the sensing parameters.     

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.     

Performance Analysis and Optimization of a Cooperative Transmission Protocol in NOMA-Assisted Cognitive Radio Networks with Discrete Energy Harvesting

In this paper, we propose a spectrum-sharing protocol for a cooperative cognitive radio network based on non-orthogonal multiple access technology, where the base station (BS) transmits the superimposed signal to the primary user and secondary user with/without the assistance of a relay station (RS) by adopting the decode-and-forward technique. RS performs discrete-time energy harvesting for opportunistically cooperative transmission. If the RS harvests sufficient energy, the system performs cooperative transmission; otherwise, the system performs direct transmission. Moreover, the outage probabilities and outage capacities of both primary and secondary systems are analyzed, and the corresponding closed-form expressions are derived. In addition, one optimization problem is formulated, where our objective is to maximize the energy efficiency of the secondary system while ensuring that of the primary system exceeds or equals a threshold value. A joint optimization algorithm of power allocation at BS and RS is considered to solve the optimization problem and to realize a mutual improvement in the performance of energy efficiency for both the primary and secondary systems. The simulation results demonstrate the validity of the analysis results and prove that the proposed transmission scheme has a higher energy efficiency than the direct transmission scheme and the transmission scheme with simultaneous wireless information and power transfer technology.     

Route selection for interference minimization to primary users in cognitive radio ad hoc networks: A cross layer approach

An opportunistic routing problem in a cognitive radio ad hoc network is investigated with an aim to minimize the interference to primary users (PUs) and under the constraint of a minimum end-to-end data rate for secondary users (SUs). Both amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques are considered for message forwarding by SU nodes in the network. Unlike popular transmit power control based solutions for interference management in cognitive radio networks, we adopt a cross layer approach. The optimization problem is formulated as a joint power control, channel assignment and route selection problem. Next, closed form expression for transmission power is derived and corresponding channel selection scheme and routing metric are designed based on this solution. The proposed route selection schemes are shown to depend not only on gains of the interference channels between SUs and PUs but also on the values of the spectrum sensing parameters at the SU nodes in the network. Two distributed routing schemes are proposed based on our analysis; (i) optimal_DF and (ii) suboptimal_AF. The routing schemes could be implemented using existing table driven as well as on demand routing protocols. Extensive simulation results are provided to evaluate performance of our proposed schemes in random multihop networks. Results show significant reduction in PUs’ average interference experience and impressive performance as opportunistic routing schemes can be achieved by our schemes compared to traditional shortest path based routing schemes. Performance improvement is also reported over prominent recent schemes.     

Transmit power and bits/channel use adaption in competitive cognitive radio networks

In this paper, we propose an optimization framework to determine the distribution of power and bits/channel use to secondary users in a competitive cognitive radio networks. The objectives of the optimization framework are to minimize total transmission power, maximize total bits/channel use and also to maintain quality of service. An upper bound on probability of bit error and lower bound on bits/channel use requirement of secondary users are considered as quality of service. The optimization problem is also constrained by total power budget across channels for a user. Simulating the framework in a centralized manner shows that more transmit power is required to allocate in a channel with higher noise power. However, allocation of bits/channel use is directly proportional to signal to interference plus noise power ratio. The proposed framework is more capable of supporting high bits/channel use requirement than existing resource allocation framework. We also develop the game theoretic user based distributed approach of the proposed framework. We see that user based distributed solution also follows centralized solution.     

Secure communication over RIS-aided underlay CR MIMO wiretap channel without Eve’s CSI

This paper investigates a reconfigurable intelligent surface (RIS)-aided underlay cognitive radio (CR) multiple-input multiple-output (MIMO) wiretap channel where the secondary transmitter (ST) communicates with primary user (PU) and secondary user (SU) in the absence of the eavesdropper’s (Eve’s) channel state information (CSI). To enhance the secrecy performance in CR MIMO wiretap channel, the power of useful signal is minimized at ST, and then the residual power is further utilized to design artificial noise (AN) based on statistical CSI at ST. Specifically, we first optimize the transmit covariance matrix at ST and the diagonal phase-shifting matrix at RIS jointly leveraging large-system approximation results. Then the power allocation for SU is optimized to obtain the minimum transmit power of useful information at ST. Besides, we further design AN with the residual power by aligning it into the null space of the SU channel and thus avert the harmful effects of AN to improve the secure communication quality of SU. Finally, through numerical simulations, we illustrate the effectiveness of the proposed algorithm and validate the existence of a trade-off between the quality-of-service (QoS) at SU and secrecy rate.     

Spectrum efficient power allocation schemes for OFDM cognitive radio with statistical interference constraints

In this paper, we study the power allocation problem for an orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) system. In a departure from the conventional power allocation schemes available in the literature for OFDM-based CR, we propose power allocation schemes that are augmented with spectral shaping. Active interference cancellation (AIC) is an effective spectral shaping technique for OFDM-based systems. Therefore, in particular, we propose AIC-based optimal and suboptimal power allocation schemes that aim to maximize the downlink transmission capacity of an OFDM-based CR system operating opportunistically within the licensed primary users (PUs) radio spectrum in an overlay approach. Since the CR transmitter may not have the perfect knowledge about the instantaneous channel quality between itself and the active PUs, the interference constraints imposed by each of the PUs are met in a statistical sense. We also study an optimal power allocation scheme that is augmented with raised cosine (RC) windowing-based spectral shaping. For a given power budget at the CR transmitter and the prescribed statistical interference constraints by the PUs, we demonstrate that although the on-the-run computational complexity of the proposed AIC-based optimal power allocation scheme is relatively higher, it may yield better transmission rate for the CR user compared to the RC windowing-based power allocation scheme. Further, the AIC-based suboptimal scheme has the least on-the-run computational complexity, and still may deliver performance that is comparable to that of the RC windowing-based power allocation scheme. The presented simulation results also show that both the AIC-based as well as the RC windowing-based power allocation schemes lead to significantly higher transmission rates for the CR user compared to the conventional (without any spectral shaping) optimal power allocation scheme.     

Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques

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.     

Optimal satisfaction degree in energy harvesting cognitive radio networks

A cognitive radio(CR) network with energy harvesting(EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model(HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree(WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user(SU) and the interference to the primary user(PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming(MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution(DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service(Qos). Numerical results are given to verify our analysis.     

Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of primary users (PU) networks are shared with secondary users (SU) and energy harvesting allows nodes of the CRN to acquire energy from the environment for operation sustainability. However, amount of energy harvested from the environment is not fixed and requires dynamic allocation of resources for obtaining optimum network and throughput capacity. In this work, we overcome the limitations of existing Q-learning based resource allocation schemes which are constrained by large state-space systems and have slow convergence. Proposed deep Q-learning based algorithm improves the resource allocation in EH-CRN, while considering quality of service (QoS), energy and interference constraints. Simulation results show that proposed algorithm provide improved convergence and better resource utilization compared to other techniques in literature.     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

On spectrum sensing in cognitive radio CDMA networks with beamforming

In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%).     

Outage Performance Analysis of NOMA in Wireless Powered Cognitive Radio Networks with AF and DF Relaying Techniques

Improving spectral efficiency under a certain energy limitation is an important design metric for future wireless communications as a response to the growing transmission demand of wireless devices. In order to improve spectral efficiency for communication systems without increasing energy consumption, this paper considers a non-orthogonal multiple access (NOMA)–based cognitive radio network, with the assistance of a wireless-powered relay station (RS), and then analyzes the system outage performance under amplified-and-forward (AF) and decoded-and-forward (DF) cooperative transmission modes. Specifically, the base station (BS) has the opportunity to cooperate by transmitting information through the RS, depending on whether the RS can harvest sufficient RF energy for cooperative transmission. That is to say, when the energy stored by the RS is sufficient for cooperative transmission, the RS will assist the BS to forward information; otherwise, the BS will send information through direct links, while the RS converts the radio frequency (RF) signals sent by the BS into energy for future transmission. Moreover, the transmission power required by the RS for cooperative transmission is usually relatively large, while the amount of harvested energy by the RS in a transmission slot is usually low, so it takes several consecutive time slots to accumulate enough transmission energy. To this end, we utilize a discrete-time Markov chain to describe the processes of charging and discharging of the RS. Subsequently, we derive the closed-form outage probabilities of both the primary and secondary systems for the considered system in AF and DF modes through mathematical analysis, and verify the accuracy of the analyses through Monte Carlo simulation. The simulation results show that the two proposed cooperative transmission schemes with AF and DF relaying techniques outperform both direct transmission and other similar schemes in both the primary and secondary system, while the DF scheme can provide better performance than the AF scheme within the range of setting values.     

Multi-channel sensing and resource allocation in energy constrained cognitive radio networks

We consider a cognitive radio network in a multi-channel licensed environment. Secondary user transmits in a channel if the channel is sensed to be vacant. This results in a tradeoff between sensing time and transmission time. When secondary users are energy constrained, energy available for transmission is less if more energy is used in sensing. This gives rise to an energy tradeoff. For multiple primary channels, secondary users must decide appropriate sensing time and transmission power in each channel to maximize average aggregate-bit throughput in each frame duration while ensuring quality-of-service of primary users. Considering time and energy as limited resources, we formulate this problem as a resource allocation problem. Initially a single secondary user scenario is considered and solution is obtained using decomposition and alternating optimization techniques. Later we extend the analysis for the case of multiple secondary users. Simulation results are presented to study effect of channel occupancy, fading and energy availability on performance of proposed method.     

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network(WSN), the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption(PCCAGM). This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption(PCCAA) is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.     

Analysis of mobility impact on interference in cognitive radio networks

Cognitive radio (CR) technology seems to be a promising candidate for solving the radio frequency (RF) spectrum occupancy problem. CRs strive to utilize the white holes in the RF spectrum in an opportunistic manner. Because interference is an inherent and a very critical design parameter for all sorts of wireless communication systems, many of the recently emerging wireless technologies prefer smaller size coverage with reduced transmit power in order to decrease interference. Prominent examples of short-range communication systems trying to achieve low interference power levels are CR relays in CR networks and femtocells in next generation wireless networks (NGWNs). It is clear that a comprehensive interference model including mobility is essential especially in elaborating the performance of such short-range communication scenarios. Therefore, in this study, a physical layer interference model in a mobile radio communication environment is investigated by taking into account all of the basic propagation mechanisms such as large- and small-scale fading under a generic single primary user (PU) and single secondary user (SU) scenario. Both one-dimensional (1D) and two-dimensional (2D) random walk models are incorporated into the physical layer signal model. The analysis and corresponding numerical results are given along with the relevant discussions.