Full-duplex Cognitive Radio Networks Research Articles

Switching algorithm in listen-and-talk-based MAC protocols for full-duplex cognitive radio networks with type 2 fuzzy cooperative spectrum sensing

Switching algorithm in listen-and-talk-based MAC protocols for full-duplex cognitive radio networks with type 2 fuzzy cooperative spectrum sensing

Facing the SNR Wall Detection in Full Duplex Cognitive Radio Networks Using a GLRT Multipath-Based Detector

Recently, with the advances in self-interference cancellation, full-duplex (FD) techniques have become a feasible approach to improve the spectrum usage in new generation wireless systems. In particular, in cognitive radio networks, FD allows a secondary user to simultaneously sense the spectrum and transmit, improving the network efficiency. However, the residual self interference (RSI) arising from an imperfect cancellation, represents a bottleneck for the spectrum sensing performance. This paper analytically demonstrates that RSI represents a colored noise which leads to the SNR Wall phenomenon making the typical OFDM signal detectors no longer robust. To overcome this drawback, we propose a generalized likelihood ratio test detector exploiting the multipath correlation due to the primary user’s (PU) channel. It is proven that the proposed method is not affected by the SNR Wall, hence it is robust w.r.t an imperfect self-interference cancellation. Specifically, best detection performance is achieved when an approximated knowledge of the maximum delay spread of the PU channel is available, otherwise it is shown that a limited performance degradation occurs. Moreover, provided results highlight that our approach outperforms the classical alternatives in different scenarios, including when PU employs a mixed numerology OFDM signal, which characterizes the 5G New Radio waveform.

A multi-layer hyper-graph routing with jamming-awareness for improved throughput in full-duplex cognitive radio networks

There are differences between routing in traditional and in Cognitive Radio Networks (CRNs). The availability of channels, periodic spectrum sensing, jammers activity, are some of these differences, etc. CRNs may contain a large number of Internet of Things (IoT) devices, hence, modeling such networks with a flat graph model generates a huge graph size. Thus, the motivation behind this work is clear bearing in mind that modeling such networks with a Multi-layer Hyper-Graph (MLHG) is the idea presented in this paper, in which each hyper-edge represents a group of CR devices, and each layer represents a licensed channel. In this paper, the main aim is to consider four key factors to maximize end-to-end network throughput, which are, transmission rate, licensed users activity, jammers activity, and needed number of time sharing. In literature, considering both jamming activity and time sharing for routing exists in few papers till now. This work proposes and develops three cutting-edge routing protocols that are aware of the four aforementioned factors, namely, Jamming-Activity-Sharing-Rate-Aware protocol, Jamming-Activity-Rate-Aware protocol, and Jamming-Activity-Sharing-Aware protocol. We compare these protocols with three recent proposed protocols in CRNs. Simulation results indicate that when jammers activity and time sharing are considered, network throughput is significantly enhanced. Specifically, our proposed jamming-aware protocols significantly outperform the existing three jamming-unaware protocols by up to 127%.

A Novel MAC Protocol with Fusion Center and Adaptive Full-Duplex Communication for Cognitive Radio Networks

Cognitive radio network is an intelligent and adaptive communication technology used to effectively utilize the radio spectrum to provide better communication. It enables the secondary (unlicensed) users to communicate over vacant channels without disrupting the communication of the primary (licensed) users. This paper proposes a novel contention-free MAC protocol for full-duplex and half-duplex cognitive radio networks. A novel concept of a fusion center-based channel allocation scheme is proposed to provide better channel allocation and reduced energy consumption. The proposed technique is compared with the existing techniques in terms of the average number of channels being sensed by each user, average wait time, collisions, throughput, and average energy consumption. The number of collisions increases as the maximum allotted time of communication increases in existing approaches. The experiment is performed with 20, 40, 60, and 80 s. The existing approaches show for 20 s, the number of collisions is 65, 61, 68, and 62; for 40 s, the number of collisions is 93, 96, 95, and 92; for 60 s, the number of collisions is 122, 126, 133, and 130; for 80 s, the number of collisions is 176, 170, 173, and 175. The proposed method approaches the number of collisions will become zero as the maximum allotted time of communication increases. The experimental results show that the proposed approach performs better than existing approaches. Moreover, it can be observed that the average waiting time of secondary users when a collision occurs is reduced with the application of a fusion center-based channel allocation scheme.

Research on Key Technology of Full Duplex Cognitive Radio Network

In traditional half duplex cognitive radio networks, secondary users can only sense the spectrum or transmit at a given time. This kind of half duplex network limits the throughput of users, because the user unit can’t transmit data in spectrum sensing. However, with the development of self-interference suppression (SIS) technology, full duplex cognitive radio network allows spectrum sensing and data transmission on a given channel at the same time. Compared with traditional half duplex network, full duplex network improves throughput and reduces conflicts. In order to explore the future development direction of spectrum sensing technology, in the context of full duplex cognitive radio, a comprehensive study of radio network is carried out, including key technologies, current challenges and future research directions.

Energy efficiency of full-duplex cognitive radio in low-power regimes under imperfect spectrum sensing

This paper investigates the energy efficiency of a full duplex (FD) cognitive radio (CR) system in the low-power regime under a practical self-interference cancellation performance and imperfect spectrum sensing. We consider an opportunistic spectrum access network in which the secondary user (SU) is capable of self-interference suppression (SIS). The SIS ability enables the SU to work in simultaneous transmit and sense (TS) mode in order to increase the quality of channel sensing. Towards this goal, we first study the sensing performance, i.e., false-alarm and miss-detection probabilities, of the FD CR networks using TS, and compare the results with traditional half duplex (HD) CR systems using transmit only mode (TO). In the next step, we show that due to imperfect spectrum sensing, the secondary network channel is best characterized as a Gaussian-Mixture (GM) channel, which is widely used to capture the asynchronism in heterogeneous cellular networks. We then analytically characterize the low-signal-to-noise-ratio (low-SNR) metrics of the minimum energy per bit and wideband slope of the spectral-efficiency curve, and obtain these fundamental limits in closed-form. Furthermore, the characterization of these fundamental metrics allows us to identify practical signaling strategies that are optimally efficient in the low-SNR regime for the considered FD CR system. The benefits in terms of energy efficiency offered by FD CR over HD CR are also clearly demonstrated.

Multiband Spectrum Sensing Using Modified Daniell Windowing Technique in Full-Duplex Cognitive Radio Networks: A Performance Study

Full-duplex cognitive radio (CR) is a promising technology for upcoming 5G wireless communication systems. This paper presents a robust fast Fourier transform (FFT) based multiband spectrum sensing using two-dimensional averaging algorithms in orthogonal frequency division multiplexing systems in full-duplex CR networks with residual self interference under Rayleigh flat fading scenario. In the proposed algorithm, we have used modified Daniell windowing technique both in time and frequency dimensions to smoothen the FFT spectrum under full-duplex scenario. The analytical expressions for the performance metrics are derived for the aforementioned algorithm. The simulated and analytical results, obtained for the proposed algorithm using modified Daniell windowing technique are found in good agreement. Finally, the comparison studies between the proposed scheme using modified Daniel windowing technique and the conventional rectangular windowing scheme clearly depict that the proposed scheme gives optimal performance even at low SNR using fewer filter lengths in time and frequency dimensions as the modified Daniell windowing algorithm is less influenced due to spectral leakage.

On Ensemble Learning-Based Secure Fusion Strategy for Robust Cooperative Sensing in Full-Duplex Cognitive Radio Networks

We propose an ensemble machine learning (EML) based robust cooperative spectrum sensing framework in full-duplex cognitive radio networks (FD-CRNs), which is robust with accuracy against malicious attacks and interference. FD communication improves the spectrum awareness capability of secondary users (SUs) by allowing them to sense and transmit simultaneously over the same frequency band. However, it also complicates the sensing environment by introducing self-interference and co-channel interference. Meanwhile, the presence of malicious attacks such as Primary User Emulation and Spectrum Sensing Data Falsification (SSDF) attacks also degrades the sensing performance. To alleviate the influence of interference and attacks, we design an EML framework that provides robust and accurate fusion performance. In such a context, we analyse the spectrum waste probability, collision probability and secondary throughput in both FD Listen-Before-Talk and Listen-And-Talk protocols. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of spectrum waste probability, collision probability and secondary throughput for any number of SSDF SUs, only at the cost of slightly higher inference time.

Yet efficient routing protocols for half- and full-duplex cognitive radio Ad-Hoc Networks over IoT environment

For the sake of fulfilling the ever-increasing demands on the Internet of Things (IoT) applications, researchers are implementing Cognitive Radio (CR) technology in Ad-Hoc Networks (AHNs). Moreover, In-Band Full-Duplex (IBFD) technology in CRAHNs is gaining scholars’ interests recently. Hence, we are considering non-time-slotted IBFD-CRAHNs to clear up the reactivation failure and frequent hand-off problems confronting Primary Users (PUs) and Secondary Users (SUs), respectively, in Half-Duplex (HD) CRAHNs. We propose two HD-CRAHN routing protocols without a common control channel intended for practicality. This is accomplished by adopting multi- and unicast transmissions in the Route Request Packets (RRQPs) delivery. The first protocol is tested with data transmission in HD mode. However, the second routing protocol adopts a channel assignment technique proposed in the literature for FD communications; thus, we apply FD data transmission on it. Additionally, this paper offers an IBFD-based CRAHN routing protocol that broadcasts the RRQP over the channels that have SUs sensing them or even transmitting over them. This is possible because of two patents that allow wireless nodes to receive (transmit) in a sweeping manner while transmitting to (receiving from) another node. The three protocols' performance is studied and compared with the most pertinent and recent protocol named Probabilistic and Deterministic Path Selection (PDPS) using an exclusive simulator, built-in Java language, over the simulator used in PDPS experiments. As far as the throughput performance metric is concerned, our simulation results show noticeable and undeniable improvements over the PDPS protocol.

JavaSim-IBFD-CRNs: Novel java simulator for in-band Full-Duplex cognitive radio networks over Internet of Things environment

Multi-channel non-time-slotted cognitive radio ad hoc networks are attractive to researchers nowadays; because they utilize the spectrum efficiently and represent a useful network design for Internet of Things (IoT) applications. From this point, a simulator that mimics these networks is developed using Java language. Further, researchers have proven that many problems facing these networks can be solved by using Full Duplex (FD) nodes. Thus, the proposed simulator is extended to support FD cognitive radio nodes. Each one of these nodes is allowed to receive in a sweeping pattern from various nodes, while transmitting to another node. In an alternative scenario, the node can transmit in a sweeping pattern to various nodes while receiving from one node in a specific direction. Both scenarios are adopted in the route request packet broadcasting phase. However, in the route reply packet delivery, the nodes are allowed to operate in FD transmit and sense or in FD transmit and receive modes. Interestingly, the simulator is tested using FD protocol, also introduced in this paper, that can be useful for IoT applications. Besides the proposed protocol is compared with very recent and close related routing protocol called Probabilistic and Deterministic Path Selection (PDPS). The results are logical; as they are compatible with the theoretical expectations, and the FD protocol surpassed PDPS by almost doubling the throughput.

Resource Allocation for IRS-Assisted Full-Duplex Cognitive Radio Systems

In this article, we investigate the resource allocation design for intelligent reflecting surface (IRS)-assisted full-duplex (FD) cognitive radio systems. In particular, a secondary network employs an FD base station (BS) for serving multiple half-duplex downlink (DL) and uplink (UL) users simultaneously. An IRS is deployed to enhance the performance of the secondary network while helping to mitigate the interference caused to the primary users (PUs). The DL transmit beamforming vectors and the UL receive beamforming vectors at the FD BS, the transmit power of the UL users, and the phase shift matrix at the IRS are jointly optimized for maximization of the total spectral efficiency of the secondary system. The design task is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the PUs’ channel state information (CSI) and their maximum interference tolerance. Since the maximum interference tolerance constraint is intractable, we apply a safe approximation to transform it into a convex constraint. To efficiently handle the resulting approximated optimization problem, which is still non-convex, we develop an iterative block coordinate descent (BCD)-based algorithm. This algorithm exploits semidefinite relaxation, a penalty method, and successive convex approximation and is guaranteed to converge to a stationary point of the approximated optimization problem. Our simulation results do not only reveal that the proposed scheme yields a substantially higher system spectral efficiency for the secondary system than several baseline schemes, but also confirm its robustness against CSI uncertainty. Besides, our results illustrate the tremendous potential of IRS for managing the various types of interference arising in FD cognitive radio networks.

BER Performance of Full-Duplex Cognitive Radio Network With Nonlinear Energy Harvesting

In this paper, the bit error rate (BER) performance of a full-duplex (FD) overlay cognitive radio (CR) network with linear/nonlinear energy harvesting (EH) capability is investigated. The considered overlay CR network consists of a primary transmitter/receiver (PT/PR) pair, a secondary transmitter (ST) and an FD mode operated secondary receiver (SR) where ST harvests energy from both PT and SR during the first communication time slot. In the second communication time slot, SR receives its intended signal from ST while cooperating with PT by transmitting its signal to PR. The bit error probability (BEP) expressions for the primary/secondary users (PU/SU) are analytically derived and validated through Monte-Carlo simulations according to both linear and nonlinear EH models applied at ST. In addition, the optimum distance value between PT and ST is determined considering the objective of minimizing BER of both PU and SU. Moreover, corresponding system performances are obtained regarding a power allocation coefficient at SR to obtain the trade-off between EH and information processing. The results show that the proposed FD-CR system with nonlinear EH improves the PU BEP performance compared to the non-cooperative (direct transmission) case while the SU benefits from the spectrum of PU with a significant BER performance.

Learning-Theoretic Multi-Channel Spectrum Sensing and Access in Full-Duplex Cognitive Radio Networks with Unknown Primary User Activities

The majority of the opportunistic spectrum access schemes in cognitive radio networks (CRNs) rely on the Listen-Before-Talk (LBT) model due to the half-duplex nature of conventional wireless radios. In this paper, we consider the problem of optimal opportunistic multi-channel spectrum sensing and access using full-duplex (FD) radios in the presence of uncertain primary user (PU) activity statistics. A joint learning and spectrum access scheme is proposed. To optimize its throughput, the SU sensing period has to be carefully tuned. However, in absence of exact knowledge of the PU activity statistics, the PU's performance may be adversely affected. To address this problem, we formulate a robust optimization problem. Our analysis shows that under some non-restrictive simplifying assumptions, the robust optimization problem is convex. We analyze the impact of the sensing period on the PU collision probability and the SU throughput, and find the optimal sensing period via convex optimization. We show that sublinear regrets can be attained by the proposed estimation and robust optimization strategy. Simulation studies also demonstrate that the resulting robust solution provides a good trade-off between optimizing the SU's throughput and protecting the PU.

Throughput Maximization in Energy Limited Full-Duplex Cognitive Radio Networks

In this paper, we consider a cognitive radio system with some of secondary users (SUs) equipped with multiple antenna full-duplex transceivers. We design a hybrid system to sense and access the spectrum opportunistically such that the SU operates on both underlay and interweave hybrid methods while the primary user (PU) may be active or inactive, respectively, to perform the sensing as agile as possible. Moreover, we maximize the throughput by allocating the spectral resources assuming limited available energy during each time frame with constraints on the interference to the primary user. We show that this problem is convex and propose a simple solution algorithm for it. Our simulations confirm the accuracy of our analytical and theoretical results and illustrate that the total achieved throughput of full-duplex cognitive radio even by considering self-interference is maximized under the assumed conditions.

Full-duplex mobile cognitive radio network under Nakagami-m fading environment

Energy detection based non-cooperative and cooperative spectrum sensing methods are considered in this paper for a full-duplex (FD) cognitive radio (CR) network. The CRs are assumed to be mobile and the wireless channels between CR(s) and primary transmitter (PT) are considered as time-selective. Such time-selective channels are characterized using Nakagami-m fading. The effect of mobility is captured using Jake’s model. The expressions for the false alarm and detection probabilities are derived under the influence of Nakagami-m distributed residual self-interference (RSI). Moreover, with double threshold at CR, we analyze the receiver operating characteristics (ROC), area under ROC curve (AUC) and the total error rate performances. All the analytical results are validated through Monte Carlo simulations.

Spectrum-Aware Routing in Full-Duplex Cognitive Radio Networks: An Optimization Framework

Routing and channel assignment schemes for cognitive radio networks (CRNs) are often designed assuming the half-duplex (HD) transmission capability per user. However, recent advances in full-duplex (FD) communications and self-interference suppression techniques challenge the traditional HD transmission capability, in which FD communication can significantly improve spectrum utilization. In this work, we investigate the routing and channel assignment problem in FD-based CRNs. Two types of FD communications are considered. The first type only allows for simultaneous transmission and reception over different channels, while the second type allows for simultaneous transmission and reception over the same channel. Specifically, for a given cognitive radio (CR) source–destination pair, we first formulate the channel assignment problem for each path between the communicating pair as an optimization problem with the main objective of minimizing the number of distinct assigned channels for that path such that the number of simultaneous active hops across the path is maximized. We show that the optimization problem is a binary linear programming problem, which is, in general, nondeterministic polynomial time-hard. Thus, we present a near-optimal solution based on a sequential fixing procedure, where the binary variables are iteratively determined by solving a sequence of relaxed programs. Accordingly, we develop a novel routing scheme that selects the best path along with the channel assignment such that the highest capacity is achieved. Simulation results are provided, which show that a careful routing and channel assignment scheme for FD CRNs can significantly improve the network performance.

Power Allocation Scheme and Performance Analysis for Multiuser Underlay Full-Duplex Cognitive Radio Networks With Energy Harvesting

This paper introduces a comprehensive wireless energy harvesting model applied for multiple secondary users (SUs) in full-duplex cognitive radio networks. In this proposed model, SUs harvest energy from a hybrid base station (HBS), in addition to the primary user (PU) network on the downlink and transmit their data on the uplink. The opportunistic user selection scheme is employed in the SU network to achieve a better SU performance. Considering the underlay spectrum sharing paradigm, the total SU transmitted powers are limited to the maximum allowable PU interference threshold. Moreover, the proposed SU system suffers from self-interference from the transmitting antenna of the HBS and co-channel interference from the PU transmitter. Closed-form expressions for the SU system outage probability, average symbol error probability, and ergodic capacity are derived. Obtained exact expressions are then simplified to their asymptotic results in the high signal-to-noise-ratio regime. Based on these results, a power allocation optimization problem is formulated to reduce the SU outage probability under the constraints of performance indices. Computer-aided numerical and simulation work is performed to validate the exact expressions.

Full-Duplex Cooperative Spectrum Sensing with Primary User Activity in Cognitive Radio Networks

ABSTRACTIn this paper, a full-duplex (FD) cognitive radio network (CRN) is considered, where the primary and secondary networks are non-time-slotted. We assume that the secondary users (SUs) or cognitive radios (CR) are equipped with FD ability, i.e. sensing and reporting can be performed simultaneously. The residual self-interference (RSI) channel associated with FD-CRN between receiving and transmitting antennas of the CR is assumed as Rayleigh distribution. The analytical expressions for false alarm and detection probabilities are derived. Moreover, to increase the sensing performance, the cooperative spectrum sensing (CSS) mechanism is employed. In contrast with the conventional time-slotted sensing performance, it is observed that the best sensing performance can be achieved by incorporating a few number of CRs. The analytical results are validated through simulations.

Spectrum Sensing in Full-Duplex Cognitive Radio Networks Under Hardware Imperfections

Direct-conversion radio transceivers can offer reprogrammable and low-cost hardware solutions for full-duplex (FD) cognitive radio networks (CRNs). However, they are susceptible to radio-frequency (RF) impairments, such as in-phase (I) and quadrature (Q) imbalance (IQI), which can significantly limit spectrum sensing capabilities. This paper is devoted to quantifying and evaluating the effects of IQI in single- and multichannel energy detectors operating in FD mode under both cooperative and noncooperative spectrum sensing scenarios. In this context, closed-form expressions are derived for the false alarm and detection probabilities in the general case, where partial self-interference suppression (SIS) and joint transmitter (TX) and receiver (RX) IQI are considered. Furthermore, simplified closed-form expressions for the special cases, where either the RF front end is ideal or the SIS technique is perfect, are also presented. The presented analytical results have been verified through extensive simulations and indicate that the IQI and partial SIS can significantly affect spectrum sensing accuracy in FD-based CRNs. Specifically, if ideal RF front end is assumed, spectrum sensing error can significantly increase, leading to a reduction in the CRN performance and a negative effect on the performance of primary (PR) networks. Hence, when designing spectrum sharing algorithms for FD-based CRNs, the hardware impairments should be considered to improve the CRN performance while minimizing the negative effects on PR users.

Throughput maximisation by optimising detection thresholds in full‐duplex cognitive radio networks

Herein the authors consider throughput maximisation for a secondary user (SU) in a full-duplex cognitive radio network (FD-CRN) when the SU has two separate antennas and a self-interference suppression capability. In the FD-CRN, the SU can simultaneously sense the spectrum throughout the whole time slot and transmit data. They propose algorithms based on brute-force search and particle swarm optimisation methods to help the SU achieve optimal detection thresholds for spectrum sensing in two different FD-CRN scenarios. In the first scenario, the SU individually performs spectrum sensing, whereas in the second scenario the SU's sensing results are improved by means of cooperative spectrum sensing. Theoretical and simulation results herein show that, for certain values of the system parameters in the above two scenarios, the system under consideration provides much higher throughput than previously proposed systems in conditions of high-transmission power or low signal-to-noise ratio of the primary signal.