Multiple Primary Users Research Articles

Algorithms and throughput analysis of cognitive radio‐based MIMO‐NOMA for the next generation of wireless communications

AbstractIn this paper, multi‐users (MU) communications are considered based on the cognitive ratio (CR) technique. Multiple‐input multiple‐output (MIMO) and non‐orthogonal multiple access (NOMA) are proposed to be merged with CR for the sake of improving the achievable throughput and satisfying user fairness. Moreover, utilizing MIMO‐NOMA enables the CR users to be served in the coverage network without the need for spectral sensing. Three scenarios are assumed in this paper to investigate the performance of each CR secondary user (SU) in the presence of single or multiple primary users (PUs). Pairing algorithms are proposed and applied to obtain optimum throughputs and outage probabilities, in which the algorithms are employed to select a PU with an SU to be coupled in a pair, depending on allocating the required power for each user, taking into account that any PU has the priority to reach its required throughput. Moreover, mathematical expressions have been derived for each case to evaluate the required power allocation factor and the achievable throughput for each user. Furthermore, the proposed algorithms show successful and satisfactory achievable rates and outage probabilities.

Many-to-one matching based cooperative spectrum sharing in overlay cognitive radio networks

Cooperative Spectrum Sharing (CSS) is a Dynamic Spectrum Access mechanism aimed at providing incentives for both primary users (PUs) and secondary users (SUs) through cooperative communication. This paper addresses the pairing problem among multiple PUs and SUs to allocate optimal fractions of spectrum resources in terms of access time, using the CSS technique. Inspired by Matching Theory, we model the CSS between PUs and SUs as a Two-sided Matching Market and explore different market equilibria. Cooperative strategies among SUs are employed to maximize the gross utility of cooperative SUs and overall utility of the secondary network, in exchange for relay services rendered to PUs. We propose a polynomial-time based many-to-one matching scheme that favors groups of collaborative SUs to attract favorable PUs from the opposite side, leading to optimal matching for SUs. On the other hand, each PU achieves stable matching when paired with a group of cooperative SUs. We provide necessary proofs for the achieved matching equilibrium. Furthermore, we formulate an optimization framework for the optimal usage of PU access time among stable PU-SU pairs and propose a near-optimal solution, achieving 96% accuracy compared to the optimal allocation yield from the benchmark method (analytical). Our proposed many-to-one matching scheme outperforms similar existing schemes and the one-to-one matching scheme in terms of SU utility, satisfaction, participation, and throughput fairness index.

A symmetric low-rank subspace clustering method for cooperative spectrum sensing in complex environments

A new cooperative spectrum sensing (CSS) method based on low-rank symmetric subspace clustering is proposed to improve spectrum sensing performance in complex environments. To align with the evolution of cellular networks and leverage spatial observations, a CSS model with multiple primary users and antennas is considered. To address the asymmetric weighting issue of data pairs, a low-rank representation with a symmetric constraint is proposed that reduces the signal dimensions. Finally, features are extracted from the symmetric coefficient matrix and clustered using the K-means algorithm. Through simulation, the effectiveness of the novel algorithm is numerically validated through comparative experiments.

Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

Distributed user pairing and effective computation offloading in aerial edge networks

Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas, such as mountainous areas. Therefore, the aerial networks are the key framework for the future communications, in which the aerial vehicle could act as the access point. Additionally, Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial networks. In this paper, we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing, power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks, namely CR-MEC aerial networks. Explicitly, this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation sub-problems. In the conceived CR-MEC aerial networks, we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory, aiming to minimize the system’s energy consumption. At the first step of UPCOMA, multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user pairs. Moreover, the stable user pairs would match to a high altitude platform who act as the base station, which is for appropriately allocating Transmission Time Slots (TSs) at the second step of UPCOMA. Additionally, a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual deadlines. Furthermore, the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low complexity. Meanwhile, the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.

Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks.

With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system's performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU's spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by 26.3% and the proposed method makes the SUs allocation more fair.

Performance Analysis of Full-Duplex Multirelaying Energy Harvesting Scheme in Presence of Multiuser Cognitive Radio Network

This paper investigates the outage performance of secondary receiver with an energy harvesting cognitive radio network employing multiple full-duplex (FD) relaying protocol in presence of multi-user spectrum sharing environment. Under this energy harvesting aided FD relaying network scenario, adaptive transmit power policies for the secondary user (SU) source, secondary FD relays and exact outage probability of the secondary user are derived analytically in presence of multiple primary users and multiple secondary destinations. Impact of self interference at the receive antenna of each FD relay in addition to all the transmitting interference at all receive nodes is being addressed. A comparative study considering all the interference together in an FD relaying protocol with that of conventional half-duplex (HD) relaying protocol is carried out. With the same diversity order, a trade-off between FD multi-relay and multi-destination is shown. Closed form expressions of secondary outage probability for FD energy harvested relay equipped CR network has also been evaluated. Maintaining quality of service (QoS) of multiple primary users (PUs), power allocation strategies at each secondary transmitting node is taken care of. Finally, all the analytical closed form expressions have been validated through Monte-Carlo simulations.

Distributed dynamic spectrum access through multi-agent deep recurrent Q-learning in cognitive radio network

This paper addresses the problem of distributed dynamic spectrum access in a cognitive radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the network consists of multiple primary users (PU) transmitting intermittently in their respective channels, while the secondary users (SU) attempt to access the channels when PUs are not transmitting. The problem is challenging considering the decentralized nature of CR network where each SU attempts to access a vacant channel, without coordination with other SUs, which result in collision and throughput loss. To address this issue, a multi-agent environment is considered where each of the SUs perform independent reinforcement learning to learn the appropriate policy to transmit opportunistically so as to minimize collisions with other users. In this article, we propose two long short-term memory (LSTM) based deep recurrent Q-network (DRQN) architectures for exploiting the temporal correlation in the transmissions by various nodes in the network. Furthermore, we investigate the effect of the architecture on success rate with varying number of users in the network and partial channel observations. Simulation results are compared with other existing reinforcement learning based techniques to establish the superiority of the proposed method.

Sum-rate maximization for cognitive relay NOMA Systems with channel uncertainty

In this paper, we study the sum-rate maximization problem for relay cognitive network based on decoding and forwarding (DF), where the primary network is implemented with the orthogonal frequency division multiple access (OFDMA) technology and the secondary users are in the underlying accessing mode. In the system, multiple primary users are included and one of them plays a role of relay. The relay forwards information to secondary users using the non-orthogonal multiple access (NOMA) technology. We attempt to improve the transmission rate of both primary and secondary users. The block power control strategy is developed by addressing interference of primary users caused by the channel sharing with secondary users. To tackle the tricky issue caused by the channel uncertainty, the worst-case method is adopted when solving the proposed sum-rate maximization problem. Considering the nonlinear objective function and constraints, the successive convex approximation (SCA) method is introduced to determine a suboptimal solution. Also, the Lagrangian dual algorithm is utilized to obtain the closed-form solutions of the transmission power and subchannel allocation. Numerical results verify that the proposed scheme significantly improves the sum-rate under the uncertainty channel gains compared to benchmarks.

Resource Allocation in Multi-Cluster Cognitive Radio Networks With Energy Harvesting for Hybrid Multi-Channel Access

Cognitive radio and energy harvesting techniques have been able to provide insight for solutions to the inefficiency in spectrum utilization and the effects of the limited storage capacity of energy batteries. Nevertheless, the requirement for interference constraint for primary user (PU) protection, the quality of service (QoS) requirements of the SUs, and the low power density of the ambient electromagnetic waves have restricted the performance of the radio frequency powered cognitive radio networks (RF-CRNs) in terms of the achievable data rate for specific energy budget or sufficient energy budget for target data rate. We are therefore motivated to investigate a radio frequency powered cognitive radio (RF-CR) with the capacity for improved achievable throughput and energy harvesting. Our model consists of multiple PUs and SUs coexisting in a practical overlapping clustered network. In each time frame for the adopted time division multiple access (TDMA) technique, SUs can exploit the multi-user benefit to harvest energy from both the PU and SUs transmissions for improved active probability. Therefore, taken into consideration the heterogeneity of each SUs in terms of their signal-to-noise (SNR), the energy harvesting rates and the inter- and intra-cluster interference, the problem is formulated into a fractional nonlinear optimization to determine the sensing duration, and power allocations that maximize the average energy efficiency of the RF-CRN subject, to constraints on energy causality, and PU protection. The performance of the proposed hybrid multi-channel access scheme is studied in terms of the trade-off between the total achievable throughput by the secondary users and the interference generated among others. Simulation results show that a trade-off exists between achieving optimal spectral efficiency and energy efficiency and, the optimal transmit power therefore depends on the primary design objective. Results equally show that the proposed hybrid multi-channel access scheme outperforms the existing scheme in literature.

Multiband Joint Spectrum Sensing via Covariance Matrix-Aware Convolutional Neural Network

This work investigates multiband spectrum sensing in a cognitive radio network where a multi-antenna secondary user attempts to detect several consecutive frequency bands occupied by multiple primary users. Specifically, we propose a multiband joint spectrum sensing approach based on the covariance matrix-aware convolutional neural network (CNN), in which the multiband sample covariance matrices are concatenated as the input of CNN. The proposed approach is free of model assumptions and the hidden correlation features between subbands can be learned to improve the spectrum sensing performance. Simulation results show that the proposed algorithm outperforms the state-of-the-art spectrum sensing methods in cases with and without the noise uncertainty.

Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems

In this paper, we consider an intelligent reflecting surface (IRS)-assisted downlink cognitive radio (CR) system, in which a secondary access point (SAP) communicates with multiple secondary users (SUs) without affecting multiple primary users (PUs) in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only a single PU and multiple SUs is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes.

CNN-aided multiple PU spectrum sensing in uncalibrated massive antennas SU system

This paper deploys the Convolutional Neural Network (CNN) to learn and set the statistical test in Spectrum Sensing (SS) task of multiple primary user (PU) sources in massive uncalibrated antennas of secondary users (SU) sharing the same spectrum resources. The proposed deep learning-based SS method (DL-SS) is based on the CNN architecture that has the capability of extracting features of the sample covariance matrices (SCMs) that are given as the network input, improving the overall performance and robustness. The proposed CNN-SS method is compared with nine recent multiple-antennas SS methods, namely the arithmetic–geometric detector (AGM), John’s detector (JD), sphericity detector (SD), generalized likelihood test (GLRT), locally most powerful invariant test (LMPIT), maximum–minimum eigenvalue detector (MME), covariance detector (CAV), Hadamard detector (HD) and volume detector (VD) methods; besides, the proposed method is also compared with five recent state-of-art CNN-based SS methodologies. Performance-complexity trade-off of the proposed and reference SS methods are corroborated via Monte Carlo Simulations (MCS). The proposed CNN-SS method under uncalibrated massive antennas reveals substantial benefits w.r.t. the reference methods and is competitive with others CNN-SS methodologies, both in terms of complexity and performance, achieving detection probability of Pd=0.9(@SNR=−20dB) under very low false alarm probability Pf=0.1. Under different figures of merit, the performance of the CNN-based SS detector has revealed to be indubitably superior regarding the state-of-art SS detectors. However, the proposed CNN-based SS detector presents relative computational complexity increases. Hence, to be effective, such a superior operational performance requires a very efficient processing structure in the SU base stations.

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.

NOMA-Based Overlay Cognitive Satellite-UAV-Terrestrial Networks with Multiple Primary Users

Satellite communication is becoming more and more significant in present and future communication systems for its unique advantages of strong survivability and seamless coverage, which can offset the defects of terrestrial communication such as being restricted by terrain and little coverage. Moreover, unmanned aerial vehicle (UAV)-assisted communication is considered as a promising development direction due to its flexibility and expansibility in the integrated satellite-terrestrial networks (ISTN). In addition, to overcome the spectrum shortage and low spectrum utilization problems, cognitive radio and nonorthogonal multiple access (NOMA) has been widely utilized to enhance spectrum efficiency, which is considered as the key technologies of the next generation communication. In this regard, our paper investigates the performance of cognitive satellite-UAV-terrestrial networks with NOMA scheme and multiple primary users. Specifically, the exact expressions of outage probability (OP) and ergodic capacity for both primary network and secondary network are derived. To gain further views, the asymptotic expression of OP and diversity orders for the two networks are provided. Finally, through numerical simulations, the correctness of the theoretical derivations is verified and the influences of critical variables on system indexes are also analyzed.

Comparing Backscatter Communication and Energy Harvesting in Massive IoT Networks

Backscatter communication (BC) and radio-frequency energy harvesting (RF-EH) are two promising technologies for extending the battery lifetime of wireless devices. Although there have been some qualitative comparisons between these two technologies, quantitative comparisons are still lacking, especially for massive IoT networks. In this paper, we address this gap in the research literature, and perform a quantitative comparison between BC and RF-EH in massive IoT networks with multiple primary users and multiple low-power devices acting as secondary users. An essential feature of our model is that it includes the interferences caused by the secondary users to the primary users, and we show that these interferences significantly impact the system performance of massive IoT networks. For the RF-EH model, the power requirements of digital-to-analog and signal amplification are taken into account. We pose and solve a power minimization problem for BC, and we show analytically when BC is better than RF-EH. The results of the numerical simulations illustrate the significant benefits of using BC in terms of saving power and supporting massive IoT, compared to using RF-EH. The results also show that the backscatter coefficients of the BC devices must be individually tunable, in order to guarantee good performance of BC.

Secrecy Performance Maximization for Underlay CR Networks with an Energy Harvesting Jammer.

This paper investigates the secrecy communication in an underlay cognitive radio (CR) networks with one primary user (PU) as well as multiple PUs, where the radio frequency (RF) energy-harvesting secondary user (SU) transmits the confidential information to the destination in the presence of a potential eavesdropper. We introduce a RF energy-harvesting secondary jammer (SJ) to secure the SU transmissions. The system works in time slots, where each time slot is divided into the energy transfer (ET) phase and the information transfer (IT) phase. In ET phase, the SU and SJ capture energy from the PU transmissions; in the IT phase, the SU uses the harvested energy to transmit information to the destination without causing the harmful interference to the PU transmissions, while the SJ utilizes the captured energy to generate jamming signals to the eavesdropper to secure the SU transmissions. We aim to maximize the secrecy rate for SU transmissionsby jointly optimizing the time allocation between ET phase and IT phase and the transmit power allocation at the SU and SJ. We first formulate the secrecy rate maximization as non-convex optimization problems. Then, we propose efficient nested form algorithms for the non-convex problems. In the outer layer, we obtain the optimal time allocation by the one dimension search method. In the inner layer, we obtain the optimal transmit power allocation by the DC programming, where the Lagrange duality method is employed to solve the convex approximation problem. Simulation results verify that the proposed schemes essentially improve the secrecy rate of the secondary network as compared to the benchmark schemes.

Multiple Radio Transmitter Localization via UAV-Based Mapping

The widespread use of unmanned aerial vehicles (UAVs) has opened a novel perspective on spectrum awareness and localization because of their ability to overcome spatial limits and the burden of dedicated infrastructure. In such scenarios, an underinvestigated problem is the simultaneous localization of multiple non-collaborative primary users (PUs) of the spectrum, whose number, transmit power, activity pattern, and signal structure are unknown. This work proposes a framework for multiple PU localization based on the received power measured by an antenna array mounted on a UAV. A score map is firstly constructed based on the measured power. Then two algorithms, k-means clustering and weighted centroid (KCWC), and Gaussian mixture model fitting (GMMF) are applied to the score map to estimate the number and the positions of the PUs. The performance is evaluated in terms of optimal subpattern assignment (OSPA) distance, compared with a genie-aided (GA) localization algorithm capable of separating the signals emitted by the PUs. Despite the blind nature of the method proposed, numerical results exhibit very good localization accuracy, with an OSPA distance below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6\,$</tex-math></inline-formula> m in large, line-of-sight (LOS)-dominated, outdoor scenarios with five PUs, even in the presence of channel impairments and UAV position and heading uncertainties.

No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks

This paper applies machine learning to optimize the transmission policies of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks, where time-division multiple access (TDMA) is used to serve multiple primary users and an energy-constrained secondary user is admitted to the primary users' time slots via NOMA. During each time slot, the secondary user performs the two tasks: data transmission and energy harvesting based on the signals received from the primary users. The goal of the paper is to maximize the secondary user's long-term throughput, by optimizing its transmit power and the time-sharing coefficient for its two tasks. The long-term throughput maximization problem is challenging due to the need for making decisions that yield long-term gains but might result in short-term losses. For example, when in a given time slot, a primary user with large channel gains transmits, intuition suggests that the secondary user should not carry out data transmission due to the strong interference from the primary user but perform energy harvesting only, which results in zero data rate for this time slot but yields potential long-term benefits. In this paper, a deep reinforcement learning (DRL) approach is applied to emulate this intuition, where the deep deterministic policy gradient (DDPG) algorithm is employed together with convex optimization. Our simulation results demonstrate that the proposed DRL assisted NOMA transmission scheme can yield significant performance gains over two benchmark schemes.

NOMA-Based Integrated Satellite-Terrestrial Relay Networks Under Spectrum Sharing Environment

With the increasing requirements for efficient utilization of satellite resources, both non-orthogonal multiple access (NOMA) scheme and cognitive radio (CR) have been recognized as one of the promising solutions, particularly, for the enhancement of spectrum efficiency. This letter investigates the performance of NOMA-based integrated satellite-terrestrial relay network (ISTRN) in the presence of multiple primary users (PUs) under spectrum sharing environment. Specifically, we derive the closed-form expressions of outage probability (OP) and ergodic capacity (EC) of the considered system under the interference constraint imposed by multiple adjacent PUs. To get further insights, the asymptotic analysis for OP at high signal-to-noise ratios (SNRs) is also obtained. Numerical results are provided to verify our analysis and reveal the impacts of key parameters on system performance.