Requirements Of Secondary Users Research Articles

Ensemble Learning Aided QPSO-Based Framework for Secrecy Energy Efficiency in FD CR-NOMA Systems

Cognitive radio (CR), non-orthogonal multiple access (NOMA), and full-duplex (FD) communications have been considered key technologies for providing spectrum utilization improvement and higher energy efficiency on the internet of things (IoT) networks and next-generation communication. However, security concerns are still an issue to be addressed because confidential information is exposed in wireless systems. To solve this problem, we design a novel artificial intelligence (AI)-based framework for maximizing the secrecy energy efficiency (SEE) in FD cooperative relay underlay CR-NOMA systems that are exposed to multiple eavesdroppers. First, we formulate the non-convex SEE optimization problem as bi-level optimization, subject to constraints that satisfy the quality-of-service requirements of secondary users. In particular, the outer problem is solved with ensemble learning (EL) to select the optimal relay. Regarding the inner problem, we propose a quantum particle swarm optimization (QPSO)-based technique to optimize power allocation. In addition, for comparison purposes, we describe a cooperative relay CR network with orthogonal multiple access (OMA), rate-splitting multiple access (RSMA), and half-duplex technologies. Moreover, we evaluate comparative schemes based on machine learning algorithms and swarm intelligence baseline schemes. Furthermore, the proposed EL-aided QPSO-based framework achieves performance close to the optimal solutions, with a meaningful reduction in computation complexity.

Performance of GA in Power Allocation for Underlay Cognitive Radio Systems

Power allocation plays a significant role in performance enhancement of cognitive radio (CR) system as it needs to not only limit the interference to primary users (PUs) but also maintains a minimum quality-of-service (QoS) requirements of secondary users (SUs) with a limited available resource. In many cases, traditional analytical optimization methods require a specific structure of objective function and constraints, hence, may not be applied directly to power allocation and also demand high computational complexity. This paper investigates the effectiveness of classical genetic algorithm (GA) in power distribution of sensing-free, location-aware underlay spectrum sharing based CR system models. GA has been implemented to optimize the various performance parameters of the system models. The performance of GA has been validated and compared with that of interior point method (IPM) and successive approximation solved by CVX toolbox. Simulation results depict that GA reaches closer to optimal value with a reduced computational time.

Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing

Cooperative spectrum sensing can effectively protect licensed users from harmful interference and satisfy the quality-of-service requirements of secondary users (SUs) in cognitive radio networks. In this paper, a novel joint quantization and confidence-based generalized (JQCG) combining scheme is proposed. A centralized approach is considered for cooperative spectrum sensing with SUs using the energy detector as a local spectrum sensing scheme. The confidence level is calculated using a fuzzy logic membership function for each cooperative SU. In the proposed JQCG combining scheme, each cooperative SU transmits multiple bits instead of transmitting one bit (hard combining) or the complete test statistic (soft combining) to report local sensing results to the fusion center (FC). We also derive optimal weights (in Neyman–Pearson sense) for each quantized level at the FC to maximize the probability of detection for a given false alarm probability. The proposed JQCG combining scheme is validated by extensive simulation results showing that it has a comparable performance to the soft combining scheme with less overhead. Extensive computer simulations show that the JQCG combining scheme significantly outperforms the hard combining and existing quantized schemes for cooperative spectrum sensing.

A Robust Power Allocation Scheme in Ad-Hoc Cognitive Radio Networks

We investigate the secondary users (SUs) power allocation in ad-hoc cognitive radio networks, considering an underlay paradigm where SUs can access the licensed spectrum owned by the primary users (PUs), if the total power to SUs and the interference power threshold from PUs and the signal-to-interference-noise-ratio (SINR) requirements of SUs are guaranteed. While prevalent works about distributed resources allocation algorithms are mainly focused on power allocation under the consideration of many practical limitations, which may not be the case in practice. We assume that SUs also are needed to satisfy heterogenous constraints with different positions by taking the fairness of SUs into account. Moreover, we develop a weighted robust distributed power allocation scheme to guarantees the fairness of SUs. Techniques from robust optimization theory are used to study an objective formulation with infinite numbers of constraints. The optimal solution is obtained by using Lagrange dual method, and the Lagrange multiplier is solved by the sub-gradient update algorithm. The feasibility and availability of our proposed power allocation scheme is confirmed by numerical results.

Adaptive Power Allocation Schemes for Spectrum Sharing in Interference-Alignment-Based Cognitive Radio Networks

Interference alignment (IA) is a promising technique for interference management and can be applied to spectrum sharing in cognitive radio (CR) networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR), and the quality of service (QoS) of the primary user (PU) may not be guaranteed. In addition, power allocation (PA) in IA-based CR networks is largely ignored, which can further improve its performance. Thus, in this paper, PA in IA-based CR networks is studied. To guarantee the QoS requirement of the PU, its minimal transmitted power is derived. Then, we propose three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency (EE) of the network, and the requirements of SUs, respectively, while guaranteeing the QoS of the PU. To reduce the complexity, the closed-form solutions of these algorithms are further studied in detail. The outage probability of the PU according to its rate threshold is also derived to analyze the performance of these algorithms. Moreover, we propose a transmission-mode adaptation scheme to further improve the PU's performance when its QoS requirement cannot be guaranteed at low SNR, and it can be easily combined with the proposed PA algorithms. Simulation results are presented to show the effectiveness of the proposed adaptive PA algorithms for IA-based CR networks.