Wideband Cognitive Radio Networks Research Articles

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

Dynamic resource allocation in IRS-assisted UAV wideband cognitive radio networks: A DDQN-TD3 approach

Intelligent reflecting surface (IRS) technology has found extensive application in the wireless communication domain, offering significant enhancements in communication performance by manipulating the reflection of electromagnetic waves. This research article delves into the domain of UAV-assisted wideband cognitive radio networks augmented by IRS and underpinned by sensing-based spectrum sharing techniques. We introduce UAVs as alternative solutions for both primary and secondary base stations to optimize the management of spectral resources. Our primary objective centers around the joint optimization of both the trajectories of the primary and secondary UAVs, power allocation at the secondary UAVs, reflection coefficients of intelligent reflecting surfaces, sensing time, and subcarrier allocation, all aimed at maximizing the achievable rate of secondary users. Given that the problem at hand is non-convex, we employ deep reinforcement learning algorithms for resolution. To address the challenge of mixed-action spaces, we implement a novel Dueling DQN-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm. The simulation outcomes illustrate that the methodology introduced in this paper substantially amplifies the performance of CR system when contrasted with benchmark methods. This is evident in the improved spectrum efficiency, elevated data transmission rates, and the minimization of interference with the primary network.

Application of Compressive Sensing (CS) to Wide-Band Cognitive Radio signals

Compressive Sensing (CS) is a digital signal processing developed theory that encloses the signal sampling and compression, based on the sparsity characteristics of signal. This can decrease sampling rate, so reduce computational complexity of the system without degrading the performance of the system. This paper describes the theoretical frame and a few key technical, then illustrates the application of compressed sensing theory to wide-band cognitive radio signals. Spectrum sensing is a critical issue in wide-band Cognitive Radio (CR) networks as it faces hard challenges such as high hardware cost, complexity, sampling rate and processing speed. Thus, this paper shows that Compressive Sensing could be exploited in wide-band Cognitive Radio networks to solve the spectrum sensing problems mentioned above.

Resource Allocation and Trajectory Design in UAV-Assisted Jamming Wideband Cognitive Radio Networks

The secure performance of wideband cognitive radio networks (CRNs) with OFDMA (orthogonal frequency-division multiple access) achieved by using physical layer security is low due to the severe channel fading and the limit transmit power of the secondary base station (SBS). To improve the secure performance, an unmanned aerial vehicle (UAV) assisted jamming wideband CRNs is studied where a UAV transmits the jamming signal to interfere with the eavesdropper (Eve) and the SBS provides communication services for the secondary users (SUs) over multiple sub-carrier channels. The total average secrecy rate of the secondary network is maximized by jointly optimizing the sub-carrier allocation, the jamming UAV trajectory, and the transmission power of the UAV and the SBS. An alternative algorithm is proposed to solve the challenging non-convex optimization problem. Simulation results verify the efficiency of our proposed algorithm and show that UAV-assisted jamming is beneficial for improving secure performance of wideband CRNs. It is also shown that our proposed scheme outperforms the benchmark schemes in terms of the total average secrecy rate.

Detection of Primary User in Wide-band Cognitive Radio Networks over Fading Channel using Compressed Sensing

In wideband cognitive radio networks, Nyquist sampling rate is very challenging problem. It required expensive high speed analog to digital converter and large storage spaces. Lately, compressive sensing has been emerged as significant solution to crack the conventional sampling rate requirements. It proved the ability to sample below Shannan-Nyquist criteria and reconstructing back the signal after considerable dimensional reduction. Mostly in cognitive radio networks, energy detection is widely used due to its simple implementation and blind detection property. However, regardless that energy detection is subject to noise uncertainty as well as shadowing and fading which deteriorate its detection performance. Several articles have been published to improve energy detection performance using large number of measurements. In this paper, since, the detection performance using small number of measurements or compressed measurements achieved significant performance using energy detection under additive white Gaussian noise channel. This motivated us to investigate the performance of compressed measurements-based detection over fading channels which has not been studied yet. The proposed algorithm has been implemented using MATLAB. We also studied the tradeoff between the compression ratios and using fraction of transmitted signal and its impact on detection performance and threshold choice. In comparison with the ordinary compressed energy detection over the Rayleigh fading channel the results reveal that the proposed enhanced compressed measurements-based energy detection is better in performance of detection.

Subcarrier Assignment Schemes Based on Q-Learning in Wideband Cognitive Radio Networks

Subcarrier assignment is of crucial importance in wideband cognitive radio (CR) networks. In order to tackle the challenge that the traditional optimization-based methods are inappropriate in the dynamic spectrum access environment, an independent Q-learning-based scheme is proposed for the case that the secondary users (SUs) cannot exchange information while a collaborative Q-learning-based scheme is proposed for the case that information can be exchange among SUs. Simulation results show that the performance achieved with the proposed collaborative Q-learning-based assignment is better than that obtained with the proposed independent Q-learning-based assignment at the cost of the computation cost.

Secure differential compressive spectrum sensing with 1‐bit quantisation

In this study, the authors propose a secure cooperative spectrum sensing algorithm that is based on compressive sampling theory and employed in wide-band cognitive radio network. The proposed algorithm seeks to reduce the hardware complexity of the spectrum sensing node by reducing the reported data from this node to the fusion centre and/or other sensing nodes. Due to the spatial correlation between the received primary users' signals at the sensing nodes, the difference between the measurement vectors of any two sensing nodes is expected to be very small when it is compared with the value of the measurement vector itself. The expected reduction in the dynamic range of the difference measurement vector motivates to use the 1-bit quantiser where only 10bit per measurement is retained, representing its sign. Moreover, the proposed algorithm is impregnable against spectrum sensing data falsification attacks. Both the fusion centre and sensing nodes utilise the proposed authentication algorithm to pick out data from the trustful nodes only to estimate its output data. According to the simulation results, the proposed system guarantees more safeguard to the license primary user while preserves the unlicensed secondary user throughput.

Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.

Optimal Resource Allocation for Harvested Energy Maximization in Wideband Cognitive Radio Network With SWIPT

Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We first aim to maximize the sum energy harvested by all the energy harvesters subject to constraints on rate, transmit power, interference, and subchannel assignment. Due to the non-convexity of the formulated problem, we relax the integer variable and introduce an auxiliary variable to transform the original problem into a convex problem. Then, the Lagrangian and subgradient methods are adopted to obtain the optimal solutions. However, this scheme may lead to a severe fairness issue among different links. In view of this fact, we further propose an energy-harvesting scheme for max–min fairness. In particular, we aim to maximize the energy harvested by the worst case individual link. We show that this problem can be solved in a similar manner to the problem of sum harvested energy maximization. Simulation results are presented to verify the convergence and fairness performance of the proposed algorithm, and to reveal a novel tradeoff between the network harvested energy and sensing time.

Energy efficiency of compressed spectrum sensing in wideband cognitive radio networks

In cognitive radio networks, wideband spectrum sensing (WSS) has been advocated as an effective approach to increase the spectrum access opportunity of the secondary users. On the other hand, because of the increase of sampling rate and sensing time duration cost by the analog-to-digital converter (ADC), energy saving has been a significant problem for WSS. In this paper, taking advantage of the frequency-domain sparsity of the wideband spectrum, a WSS scheme combining compressed sensing and multi-band joint detection technique is proposed to reduce the energy consumption. Based on extensive analysis and simulation, we identify the sparsity order of the wideband spectrum, the received signal-to-noise ratio (SNR) of the primary signal, and the compression rate employed in sampling as three key factors that affect the sensing performance. In particular, we derive a closed-form analytical model of the scheme. Based on these observations, the energy efficiency, defined as the ratio of the spectrum access opportunity to the energy consumption, is maximized through the optimization process of the compression rate under the sensing performance constraints. We also indicate the uniqueness of the optimal compression rate for maximizing the energy efficiency. Numerical and simulation results show that our proposed scheme is more energy efficient if the wideband spectrum is sparser in the frequency domain.

Application of Compressed Sampling for Spectrum Sensing and Channel Estimation in Wideband Cognitive Radio Networks

Application of Compressed Sampling for Spectrum Sensing and Channel Estimation in Wideband Cognitive Radio Networks

Joint sensing and power allocation in wideband cognitive radio networks

This paper investigates the optimization problem in wideband cognitive radio networks. It focuses on jointly optimizing the decision threshold, sensing time and transmission power to maximize the aggregate throughput under the constraints of local interference, false-alarm probability, missed detection probability and maximal total power of each secondary user. Sequential parameter optimization strategy is introduced to deal with different optimization parameters in the optimization problem. It starts with power allocation first, with the optimal power allocation vector obtained by adaptive power allocation strategy, the gold section search method combined with Taguchi algorithm is proposed to find the optimal sensing time with the optimal power allocation vector, and the optimal decision threshold with the optimal power allocation vector and sensing time is optimized through iterative process. Simulation results show that the proposed algorithm can achieve a better performance and is effective in solving such optimization problems.

Power saving and improving the throughput of spectrum sharing in wideband cognitive radio networks

This paper considers a wideband cognitive radio network which can simultaneously sense multiple narrowband channels and thus aggregate the detected available channels for transmission and proposes a novel cognitive radio system that exhibits improved sensing throughput and can save power consumption of secondary user (SU) compared to the conventional cognitive radio system studied so far. More specifically, under the proposed cognitive radio system, we study the problem of designing the optimal sensing time and power allocation strategy, in order to maximize the ergodic throughput of the proposed cognitive radio system under two different schemes, namely the wideband sensing-based spectrum sharing scheme and the wideband opportunistic spectrum access scheme. In our analysis, besides the average interference power constraint at primary user, the average transmit power constraint of SU is also considered for the two schemes and then a subgradient algorithm is developed to obtain the optimal sensing time and the corresponding power allocation strategy. Finally, numerical simulations are presented to verify the performance of the two proposed schemes.

A Dynamic QoS Model for Improving the Throughput of Wideband Spectrum Sharing in Cognitive Radio Networks

This paper considers a wideband cognitive radio network (WCRN) which can simultaneously sense multiple narrowband channels and thus aggregate the detected available channels for transmission and studies the ergodic throughput of the WCRN that operated under: the wideband sensing-based spectrum sharing (WSSS) scheme and the wideband opportunistic spectrum access (WOSA) scheme. In our analysis, besides the average interference power constraint at PU, the average transmit power constraint of SU is also considered for the two schemes and a novel cognitive radio sensing frame that allows data transmission and spectrum sensing at the same time is utilized, and then the maximization throughput problem is solved by developing a gradient projection method. Finally, numerical simulations are presented to verify the performance of the two proposed schemes.

Distributed Cooperative Wideband Spectrum Sensing Algorithm Based on Subspace Methods

We present a novel distributed cooperative spectrum sensing algorithm from compressive sampling in wideband cognitive radio (CR) networks. Each CR utilizes compressive sampling to reduce data acquisition costs. A subspace method is then adopted to directly detect occupied channels without reconstructing the sparse spectrum. To obtain the spatial diversity gain, global signal subspace is estimated by the distributed projection approximation subspace tracking (DPAST) algorithm in which the CRs exchange information locally and cooperate without the need for a fusion center. Then, the orthogonality property of the signal subspace and noise subspace can be exploited to find spectral support to complete the spectrum sensing. We study the convergence behavior of the DPAST algorithm and evaluate the performance of spectrum sensing. Simulation results indicate that the DPAST can effectively estimate the global signal subspace, and the proposed compressed wideband spectrum sensing scheme performs better than spectrum sensing at a single CR.

Gradual Compressive Spectrum Sensing for Wideband Cognitive Radio Network

In this paper, a Gradual Compressive spectrum sensing method is presented for collaborated users in wideband cognitive radio (CR) network. By taking the advantage of compressive sensing (CS), we can reconstructs the wideband spectrum using sub-Nyquist samples. Furthermore, we employ gradual signal acquisition and recovery that can terminate the process once the result of spectral recovery converge. This proposed method is flexible for different wideband signal sparsity and signal to noise ratio, leading to enhanced network throughput. Simulations testify the effectiveness of the proposed method in CR networks.

Distributed Compressed Spectrum Sensing via Cooperative Support Fusion

Spectrum sensing in wideband cognitive radio (CR) networks faces several significant practical challenges, such as extremely high sampling rates required for wideband processing, impact of frequency-selective wireless fading and shadowing, and limitation in power and computing resources of single cognitive radio. In this paper, a distributed compressed spectrum sensing scheme is proposed to overcome these challenges. To alleviate the sampling bottleneck, compressed sensing mechanism is used at each CR by utilizing the inherent sparsity of the monitored wideband spectrum. Specifically, partially known support (PKS) of the sparse spectrum is incorporated into local reconstruction procedure, which can further reduce the required sampling rate to achieve a given recovery quality or improve the quality given the same sampling rate. To mitigate the impact of fading and shadowing, multiple CRs exploit spatial diversity by exchanging local support information among them. The fused support information is used to guide local reconstruction at individual CRs. In consideration of limited power per CR, local support information percolates over the network via only one-hop local information exchange. Simulation results testify the effectiveness of the proposed scheme by comparing with several existing schemes in terms of detection performance, communication load, and computational complexity. Moreover, the impact of system parameters is also investigated through simulations.

인지 무선 네트워크에서 상관관계를 갖는 다중 신호를 위한 협력 베이지안 압축 스펙트럼 센싱

본 논문에서는 분산된 광대역 인지 무선 네트워크 환경에서 상관관계를 갖는 다중 신호를 위한 협력 압축 스펙트럼 센싱 기법을 제안한다. 압축 센싱 (Compressed Sensing)은 나이퀴스트율 (Nyquist Rate) 이하로 샘플링된 신호를 높은 확률로 복구할 수 있는 신호처리 기법으로 기존의 광대역 스펙트럼 센싱을 위해서 필요로 했던 고속의 아날로그-디지털 변환기 구현 문제를 해결할 수 있다. 압축 센싱에서는 압축된 신호를 원본 신호로 정확하게 복구하는 복구 알고리즘을 설계하는 것이 하나의 핵심 문제이다. 본 논문에서는 나이퀴스트율 이하로 압축된 신호의 복구 성능을 높이기 위하여 연속된 다중 입력 신호로 구성된 Multiple Measurement Vector 모델을 이용하였고, 입력 신호 사이의 시간적 상관관계를 이용하는 협력 베이지안 복구 알고리즘을 제안한다. In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.

Study the effect of PAPR on wideband cognitive OFDM radio networks

Cognitive radio (CR) technology is viewed as a novel approach for maximizing the utilization of the radio electromagnetic spectrum. Spectrum sensing methods are often used for finding free channels to be used by CR. Recently, Orthogonal Frequency Division Multiplexing system (OFDM) has been suggested as a candidate technology for multicarrier-based CR systems. However, one problem that appears in OFDM systems is the high Peak to Average Power Ratio (PAPR). In this paper, the effect of PAPR reduction of the primary signal on the performance of the multiband joint detection for wideband spectrum sensing and the profit of the primary user will be investigated. Moreover, the optimal solutions for the multi-band joint detection for the non-cooperative and cooperative schemes will be analyzed by considering the primary user's PAPR reduction. Also, the wideband cooperative spectrum sensing to improve the signal detection with high reduction in the PAPR will be suggested. Simulation results show that the PAPR reduction decreases the total price of the primary user and the aggregate opportunistic throughput of the secondary user. The cooperative scheme is effective in improving the performance in terms of the aggregate opportunistic throughput with PAPR reduction.

Joint optimisation algorithm of cooperative spectrum sensing with cooperative overhead and sub‐band transmission power for wideband cognitive radio network

AbstractCognitive radio (CR) allows a secondary user (SU) to utilise the spectrum allocated to the primary user (PU) on the premise of detecting the absence of PU by spectrum sensing. The SU often adopts cooperative spectrum sensing to cope with the hidden terminal problem if the PU is shadowed or in serious multi‐path fading; however, the cooperative overhead may decrease the throughput of the SU. In this paper, we consider a wideband CR network that can reuse multiple idle sub‐bands after cooperative spectrum sensing and then analyse the total throughput of the SU over all the sub‐bands. A multi‐variable nonconvex optimisation problem of cooperative spectrum sensing with cooperative overhead and sub‐band transmission power is proposed, which maximises the total throughput of the SU subject to the constraints of the false‐alarm probability, the detection probability, the cooperative overhead and the maximal total power of the SU. For obtaining the optimal solution, we divide the optimisation problem into two sub‐optimisation problems: one for optimising the cooperative spectrum sensing including the sensing time and the number of SUs and the other one for optimising the sub‐band transmission power. We have proven the convex optimisation of the two sub‐optimisation problems and proposed a joint optimisation algorithm of the sensing time, the number of SUs and the sub‐band transmission power. The simulation results show that the proposed joint optimisation algorithm can achieve the largest throughput with the false‐alarm probability and the detection probability within their limits. Copyright © 2013 John Wiley & Sons, Ltd.