Shortage Of Spectrum Resources Research Articles

GS-QRNN: A High-Efficiency Automatic Modulation Classifier for Cognitive Radio IoT

Cognitive radio (CR) has been introduced into the Internet of Things (IoT) domain as a potential solution to the critical spectrum resource shortage caused by a dramatic increase of IoT solutions within a limited operational spectrum. One technique of use within traditional CR processing for sensing communication activity across a spectrum of interest is automatic modulation classification (AMC). However, utilizing AMC within resource-limited cognitive radio-enabled IoT (CR-IoT) devices poses a significant challenge. In this article, we present a high-efficiency automatic modulation classifier architecture whose core is built upon the implementation of stacking quasirecurrent neural network (S-QRNN) layers acting as a feature extraction stage. This architecture utilizes the low-latency feature extraction capability of convolutional layers, and a minimalist recurrent pooling function that mimics recurrent-layer operations to aggregate the extracted features over time steps for higher classification accuracy. Additionally, implementing dense layers between two consecutive QRNN layers helps keep the network growth rate low. Therefore, the proposed S-QRNN classifier overall exhibits higher efficiency, fitting well within the limitations of CR-IoT devices. In order to demonstrate higher performance efficiency of our classifier, we conduct a comprehensive evaluation of our approach against the state-of-the-art AMC classifiers. Our analysis metrics of the classifier performance focuses on trainability, classification accuracy, execution latency, and resulting overall efficiency. Our results demonstrate that our proposed S-QRNN classifier exhibits higher trainability and, on average, a 75.83% higher efficiency, while it has, on average, a 26.54% lower execution latency. We then expand upon S-QRNN’s foundation by introducing gated recurrent units into our classifier to initially extract temporal features of the received constellations. The resulting GS-QRNN classifier demonstrates an efficiency increase by an average of 191% compared to the gated-enhanced recurrent-neural-network-based AMC classifier, with our GS-QRNN classifier average execution latency being 59.31% lower.

Integrated Resource Allocation and Task Scheduling for Full-Duplex Mobile Edge Computing

Mobile edge computing (MEC) which enables communication, computation and storage capabilities at the network edge, is an emerging paradigm for 5G and beyond 5G networks. Resource-limited mobile devices can offload their tasks to the MEC server to reduce computation latency and save energy consumption. However, limited wireless spectrum resources make it hard for MEC to guarantee low latency when a large number of users offload the computation tasks or download the computed results. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band is a promising solution to the shortage of spectrum resources. In this paper, we study a multi-user FD-MEC system involving all the phases of uploading, computing, and downloading. With the help of an FD-capable base station (BS), two half-duplex (HD) users can form an FD pair and share the same time-frequency resource for task uploading and result downloading. We formulate a latency minimization problem considering power control, time scheduling, offloading decision, and user pairing strategy, which is a mixed-integer non-linear programming (MINLP) problem. The problem can be decomposed into two subproblems. The first subproblem is delay minimization for each possible FD pair, and the second one is to decide the offloading and user pairing strategy, and the user scheduling order. We analyze the problem in two cases: (1) the computing delay at the edge server is negligible and (2) the computing delay at the edge server is non-negligible. For both cases, the first subproblem is solved using a bisection algorithm. Then, we solve the second subproblem in the two cases by a relaxing-based algorithm and a concave-convex procedure (CCCP)-based algorithm, respectively. Finally, numerical results demonstrate that with our proposed strategies, the overall latency can be significantly reduced by integrating FD communication with the MEC system.

Maximizing Energy Efficiency in Hybrid Overlay-Underlay Cognitive Radio Networks Based on Energy Harvesting-Cooperative Spectrum Sensing

Spectrum demand has increased with the rapid growth of wireless devices and wireless service usage. The rapid development of 5G smart cities and the industrial Internet of Things makes the problem of spectrum resource shortage and increased energy consumption even more severe. To address the issues of high energy consumption for spectrum sensing and low user access rate in the cognitive radio networks (CRN) model powered entirely by energy harvesting, we propose a novel energy harvesting (EH)-distributed cooperative spectrum sensing (DCSS) architecture that allows SUs to acquire from the surrounding environment and radio frequency (RF) signals energy, and an improved distributed cooperative spectrum sensing scheme based on energy-correlation is proposed. First, we formulate an optimization problem to select a leader for each channel; then formulate another optimization problem to select the corresponding cooperative secondary users (SUs). Each channel has a fixed SUs cluster in each time slot to sense the main user state, which can reduce the energy consumption of SUs sensing and can reduce the sensing time, and the remaining time can be used for data transmission to improve throughput, and finally achieve the purpose of improving energy efficiency. Simulation results show that our proposed scheme significantly outperforms the centralized scheme in terms of SUs access capability and energy efficiency.

Unlicensed assisted transmission in vehicular edge computing networks

Recently, the emerging computation-intensive in-vehicle applications and the exponentially growing data have posed a serious challenge to resource-limited in-vehicle devices. By integrating the mobile edge computing (MEC) into the vehicle networks, vehicular edge computing (VEC) is envisioned as a promising solution. In this paper, we divide the VEC system into small time slots, which effectively simulates dense small-scale computing scenarios in the rapidly varying channel environment over time. Furthermore, to alleviate the shortage of spectrum resources, vehicles can offload part of computing tasks to the edge server not only through licensed channels but also through unlicensed channels. However, dynamic topology changes and complex communication characteristics of the vehicle networks lead to the strategies of task offloading and resource allocation being difficult to obtain. By decoupling the non-convex optimizing problem and splitting it into multiple sub-problems, we design a joint power, spectrum and computing resource allocation strategy with low complexity, in order to minimize the total energy consumption of all vehicles for processing tasks. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme that employing licensed and unlicensed access in VEC networks can reduce the energy consumption of vehicles for processing tasks in comparison with baseline schemes.

Analysis of Co-Channel Interference in Connected Vehicles WLAN with UAV

Unmanned aerial vehicle (UAV) has the advantages of flexibility, strong controllability, and easy deployment. It provides wireless communication with low delay and high throughput. However, under the background of the increasing shortage of spectrum resources, the frequency band resources of UAV systems are tight, resulting in increasingly serious co-channel interference between services, and the communication quality cannot be effectively guaranteed. Therefore, it is very important to evaluate the co-channel interference of UAV system and improve its spectral efficiency. This paper introduces the interference model for the research and analysis of the frequent coexistence of UAV and connected vehicles WLAN system and applies the model to evaluate the co-channel interference of UAV in the ISM frequency band of 2.4 GHz. The actual receiver filter is modeled and analyzed, the FDR mathematical expression for changing the protection band is derived, and two UAV interference signal waveforms, cyclic prefix orthogonal frequency division multiplexing waveform and windowed orthogonal frequency division multiplexing waveform, are proposed to effectively improve the utilization of frequency resources. After simulation, the interference of the interfered connected vehicles WLAN system is calculated and compared with the relevant service interference threshold standards issued by the International Telecommunication Union. To ensure that the UAV will not interfere with the connected vehicles WLAN system in the ISM frequency band of 2.4 GHz, the protection height of the UAV system in China is 6.2 km.

Spectrum sensing in cognitive radio: A deep learning based model

AbstractSpectrum sensing is an efficient technology for addressing the shortage of spectrum resources. Widely used methods usually employ model‐based features as the test statistics, such as energies and eigenvalues, ignoring the temporal correlation aspect. Deep learning based methods have the potential to focus on various aspects, including temporal correlation. However, the existing ones are not good at capturing the temporal correlation features from spectrum data as traditional convolutional neural network (CNN) and long short‐term memory network (LSTM) are used for feature extraction. Traditional CNNs were not designed to capture the global temporal correlations from time series data. Standard LSTM captures the temporal correlations based on the data collected from previous time slots only and cannot emphasize some important parts of a time series. This article proposes a data‐driven deep learning based model to classify the received raw signals automatically, where the received signal data is considered time‐series data. The proposed deep neural network (DNN) model is mainly featured with 1‐dimensional convolutional neural network (1D CNN), bidirectional long short‐term memory network (BiLSTM), and self‐attention (SA). The 1D CNN and BiLSTM are responsible for extracting the local features and global correlations from the time series data, and BiLSTM could extract sufficient features in opposite directions. The SA layer enables the classifier network to emphasize those important features obtained by BiLSTM. The simulation results demonstrate that our model performs better than a number of existing DNN models in terms of the probabilities of missed detection and false alarm, especially when the signal to noise ratio is low. Moreover, the impacts of the modulation scheme and sample length on the detection performance are studied.

Power Loss Suppression for Time-Modulated Arrays in Radar-Communication Integration

In order to minimize the weight and volume of the radio frequency equipment while alleviating the shortage of spectrum resources, electronic system integration has become a topical area in both civil and military fields, and the radar-communications integrated technology based on the time-modulated array (TMA) has also been proposed due to its low complexity and easy reconfiguration property. The TMA has power losses caused by sideband signals radiation, which is inherent drawback. However, existing TMA power loss optimization methods are not suitable for radar-communication integration, because of the existence of multiple harmonic components. In this paper, a closed-form expression of TMA power losses for radar-communications integration is derived and the sideband radiation is optimized. Firstly, some preliminaries about TMA are shown. Then, the radar-communications integration strategy based on TMA is introduced. Moreover, a closed-form expression of TMA power losses caused by sideband signals radiation is derived, and the direct power losses optimization method is proposed based on the derived results. Finally, the theoretical results are validated by simulations.

A Novel Binary Slime Mould Algorithm with AU Strategy for Cognitive Radio Spectrum Allocation

Spectrum has now become a scarce resource due to the continuous development of wireless communication technology. Cognitive radio technology is considered to be a new method to solve the shortage of spectrum resources. The spectrum allocation model of cognitive radio can effectively avoid the waste of spectrum resources. A novel binary version of slime mould algorithm is proposed for the spectrum allocation model to solve the spectrum allocation scheme. In addition, adding unselected factors strategy can make the approach find a better solution. Compared with other algorithms, the novel binary slime mould algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation. The resulting spectrum allocation scheme can achieve efficient use of network resources.

Vehicle Pairing, Spectrum Assignment and Power Control for Non-Safety Critical Services in Heterogeneous Vehicular Networks

To accommodate the ever-increasing demands for non-safety critical services in vehicular networks, heterogeneous vehicular networks (HVNs) have been proposed and drawn considerable attention. It is known that the performance of HVNs suffers from a shortage of spectrum resource, and the relatively low utilization of licensed spectrum makes the spectrum shortage even worse. To cope with this, by introducing unlicensed spectrum into HVNs, we investigate the spectrum efficiency (SE)-oriented collaborative transmission. The SE for HVNs depends highly on the channel state information (CSI) of the effective communication links which are normally determined by the vehicle pairing. So, we first propose a joint social-physical domain vehicle pairing algorithm (JSPD-VPA), and then formulate an optimization problem to maximize the SE of vehicle-to-everything (V2X) communications while guaranteeing the quality of services (QoS) requirements of cellular users in licensed spectrum and users in unlicensed spectrum. Considering that the formulated problem is NP-Hard problem, to solve it, the formulated problem is decomposed into two subproblems, i.e., spectrum assignment subproblem and power control subproblem. An optimal hypergraph coloring algorithm is proposed to solve the spectrum assignment subproblem, and a low-complexity three-dimensional geometry approach is developed to solve the power control subproblem. Simulation results reveal that the proposed vehicle pairing, spectrum assignment and power control algorithm (VP-SA-PCA) can greatly enhance the SE and outperforms the baselines around 7%-37% within a certain range. Moreover, the superiority of the proposed JSPD-VPA has been validated.

NOMA-Based Cognitive Spectrum Access for 5G-Enabled Internet of Things

With the rapid development of 5th generation (5G) communications, 5G-enabled Internet of Things (IoT) will provide high-speed and low-latency data transmissions as well as better communication coverage. 5G-enabled IoT can achieve efficient and reliable connections of massive nodes via the technical advantages of ultra-reliable low-latency communication (uRLLC), massive machine-type communication (mMTC) and enhanced mobile broadband (eMBB). However, the shortage of spectrum resources has become a bottleneck restricting the development of 5G-enabled IoT. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are emerging as promising spectrum sharing technologies to improve spectrum utilization. In this article, by integrating CR and NOMA, NOMA-based cognitive spectrum access is studied to improve the spectral efficiency of 5G-enabled IoT, by which the IoT may access the spectrum licensed to a primary user (PU) via NOMA. First, underlay, overlay and hybrid spectrum access modes based on NOMA are described, respectively, and two successive interference cancellation (SIC) schemes are proposed to guarantee the decoding performance of IoT and PU, respectively. Second, subcarrier aggregation-based NOMA and interference suppression-based NOMA decoding are presented to improve the utilization of discrete idle spectrum and decrease the impact of PU's interference on decoding, respectively. Third, grant-free NOMA and compressed sensing-based NOMA decoding are proposed to improve the performance of URLLC and mMTC, respectively. Fourth, cooperative NOMA is presented to improve the transmission performance of PU and IoT in fading channels. Finally, some open research works and challenges are discussed.

Research on spectrum scheduling based on discrete artificial bee colony algorithm

With the increasing shortage of spectrum resources on the earth, how to ensure the high-quality transmission of satellite wireless signals to the ground has become one of the most important problems in the current satellite ground communication system. In order to schedule spectrum resources more reasonably and efficiently, we propose a spectrum resource scheduling model based on adaptive chaotic distributed differential artificial bee colony algorithm. The algorithm uses chaotic distribution to generate initial solution. In the search phase, differential evolution strategy and artificial bee colony algorithm are used to work in parallel to find the local optimal solution. The selection operator is used to calculate the probability of selecting new bee sources, which improves the reliability and convergence speed of the algorithm. Finally, the results show that in single-channel and multi-channel scenes, the algorithm can help to improve the fairness and throughput.

5G heterogeneous network selection and resource allocation optimization based on cuckoo search algorithm

In order to solve the problem of spectrum resource shortage and high-speed access in 5G network, the multi-agent system is embedded into the standard cuckoo algorithm, and the multi-agent cuckoo algorithm is proposed. Firstly, the spectrum information of network channel sharing available to users is obtained, and the cuckoo search algorithm is used to optimize under the condition of satisfying the quality of service (QoS) guarantee of users, and the optimal allocation scheme is obtained by iterating for many times. The use steps are illustrated by examples. Compared with the traditional genetic algorithm, the calculation complexity can be reduced, and it can also be extended to more users and networks. In this algorithm, each cuckoo represents an agent, and all the agents constitute a von-Neumann structure. Through the neighborhood competition cooperation operator, mutation operator, self-learning operator and the evolution mechanism of cuckoo algorithm, they can continuously enhance the energy and improve the adaptability, and can quickly and accurately find the optimal solution of the problem.

Performance Evaluation of NOMA-Based Cognitive Integrated Satellite Terrestrial Relay Networks With Primary Interference

Satellite communication has attracted great interests due to extensive coverage and no terrain restriction, which can make up for the shortcomings of terrestrial communication, especially in remote areas. Hence, integrated satellite-terrestrial relay network (ISTRN) is considered as a promising research direction. However, the shortage of spectrum resources is becoming the bottleneck of the future development of ISTRN because of the low utilization of spectrum. In this regard, non-orthogonal multiple access (NOMA) scheme and cognitive radio are considered as great ways to solve this problem. This paper investigates the performance of a NOMA-based ISTRN under spectrum sharing environment with primary interference. Particularly, the exact closed-form expressions of outage probability and ergodic capacity are derived to evaluate the system performance, where the interference constraint caused by the multiple primary users and the interference of primary transmitter have been considered. Furthermore, in order to reveal the impacts of key parameters on system performance at high signal-to-noise ratios efficiently, the asymptotic analysis of OP is also provided. After that, we obtain the relationship between system performance and the power allocation factor, the number of primary users, interference parameter or the channel environment, which will enlighten the research of NOMA-based cognitive ISTRN. At last, Monte Carlo results are provided to verify the effectiveness of our theoretical analysis.

Coexistence Analysis of D2D‐Unlicensed and Wi‐Fi Communications

By enabling direct communications between nearby user equipment (UE), device‐to‐device (D2D) communication has become one of the key technologies in 5th generation (5G) mobile networks. D2D communication brings new communication opportunities for mobile devices, especially in a highly dense network. In this paper, D2D communication in the unlicensed spectrum, namely, D2D‐Unlicensed (D2D‐U), is discussed. The use of unlicensed frequency bands can ease the shortage of spectrum resources and improve network performance. However, the D2D‐U in 5G has significant effects on the network performance of existing unlicensed networks sharing the same frequency bands, such as Wi‐Fi and Bluetooth. Therefore, it is necessary to design a fair coexistence scheme for D2D‐U. To understand the coexistence problem, in this paper, we first formulate the network performance of D2D‐U and Wi‐Fi under two different coexistence schemes, namely, listen before talk (LBT) and duty cycle mechanism (DCM). Then, we use computer simulations to investigate a mode selection scheme that switches between these two schemes and point out the best possible solution for the coexistence between D2D‐U and Wi‐Fi.

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on underlay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.

Research on 6G mobile communication system

6G will have super flexibility in the utilization of time, frequency and space resources. The time slot can be compressed to use the high frequency band more effectively and meet the demand of delay sensitive service. Terahertz frequency band is used to make up for the current shortage of spectrum resources. The 6G system will adopt the technical architecture of multi frequency close integration and no cellular cell communication network in air, sky and ground areas. 6G system will realize the integration of network and intelligence, communication and sensor, and adopt novel discontinuous communication technology, wireless power transmission and energy harvesting technology, so as to access all people, information and goods with ultra real experience, and completely eliminate the restrictions of working place and time.

Deep STFT-CNN for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the crucial technologies used to solve the shortage of spectrum resources. In this letter, based on the short-time Fourier transform (STFT) and convolutional neural network (CNN), we firstly develop a STFT-CNN method for spectrum sensing. The proposed method exploits the time-frequency domain information of the signal samples and achieves the state of the art detection performance. In particular, the method is suitable for various primary users’ signals and does not need any priori information. Besides, we also analyze the signal-to-noise ratio robustness and the generalization ability of the proposed algorithm. Finally, simulation results demonstrate that the proposed method outperforms other popular spectrum sensing methods. Notably, the proposed method can achieve a detection probability of 90.2% with a false alarm probability of 10% at SNR = −15dB.

Demand Response Management Research Based on Cognitive Radio for Smart Grid

Cognitive radio is introduced into the demand response management (DRM) of smart grid with the hope of alleviating the shortage of spectrum resources and improving communication quality. In this paper, we adopt an energy detection algorithm based on generalized stochastic resonance (GSRED) to improve the spectrum sensing accuracy under the circumstances of low signal-to-noise ratio without increasing system overhead. Specifically, a DRM scheme based on real-time pricing is investigated, and the social welfare is taken as the main index to measure system control performance. Furthermore, considering the adverse effects incurred by incorrect spectrum sensing, we incorporate the probability of the DRM system causing interference to primary user and spectrum loss rate into the evaluation index of the system control performance and give the final expression of the global optimization problem. The influence of sensing time on system communication outage probability and spectrum loss rate is elaborated in detail through theoretical derivation and simulation analysis. Simulation results show that the GSRED algorithm has higher detection probability under the same conditions compared with the traditional energy detection algorithm, thus guaranteeing lower communication outage probability and spectrum loss rate.

Throughput Maximization for Multiple D2D Group Communications Underlaying Cellular Networks

In the scenario where multiple device-to-device (D2D) users and cellular users coexist, the large number of D2D users not only results in the shortage of spectrum resources but also brings interference to the communication of cellular users. In this paper, we establish a clustering model centered on cellular users and propose a resource allocation algorithm based on a D2D clustering model. On the basis of ensuring the throughput requirements of cellular users, this algorithm reconstructs various matrices in the graph theory through probability models, sets the priority of D2D pairs, and maximizes the number of user accesses under interference tolerance. Besides, on the optimization of the number of users, we adopt the Rubinstein game model to adjust the game order according to the priority and optimize the bandwidth allocation mode, so as to improve the overall throughput of the network. Simulation results show that the proposed algorithm can increase the number of users and the network throughput, while shortening the spectrum allocation time by more than half.

Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks.

Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.