Shortage Of Spectrum Resources Research Articles

Joint Optimization Scheme for Subcarrier Selection and Power Allocation in Multicarrier Dual-Function Radar-Communication System

Dual-function radar-communications (DFRC) system has been recognized as a promising solution to alleviate the radio frequency spectrum congestion and the shortage of spectrum resources. In this article, we address the problem of designing the joint subcarrier selection and power allocation scheme to minimize the power consumption of a DFRC system, under a general scenario in which the DFRC system is capable of performing a primary radar purpose and a secondary communications purpose in the meantime. In particular, the key mechanism is to minimize the total radiated power of the multicarrier DFRC system by jointly selecting the best possible subcarriers for radar and communications purposes in sequence and allocating the optimal power resource on the corresponding subcarriers, under the constraints of a predefined mutual information for target characterization and a desired communications data rate for information transmission. The resulting problem is formulated as a two-variable nonconvex optimization problem, one for subcarrier selection and the other for power allocation. Then, after convex relaxation reformulation and problem partition, an efficient three-step solution technique is developed for the joint optimization scheme, which combines the cyclic minimization algorithm and Karush-Kuhn-Tuckers optimality conditions. Finally, numerical results are provided to validate the theoretical findings and to verify the effectiveness of the proposed joint optimization scheme.

Sparse code multiple access for downlink multiple access of 5G wireless networks

Mobile data traffic as the two main forces for the development of future communications, mobile Internet and Internet of Things give the fifth generation of mobile communications (5G) unlimited possibilities and prospects. A large number of mobile device access and shortage of spectrum resources are also issues that 5G needs to face. Based on the understanding of wireless communication multiple access, a low complexity FPGA implementation solution based on 5G wireless communication sparse code multiple access system is proposed, and the synthesizable Verilog language is used in QuarLusII and ModelSim platforms. The integrated design of the circuit and the FPGA verification are completed, and the results prove that the design is complete and can be used in practice. In the scenario of downlink multiple access, a high-performance blind detection algorithm based on sparsity constraints is also proposed. Considering the sparsity of the system, a corresponding mathematical model is established, and a probability parameter is used to characterize the sparsity of the system, thereby approaching the maximum posterior probability detection performance. In addition, using the sparsity of the system, we developed a constrained detection and derived a priori metric, thereby reducing complexity. Through verification, the performance of the blind detection algorithm proposed in this paper can approach MAP detection, and has significant gains in performance and complexity compared to traditional SD algorithms.

Performance Evaluation of Key Performance of 5G Mobile Communication System And Development Countermeasures

This paper mainly studies the key performance of 5G mobile communication system and its development countermeasures. This paper introduces the challenges faced by 5G mobile communication system, which mainly come from increasing mobile data volume, connecting mass terminals anytime and anywhere, shortage of spectrum resources, and green environmental protection and energy efficiency; In order to study the key performance of 5G mobile communication system more comprehensively, first, this paper analyzes the practical significance of establishing the key performance indexes system of mobile communication system. Second, the performance requirements of mobile communication systems in six main application scenes are discussed. Third, the key performance indexes system of mobile communication system is constructed; This paper introduces the purpose and steps of performance evaluation of key performance of mobile communication system, and compares and evaluates the key performance of 5G and 4G in wide-area continuous coverage scene based on AHP. Suggestions on the development of 5G mobile communication system are put forward from four aspects: policy and system, demand and service, technology and talents, and sustainable development.

Dynamic Spectrum Access Based on Improved SARSA Algorithm

Dynamic spectrum access is the core technology of cognitive radio, accurate spectrum access can effectively alleviate the shortage of spectrum resources and realize the "secondary utilization"of spectrum resources. For this reason, this paper proposes a dynamic spectrum access based on improved Sarsa algorithm. The algorithm uses a simulated annealing strategy instead of the ε-greedy strategy in action selection, which can effectively overcome the problem of the traditional Sarsa algorithm falling into a local optimum. Simulation experiments show that the algorithm proposed in this paper can effectively improve the throughput of cognitive users and reduce the probability of collision with the main user, what’s more, it also has a significant increase in speed.

Communication‐awareness joint beams and power allocation scheme of radar network for manoeuvring targets tracking

The coexistence of radar and communication system is a crucial task owing to the shortage of spectrum resources. In this study, the authors consider a multiple manoeuvring targets tracking problem via a radar network in the presence of several non-cooperative, spatially random, and spectral overlapped communication areas (CAs). Instead of costly inner pattern optimisation, a communication awareness joint beam and power allocation (CJBPA) scheme of radar network is proposed to achieve high flexibility in coexistence. In CJBPA, radars and CAs treat the signals from each other as interference, which are formulated and discussed from the perspective of spectrum and space. The CJBPA optimises the overall tracking performance, which is characterised by the posterior Cramer-Rao lower bound under the consideration of coexistence. The resultant non-convex problem is relaxed and decoupled as an integrated two-staged optimisation. To accelerate the numerical procedures of the optimisations, custom-built primal dual interior point methods are derived by the structure information of the problems. The effectiveness of the proposed CJBPA scheme is assessed through simulations, showing that by using spatial and spectral information of undesired signals, the tracking performance can be optimised while the impacts of interference on both radars and CAs are restricted.

Secure Transmission Method of Spectrum Watermark for Fine-Grained Spectrum Management

Fine-grained spectrum management is promising in addressing the contradiction between the access requirements for massive radio systems and the shortage of spectrum resources. It is difficult to identify the radio system and securely transmit its spectrum identity information due to the complexity, openness, and variability of the electromagnetic environment, which has become a major challenge for the fine-grained spectrum management. In this paper, we consider a practical and proactive spectrum monitoring scenario, where the spectrum monitoring node monitors the communication signal sent by a source node to a destination node for the fine-grained spectrum management. A malicious node eavesdrops on the transmission for accessing and deceiving them. We propose a secure transmission method of spectrum watermark for the fine-grained spectrum management. The spectrum watermark for identifying the radio system is hidden in the normal communication signal via adjusting the embedding parameters, and has no effect on the required transmission performance. We investigate the transmission performance of the spectrum watermark in two typical use cases. In first case, the transmission rate of the communication signal is given and fixed. In another case, the transmission rate of the communication signal can be adaptively adjusted according to the channel quality. The analysis and simulation results show that the proposed method achieves the optimal and secure transmission of the spectrum watermark by adjusting the embedding parameters of the spectrum watermark while ensuring the required and optimal transmission performance of the communication signal.

Machine Learning-Based Energy-Spectrum Two-Dimensional Cognition in Energy Harvesting CRNs

Energy harvesting cognitive radio network (EH-CRN) is a promising approach to address the shortage of spectrum resources and the increase of energy consumption simultaneously in wireless networks. In this article, we propose a novel machine learning (ML)-based energy-spectrum two-dimensional (2D) cognition technology to improve the sensing accuracy as well as the network throughput in EH-CRNs, which consists of sensing, prediction and decision modules. More specifically, we first study the 2D sensing module which is achieved by a carefully constructed dynamic Bayesian network (DBN) to effectively exploit the coupling between spectrum usage and energy harvesting in EH-CRNs. Then we propose a deep neural network (DNN) based 2D transmission decision module to optimize the transmission energy of secondary users (SUs). With our proposed novel 2D cognition scheme, SUs can characterize the energy-spectrum correlation and transmit data with optimal transmission energy. The proposed ML-based 2D cognition is evaluated via extensive simulations in terms of sensing accuracy, prediction accuracy, and network throughput, and simulation results indicate that our proposed scheme significantly outperforms the conventional one-dimensional (1D) cognition scheme working in spectrum or energy dimension only.

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

With the rapid increase of vehicles, the explosive growth of data flow and the increasing shortage of spectrum resources, the performance of existing task offloading scheme is poor, and the on-board terminal can’t achieve efficient computing. Therefore, this article proposes a task offload strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. Firstly, the system architecture of Internet of vehicles is designed. The Road Side Unit receives the vehicle data in community and transmits it to Mobile Edge Computing server for data analysis, while the control center collects all vehicle information. Then, the calculation model, communication model, interference model and privacy issues are constructed to ensure the rationality of task offloading in Internet of vehicles. Finally, the user cost function is minimized as objective function, and double-layer deep Q-network in deep reinforcement learning algorithm is used to solve the problem for real-time change of network state caused by user movement. The results show that the proposed offloading strategy can achieve fast convergence. Besides, the impact of user number, vehicle speed and MEC computing power on user cost is the least compared with other offloading schemes. The task offloading rate of our proposed strategy is the highest with better performance, which is more suitable for the scenario of Internet of vehicles.

Signal Estimation in Cognitive Satellite Networks for Satellite-Based Industrial Internet of Things

Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved ${M_2}{M_\infty }$ method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT.

Energy Sharing-Based Energy and User Joint Allocation Method in Heterogeneous Network

Heterogeneous network, which is a key technology of fifth generation(5G) mobile communication system, can effectively solve the spectrum resource shortage in the communication system. However, with densified deployment of base stations (BSs) and growth of user quantity and communication data size, the system energy consumption of BSs has imposed enormous economic pressure on network operators. As a result, energy consumption decreases and cost of communication system becomes a problem that requires urgent solutions. Renewable energy power generation devices of BSs have provided an opportunity for solving this problem with the development of smart power grids. However, the production rate of renewable energy sources presents intense volatility because of the influence of weather factors. This condition brings new challenges to the energy allocation of communication system. Therefore, an energy sharing link between BSs was established in a heterogeneous wireless network, and the distance between macro-BS and micro-BS was taken as an index. The energy consumption-based energy and user joint allocation method and energy cost-based energy and user joint allocation method were proposed. The two multi-objective optimization problems were converted into two convex optimization problems, and the optimal allocation strategies were solved using convex optimization toolbox. Simulation results indicate that the energy consumption-based allocation method takes energy consumption as the optimization objective and makes the shared link transmit energy sources as few as possible. This way reduces energy consumptions of the link and the system. Starting from the economic angle, the energy cost-based allocation method considers the influence of energy price, coordinates energy allocation between BSs based on energy cost using the shared link, and optimizes energy allocation among BSs at each time slot. Therefore, the method can reduce the energy cost of the system by a large margin.

Energy efficiency resource allocation for D2D communication network based on relay selection

In order to solve the problem of spectrum resource shortage and energy consumption, we put forward a new model that combines with D2D communication and energy harvesting technology: energy harvesting-aided D2D communication network under the cognitive radio (EHA-CRD), where the D2D users harvest energy from the base station and the D2D source communicate with D2D destination by D2D relays. Our goals are to investigate the maximization energy efficiency (EE) of the network by joint time allocation and relay selection while taking into the constraints of the signal-to-noise ratio of D2D and the rates of the Cellular users. During this process, the energy collection time and communication time are randomly allocated. The maximization problem of EE can be divided into two sub-problems: (1) relay selection problem; (2) time optimization problem. For the first sub-problem, we propose a weighted sum maximum algorithm to select the best relay. For the last sub-problem, the EE maximization problem is non-convex problem with time. Thus, by using fractional programming theory, we transform it into a standard convex optimization problem, and we propose the optimization iterative algorithm to solve the convex optimization problem for obtaining the optimal solution. And, the simulation results show that the proposed relay selection algorithm and time optimization algorithm are significantly improved compared with the existing algorithms.

Energy-Efficient Cooperative Spectrum Sensing Strategy for Cognitive Wireless Sensor Networks Based on Particle Swarm Optimization

Cognitive wireless sensor networks (CWSNs) can use the idle authorized frequency band to solve the problem of spectrum resource shortage in traditional wireless sensor network. By employing spectrum hole in the authorized frequency band, the spectrum sensing technology can degrade the coexistent interference and enhance the performance of whole sensor network. Due to the characteristics of limited battery energy and low processing capacity with sensor nodes, it is necessary to enhance the energy efficiency while improving spectrum sensing performance. In this paper, a cooperative spectrum sensing strategy for CWSNs based on particle swarm optimization is proposed. Firstly, the system throughput and energy consumption are quantitatively analyzed, and the mathematical model related to energy efficiency is established. Secondly, the particle swarm optimization (PSO) algorithm is used to obtain the optimal selected nodes set under the limited conditions of false alarm probability and detection probability. To avoid local optimization in the process of problem solving, Cauchy mutation method is introduced to optimize the parameter selection of fitness function. The experimental results illustrate that our proposed method can improve the throughput of the system while ensuring the sensing performance, and achieve the energy efficiency effectively.

Resource Management in LTE-U Systems: Past, Present, and Future

With the tremendous growth in mobile data traffic, wireless cellular networks are facing a rigorous challenge to increase network capacity. Despite that many advanced technologies are used, the shortage of spectrum resource is still the main bottleneck for capacity enhancement. To address the challenge, the cellular networks have been motivated to seek for more fruitful radio spectra. Amongst many others, the unlicensed 5 GHz spectrum is a promising candidate due to its low channel attenuation, large available bandwidth, and easy to utilize. Therefore, the innovative technology of long-term evolution (LTE) using the unlicensed spectrum, known as LTE-Unlicensed (LTE-U), has been widely investigated as a promising means to increase the data rate of cellular networks. The unique characteristics of the unlicensed spectrum bring new challenges to resource management in the LTE-U system. During the last few years, there have been a lot of resource management designs for the newly-born but vigorous LTE-U technologies. This paper provides a comprehensive overview of the state-of-the-art resource management scenarios in LTE-U systems, including single small cell base station (SBS), multiple SBSs, device-to-device (D2D) networks, vehicular networks, and unmanned aerial vehicle (UAV) systems. The future research issues of resource management in LTE-U for 5G are also outlined.

Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio

In order to ease the conflict between the bandwidth demand of high-rate wireless communication and the shortage of spectrum resources, a wideband spectrum sensing method based on reconfigurable filter bank (RFB) with adjustable resolution is presented. The wideband signals are uniformly divided into multi-narrowband signals by RFB, which is designed by polyphase uniform Discrete Fourier Transform (DFT) modulation, and each sub-band is sensed by energy detection. According to the idle proportion of detected sub-bands, the number of RFB sub-bands is reset in next spectrum-sensing time. By simulating with collected wideband dataset, the influence of filter bank sub-bands number and idle state proportion on the sensing results is analyzed, and then on the basis of the trade-off between spectrum-sensing resolution and computational complexity, the optimal sub-bands number of filter bank is selected, so as to improve the detection performance and save resources.

NOMA-Based Resource Allocation for Cluster-Based Cognitive Industrial Internet of Things

The development of Industrial Internet of Things (IIoT) has been limited due to the shortage of spectrum resources. Based on cognitive radio, the cognitive IIoT (CIIoT) has been proposed to improve spectrum utilization via sensing and accessing the idle spectrum. To improve sensing and transmission performance of the CIIoT, a cluster-based CIIoT is proposed, in this article, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA). The frame structure of the CIIoT is designed, and the spectrum access probability and average total throughput of the CIIoT are deduced. A joint resource optimization for sensing time, node powers, and the number of clusters is formulated to maximize the average total throughput. The optimal solution is obtained via sensing and power optimization. The clustering algorithm and cluster head alternation are proposed to improve transmission performance and ensure energy balance, respectively. The simulations have indicated that the NOMA for the cluster-based CIIoT can better guarantee the transmission performance of each node, especially the node decoded first, than the traditional NOMA and orthogonal multiple access.

Hybrid Adaptive Channel Access for LTE-U Systems

To alleviate the shortage of spectrum resource on licensed bands, long-term evolution in unlicensed bands (LTE-U) has been proposed. To facilitate the fair coexistence between LTE-U and the legacy WiFi networks on unlicensed bands, two well-known channel access mechanisms named as duty-cycle muting (DCM) and listen-before-talk (LBT) have been proposed and studied extensively. Accordingly, in this paper, a unified hybrid adaptive channel access scheme is proposed, which takes advantages of both the DCM and LBT mechanisms. Our proposal can realize the flexible handoff between the DCM and LBT mechanisms to meet the requirements of different markets. It can also adaptively adjust the important parameters, such as the back-off window size and the duty-cycle time fraction based on the WiFi traffic and the available licensed spectrum resource while guaranteeing the fair coexistence between the WiFi and LTE-U systems. Moreover, joint spectrum and transmission power allocation is also studied to improve the spectra efficiency (SE) on both licensed and unlicensed bands. Finally, analytical and numerical results are demonstrated to verify the effectiveness of the proposed scheme.

A Novel Multichannel Internet of Things Based on Dynamic Spectrum Sharing in 5G Communication

The shortage of spectrum resources has limited the development of Internet of Things (IoT). Fifth generation (5G) network can flexibly support a variety of devices and services, which makes it possible to combine 5G with IoT. In this paper, a novel multichannel IoT is proposed to dynamically share the spectrum with 5G communication, where an IoT node including transmitter and receiver is designed to perform 5G communication and IoT communication simultaneously. The subchannel sets allocated for 5G communication and IoT communication are defined by two complementary spectrum marker vectors, respectively. Two independent spectrum sequences are generated by calculating the inner products of spectrum marker vectors, presudo-random phases and power scaling vectors. Two time-domain fundamental modulation waveforms generated by the inverse fast Fourier transform of the spectrum sequences are used to modulate 5G data and IoT data, respectively. The receiver can detect the data using the same spectrum marker vectors as the transmitter. The BER performances of the system using binary modulation and cyclic code shift keying modulation in the cases of spectrum marker error and multiple access are analyzed, respectively. A subchannel and power optimization unit is formulated as a joint optimization problem, which seeks to maximize the 5G throughput under the constraints of minimal IoT throughput, maximal power, and maximal interference. An alternative optimization problem is proposed to maximize the IoT throughput while guaranteeing the minimal 5G throughput. A joint optimization algorithm based on Lagrange dual decomposition is proposed to achieve the optimal solution. Simulation results indicate that the proposed IoT can improve the 5G throughput significantly while the IoT throughput is guaranteed.

Rate and Energy Efficiency Improvements for 5G-Based IoT With Simultaneous Transfer

Internet of Things (IoT) is facing the shortage of spectrum resources due to the rapid growth of IoT terminals and big data services. Fifth generation (5G) network owns sufficient spectrum resources and supplies large data volume business, which can help to expand the communication resources of the IoT by combing IoT with 5G network. In this paper, a 5G-based IoT is designed to transfer both 5G and IoT information simultaneously. Two simultaneous transfer models including time switching model and power splitting model are proposed to carry out 5G and IoT communications using different time slots and power streams, respectively. For these two models, we have formulated joint optimization problem of allocation factors and node powers to maximize the 5G transmission rate while the IoT transmission rate and the total power are constrained. An alternative optimization problem is also proposed to maximize the IoT transmission rate while guaranteeing the minimal 5G transmission rate. A joint optimization algorithm based on the Lagrange dual optimization is proposed to obtain the solution to the optimization problems. An energy efficiency model is proposed to minimize the consumed total power of the IoT while keeping the minimal 5G and IoT transmission rates. Simulation results are given to evaluate the performance of our proposed models from diverse perspectives.

A D2D Wireless Resource Allocation Scheme Based on Overall Fairness

D2D user equipment (DUE) multiplex wireless resources of non-orthogonal cellular user equipment (CUE), which can solve the problem of spectrum resource shortage. However, these interferences not only affect the throughput of DUE and CUE, but also undermine their fairness in receiving services. In order to ensure the fairness of CUE and DUE in quality of service and the fairness in using spectrum resources, a D2D wireless resource allocation scheme based on overall fairness is proposed. First of all, it allows multiple D2D users to multiplex the resource of a CUE which increases the access rate of D2D pairs and the throughput of the marginal users; Then it clusters and reconstructs multiple D2D pairs based on graph coloring theory and the reconstructed cluster is taken as a unit; Finally, maximizing the weight value of each D2D reconstructed cluster and its matching cellular users on a resource block (RB) k. Simulation results show that the proposed algorithm can significantly increase the overall fairness and the throughput of the marginal users, access rate of D2D pairs compared with other algorithms.

Mode Selection and Resource Allocation Algorithm in Energy-Harvesting D2D Heterogeneous Network

With the rapid increase of smart mobile devices and the improvement of people's demand for wireless communication, the insufficient system capacity and shortage of spectrum resources is gradually emerging. The current wireless communication technology is facing enormous challenges. Device-to-Device (D2D) technology has become a key technology in the future 5G network, with its flexible working mode, low energy consumption, low latency and high capacity, etc. At the same time, D2D communication devices are usually powered by batteries and have a limited lifetime. The lack of spectrum resources and the single power supply for D2D User Equipment (DUE) have become an important issue for mobile communication. Energy harvesting (EH) can provide the power supply. The joint problem of mode selection and resource allocation is challenging issues in energy harvesting D2D heterogeneous networks (EH-DHNs). In this paper, mode selection and resource allocation problem with DUEs multiplexing cellular user equipments (CUEs) uplink spectrum resources for EH-DHNs is investigated. Our object is to maximize the system throughput, and the energy harvesting constraint and the quality of service of CUEs are considered. In order to tackle these issues. The mode selection and resource allocation system throughput maximization problem in EH-DHN is formulated. The formulated problem is solved by the convex optimization theory and greedy strategy. We proposed a Mode Selection and Resource Allocation algorithm (MSRA). In order to illustrate the advantage of the MSRA algorithm, we compared it with other algorithms. We also analyzed the convergence of the MSRA and the performance with different system configurations on system throughput. Comparison shows that the MSRA algorithm can improve system throughput effectively.