Spectrum Allocation Method Research Articles

Deep-reinforcement-learning-based RMSCA for space division multiplexing networks with multi-core fibers [Invited Tutorial

The escalating demands for network capacities catalyze the adoption of space division multiplexing (SDM) technologies. With continuous advances in multi-core fiber (MCF) fabrication, MCF-based SDM networks are positioned as a viable and promising solution to achieve higher transmission capacities in multi-dimensional optical networks. However, with the extensive network resources offered by MCF-based SDM networks comes the challenge of traditional routing, modulation, spectrum, and core allocation (RMSCA) methods to achieve appropriate performance. This paper proposes an RMSCA approach based on deep reinforcement learning (DRL) for MCF-based elastic optical networks (MCF-EONs). Within the solution, a novel state representation with essential network information and a fragmentation-aware reward function were designed to direct the agent in learning effective RMSCA policies. Additionally, we adopted a proximal policy optimization algorithm featuring an action mask to enhance the sampling efficiency of the DRL agent and speed up the training process. The performance of the proposed algorithm was evaluated with two different network topologies with varying traffic loads and fibers with different numbers of cores. The results confirmed that the proposed algorithm outperforms the heuristics and the state-of-the-art DRL-based RMSCA algorithm in reducing the service blocking probability by around 83% and 51%, respectively. Moreover, the proposed algorithm can be applied to networks with and without core switching capability and has an inference complexity compatible with real-world deployment requirements.

Optimizing V2X Communication: Spectrum Resource Allocation and Power Control Strategies for Next-Generation Wireless Technologies

The upcoming wireless technology developments in the next generations are expected to substantially transform the vehicle-to-everything (V2X) communication network. The challenge of limited spectrum resources in V2X communication, caused by the need for high data rates, necessitates a thorough analysis of spectrum resource allocation and power control. This complex problem falls under the domain of mixed-integer nonlinear programming; a strategic approach is implemented to overcome these issues, which divides the main challenge into two sub-problems. The issue of resource allocation is addressed by implementing a multiaccess spectrum allocation method, which is deliberately designed to optimize the utilization of the spectrum resources that are currently accessible. Concurrently, the power control issue is resolved by employing a continuous convex approximation technique, which effectively converts non-convex power-allocation issues into convex equivalents. This approach helps to alleviate interference between users. Finally, the simulation results prove that the proposed approaches can improve vehicle performance. The algorithms proposed in this article significantly improve the system throughput and access rate of vehicular user equipment (VUEs) while ensuring the data rate of cellular user equipment (CUEs).

Spectrum Allocation and User Scheduling Based on Combinatorial Multi-Armed Bandit for 5G Massive MIMO.

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.

An Intelligent Optimization Method for Wireless Communication Network Resources Based on Reinforcement Learning

The scarcity of spectrum resources is becoming increasingly severe, and the traditional static spectrum allocation method leads to low spectrum utilization. To address these issues, this article introduces reinforcement learning (RL) into wireless communication systems and proposes an optimization method for wireless resource allocation in deep Q networks (DQN). This paper uses the deep reinforcement learning (DRL) method to model the selection process of cluster heads and relay nodes as a Markov decision process, and then uses DQN to establish a cluster head and relay node selection mechanism aimed at maximizing the life cycle of wireless sensor network (WSN). Experimental results show that compared with the existing algorithms, the proposed algorithm effectively reduces the energy consumption of the network and extends the life cycle of WSN. Ultimately, it is possible to reconfigure relevant spectrum resources to obtain available spectrum resources, and idle spectrum resources can be discovered at any time.

Fuzzy Tree Schematic for Spectrum Handoff Reduction in Cognitive Radio Networks

Spectrum is a limited resource, so it must be used effectively. The fixed spectrum allocation method currently in operation may not sustain the growth in the network access users, which continues to increase over time. Many studies have shown that the spectrum deficiency problem can be solved with cognitive radio network (CRN) technology. CRN allows flexible spectrum utilization. Spectrum handoff is one of the fundamental prerequisites for dynamic spectrum access in cognitive radio networks. The ping-pong effect is one of the problems caused by the frequent occurrence of unnecessary handoff spectrum. So, in CRN, the right handoff decision is needed to avoid the ping-pong effect. The proposed fuzzy system carries the concept of a fuzzy tree that uses two fuzzy inference systems (FIS), which work in two stages, namely FIS-1 and FIS-2. In the early stages, the FIS-1 controls the secondary users (SU) power and prevents interference with the primary users (PU). FIS-2 is used to execute precise spectrum handoff decisions. The test results show that the amount of handoff spectrum has decreased by up to 43 % compared to other methods, which are only 60 %. This results in a decrease of 16.67% from the previous method. Thus, the ping-pong effect can be minimized with the right handoff decisions.

Joint Optimization Method of User Association and Spectrum Allocation for Multi-UAV-Assisted Communication

Joint Optimization Method of User Association and Spectrum Allocation for Multi-UAV-Assisted Communication

Joint Optimization Method of User Association and Spectrum Allocation for Multi-UAV-Assisted Communication

Joint Optimization Method of User Association and Spectrum Allocation for Multi-UAV-Assisted Communication

An Enhanced Dynamic Spectrum Allocation Method on Throughput Maximization in Urban 5G FBMC Heterogeneous Network

Reports have shown that the demand for data managed by wireless systems is expected to grow by more than 500 exabytes by 2025 and beyond. 5G networks are predicted to meet these demands, provided that the spectrum resources are well managed. In this paper, an enhanced dynamic spectrum allocation (E-DSA) method is proposed, which incorporates a cooperative type of game theory called the Nash bargaining solution (NBS). It was assumed that there is one primary user (PU) and two secondary users (SU) in the network and their spectrum allocation was analyzed by testing the validity of the algorithm itself by using price weight factors to control the costs of the spectrum sharing. The solution was established by combining a proposed multiplexing method called the Filter Bank Multicarrier (FBMC) for 5G configuration, with the E-DSA algorithm to maximize the throughput of a heterogeneous 5G network. It was shown that the throughputs for 5G with E-DSA implementation were always higher than those of the ones without E-DSA. The simulation was done using the LabVIEW communication software and was analyzed based on a 5G urban macro and micro network configuration to validate the heterogeneity of the network.

Stochastic Resource Algorithm for Effective Utilization of Bandwidth for IoT Sensor Networks in Arecanut Agriculture Applications

Cognitive radio technology is growing positive solution for the effective utilization of the bandwidth for IoT device networks. The fixed spectrum allocation scheme leads to insufficient spectrum utilization, to overcome this problem, the stochastic resource allocation algorithm is proposed in the present work. This method of internet spectrum allocation considerably increases the performance of IoT sensor network by utilizing the internet spectrum by non-cognitive inactive IoT sensors (white space/spectrum holes) by continuous spectrum sensing. In this paper we have discussed the effective resource allocation for interweave and underlay conditions and also at variable interference constraints (short time and longtime spectrum sensing) of IoT network. The arecanut plantation for agriculture scenario is considered in the present paper and the sensor environment simulation is performed using MATLAB.

Electromagnetic Spectrum Allocation Method for Multi-Service Irregular Frequency-Using Devices in the Space–Air–Ground Integrated Network

The management and allocation of electromagnetic spectrum resources is the inner driving force of the construction of the space-air-ground integrated network. Existing spectrum allocation methods are difficult to adapt to the scenario where the working bandwidth of multi-service frequency-using devices is irregular and the working priorities are different. In this paper, an orthogonal genetic algorithm based on the idea of mixed niches is proposed to transform the problem of frequency allocation into the optimization problem of minimizing the electromagnetic interference between frequency-using devices in the integrated network. At the same time, a system model is constructed that takes the minimum interference effect of low-priority-to-high-priority devices as the objective function and takes the protection frequency and natural frequency as the constraint conditions. In this paper, we not only introduce the thought of niches to improve the diversity of the population but also use an orthogonal uniform crossover operator to improve the search efficiency. At the same time, we use a standard genetic algorithm and a micro genetic algorithm to optimize the model. The global searchability and local search precision of the proposed algorithm are all improved. Simulation results show that compared with the existing methods, the proposed algorithm has the advantages of fast convergence, strong stability and good optimization effect.

Online Double Auction for Wireless Spectrum Allocation With General Conflict Graph

The spectrum usage often comes in an online fashion. Considering the selfish behaviors of both primary users(PUs) and sencond users(SUs), we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> double spectrum allocation methods. We propose a truthful online double auction for spectrum allocation. Preempting existing spectrum usage is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> allowed. We design a strategyproof mechanism for both the primary user side and the SU side. In previous studies, users who do not interfere with each other have not been reasonably allocated to use channels. There is one and only one user on the used spectrum channel at a certain time, which will result in discarding many user requests. Our model allows multiple users to use a spectrum channel at the same time. Aiming at the shortcomings of previous research, we propose the concept of grouping for online auctions, so that all SUs that do not interfere with each other can use spectrum channels at the same time. This will greatly increase the number of users who use the spectrum at the same time. We thus increase the utilization rate of the user market, and a large number of users will not be idle and abandoned.In the design of our experiments, the grouping model proposed in this paper has obtained at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$43{\%}$</tex-math></inline-formula> utilization rate on the set channel, and our experiments have obtained good generality for different interference radius.

Low complexity multichannel spectrum prediction algorithm based on optimized neural network for spectrum allocation in cognitive radio internet of things

AbstractCognitive radio has been nominated as a key technology for the internet of things (IoT), due to its intelligent functionalities ensuring continuous connectivity for IoT objects. Spectrum prediction, as one of the core CR functions, has emerged as a leading tool to alleviate the spectrum scarcity problem. Spectrum prediction minimizes sensing and decision‐making delays, and thereby it reduces collisions with primary users and guarantees safe access for secondary users (SUs). Thus, it became an inseparable part of many new spectrum allocation and mobility methods. In this work, the proposed cognitive radio IoT model consists of local sensors (LS) that perform sensing instead of SUs, and a cognitive base station that receives sensing results from different LSs to predict the next occupancy information and allocate frequencies for SUs. The predictor is followed by a channel extraction block for efficient spectrum allocation. Then, a low complexity spectrum prediction and preallocation system based on optimized neural network architecture is presented. Two nonlinear neural network models, time delay neural network and nonlinear autoregressive with exogenous input, that are trained on a real spectral occupancy dataset, are optimized using the Bayesian optimization algorithm then compared. The best predictor forecasts the next occupancy rate of multiple channels simultaneously based on three dimensions, area, time, and frequency. Performance evaluation was conducted through accuracy, mean squared error (MSE), and regression fit. The highest prediction accuracy was93.5%, the regression coefficient was0.98, and a reduced MSE of0.0013obtained. Results show that the considered scheme is efficient in forecasting the spectrum availability of different bands within the IoT spectrum resources.

Efficient Rate Splitting Multiple Access Scheme Based Cooperative Bargaining Solutions

Due to the increasingly new communication technologies and growing traffic demands, dy-namic spectrum allocation method becomes insufficient to guarantee the satisfaction of main wireless com-munication requirements such as spectrum and energy efficiency. In order to meet different requests, rate splitting multiple access (RSMA) has been recently presented as a generalized multiple access technique. In this study, we design a novel RSMA resource management scheme based on the cooperative bargaining game theory. According to the fundamental ideas of pessimistic utilitarian and optimistic max-min bargaining so-lutions, we can leverage a mutual consensus among different network users to improve the total system per-formance. Based on our interactive bargaining approach, the proposed scheme adaptively controls the spec-trum and power resources in the RSMA system. To get a desirable fair-efficient solution, our bargaining strategy can make control decisions in a coordinated manner while ensuring the relevant trade-off between conflicting criteria. Under the diverse RSMA traffic scenario, the numerical simulation results demonstrate the efficiency of our proposed scheme over the existing state-of-the-art protocols. Finally, some open issues are spotted to shed lights on the need for further studies and future research efforts.

A Research on Routing and Spectrum Allocation Algorithm for Elastic Optical Networks Based on Deep Learning

Dynamic allocation request and spectrum release will lead to spectrum fragmentation, which will affect the allocation of subsequent services and spectrum resource utilization of elastic optical network. This paper proposes a new routing and spectrum allocation algorithm based on deep learning, which will find the best routing and spectrum allocation method for a specific network, so as to improve the overall network performance. Simulation results show that compared with the traditional resource allocation strategy, the neural network model used in this paper can improve the degree of spectrum fragmentation and reduce the network blocking probability.

A novel spectrum allocation method in the photovoltaic-thermochemical hybrid solar system

This paper proposes a novel spectrum allocation method for the spectral splitting photovoltaic-thermochemical hybrid system. In this new spectrum allocation method, the spectral beam splitter of the hybrid system has varying cutoff wavelengths along the reaction tube to adjust the wavelengths range of the spectrums allocated to the different segments of the thermochemical reactor. The solar thermal energy distribution on the thermochemical reactor is optimized to be matched with the energy demands of the thermochemical reaction. We have carried out the theoretical and experimental investigations of this new spectrum allocation method in our proposed photovoltaic-thermochemical hybrid system. Analysis shows that the solar energy allocated to the outlet segment of the reaction tube is reduced and the solar thermal energy distribution along the thermochemical reactor is optimized effectively. As a result, not only the heat loss of the thermochemical reactor is reduced, but the power output of the photovoltaic cells is improved. The experimental results show that the solar-to-electric efficiency of the hybrid system with the new spectrum allocation method reaches 23.3%, which is 4% higher than the conventional system with uniform cutoff wavelength.

Novel fragmentation-aware algorithms in space Division Multiplexing Elastic Optical Networks

Space Division Multiplexing Elastic Optical Networks (SDM-EONs) has been presented to provide flexibility and high speed transmission to increase network capacity and optimal use of network resources. Dynamic establishment and release of connections causes fragmentation in EONs, thus increasing blocking probability as a result of increased fragmentation. In this paper, we present three novel algorithms that focus on how to allocate resources in SDM-EONs under Multi-Core Fiber (MCF) in order to reduce blocking probability by reducing bandwidth fragmentation. The Core Classification Aware of Fragmentation (CCAF) is a method based on the core classification mechanism that uses the concept of demand splitting and a cost function to determine the appropriate spectral space for a new request. The Fragmentation Aware Spectrum and Core Assignment (FASCA-without priority) is a routing and spectrum allocation method that uses a cost function to specify the appropriate spectral space to improve spectrum efficiency and reduce fragmentation. The Fragmentation Aware Spectrum and Core Assignment (FASCA-with priority) is a routing and spectrum allocation method that considers priority of requests to improve spectrum efficiency and reduce fragmentation. Simulation results show that the proposed CCAF algorithm can achieve better performance compared with the FASCA-without priority and FASCA-with priority in terms of blocking probability and spectrum utilization. Moreover, FASCA-without priority can provide better performance than FASCA-with priority.

A survivable VON embedding algorithm based on resource awareness of correlation lightpaths in elastic optical networks

To address the problems of low spectrum utilization and high bandwidth blocking probability of virtual optical network embedding in elastic optical networks, we propose a Survivable virtual optical network Embedding algorithm based on resource awareness of correlation lightpaths (SE-RACL), which can further optimize network resource allocation compared to traditional single lightpath resource allocation algorithms. Firstly, according to the multi-dimensional attributes of nodes, the importance sorting formulas of virtual nodes and physical nodes are designed to increase the cooperation between virtual node embedding and link embedding and reduce the bandwidth consumption of link embedding. Secondly, in the spectrum allocation process of the working lightpath, the spectrum block matching degree formula is designed by combining the spectrum fragmentation degree changes and resource reductions of correlation lightpaths, which is used to optimize the network spectrum resource allocation. Finally, in order to improve the sharing degree of protection resources and the success probability of spectrum allocation under single link failure, a correlation lightpaths spectrum allocation method based on the minimum free frequency slot consumption is proposed. Simulation results show that the SE-RACL algorithm can effectively reduce the bandwidth blocking probability and enhance the spectrum utilization.

Research on dynamic spectrum allocation method based on number of users in wireless communication system

As wireless communication has developed rapidly, spectrum plays an essential role in system performance. How to allocate the limited spectrum is a critical problem. In this paper, we research spectrum allocation algorithms in the wireless system, such as Simultaneous Water-Filling (SWF), Fractional Frequency Reuse(FFR), and Forward-Looking Game(FLG). Then we combine the SWF and FFR to make a new method. We demonstrate the performance of each method. Our method saves spectrum in a wireless system, and it can also reduce the interference between different users. Our method, namely Dynamic Method, has an actual meaning because the number of users always changes in some cell, and this method can adjust to the wireless system dynamically. In this way, our method might have a more general use.

Static routing and spectrum allocation method for design of low-power elastic optical networks with multifiber environments

This paper proposes a static Routing and Spectrum Allocation (RSA) method to design low-power elastic optical networks. In the elastic optical networks, data are transmitted via lightpaths, which are routes flexibly assigned frequency resources according to transmission distances and traffic demands. When designing the elastic optical networks with a static lightpath establishment policy, lightpaths are generally established so as to minimize frequency bandwidth allocated in order to save frequency resources necessary for constructing the networks. The proposed method focuses on not only this performance metric but also the power consumption of optical amplifiers. An optical amplifier in an optical fiber works when any of optical signals pass through it, and thus in multifiber environments where each link consists of multiple optical fibers, lightpaths should be collectively amplified from the view point of the power efficiency. Therefore, the relationship between these two performance metrics is a trade-off. The proposed method develops an Integer Linear Programming (ILP) formulation for the RSA problem taking the trade-off into account. Furthermore, the proposed method provides a heuristic algorithm that performs route selection and frequency spectrum allocation for reducing the total power consumption of optical amplifiers while saving necessary frequency resources. Through numerical experiments, we show the performance of the proposed method.

Study on Spectrum Allocation and Optimization of Wireless Communication Networks Based on SFOA

Wireless communication network spectrum is a limited resource. With the rapid increase of mobile communication services in recent years, traditional spectrum allocation methods are only based on a fixed spectrum allocation strategy, which often results in uneven and wasteful resource allocation. Therefore, spectrum allocation and the optimization problem need to be solved urgently. The application of semantic mobile computing in the Internet of Things and the research of emerging bionic models provide new ideas for this problem. In order to solve the problem of spectrum optimization and allocation, this paper proposes an optimization algorithm that simulates fisherman fishing to reasonably arrange the allocation and optimization of wireless network spectrum. This paper selects SFOA and the other two algorithms, designs experimental functions to perform calculations separately, obtains relevant data indicators, and uses comparative analysis to analyze. The analysis shows that in terms of performance, the success rate of SFOA is higher than that of PSO, and the success rate of the two function calculations has reached 100%. In the signal‐to‐noise ratio analysis, when the signal‐to‐noise ratio is ‐4 dB, the throughput of GPSO reaches the maximum value of 0.17, the throughput of PSO reaches the maximum value of 0.56, and the throughput of SFOA reaches 1, which shows that SFOA is adopted. The stability and accuracy of the algorithm are higher than the other two algorithms, and in the case of high signal‐to‐noise ratio, the advantages of the SFOA algorithm are also more obvious. This shows that the use of this algorithm will be very helpful for spectrum allocation and optimization. Because SFOA has high stability and accuracy, through reasonable adjustment and improvement, it can make good use of spectrum allocation and optimization. Chinese wireless communication network and the development of Internet of Things technology are of great significance.