Spectrum Allocation Algorithm Research Articles

Fragmentation and ISRS-Aware Survivable Routing, Band, Modulation, and Spectrum Allocation Algorithm in Multi-Band Elastic Optical Networks

Fragmentation and ISRS-Aware Survivable Routing, Band, Modulation, and Spectrum Allocation Algorithm in Multi-Band Elastic Optical Networks

Security enhanced routing and spectrum allocation against crosstalk attacks for confidential lightpath in elastic optical networks.

Elastic optical network (EON) is a critical transmission infrastructure for emerging new applications due to its spectral efficiency and flexibility. Nowadays, numerous confidential lightpaths (CLPs) are carried over EON to support security-sensitive users. However, they are vulnerable to crosstalk attacks at the optical layer, typically aimed at eavesdropping on the carried data or even disrupting connections. Due to the transparent nature of the optical signals, such attacks are difficult to detect and could last for a long time, resulting in data leakage even spreading throughout the network. This paper presents a novel routing and spectrum allocation (RSA) algorithm to protect CLPs from crosstalk attacks. We investigate intra-channel and inter-channel crosstalk attacks and develop a metric to quantify crosstalk leakage risks (CLRs). We first formulate an ILP model to plan CLPs with a minimum CLR. To solve the same problem for large-scale networks, we also propose a heuristic algorithm, i.e., crosstalk-attack-aware RSA. Results indicate that the proposed algorithm is capable of reducing CLR by 23%.

Dynamic Routing and Spectrum Allocation Algorithms in Elastic Optical Networks for Satellite Internet (Invited)

Dynamic Routing and Spectrum Allocation Algorithms in Elastic Optical Networks for Satellite Internet (Invited)

ISRS impact-reduced routing, modulation, band, and spectrum allocation algorithm in C + L-bands elastic optical networks

C + L-bands elastic optical networks (EONs) cope with growing traffic demands by leveraging the C and L bands of deployed optical fibers. In this scenario, the inter-band stimulated Raman scattering (ISRS) becomes more serious and degrades the quality of transmission (QoT) of services, and should be considered to guarantee the lightpaths establishment for services in the routing, modulation, band, and spectrum allocation (RMBSA). In this paper, we propose an ISRS impact-reduced RMBSA algorithm that reduces the ISRS impact on services at the RMBSA process. In the routing phase, selecting the route with more available contiguous aligned frequency slots (FSs) and shorter transmission distance, the less ISRS impact of requests can be suffered and the spectrum resources of each link can be used as much as possible. In the modulation phase, the lower-order modulation format which can achieve the least required FSs and has higher OSNR tolerance is selected. In the band and spectrum allocation phase, allocation of higher-order modulation format requests to the C-band to reduce the ISRS impact from C-band to L-band. Meanwhile, centralized assignment of more required FSs requests to reduce the ISRS impact of these requests on other requests. Simulation results indicate that the proposed RMBSA algorithm can effectively decrease the blocking probability, improve the resource utilization and request average optical signal-to-noise ratio.

Algorithm Design and Convergence Analysis for Coexistence of Cognitive Radio Networks in Unlicensed Spectrum.

This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead.

Joint Channel and Power Assignment for Underwater Cognitive Acoustic Networks on Marine Mammal-Friendly

When marine animals and underwater acoustic sensor networks (UASNs) share spectrum resources, problems such as serious harm caused to marine animals by underwater acoustic systems and scarcity of underwater spectrum resources are encountered. To address these issues, a mammal-friendly underwater acoustic sensor network channel power allocation algorithm is proposed. Firstly, marine animals are treated as authorized users and sensor nodes as unauthorized users. Considering the interference level of sensor nodes on authorized users, this approach improves network service quality and achieves a mammal-friendly underwater communication mechanism. Secondly, to maximize the utility of unauthorized users, the algorithm incorporates a network interference level and node remaining energy into a game-theoretical framework. Using channel allocation and power control, a game model is constructed with a unique Nash equilibrium point. Finally, through simulation, it can be found that the proposed algorithm can obtain a stable optimal power value, and with the increase of network load, the system capacity of the proposed algorithm is significantly improved than that of the traditional cognitive radio technology and the common spectrum allocation algorithm, and the transmitted power of nodes can be controlled according to the size of the residual energy, so as to comprehensively improve the overall performance of the network.

Experimental evaluation of a latency-aware routing and spectrum assignment mechanism based on deep reinforcement learning

The introduction of futuristic and challenging use cases of 5G and 6G communications will demand strict requirements in terms of high bandwidth and low latency. Optical backbone networks need to tackle these new network scenarios by offering highly efficient, flexible, and scalable technologies and solutions. In this context, elastic optical networks (EONs) have been recognized as a promising technological transport infrastructure for the future Internet since they can manage the optical spectrum with enhanced flexibility and efficiency. The service provisioning in EONs is a challenging issue to tackle since the routing and spectrum assignment (RSA) is characterized by a high degree of complexity. This work presents an approach for RSA in EONs leveraging the advantages of deep reinforcement learning (DRL) solutions. The devised approach jointly considers the constraints imposed by the optical technologies and the demanded connectivity service requirements (i.e., guaranteed bandwidth and maximum end-to-end latency) when computing and selecting the optical path and spectral resources. We first evaluate our approach through simulation experiments considering two reference network topologies, demonstrating its effectiveness in reducing the bandwidth blocking ratio, the path computation time, and the number of rejected connectivity services requiring lower latencies when compared to a baseline k-shortest path routing and first-fit spectrum allocation algorithm. Then, the trained DRL agent is integrated within a real proof of concept to attain an ML-assisted SDN control plane in the CTTC ADRENALINE testbed. The attained performance improvements highlight the potential benefits brought by using DRL mechanisms and its feasible integration within production EON transport infrastructures.

Symmetry-free algorithm for spectrum allocation: parallel implementations and evaluation

Our symmetry-free model for spectrum allocation (SA) in networks of general topology leverages two properties: (1) SA is equivalent to a connection permutation problem, and (2) in assigning spectrum, it is sufficient to consider the allocation made by the first-fit (FF) algorithm. This model opens up algorithmic approaches that altogether sidestep spectrum symmetry, i.e., eliminate from consideration the exponential number of equivalent solutions resulting from spectrum slot permutations. Recursive FF (RFF) is such an algorithm; it applies FF recursively to search the connection permutation space and solve the SA problem optimally. Moreover, parallelism is inherent in the spectrum symmetry-free model, as the connection permutation space may be naturally decomposed into non-overlapping subsets that can be searched independently. Accordingly, RFF admits multi-threaded implementations that may be tailored to the computing environment at hand. In this work, we present two strategies for parallelizing the execution of RFF, and we evaluate them experimentally using a comprehensive set of metrics. Our experiments indicate that RFF explores a vast number of symmetry-free solutions, and for moderate-sized networks, it takes mere seconds to yield solutions that are either optimal or very close to the lower bound.

Dynamic resource allocation for multicast in SDM-EON: time-decoupled dynamic path cross talk and joint weight

With the development of the Internet and the rise of multicast, enabling multicast in a space division multiplexing elastic optical network (SDM-EON) is considered highly necessary. This paper focuses on the dynamic resource allocation for multicast in SDM-EON. First, to ensure service survivability and reduce the complexity of inter-core cross talk computation, we design a path-based strict cross talk avoidance routing, modulation, core, and spectrum allocation (PSCA-RMCSA) algorithm. Then, based on PSCA-RMCSA, a joint weight and PSCA-RMCSA (JW-PSCA-RMCSA) algorithm is proposed to further reduce the blocking probability (BBP) of services. Simulation results show that PSCA-RMCSA reduces the BBP by up to 7.55% and the resource allocation time by up to 88.63%, while JW-PSCA-RMCSA reduces the BBP by up to 10.43% and the resource allocation time by up to 82.11%, when compared to the shortest path tree RMSCA.

Impairment- and fragmentation-aware, energy-efficient dynamic RMSCA for SDM-EONs

This paper presents a routing, modulation, spectrum, and core allocation (RMSCA) algorithm for space-division multiplexing-based elastic optical networks (SDM-EONs). A network state-dependent route and core selection method is proposed using a multi-attribute decision-making method based on the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluations (PROMETHEE) methods. This systematic resource allocation allows the network designer to choose which resources are most valuable. It is followed by a spectrum allocation algorithm using a weighted score function to rate and select the best spectrum blocks. Physical layer impairments, including inter-core cross talk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered alongside fragmentation and energy consumption. The proposed RMSCA approach is compared with published benchmarks incorporating quality of transmission constraints and evaluated on two network topologies, NSFNET (7- and 12-core multicore fiber links) and COST. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, network fragmentation, and energy consumption compared to standard and published benchmarks.

An improved monarch butterfly spectrum allocation algorithm for multi-source data stream in complex electromagnetic environment

In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication. Due to the characteristics of massive, heterogeneous, and multi-source, the generated multi-source data stream brings difficulties to spectrum cognition. As a result, unreasonable spectrum allocation strategy leads to low utilization of spectrum resources. Optimizing spectrum allocation strategy can effectively improve spectrum utilization. Aiming at the problem of trapped local optimum solution in the genetic algorithm (GA) and particle swarm optimization algorithm (PSO), an improved monarch butterfly algorithm is proposed. Firstly, this paper employs the simulated annealing algorithm to select the migration rate, which increases the diversity of monarch butterfly population. Secondly, chaos mapping algorithm is utilized to improve the optimization ability and convergence speed. Finally, in the view of the problem that the monarch butterfly algorithm is easy to fall into the local optimal solution, there is no better way to escape from the local optimal solution. The Wolf pack updating operator is selected to improve the diversity of the population to generate new monarch butterflies. This method updates the population by generating new monarch butterfly individuals, so as to increasing the diversity of the population. The experimental results show that the improved monarch butterfly algorithm outperforms the other two algorithms in terms of convergence speed and system revenue.

Crosstalk Classification Based on Synthetically Consider Crosstalk and Fragmentation RMCSA in Multi-Core Fiber-Based EONs

Space division multiplexing elastic optical networks (SDM-EONs) based on multi-core fiber (MCF) technologies have attracted widespread attention as a potential means of enhancing large capacity and high flexibility. However, inter-core crosstalk (XT) degrades the quality of transmission. The algorithm for minimizing XT leads to an increase in spectrum fragmentation in the lightpath, which influences the spectrum utilization. Therefore, the question of how to comprehensively consider the two factors and improve the network performance is an issue worthy of study. This paper focuses on maximizing spectrum resource utilization while satisfying the XT constraints. Firstly, we optimize a three-dimensional metric model to evaluate XT and fragmentation more exactly in SDM-EONs. Furthermore, a crosstalk classification (CC) algorithm, which can adjust the XT constraints according to the actual situation of the network, is proposed. Moreover, to match the CC algorithm, we describe the crosstalk and fragmentation in the network and propose a synthetically consider crosstalk and fragmentation (SCCF) algorithm. Finally, simulation results show that the proposed CC-SCCF routing, modulation, core, and spectrum allocation algorithm reduces the XT on existing lightpaths, and also provides a lower probability of blocking and greater spectrum utilization.

Improved spectrum allocation scheme for TV white space networks using a hybrid of firefly, genetic, and ant colony optimization algorithms

This study proposes a novel hybrid Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization Algorithm (FAGAACO) for spectrum allocation in TV White Space (TVWS) networks. The Genetic Algorithm (GA) was used in the design to provide cross-over chromosomes to both the Firefly Algorithm (FA) and the Ant Colony Optimization Algorithm (ACO), thereby improving the exploration abilities of FA and ACO and preventing FA and ACO from becoming trapped in local optimum. The proposed algorithm was implemented using MATLAB R2018a. Simulation results show that in comparison with a hybrid of the Firefly Algorithm and Genetic Algorithm (FAGA), the proposed algorithm achieved 13.03% higher throughput, 1.3% improved objective function value and 5.03% higher runtime due to the good accuracy of the proposed algorithm. Based on these improvements, the proposed algorithm is therefore an efficient spectrum allocation technique in TVWS networks.

Contract-Theory-Based Secure Spectrum Sharing Framework in Internet of Vehicles

Internet of vehicles (IoV) enables a large number of applications for consumers by integrating massive electronics, which aggravates the spectrum scarcity and data security, and degrades the quality of services (QoS) and experiences of consumers obviously. Focusing on enhancing the security and improving spectrum utilization, this article proposes a contract-theory-based secure spectrum sharing framework, which adopts the consortium blockchain to guarantee security of spectrum sharing and utilizes the practical Byzantine fault tolerant consensus algorithm to achieve consensus in IoV. Smart contracts are then formed based on insurance contracts to promote primary users to share their idle spectrum resources. A preference-based spectrum allocation algorithm is designed to maximize the total utility of secondary users (SUs). Simulation results show that, compared with the existing schemes, the proposed scheme achieves better performance in terms of the utility of SUs and the spectrum utilization greatly, which provides more access opportunities to consumers in IoV.

Network dynamic routing and spectrum allocation algorithm based on blockchain technology

Network dynamic routing and spectrum allocation algorithm based on blockchain technology

Intelligent Community and Real Estate Management Based on Machine Learning.

When studying intelligent community and property management system, network service has an important impact on intelligent community construction and property management service. How to use machine learning and other technologies to improve the network service of intelligent community and integrate it into real estate property management is worthy of further research. This paper introduces the basic model of machine learning and proposes a network data prediction model based on time series. For the time dimension, an improved prediction algorithm model of machine learning is proposed. For mobile data allocation, from the perspective of ensuring the current and future continuity of the spectrum after spectrum allocation, this paper proposes a spectrum allocation algorithm based on the joint measurement of time domain and frequency domain. In addition, the VP-tree algorithm is used to construct the spatial vector relationship of the intelligent community. At the same time, in the time trend and periodicity of the mobile data in the intelligent community network, the attention mechanism is introduced to realize the distribution of mobile data and traffic prediction in the intelligent community by machine learning. This paper analyzes the requirements of the property management system, designs the property management information system including the field subsystem layer, data acquisition layer, and cloud service layer, introduces the property management module and customer service module in detail, and carries out the system test. The test results show that the system runs well. Finally, aiming at the problems existing in the property management industry, this paper puts forward the development strategy of property management.

Flexibility and fragmentation aware routing, core and spectrum allocation for hybrid AoD nodes in SDM-EONs.

Space division multiplexing elastic optical network (SDM-EON) enables high-capacity transmission, in which the network nodes should provide high switching flexibility while limiting the complexity and costs of nodes. Architecture on demand (AoD) nodes can meet these requirements but the slow configuration time of optical backplane in AoD nodes makes it difficult to serve latency-sensitive requests. In this paper, we propose a hybrid optical backplane based on micro-electromechanical systems (MEMS) and semiconductor optical amplifier (SOA) switches to provide fast configuration time for AoD nodes. Moreover, we propose quantitative measures of node switching flexibility in SDM-EONs and of link configuration speed in AoD nodes. Based on the hybrid backplane architecture and the measurement approaches, we propose a flexibility and fragmentation aware routing, spectrum and core allocation algorithm and an AoD synthesis algorithm. Simulation results show that the hybrid AoD nodes with support of spatial lane change can reduce network blocking probability. The AoD based on this hybrid backplane structure can improve the network performance by 32.8% compared to the AoD based on the traditional MEMS. Compared with traditional reconfigurable optical add/drop multiplexers (ROADMs), the hybrid AoD nodes can control the number of wavelength selective switch (WSS) ports.

Predicting target data rates for dynamic spectrum allocation using Gaussian process regression

Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers only the performance of the weakest user. To resolve this issue, we propose a new target data rate setting algorithm for dynamic spectrum allocation. In this algorithm, a Gaussian process regression model is trained to predict the target data rate. All users that perform below the defined target rate will have their frequency band allocations changed to one that guarantees a better performance. Through simulations, we show that the maximum data rate achieved by the weakest user in our algorithm is 121.7% higher than the selfish algorithm.

Routing and spectrum assignment employing long short-term memory technique for elastic optical networks

With the prevalence of some high bandwidth-demanding applications, such as cloud computing, traditional wavelength-division-multiplexing passive optical networks have difficulties in satisfying such growing bandwidth demands due to its limited allocation-flexibility and utilization-efficiency. Therefore, elastic optical networks (EONs). In order to realize the flexibility in EONs, sophisticated routing and spectrum allocation (RSA) algorithms areone of the keyenabling technologies. However, most of the previous RSA algorithms were proposed with invariant routing and spectrum allocation strategies, which ignored considering the time-varying characteristics of EONs due to the variable network architecture and service provisioning. And such time-varying characteristics can deteriorate the spectrum fragmentation and the service blocking performances of EONs, which stimulates the application of various machine-learning technologies in EONs. In this paper, a long short-term memory based routing and spectrum assignment (LSTM-RSA) algorithm is proposed for EONs. By employing the long short-term memory technique to sense the complex status of EONs (e.g. spectral usage on the selected paths), the proposed LSTM-RSA algorithm gradually learns successful strategies through accumulating operation experience in the process of interaction and obtains higher returns through enhanced operation, which helps improve the spectrum fragmentation and the service blocking performances in EONs. Simulation results show that the spectrum fragmentation rate and the blocking rate of the proposed LSTM-RSA algorithm are reduced by about 6% and 8.9%, respectively, when compared to the traditional shortest-path-routing first-fitting RSA algorithm.

Dynamic impairment-aware RMCSA in multi-core fiber-based elastic optical networks

Space division multiplex elastic optical networks (SDM-EONs) based on multi-core fiber (MCF) have been regarded as one of the promising solutions to address the future network capacity crunch. Inter-core crosstalk (XT) and intra-core physical layer impairments (PLI) exist in the MCF and degrade the quality of transmission (QoT) for lightpaths. Meanwhile, the establishment of new lightpaths affects the QoT of existing lightpaths due to dynamic spectrum occupancy in the network. This paper focuses on resource allocation under the constraint of physical impairment. Firstly, a dynamic impairment (DI) evaluation model is established to assess the level of impairment influence in terms of dynamic spectrum occupancy. Then, a DI-aware routing, modulation, core, and spectrum allocation (RMCSA) algorithm is proposed, which considers the influence of new lightpath establishment on existing lightpaths. Here, the core and spectrum resources with the least impact of impairment on existing lightpaths are selected preferentially to improve the QoT of lightpaths. Simulation results indicate that the proposed DI-aware RMCSA algorithm reduces the accumulated physical impairment on existing lightpaths, providing a lower probability of blocking and greater spectrum utilization.