Spectrum Assignment Scheme Research Articles

PtrNet-RSA: A Pointer Network-Based QoT-Aware Routing and Spectrum Assignment Scheme in Elastic Optical Networks

PtrNet-RSA: A Pointer Network-Based QoT-Aware Routing and Spectrum Assignment Scheme in Elastic Optical Networks

DRL assisted multi-objective algorithm for multicast scheduling in elastic optical network

Multicasting, as a main transmission mode for most of the current applications in elastic optical network, has attracted more and more research attention. In this paper, we investigate multicast scheduling model and solving algorithm. First, we model the multicast scheduling problem as a multi-objective optimization problem (MOP) by minimizing the bandwidth resources and maximizing the user service quality, and then, we propose a deep reinforcement learning assisted multi-objective algorithm for the model (DRL-MM), in which we design source node selection strategy, routing scheme, modulation scheme and spectrum assignment scheme for each multicast session. To identify the superiority of the proposed DRL-MM, we conduct the experiments and compare DRL-MM with an approximation based Steiner tree algorithm (STA-RSA) and a load-balancing routing tree-based algorithm (LD-RSA) through the experiments. The results show that DRL-MM outperforms STA-RSA and LD-RSA in terms of both bandwidth resource usage and user service quality.

UE admittance and contention free resource allocation for interference mitigation in HetNet cognitive femtocells

Femtocells are designed to co-exist alongside macrocells providing higher spectrum efficiency, spatial frequency reuse, to name a few. However, when deployed in the two-tier architecture with macrocells, interference is imminent thereby it is necessary to mitigate the inherent co-tier and cross-tier interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning, adapting, and reasoning leading to informed decisions. This paper proposes a joint threshold power based admittance and contention free resource allocation scheme (known as Proposed SA) for interference mitigation in HetNet cognitive femtocells (CF). In the proposed SA, a CF sets a threshold value on the mutual interference between itself and a close-by mobile user equipment (MUE). To mitigate cross-tier interference, a CF classifies MUEs which fall above this threshold value (high interference value) as Undesired MUEs (UMUEs), whereas MUEs which fall below this threshold are classified as Desired MUEs (DMUEs). To mitigate co-tier interference, proposed SA introduces a scheduling engine which employs matching policy attributes and assigns resource blocks (RBs) of unique DMUEs to CFs to avoid any possible contention problems, thus providing improved co-tier interference. System level simulations have been performed to demonstrate working and effectiveness of proposed SA. Key performance indicators including choice of maximum transmit power for MUE, co and cross tier interference mitigation, as well impact of MUE mobility has been investigated. Performance analysis of the Proposed SA has also found that the scheme performs much better in relation to a random spectrum assignment (Random SA) scheme. The proposed SA caters the mobility of the MUEs very well allowing more and more admittance thereby providing improved interference results and making it a viable candidate for Femtocell Access points (FAPs) implementation.

A survivable multipath resource allocation strategy based on fragmentation-sensitive fragmentation-aware in space division multiplexing elastic optical networks

The space division multiplexing elastic optical network (SDM-EONs) can effectively supply the large-capacity for the current optical network. If a link failure occurs, it will bring huge data losses or disruptions for the operator. We propose a survivable multipath fragmentation-sensitive fragmentation-aware routing, core and spectrum assignment (SM-FSFA-RSCA) scheme to solving the problem of survivability of the request in SDM-EONs and spectrum fragmentation that caused by the dynamic allocation and release of spectrum resources. We design a fragmentation-sensitive fragmentation metric (FSFM) to estimate the spectrum fragmentation in the established lightpaths, and design a dynamic routing and core selection strategy for the request to control spectrum fragmentation in the core according to FSFM. We consider survivable multipath routing strategy for saving spectrum resources, each path of the multipath can be used as a working path or a protection path. In addition, for spectrum allocation, a survivable multipath with shared spectrum allocation strategy is adopted to maximize the utilization of frequency resources. The simulation results show that the algorithm proposed in this paper significantly reduces the bandwidth blocking probability,fragmentation ratio and also improves the spectrum utilization.

SNR Re-Verification-Based Routing, Band, Modulation, and Spectrum Assignment in Hybrid C-C+L Optical Networks

Band division multiplexing technology is a promising medium-term transitional solution to deal with the traffic flood, by leveraging the existing optical fiber transmission infrastructure. Additionally, the deployment of multi-band optical networks requires updating and upgrading key components, such as multi-band amplifiers and multi-band optical cross-connects. From cost view, it would be a promising way for operators to expand transmission capacity on some of the links with high priority, which are used heavily. Therefore, the application scenario of this paper is positioned in the C-C+L hybrid optical networks. In this scenario, the introduction of the L-band brings additional inter-band stimulated Raman scattering (ISRS) which is a non-negligible nonlinear effect, leading new constraint for service provisioning. This paper proposes an ISRS-aware routing, band, modulation, and spectrum assignment (RBMSA) scheme, considering the impact of ISRS generated by new requests on existing requests. By implementing the SNR re-verification, not only the transmission quality of the current request can be realized, but also the service quality of the existing requests will not be seriously degraded to be unacceptable. A two-dimensional model of request bandwidth and duration is established for band selection. Priority is provided for arranging the requests in the L-band when it is available. For each single request, when the least number of slots required by adopting different modulation formats is the same, lower-order modulation is preferred to improve its SNR tolerance of this request under the ISRS impact. Simulation results indicate that the proposed scheme can effectively decrease the blocking probability, and improve the channel SNR for the successfully established requests.

Supervised graph convolution networks for OSNR and power estimation in optical mesh networks

The optical signal-to-noise ratio (OSNR) and received optical channel power are critical parameters in determining the quality of transmission. The OSNR and received optical channel power are influenced by network impairments such as fiber loss, amplified stimulated emission noise, and nonlinear impairments. Furthermore, environmental effects and routing, modulation, and spectrum assignment schemes influence the OSNR and thus the reach of the optical channels. These impairments and effects vary with the spectral loads that are hard to predict in brownfield networks. Several deep neural network (DNN)-based methods have been explored to estimate the OSNR and nonlinear noise. However, these methods ignore the network topology. This paper bridges this gap by leveraging supervised graph convolution neural networks (GCNs), which operate directly on graphs for OSNR and received power estimation in an optical mesh network. We also develop and implement a novel graph windowed neural network (GWinN) to reduce the over-smoothing effects of a GCN and thus learn localized behaviors like fiber cuts. We apply a DNN, GCN, and GWinN in practice to a testbed of 8 reconfigurable optical add-drop multiplexers and 22 amplifiers. Our procedure accurately estimates the OSNR with a prediction error mean and a standard deviation of ( − 0.02 d B , 0.35 dB) for a reference OSNR ranging from (16 dB) to (24 dB).

A half subcarrier guard band spectrum assignment scheme for multi-user FBMC systems

A half subcarrier guard band spectrum assignment scheme for multi-user FBMC systems

RDRSA: A reactive defragmentation based on rerouting and spectrum assignment (RDRSA) for spectrum convertible elastic optical network

In this paper, a reactive defragmentation rerouting and spectrum assignment (RDRSA) strategy when existing connection requests terminated and network resources released is presented. In RDRSA with spectrum conversion (SC), the existing connections are re-established to new route or reassigned to new frequency slots. Defragmentation has three advantages over the conventional RSA: (1) at the time of connection setup, shorter routes may be unavailable, but after the termination of the existing requests; possibility of re-assignment of shorter route becomes high, (2) fragmentation of network also reduces which results load balancing over the entire network, (3) network resource utilization increases due to more connection acceptance. The performance of RDRSA-SC is compared with the conventional RSA with spectrum conversion (SC), and non spectrum conversion (NSC). The simulation results on NSFNET and ARPANET network topologies show that the proposed RDRSA-SC has lower fragmentation and bandwidth blocking probability, and more utilization of network capacity than the existing RSA-SC and RSA-NSC.

Time and bandwidth aware traffic balancing in elastic optical networks – A two dimensional approach

The time scheduled traffic in spectrally efficient elastic optical networks (EONs) is periodic and exhibits non uniform pattern, in both, the time as well as the requested bandwidth. In time domain, it remains heavy during the office hours and slacks during the non-office hours; while in the bandwidth domain, it is distributed non-uniformly along the network links and also, may vary from few Gbps to hundreds of Gbps. This inefficient utilization of spectral resources results in increased blocking of connection requests. Hence, to address the problem of traffic unevenness in time and bandwidth domains in the EONs, traffic balancing based routing and spectrum assignment (TBRSA) strategies for scheduled traffic are proposed in this manuscript. Traffic balancing is on the basis of service level agreements (SLA) between the clients and the network service providers. The characteristics of traffic and the advanced features of EONs have been exploited to perform the traffic balancing. The proposed strategies have been compared with the existing benchmark strategy. The metrics of comparison are bandwidth blocking probability (BBP), spectrum utilization ratio (SUR), fragmentation index (FI) and a newly introduced metric, spectrum time utilization ratio (STUR). From the simulation results, it has been conclusively established that the proposed traffic balancing based RSA strategies outperform the benchmark strategy in all the parameters of interest.

Design of a novel XT-aware energy and spectrum efficient RSCA scheme in offline SDM-EON

This paper proposes a methodology E-S-RSCA to integrate energy efficiency with spectrum efficient design of crosstalk aware routing, spectrum and core assignment (RSCA) schemes for offline traffic in space division multiplexed elastic optical network (SDM-EON) while allocating traffic in selected multiple cores in SDM-EON. Mixed Integer Linear Programming (MILP) model has been formulated for the scheme. The offline RSCA being an NP-hard problem is intractable in nature and a heuristic algorithm with polynomial run time complexity is developed. Simulation study of E-S-RSCA vis-à-vis MILP establishes near optimal performance of the heuristic. We also propose a spectrum efficient scheme S-RSCA in order to validate energy efficiency of E-S-RSCA. The scheme E-S-RSCA is compared with S-RSCA and also an existing work ex-S-RSCA (which allocates spectrum in selected single core of SDM-EON). It is observed that (i) E-S-RSCA consumes least energy due to its energy awareness and (ii) both the heuristics E-S-RSCA and S-RSCA (due to their multi-core allocation based approach) outperform ex-S-RSCA in terms of spectrum efficiency.

Temporal and Spectral 2D Fragmentation-Aware RMSA Algorithm for Advance Reservation Requests in EONs

In this paper, we explore the problem of routing, modulation and spectrum assignment (RMSA) for advance reservation (AR) requests in elastic optical networks (EONs), with the objective to further reduce the blocking probability and improve the spectrum utilization. On the one hand, we propose a minimum resource consumption routing policy (MRC) for leaving more free resources for future requests. On the other hand, we introduce a two-dimensional (2D) path resource model, in which each AR request can be transformed into a rectangle block whose length and width are related to request bandwidth and request duration respectively. Therefore, the spectrum assignment problem of AR requests is simplified to the two-dimensional rectangle packing problem. Furthermore, we design several factors to evaluate the two-dimensional fragmentation caused by a rectangle packing operation, where we consider the fragmentation situation in the rectangle block neighborhood for the first time. Based on these factors, we propose a two-dimensional fragmentation-aware spectrum assignment strategy (2D-FA). Through the combination of MRC and 2D-FA, we propose a novel RMSA algorithm named MRC-2D-FA for dynamically provisioning AR requests in EONs. Simulation results show that MRC-2D-FA can achieve lower bandwidth blocking probability and higher spectrum utilization compared with several well-performed benchmark algorithms.

Low-complexity physical layer impairment aware spectrum assignment based on discretized Gaussian model for nonlinear noise in elastic optical networks

A physical layer impairment (PLI)-aware spectrum assignment (SA) algorithm has been introduced for elastic optical networks (EON) using a discretized Gaussian model for nonlinear interfering noise (NLIN). This SA scheme is derived exploiting achievable rate and minimum signal-to-noise (SNR) margin as our design evaluation metric. Comparing to previously proposed techniques, the proposed SA method shows a significant performance improvement in a fiber-optic link, especially in large traffic values, both with optimum variable and flat channel power allocation.Comparison of numerical results performed for the DTG network shows up to 3.2 dB improvement in the minimum SNR margin for both flat and variable power optimization scenarios. The low complexity of the introduced SA approach makes it useful for PLI-aware heuristic as well as mixed integer linear programming based network planning schemes for EONs.

A Predictor-Based Model Reference Adaptive Controller for Time-Delay Systems

This paper presents novel modifications to a predictor-based model reference adaptive controller (PMRAC) to compensate for input delays in uncertain nonlinear systems. A delay-dependent stability condition, derived using a Lyapunov–Krasovskii functional, indicates that there exists a time delay margin, below which the asymptotic stability of the tracking and estimation errors as well as the input-to-state stability of the closed-loop system are guaranteed. Numerical experiments are carried out with comparisons to the original PMRAC and to a finite spectrum assignment scheme to validate the theoretical claims. The results also illustrate the effectiveness of the proposed delay compensation formulation and the asymptotic behaviors of the tracking and estimation errors.

Network blocking probability-based evaluation of proposed spectrum assignment strategy for a designed elastic optical network link

Elastic optical networks (EONs), which are spectrum-efficient new generation optical networks, have been replacing traditional dense wavelength division multiplexing-based networks in backbone networks. The allocation of spectral resources while overcoming spectrum contiguity and continuity constraints is a challenging task in EONs. This paper presents a RSA simulation framework for a designed EON link, and the performance of existing spectrum assignment (SA) strategies (first fit, random fit and last fit) has been evaluated. This paper further presents a SA strategy based upon the relative difference between available spectrum slots width and the required slot width for an incoming traffic demand. The performance of proposed SA technique has been evaluated by carrying out simulations using the designed RSA simulation framework under different network load conditions. The comparative analysis with existing SA strategies shows that the proposed strategy is successful in reducing network blocking probability effectively.

A Novel Spectrum Assignment Scheme for Time-Varying Traffic in Flexgrid Optical Networks

Abstract In real-time communication, the bandwidth required by traffic may fluctuate over time, and the network should allocate spectrum flexibly to satisfy such demands. In this paper, a novel spectrum assignment scheme is proposed. It reduces the spectrum fragmentation by minimizing the number of spectrum-free windows and establishes a virtual sub-connection in a non-contiguous spectrum block for the incremental bandwidths of time-varying traffic, to eliminate the traffic relocation and interruption caused by traffic change. Numerical results demonstrate that the scheme proposed in this paper effectively reduces the network blocking probability, decreases the traffic interrupt ratio, and improves the performance of serving traffic.

A Fair Multi-Channel Assignment Algorithm With Practical Implementation in Distributed Cognitive Radio Networks

Designing an efficient spectrum assignment (SA) mechanism is a key issue for realizing dynamic spectrum access in cognitive radio network. In multi-channel selection based SA schemes, secondary users (SUs) are able to utilize multiple channels simultaneously to enhance the network throughput. However, a fairness problem may happen if few SUs utilize too many idle data channels that other SUs are left with no idle channels, thus increasing the blocking probability and reducing the fairness. Aiming at improving the network throughput with multi-channel selection capability while maintaining fairness among the SUs, in this paper, we propose a fair multi-channel assignment scheme (FMCA) for distributed cognitive radio networks. For the FMCA scheme, we design a new MAC framework for sensing and access contention resolution, which is integrated into the FMCA scheme. Channel-aggregation (CA) technique is used in each SU to enable the multi-channel selection ability. Considering both of the idle data channel utilization efficiency and the transmit power budget constrained CA ability of each SU, we analytically formulate a channel assignment problem according to the well-known Jain’s fairness criterion. Our objective is to find a channel assignment with maximal fairness index for all SUs. The optimization problem is turned out to be a quadratic integer programming (QIP). According to the definition of Jain’s fairness criterion, we design an algorithm to get the optimal solution of the QIP. With the optimal channel assignment solution, the FMCA scheme is realized in the channel assignment phase of the proposed MAC protocol. Extensive simulation results show that the proposed FMCA scheme gets a good tradeoff between throughput and fairness compared with the existing SA schemes.

Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks

Recently, network traffic has been growing exponentially and almost reached the physical capacity limit of single mode fibers. Space division multiplexing (SDM) is a promising technology to overcome the looming fiber capacity crunch. Especially, few-mode multi-core fibers (FM-MCFs) can aggregate multiple cores into one fiber and two or more modes can be transmitted in one core, which can greatly increase the capacity yet introduce crosstalk constraints including inter- and intra-core crosstalk. To our best knowledge, there is no accurate crosstalk calculation model study in SDM optical networks with FM-MCFs. To address this issue, we first introduce the machine learning into the crosstalk prediction phase and propose a novel crosstalk estimation model (CEM) exploiting the beam propagation method called CEM-beam propagation method (BPM)-machine learning (ML), which can be used to evaluate the crosstalk during the design for the resource allocation scheme. Then, a crosstalk aware core, mode, and spectrum assignment (CA-CMSA) strategy is presented. The simulation results for crosstalk estimation at the wavelength level indicate that the crosstalk at lower frequencies is less than that at higher frequencies. Thus, the lower frequencies are always the first choice in the spectrum resource assignment phase. In addition, for our specific training set, the Levenberg-Marquardt (LM) algorithm based on machine learning performs better on the training, including regression values measurement and time consumption. The simulation results of the proposed CA-CMSA scheme also show that the resource allocation algorithm based on LM can improve resource utilization without increasing total connection set-up time. Thus, it will be the best choice for the resource assignment in SDM networks with FM-MCFs.

Least loaded and route fragmentation aware RSA strategies for elastic optical networks

Abstract Elastic optical networks (EONs) provide flexibility to assign wide range of spectral resources to the connection requests. In this manuscript, we address two issues related to spectrum assignment in EONs: the non uniform spectrum assignment along different links of the route and the spectrum fragmentation in the network. To address these issues, two routing and spectrum assignment (RSA) strategies have been proposed: Least Loaded RSA (LLRSA) and Route Fragmentation Aware RSA (RFARSA). The LLRSA allocates spectrum homogeneously along different links in the network, where as RFARSA accords priority to the routes which are less fragmented. To highlight the salient features of the two strategies, two new metrics, route fragmentation index (RFI) and standard deviation (SD) are introduced. RFI is defined as the ratio of non-contiguous FSs to the total available free FSs on the route, and SD relates to the measure of non-uniformity in the allocation of resources on the links in the network. A simulation program has been developed to evaluate the performance of the proposed (LLRSA and RFARSA) strategies, and the existing strategies of shortest path RSA (SPRSA) and spectrum compactness based defragmentation (SCD) strategies, on the metric of RFI, bandwidth blocking probability (BBP), network capacity utilized, and SD. The variation in the metrics on the basis of number of requests and the bandwidth (number of FSs) requested has been studied. It has been conclusively established that the proposed strategies (LLRSA and RFARSA) outperform the existing strategies in terms of all the metrics.

Fragmentation-Avoiding Spectrum Assignment Strategy Based on Spectrum Partition for Elastic Optical Networks

A fragmentation-avoiding spectrum assignment strategy based on spectrum partition is proposed, which is used to resolve the spectrum fragmentation problem in elastic optical networks. For alleviating spectrum fragmentation, a spectrum partition policy, splitting the whole optical spectrum into several dedicated partitions, is presented. Based on this, a joint first-last-fit spectrum assignment policy is presented to enhance the probability of successful transmission of request and spectrum efficiency, where each partition is first used to transmit requests with the same rate in the first-fit policy; and other partitions are used to search available spectrum resources in the last-fit policy when there are no available spectrum resources in the dedicated partition. Meanwhile, a partition selection formula is designed to minimize the interference of spectrum resources during the last-fit spectrum assignment. Moreover, a reconfiguration mechanism, moving requests that are not transmitted in their dedicated partition to their dedicated partition, is also studied. The simulation results indicate that the proposed algorithm can reduce the bandwidth blocking probability and improve spectrum efficiency.

New bi-level programming model for routing and spectrum assignment in elastic optical network

The routing and spectrum assignment problems in elastic optical networks are well-known NP-hard problem, and are even more complex and challenging when the energy consumption is taken into account. To tackle this challenging problem, we establish a bi-level programming model with the energy consumption of the optical networks and the maximum index of used frequency slots as the leader’s and follower’s objectives to be minimized, respectively, which are used to determine the optimal schemes of routing and spectrum assignments. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. We use heuristic algorithm to sort the connection requests and design a genetic algorithm with tailor-made crossover, mutation and local search operator to look for an optimal routing and spectrum assignment scheme. Finally, simulation experiments are conducted, and the experimental results indicate the effectiveness of the proposed model and algorithms.