The possibility of flexibly assigning spectrum resources with channels of different sizes greatly improves the spectral efficiency of optical networks, but can also lead to unwanted spectrum fragmentation. We study this problem in a scenario where traffic demands are categorized in two types (low or high bit-rate) by assessing the performance of three allocation policies. Our first contribution consists of exact Markov chain models for these allocation policies, which allow us to numerically compute the relevant performance measures. However, these exact models do not scale to large systems, in the sense that the computations required to determine the blocking probabilities—which measure the performance of the allocation policies—become intractable. In order to address this, we first extend an approximate reduced-state Markov chain model that is available in the literature to the three considered allocation policies. These reduced-state Markov chain models allow us to tractably compute approximations of the blocking probabilities, but the accuracy of these approximations cannot be easily verified. Our main contribution then is the introduction of reduced-state imprecise Markov chain models that allow us to derive guaranteed lower and upper bounds on blocking probabilities, for the three allocation policies separately or for all possible allocation policies simultaneously.
A rich body of research work on flexi-grid optical networks has appeared in the last few years. The reader is referred to @cite_0 and @cite_18 for a thorough overview. More specifically, both the static and the dynamic Routing and Spectrum Assignment (RSA) problem in flexi-grid networks has been extensively addressed; for a survey of the most relevant literature we refer to @cite_27 . In the present contribution, we focus on a single-link dynamic scenario. The arrival and departure processes of traffic requests are random, and requests are either accommodated in a spectrum portion of the link or rejected in real-time.
@cite_0 @cite_27 @cite_18
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Routing and Spectrum Assignment (RSA) algorithms play a key role in achieving good spectrum utilization in Elastic Optical Networks (EONs). A metric that is often used to evaluate RSA algorithms is the overall blocking probability experienced by circuit requests. In a previous study, the authors introduced an additional performance indicator that can be used to quantify the level of fairness (or lack thereof) experienced by two types of service requests, each type requiring a distinct amount of reserved spectrum. Their study is extended in this paper to account for circuit requests with uneven arrival rates. Routing and Spectrum Allocation (RSA) algorithms are used to compute both routing and spectrum allocation of connection demands in Elastic Optical Networks (EONs). A number of RSA algorithms have been proposed. Their performance is usually measured in terms of overall blocking probability. In this paper the authors pursue an additional performance indicator that is introduced to quantify the level of fairness (or unfairness) experienced by two types of demands, each type requiring a distinct spectrum bandwidth size. A new RSA algorithm is also proposed to jointly control blocking and fairness by partitioning the spectrum into dedicated and shared bands. Simulation results are used to estimate the trade-off between the blocking probability experienced by all demands and the level of fairness as it is achieved by a total of six RSA algorithms. We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into @math orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm. Next generation fixed wireless broadband networks are being increasingly deployed as mesh networks in order to provide and extend access to the internet. These networks are characterized by the use of multiple orthogonal channels and nodes with the ability to simultaneously communicate with many neighbors using multiple radios (interfaces) over orthogonal channels. Networks based on the IEEE 802.11a b g and 802.16 standards are examples of these systems. However, due to the limited number of available orthogonal channels, interference is still a factor in such networks. In this paper, we propose a network model that captures the key practical aspects of such systems and characterize the constraints binding their behavior. We provide necessary conditions to verify the feasibility of rate vectors in these networks, and use them to derive upper bounds on the capacity in terms of achievable throughput, using a fast primal-dual algorithm. We then develop two link channel assignment schemes, one static and the other dynamic, in order to derive lower bounds on the achievable throughput. We demonstrate through simulations that the dynamic link channel assignment scheme performs close to optimal on the average, while the static link channel assignment algorithm also performs very well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.