Maximizing system throughput and bandwidth utilization while guaranteeing fairness among users is a core technical challenge in the design of next generation cellular networks. In this context, fractional frequency reuse (FFR) is a helpful tool if it is flexible in terms of resource and power allocation as well as scheduling. Complementary to this tool, a well-chosen association of users to base station sectors can yet be another important factor, particularly when users are distributed unevenly in space. Here we propose a load balancing scheme that combines flexible FFR with an unrestricted association of users to sectors or base stations, thereby effectively increasing the user data rates in the dense regions of the network. In a first step, we perform virtual joint power allocation and scheduling so as to determine the best sectors for maximizing the throughput for a given user. In a second step, users are actually associated to the best sectors, which are not necessarily the ones providing the strongest signal. Simulation results show that our proposed mechanism provides fairness by load balancing while still maintaining the throughput over all users.

Radio Resource Management(RRM) has become critical with the current growth in the development of new technologies and services in the field of cellular communications. Efficient management of available radio resources and the possibility of dynamically selling unused spectrum are crucial challenges for the telecom operators. We propose an auction model for efficiently marketing radio resources and a dynamic pricing method for setting the prices of communication resources for sale. Simulation results demonstrate the effectiveness of the proposed pricing method in maximizing the revenue and success rate of the bid compared to the random pricing.

An important issue of research in optical burst switching (OBS) networks is to minimize the loss of bursts due to contention at the intermediate nodes. These contention losses can be minimized with the design of efficient path selection algorithms at the ingress node. Path selection algorithms that learn the optimal path dynamically with the changing traffic conditions outperform the deterministic path selection algorithms. Usually in the single agent path selection algorithms, a path is selected by the agent based on the feedback received at the ingress node which does not capture the effect of the paths selected by the other nodes in the network. We develop a multi-agent approach for path selection that includes the effect of the selection made by all the other nodes in the network. The proposed path selection algorithm uses agents at different source nodes to collectively learn the network dynamics and select the best outgoing path for each burst. We present simulation results to demonstrate the effectiveness of the proposed algorithm over the other similar algorithms in the literature.

Contention losses which usually do not indicate congestion is a major issue that hinders the deployment of optical burst switching (OBS) networks. Development of efficient path and wavelength selection algorithms is crucial to minimize the burst loss probability (BLP) in OBS networks. In this paper, we handle path selection and wavelength selection in a joint fashion. We formulate the problem of selecting a pair of path and wavelength jointly as a multi-armed bandit problem (MABP) and discuss the difficulties in solving MABP directly. We then rewrite the Q-learning formalism to solve the MABP without explicit model in an online fashion and propose an algorithm to solve the problem near-optimally. The proposed algorithm selects a pair of path and wavelength at each ingress node to minimize the BLP on the long run. Simulation results demonstrate the effectiveness of our algorithm in minimizing the BLP with better link utilization compared to the other proposals in the literature.

Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Contention among the bursts that arrive simultaneously at a core node leads to burst loss which affects the throughput of higher layer traffic. Development of efficient algorithms for path selection and wavelength selection is crucial to minimize the burst loss probability (BLP) in OBS networks. In this paper, we formulate path selection and wavelength selection in OBS networks as a multi-armed bandit problem and discuss the difficulties to solve them optimally. We propose algorithms based on Q-learning to solve these problems near-optimally. At an egress node, the path selection algorithm evaluates the Q values for a set of precomputed paths and chooses a path that corresponds to minimum BLP. Similarly, Q-learning algorithm for wavelength selection selects a wavelength in a pre-routed path such that the BLP is minimized. We do not assume wavelength conversion and buffering at the core nodes and hence, selection of path and wavelength is done only at the edge nodes. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the other adaptive algorithms available in the literature.

Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Development of efficient algorithms for path selection and wavelength selection is crucial in minimizing the burst loss probability (BLP) in OBS networks. In this paper, we present novel Reinforcement Learning algorithms for path selection and wavelength selection in the context of OBS networks. We develop an online path selection algorithm based on Q-learning to minimize the BLP by choosing an optimal path among a set of predetermined routes between every pair of ingress and egress nodes. We also propose a Q-learning algorithm for wavelength selection that selects an optimal wavelength among the available wavelengths in a pre-routed path with an objective of minimizing the BLP. We assume no wavelength conversion and buffering to be available at the core nodes of the OBS network. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the best known adaptive techniques for path selection and wavelength selection available in the literature.

In this paper, we discuss issues involved in developing autonomic Optical Burst Switched (OBS) networks. We develop an OBS network system, the first of its kind, which is self-aware, self-protecting, and self-optimizing, which are essential requirements of an autonomic network system. We use learning automata to autonomously learn the network state and make intelligent choices of route and wavelength, for burst transmission. We develop, for the first time, a self-protecting mechanism, to guard against contention losses and to adapt to network component (link/node) failures. For each connection (flow), at any point of time, this system either works without protection or chooses from one of many available protection mechanisms, based on the current network conditions and the performance requirements. Further, we develop a self-restoration mechanism based on deflection routing, wherein learning automata are used to identify an efficient alternate route to the destination, when there is a failure on the primary route. We show through extensive simulation studies that our mechanisms significantly improve burst loss probability over their existing counterparts.