Jerome Talim

Optimal scheduling in high-speed downlink packet access networks

We present an analytic model and a methodology to determine the optimal packet scheduling policy in a High-Speed Downlink Packet Access (HSDPA) system. The optimal policy is the one that maximizes cell throughput while maintaining a level of fairness between the users in the cell. A discrete stochastic dynamic programming model for the HSDPA downlink scheduler is presented. Value iteration is then used to solve for the optimal scheduling policy. We use a FSMC (Finite State Markov Channel) to model the HSDPA downlink channel. A near-optimal heuristic scheduling policy is developed. Simulation is used to study the performance of the resulting heuristic policy and compare it to the computed optimal policy. The results show that the performance of the heuristic policy is very close to that of the optimal policy. The heuristic policy has much less computational complexity, which makes it easy to deploy, with only slight reduction in performance compared to the optimal policy.

Optimal resource allocation and fairness control in all-optical WDM networks

This paper investigates the problem of optimal wavelength allocation and fairness control in all-optical wavelength-division-multiplexing networks. A fundamental network topology, consisting of a two-hop path network, is studied for three classes of traffic. Each class corresponds to a source-destination pair. For each class, call interarrival and holding times are exponentially distributed. The objective is to determine a wavelength allocation policy in order to maximize the weighted sum of users of all classes (i.e., class-based utilization). This method is able to provide differentiated services and fairness management in the network. The problem can be formulated as a Markov decision process (MDP) to compute the optimal allocation policy. The policy iteration algorithm is employed to numerically compute the optimal allocation policy. It has been analytically and numerically shown that the optimal policy has the form of a monotonic nondecreasing switching curve for each class. Since the implementation of an MDP-based allocation scheme is practically infeasible for realistic networks, we develop approximations and derive a heuristic algorithm for ring networks. Simulation results compare the performance of the optimal policy and the heuristic algorithm, with those of complete sharing and complete partitioning policies.

A call admission control for service differentiation and fairness management in WDM grooming networks

We investigate a call admission control (CAC) mechanism for providing fairness control and service differentiation in a WDM network with grooming capabilities. A WDM grooming network can handle different classes of traffic streams which differ in their bandwidth requirements. We assume that for each class, call interarrival and holding times are exponentially distributed. Using a Markov Decision Process approach, an optimal CAC policy is derived for providing fairness in the network. The Policy Iteration algorithm is used to numerically compute the optimal policy. Furthermore, we propose a heuristic decomposition algorithm with lower computational complexity and good performance. Simulation results compare the performance of our proposed policy with those of Complete Sharing and Complete Partitioning policies. Comparisons show that our proposed policy provides the best performance in most cases. Although this approach is motivated by WDM networks, it may be deployed to determine the optimal resource allocation in many problems in wireless and wired telecommunications systems.

A new access control scheme for metropolitan packet ring networks

This paper focuses on fair medium sharing in metropolitan packet ring networks. We propose a new fair access control scheme called virtual source queuing (VSQ) for these networks. A simple feedback control scheme for VSQ to avoid packet loss on the ring is also proposed. We compare the implementation complexity and performance of VSQ with those of the IEEE 802.17 resilient packet ring (RPR) standard. The simulation and analytical results show that our scheme can achieve fairness guarantee with less convergence time. It also has less access delay and requires ring buffer compared to RPR.

A Markov decision process model for dynamic wavelength allocation in WDM networks

This paper outlines an optimal dynamic wavelength allocation in all-optical WDM networks. A simple topology consists of a 2-hop path network with three nodes is studied for three classes of traffic where each class corresponds to different source-destination pair. For each class, call interarrival and holding times are exponentially distributed. The objective is to determine a wavelength allocation policy in order to maximize the weighted sum of users of all classes. Consequently, this method is able to provide differentiated services in the network. The problem can be formulated as a Markov decision process to compute the optimal resource allocation policy. It has been shown numerically that for two and three classes of users, the optimal policy is of threshold type and monotonic. Simulation results compare the performance of the optimal policy, with that of complete sharing and complete partitioning policies.

Optimal Scheduling Policy Determination for High Speed Downlink Packet Access

In this paper, we present an analytic model and methodology to determine optimal scheduling policy that involves two dimension space allocation: time and code, in high speed downlink packet access (HSDPA) system. A discrete stochastic dynamic programming model for the HSDPA downlink scheduler is presented. Value iteration is then used to solve for optimal policy. This framework is used to find the optimal scheduling policy for the case of two users sharing the same cell. Simulation is used to study the performance of the resulted optimal policy using round robin (RR) scheduler as a baseline. The policy granularity is introduced to reduce the computational complexity by reducing the action space. The results showed that finer granularity (down to 5 codes) enhances the performance significantly. However, the enhancement gained when using even finer granularity was marginal and does not justify the added complexity. The behaviour of the value function was observed to characterize the optimal scheduling policy. These observations is then used to develop a heuristic scheduling policy. The devised heuristic policy has much less computational complexity which makes it easy to deploy and with only slight reduction in performance compared to the optimal policy according to the simulation results.

Call admission and fairness control in WDM networks with grooming capabilities

We investigate a call admission control (CAC) mechanism to provide fairness control and service differentiation in a WDM network with grooming capabilities. A WDM grooming network can handle different classes of traffic streams which differ by their bandwidth requirements. We assume that for each class, call interarrival and holding times are exponentially distributed. Using a Markov decision process approach, an optimal CAC policy is derived to provide fairness in the network. The policy iteration algorithm is used to numerically compute the optimal policy. Furthermore, we propose a heuristic decomposition algorithm with lower computational complexity and very good performance. Simulation results compare the performance of our proposed policy, with that of complete sharing and complete partitioning policies.

Service differentiation and fairness control in WDM grooming networks

We investigate a call admission control (CAC) mechanism to provide service differentiation and fairness control in a WDM network with grooming capabilities. A WDM grooming network can handle different classes of traffic streams which differ by their bandwidth requirements. We assume that for each class, call interarrival and holding times are exponentially distributed. Using a Markov decision process approach, an optimal CAC policy is derived to provide service differentiation in the network. The policy iteration algorithm is used to numerically compute the optimal policy. Furthermore, we propose an heuristic decomposition algorithm with lower computational complexity and very good performance. Simulation results compare the performance of our proposed policy with that of complete sharing and complete partitioning policies.

Random Distribution for Data Survival in Unattended Wireless Sensor Networks

In Unattended Wireless Sensor Networks, data are stored locally and wait for a sink to offload. Since there is no real time communication between the sensors and the sink, a powerful mobile adversary can corrupt the collected data between the sink visits. In this paper, we propose simple and effective algorithms to limit data corruption in homogeneous unattended wireless sensor networks, without implementing cryptography. The key feature of our algorithms is to randomly distribute the data. We evaluate their performance using numerical simulation. The proposed algorithms successfully defeat the adversary objectives even when it has enough energy and time to corrupt all the nodes in the entire network.

An analytical model for fair rate calculation in resilient packet rings

Resilient packet ring (RPR) is a new medium access control (MAC) protocol for high-speed ring networks. It supports spatial reuse and, therefore, maintaining fairness among different nodes is a challenging task in RPR. To ensure fairness among nodes, a fairness algorithm is employed at each RPR node. In case of congestion, the fairness algorithm advertises a fair rate to all upstream nodes contributing to the congestion. In this paper, we develop an analytical model for fair rate calculation in the standard RPR fairness algorithm in the parking lot scenario. We first ignore the link propagation delay and model the system using a nonlinear discrete-time low-pass filter. We, then, consider the link propagation delay and develop a more realistic model. We verify our model by simulation results and analyze the effect of various parameters on the convergence time. Finally, we determine the low-pass filter coefficient to ensure that convergence time of the algorithm is within its minimum range