Cherry Wakayama

VHDL-AMS behavioral modeling and simulation of a /spl Pi//4 DQPSK transceiver system

This work describes a methodology for top-down design, modeling, and simulation of complete /spl Pi//4 DQPSK system using hardware description language VHDL-AMS. Two system implementations are considered: with and without Viterbi encoder/decoder. VHDL-AMS implementations of various RF blocks (e.g. a realistic channel model) are developed, the system is simulated, and bit error rate is evaluated in the presence of noise. We show that the results of VHDL-AMS simulations for basic /spl Pi//4 DQPSK system in Mentor Graphics ADVance-MS match both Agilent ADS results and theoretical calculations. Adding a simple Viterbi encoder and decoder in VHDL to the basic system results in an approximate 1.4 dB SNR improvement. This work together is targeted towards engineers who work on behavioral modeling and simulation of complete RF systems using hardware description languages.

Rollout Algorithms for Data Storage- and Energy-Aware Data Retrieval Using Autonomous Underwater Vehicles

Increasingly effective underwater networks will be required to meet the growing demand for undersea data. The impending exploitation of non-acoustic underwater communication modes and the proliferation of autonomous underwater vehicles (AUVs) will enable the development of underwater networks able to use multiple modes of wireless communications and AUVs to transport data. In this paradigm, planning the routes for AUVs to collect data from underwater sensors becomes critical due to the dynamic nature of the undersea environment and the data collection process. This work proposes a dynamic path planning framework that enables judicious decisions on which network nodes the AUVs should visit next, based on the most recent network-status information. Routing decisions are aware of the AUVs own data-storage and energy constraints. Motivated by the intractability of optimal AUV routing, we propose a rollout algorithm as an enabler for dynamic AUV routing. Numerical tests illustrate the performance of the proposed algorithm.

Utilizing kinematics and selective sweeping in reinforcement learning-based routing algorithms for underwater networks

Effective utilization of mobile ad hoc underwater distributed networks is challenging due to high system costs and the harsh environment characterized by low bandwidth, large latency, high energy consumption, and node mobility. This work addresses the routing issue, which is critical in successfully establishing and utilizing an underwater network. In particular, it focuses on reinforcement learning (RL)-based routing algorithms, which possess the ability to explore the network environment and adapt routing decisions to the constantly changing topology of the network due to node mobility and energy usage. This paper presents a routing algorithm based on Q-learning, one of the RL approaches, with additional Kinematic and Sweeping features, therefore referred to as QKS. These two additional features are introduced to address the potential slow convergence associated with pure RL algorithms. The results of a detailed packet-level simulation have been obtained using the NS-2 open-source network simulator with underwater modeling additions. The energy efficiency, convergence, and delivery performance of QKS are compared with two other routing protocols for underwater networks, a basic flooding approach (ICRP (Liang, 2007)) and a basic Q-learning implementation (QELAR (Hu, 2010)), using simulations of networks with both fixed and mobile nodes.

Adaptive ping control for track-holding in multistatic active sonar networks

Distributed multistatic active sonar networks provide an Anti-Submarine Warfare capability against small, quiet, threat submarines in the harsh clutter-saturated littoral and deeper ocean environments. Adaptive ping control techniques provide the potential to significantly increase the multistatic networks performance, by pinging (in an optimum sense) the right source, at the right time, with the right waveform. This paper describes an automatic, adaptive ping control algorithm. It specifically addresses the “trackhold” objective, which is to adapt multistatic sonar operations to maintain and hold one or more target tracks which have been previously initiated (detected). The approach is unique in that it includes both sonar performance modeling and multistatic tracker outputs, in a closed-loop control structure. The paper motivates the approach, describes the algorithm, and shows some validating results. The evaluation utilizes a simple sonar performance model, a ping contact simulator, and a multistatic target tracker. Results are shown for a simple simulated scenario, showing the advantages of this adaptive ping control algorithm compared to using a preplanned, non-adaptive ping transmission schedule.

Noise aware behavioral modeling of the Ε-Δ fractional-N frequency synthesizer

This paper presents the behavioral model of a Ε-Δ fractional-N frequency synthesizer in terms of different noise sources and non-ideal effects. To accurately predict the phase noise of the synthesizer, different jitter noise sources such as phase modulation (PM) noise in phase-frequency detector and divider, frequency modulation (FM) noise in VCO are properly depicted. The Ε-Δ modulator, with its divider value dithered and quantization noise dynamically injected to the PLL, is described in behavioral model, which allows the designer to study the quantization noise impaction to the PLL phase noise. All the models are implemented in VHDL-AMS and simulated using Mentor Graphics ADvance-MS (ADMS). Our behavioral modeling method enables a fast simulation of the PLL system and an accurate phase noise prediction.

Simulation-driven task prioritization using a restless bandit model for active sonar missions

We consider a task prioritization problem of an active sonar tracking system when available ping resources may not be sufficient to sustain all tracking tasks at any particular time. In this problem, the time-varying conditions of a tracking task are represented by a finite-state discrete-time Markov decision process. The objective is to find a policy which decides at each time interval which tracking tasks to perform so as to maximize the aggregate reward over time. This paper addresses the derivation of the Markov chain parameters from the sonar tracking system simulations, the establishment of task prioritization as a restless bandit (TPRB) problem, and the TPRB policy obtained by a primal-dual index heuristic based on a first-order linear programming relaxation to the TPRB problem. The superior performance of the resulting TPRB policy is demonstrated using Monte Carlo simulations on various multi-target scenarios.

A quantum-dot light-harvesting architecture using deterministic phase control

Efficient solar-energy harvesting is fundamental to solar cell technology. Much research effort has been devoted to the construction of new light-harvesting structures, including the use of semiconductor quantum dots (QDs), to improve the widespread availability of solar cells. In this paper, a new light-harvesting architecture is considered, which utilizes quantum dots. The proposed architecture is composed of quantum phase-locked loops (QPLLs) to enhance the harvesting efficiency of QD solar cells by utilizing feedback control principles. The purpose of QPLL is to synchronize the phases of monochromatic light harvested by the antenna systems. This paper addresses a deterministic modeling and control formulation of the QPLL. The QPLL consists of a tracking controller and a proportional-integral (PI) controller. Simulation results for the controllers are presented and discussed.