Discrete-time controlled Markov processes with average cost criterion: a survey
SIAM Journal on Control and Optimization
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Optimization over Time
Dynamic Programming: Models and Applications
Dynamic Programming: Models and Applications
Neuro-Dynamic Programming
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Convex Optimization
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Fairness and optimal stochastic control for heterogeneous networks
IEEE/ACM Transactions on Networking (TON)
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Information Relaxations and Duality in Stochastic Dynamic Programs
Operations Research
Utility-based asynchronous flow control algorithm for wireless sensor networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Fast algorithms for resource allocation in wireless cellular networks
IEEE/ACM Transactions on Networking (TON)
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We consider the problem of choosing the data flow rate on a wireless link with randomly varying channel gain, to optimally trade off average transmit power and the average utility of the smoothed data flow rate. The smoothing allows us to model the demands of an application that can tolerate variations in flow over a certain time interval; we will see that this smoothing leads to a substantially different optimal data flow rate policy than without smoothing. We pose the problem as a convex stochastic control problem. For the case of a single flow, the optimal data flow rate policy can be numerically computed using stochastic dynamic programming. For the case of multiple flows on a single link, we propose an approximate dynamic programming approach to obtain suboptimal data flow rate policies. We illustrate, through numerical examples, that these approximate policies can perform very well.