We present an optimal adaptive modulation and coding policy that minimizes the transmission latency and modulation/coding switching cost across a finite-state Markovian fading channel. We formulate the optimal tradeoff between transmission latency and modulation/coding switching cost as a discounted infinite horizon Markov Decision Problem (MDP). By exploiting special structures of the formulated MDP and under certain sufficient conditions, we show that optimal modulation and coding selection policies are monotone in the state variables. These monotone optimal policies are computationally inexpensive to implement and are scalable in terms of channel and switching cost parameters. Numerical results confirm the monotonicity and threshold-based structure of the optimal Modulation and Coding Scheme (MCS) selection policies under the proposed sufficient conditions.

We present a power and retransmission control policy that achieves an optimal tradeoff between transmission power, delay, and packet drop penalty costs over a radio fading channel. We formulate the underlying power and retransmission control as a Markov decision problem (MDP). Under certain sufficient conditions, we show that the optimal transmission power monotonically decreases in the channel quality and the number of retransmission slots left. These structural results lead to power allocation policies that are computationally inexpensive for on-line implementation and are scalable in terms of transmission parameters. Numerical examples confirm the monotonicity results and demonstrate the improvement in the transmission cost enabled by the optimal policy.

We present an optimal Adaptive Modulation and Coding (AMC) policy that minimizes the transmission latency and modulation/coding switching cost across a finite-state Markovian fading channel. We formulate the optimal tradeoff between the transmission latency and the modulation/coding switching cost as a stochastic shortest path Markov decision problem (MDP). By exploiting special structures of the formulated MDP and under certain sufficient conditions, we show that optimal modulation and coding selection policies are monotone in the state variables. These monotone optimal policies are computationally inexpensive to implement and are scalable in terms of channel and switching cost parameters. Numerical results confirm the monotonicity and threshold-based structure of the optimal MCS selection policies under the proposed sufficient conditions.

In modern network centric warfare (NCW) there is a dedicated platform (airplane) assigned to every group of aircraft that specializes in the hard-kill of the enemy guidance-radars by deploying high speed anti-radiation missiles (HARM)s. In this paper we consider the problem of optimal launch control of the HARMs. We formulate the optimal trade-off between the cost of the HARMs and the latency in performing the hard-kill of the enemy radar as a partially observable Markov decision process (POMDP). Next, by reformulating this POMDP as a Markovian search problem, we prove that optimal missile launch control policies are threshold-based policies in nature. We then present optimal threshold policies that unlike their POMDP counterparts are computationally efficient and inexpensive to implement in real time combat systems. Numerical results demonstrate the effectiveness of these threshold based missile deployment algorithms

High-speed downlink packet access (HSDPA) achieves high data rates and high spectral efficiency by using adaptive modulation and coding schemes and employing multicode CDMA. In this paper, we present opportunistic algorithms for scheduling HSDPA users and selecting modulation/coding and multicode schemes that exploit channel and buffer variations to increase the probability of uninterrupted media play-out. First, we introduce a stochastic discrete event model for a HSDPA system. By employing the discrete event model, we transform the scheduling problem of providing uninterrupted play-out to a feasibility problem that considers two sets of stochastic quality-of-service (QoS) constraints: stability constraints and robustness constraints. A methodology for obtaining a feasible solution is then proposed by starting with a so-called stable algorithm that satisfies the stability QoS constraints. Next, we present stochastic approximation algorithms that adapt the parameters of the stable algorithm in a way that a feasible point for the robustness QoS is reached within the feasibility region of the stability QoS

High-speed downlink packet access (HSDPA) achieves high data rates and high spectral efficiency by using adaptive modulation and coding (AMC) schemes and employing multi-code operation of CDMA. In this paper we present opportunistic algorithms for scheduling HSDPA users and selecting modulation and coding schemes that exploit channel variations to increase the probability of an uninterrupted media play-out. First we introduce a discrete event model for HSDPA system to transform the scheduling problem for providing an uninterrupted play-out to a feasibility problem that considers short term and long term quality of service (QoS) constraints. A methodology for obtaining a feasible solution is then proposed by starting with a so called stable algorithm that satisfies the long term QoS constraints (if possible with any scheduling policy). Next, we present stochastic approximation algorithms that adapt the parameters of the stable algorithm in a way that a feasible point for the short term QoS is reached within the feasibility region of the long term QoS.