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Dynamic Power Allocation over Block-Fading Channels with Delay Constraint

The problem of allocating power over a nonergodic Gaussian block fading channel is addressed for delay constrained applications, where transmission takes place over a limited number of time slots. We propose an algorithm in which the transmission power is determined at each time slot based on the channel condition at the current and future time slots, where a Markov model is used to capture the correlation between channel coefficients in different time slots. The problem is formulated in the framework of finite-horizon dynamic programming, where the optimal transmission strategy is assigned based on the relative importance of power and the quality of service (QoS). Depending on the importance of meeting the QoS constraint compared to the cost of power, the best power level is dynamically assigned by the algorithm, taking into account the channel state and the chance of meeting the QoS constraint. 
 
The performance of the proposed dynamic power allocation algorithm is evaluated for different channel states and QoS constraints. We compare the performance of the algorithm with schemes having strict constraints on power. Simulation results show that due to the flexibility given to the algorithm by removing the strict power constraint, the dynamic power allocation algorithm outperforms the optimal power constrained algorithm. Also, the results indicate that increasing the cost of power at the transmitter changes the system dynamics in a way that keeps the balance between QoS and power consumption.