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- W2181302844 abstract "The common criterion used in the power allocation problem for parallel memoryless Gaussian channels is to maximize overall mutual information (namely, to achieve the capacity), resulting in the well-known water-filling policy. Such a capacity-achieving power allocation, although theoretically interesting and beneficial in conceptually elucidating the behavior of coding systems, does not match well with practical situations as capacity is an asymptotic rate requiring the codeword length to grow to infinity. In addition, the overall system capacity can only be achieved when the coding scheme of each channel is optimally and continuously adapted to the allotted power. However in a practical system, the adopted codes are by no means optimal in terms of achieving capacity and have only a finite number of rate choices. Furthermore, a common quantity of interest is the effective system throughput. In light of these observations, we study in this paper the problem of determining the power allocation strategy for a system of coded parallel Gaussian channels with the objective of maximizing effective throughput under finite-length and fixed-rate coding constraints. An approximating formula of the system’s effective throughput is proposed for the case of convolutional codes and used to identify the optimal power allocation for each parallel channel. Our results show that the proposed power allocation policies can be graphically represented as a variation of the water-filling principle and achieves a near-optimal throughput. ii" @default.
- W2181302844 created "2016-06-24" @default.
- W2181302844 creator A5023085826 @default.
- W2181302844 date "2011-01-01" @default.
- W2181302844 modified "2023-09-26" @default.
- W2181302844 title "Throughput-Oriented Power Allocation Policies for Parallel Gaussian Channels Under Finite-Length and Fixed-Rate Coding Constraints" @default.
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