2014Advanced materials researchRequires access

Adaptive Bit Allocation Algorithm for OFDM System

Wei Wei, Wen Dong An

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Abstract

For the greedy algorithm required a large number of iterations, this paper proposed one adaptive bit allocation algorithm based on Hughes-Hartogs algorithm which was improved greedy algorithm .Under the constraint of bit error rate and data transmission rate, the algorithm first used the Chow algorithm to allocate some of the bits, and then used the greedy algorithm to allocate the remaining bits. When minimize the total power by this algorithm, the iterations of this algorithm were significantly less than the greedy algorithm. By computer simulation, the results showed that performance of this algorithm was very close to the greedy algorithm, and the number of this algorithm’s iterations was 7.4%~34% of the greedy algorithm.

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What this paper is about

For the greedy algorithm required a large number of iterations, this paper proposed one adaptive bit allocation algorithm based on Hughes-Hartogs algorithm which was improved greedy algorithm .Under the constraint of bit error rate and data transmission rate, the algorithm first used the Chow algorithm to allocate some of the bits, and then used the greedy algorithm to allocate the remaining bits. When minimize the total power by this algorithm, the iterations of this algorithm were significantly less than the greedy algorithm. By computer simulation, the results showed that performance of this algorithm was very close to the greedy algorithm, and the number of this algorithm’s iterations was 7.4%~34% of the greedy algorithm.

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Available abstract

For the greedy algorithm required a large number of iterations, this paper proposed one adaptive bit allocation algorithm based on Hughes-Hartogs algorithm which was improved greedy algorithm .Under the constraint of bit error rate and data transmission rate, the algorithm first used the Chow algorithm to allocate some of the bits, and then used the greedy algorithm to allocate the remaining bits. When minimize the total power by this algorithm, the iterations of this algorithm were significantly less than the greedy algorithm. By computer simulation, the results showed that performance of this algorithm was very close to the greedy algorithm, and the number of this algorithm’s iterations was 7.4%~34% of the greedy algorithm.

Key concepts: Greedy algorithm, Algorithm, Greedy randomized adaptive search procedure, Ramer–Douglas–Peucker algorithm, Computer science, Population-based incremental learning, Orthogonal frequency-division multiplexing, Dinic's algorithm

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