A Low-Complexity Bit-Loading Algorithm for OFDM Systems Under Spectral Mask Constraint

This letter aims to solve the power minimization problem for interference-free OFDM systems under achievable throughput and spectral mask constraints. To this end, we use a general analytic formula to yield an efficient initial bit vector, i.e., given a total number of bits, the total power use corr...

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Vydané v:IEEE communications letters Ročník 20; číslo 6; s. 1076 - 1079
Hlavní autori: Thanh Nhan Vo, Amis, Karine, Chonavel, Thierry, Siohan, Pierre
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
Algorithm design and analysis
Algorithms
Bit rate
Bit-loading
Engineering Sciences
greedy Algorithm
Low-complexity algorithm
Marginadaptive
Masks
Mathematical analysis
Minimization
OFDM
Optimization
Orthogonal Frequency Division Multiplexing
Power consumption
Power demand
Signal and Image processing
Signal to noise ratio
Spectra
Spectral mask constraint
Throughput
Tolerances
Vectors (mathematics)
Low-complexity algorithm
Spectral mask constraint
Bit-loading
greedy Algorithm
Margin-adaptive
OFDM
Spectral mask
Bit-loading algorithm
Optimization
ISSN:1089-7798, 1558-2558
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Shrnutí:This letter aims to solve the power minimization problem for interference-free OFDM systems under achievable throughput and spectral mask constraints. To this end, we use a general analytic formula to yield an efficient initial bit vector, i.e., given a total number of bits, the total power use corresponding to an efficient bit vector is minimum. We propose a low-complexity algorithm that achieves the global optimum of the problem. We theoretically prove that the number of bits required to add/remove, starting from the efficient initial bit vector, is upper bounded by the sum of half the number of active subcarriers and an arbitrary tolerance value. Simulation results show that the total power consumption is minimized with significant reduction of the computation cost as compared to other optimum algorithms.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2016.2521358