Computationally Efficient Resource Allocation in OFDM Systems: Genetic Algorithm Approach

Reddy, Yenumula B.; Gajendar, N.; Taylor, Peter G.; Madden, David

doi:10.1109/itng.2007.57

Cited by 25 publications

(14 citation statements)

References 20 publications

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“…Genetic Algorithms (GAs) [11][12][13][14][15][17][18][19][20][21][22] are a class of evolutionary algorithms. They provide novel approaches to problem solving technique inspired by biological evolution.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

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On the use of NSGA-II for multi-objective resource allocation in MIMO-OFDMA systems

Sharma

Anupama

2011

Wireless Netw

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This paper investigates the problem of dynamic subcarrier and bit allocation in downlink of Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiple Access (OFDMA) Systems. Using Singular Value Decomposition, the MIMO fading channel of each subcarrier is transformed into an equivalent bank of parallel Single Input Single Output sub-channels. To achieve the capacity bound, one must solve a multiuser subcarrier allocation and the optimal bit allocation jointly. To alleviate the computational complexity of joint subcarrier and bit allocation, several suboptimal solutions have been proposed. These suboptimal solutions handle subcarrier and bits individually. We propose the use of Non-dominated Sorting Genetic Algorithm (NSGA)-II, which is a multi-objective Genetic Algorithm, for joint allocation of bits and subcarriers, in the downlink of MIMO-OFDMA system. NSGA-II is intended for optimization problems involving multiple conflicting objectives. Here the two conflicting objectives are Rate Maximization and Transmit Power Minimization. The simulation results indicate remarkable improvement in terms of convergence over previous approaches involving Evolutionary algorithms. At the same time capacity achieved by the proposed algorithm is found to be comparable with that of previous algorithms.

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Section: Genetic Algorithmsmentioning

confidence: 99%

“…In [17] the MA optimization problem was addressed using GA, where each individual chromosome was coded as an array of elements that represented subcarriers. Here we extend the analogy to simultaneously allocate subcarriers and bits, while taking constraint (C 1 and C 2 ) into consideration.…”

Section: Nsga-iimentioning

confidence: 99%

On the use of NSGA-II for multi-objective resource allocation in MIMO-OFDMA systems

Sharma

Anupama

2011

Wireless Netw

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“…This, in turn, calls for some dynamic resource allocation solution that in addition to being dynamic can consider allocation of both the resources simultaneously, in an optimized fashion. More specifically to our problem , Wang [17] proposed an adaptive subcarrier and bit allocation for multiuser OFDM system based on genetic algorithm while Reddy [18][19][20] proposed also a genetic algorithm approach for adaptive sub-carrier, bit and power allocation. However, each considered resource or parameter to be allocated is taken to be independent of the other.…”

Section: Related Workmentioning

confidence: 99%

“…Bits are allocated randomly in first generation and then power is allocated to each sub-carrier according to the number of bits allocated to each sub-carrier. Furthermore, Reddy [18][19][20] considered a multi-user scenario where each user can either have one or multiple sub-carriers, in contrast to our considered scenario where each user is allocated only one sub-carrier.…”

Section: Related Workmentioning

confidence: 99%

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Simultaneous Power and Bit Allocation through Genetic Algorithm in MB-OFDM Systems

Taj

Hammami

2008

2008 10th International Conference on Advanced Communication Technology

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Multicarrier systems, such as MB-OFDM system, have shown better performance measures when system parameters (power, bits) are optimized, simultaneously, over different frequencies with respect to channel gain factor. In this paper, the parameter allocation problem in a multi-band OFDM (MB-OFDM) system for ultra-wide band is described considering UWB spectrum that is divided into bands that are 528 MHz wide. Within each sub-band, the bits and power are allocated in an optimized way. A Genetic Algorithm is designed that takes into account the problem of parameter allocation (power, bits) to different sub-bands in MB-OFDM scheme. The proposed solution tends to converge after a specific number of generations depending upon the number of bits allocated to each sub-carrier that in turn is dependent on channel gain.

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Dynamic Channel Allocation to Interfered Cellular Networks

Kapsalis¹,

Cottis²

2014

Wireless Pers Commun

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Computationally Efficient Resource Allocation in OFDM Systems: Genetic Algorithm Approach

Cited by 25 publications

References 20 publications

On the use of NSGA-II for multi-objective resource allocation in MIMO-OFDMA systems

On the use of NSGA-II for multi-objective resource allocation in MIMO-OFDMA systems

Simultaneous Power and Bit Allocation through Genetic Algorithm in MB-OFDM Systems

Dynamic Channel Allocation to Interfered Cellular Networks

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