Optimal Chunk Based Resource Allocation For Ofdma Systems With Mulitple Ber Requirements
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Optimal Chunk-based Resource Allocation for OFDMA Systems with Mulitple BER Requirements
In wireless orthogonal frequency division multiple-access (OFDMA) standards, subcarriers are grouped into chunks and a chunk of subcarriers is made as the minimum allocation unit for subcarrier allocation. We investigate the chunk-based resource allocation for OFDMA downlink, where data streams contain packets with diverse bit-errorrate (BER) requirements. Supposing that adaptive transmissions are based on a number of discrete modulation and coding modes, we derive the optimal resource allocation scheme that maximizes the weighted sum of average user rates under the multiple BER and total power constraints. With proper formulation, the relevant optimization problem is cast as an integer linear program (ILP). We can rigorously prove that the zero duality gap holds for the formulated ILP and its dual problem. Furthermore, it is shown that the optimal strategy for this problem can be obtained through Lagrange dual-based gradient iterations with fast convergence and low computational complexity per iteration. Relying on the stochastic optimization tools, we further develop a novel on-line algorithm capable of dynamically learning the underlying channel distribution and asymptotically approaching the optimal strategy without knowledge of intended wireless channels a priori. In addition, we extend the proposed approach to maximizing the a-fair utility functions of average user rates, and show that such a utility maximization can nicely balance the trade-off between the total throughput and fairness among users. Likewise, the optimal solution for the primal problem can be yielded through solving its dual problem. Numerical results are provided to gauge the performance of the proposed schemes and attain our benchmarks, followed by conclusions and later on the discussion of future directions in this field.
Optimal Chunk-Based Network Scheduling
Author: He Tianzhou
language: en
Publisher: LAP Lambert Academic Publishing
Release Date: 2015-05-21
Efficient resource allocation for diverse data streams has attracted interest in future network design. We investigate the chunk-based resource allocation for OFDMA downlink, where data streams contain packets with diverse bit-error-rate (BER) requirements. Supposing that adaptive transmissions are based on a number of discrete modulation and coding modes, we derive the optimal resource allocation scheme that maximizes the weighted sum of average user rates under the multiple BER and total power constraints. With proper formulation, the relevant optimization problem is cast as an integer linear program. It is shown that the optimal strategy for this problem can be obtained through Lagrange dual-based gradient iterations with fast convergence and low computational complexity per iteration. A novel on-line algorithm is developed to approach the optimal strategy without knowledge of intended wireless channels a priori. Furthermore, the proposed approach is generalized to utility maximization of average user rates to balance the trade-off between the total throughput and fairness among users. Numerical results are provided to gauge the performance of the proposed schemes.
Genetic and Evolutionary Computing
This volume of Advances in Intelligent Systems and Computing highlights papers presented at the 11th International Conference on Genetic and Evolutionary Computing (ICGEC 2017). Held from 6 to 8 November 2017 in Kaohsiung, Taiwan, the conference was co-sponsored by Springer, Fujian University of Technology in China, National University of Kaohsiung, Harbin Institute of Technology, National Kaohsiung University of Applied Sciences, and VŠB -Technical University of Ostrava. The conference was intended as an international forum for researchers and professionals engaged in all areas of genetic computing, intelligent computing, evolutionary and grid computing.