Adaptive Subcarrier And Power Allocation Algorithm For Multiuser OFDM System With Proportional Rate Constraints

1. IntroductionThe growing demand for wireless multimedia services requires reliable and high-rate data communications over a wireless channel. But under the current mobile environment, conventional modulation techniques hardly meet the requirements of high-speed communications for limited spectrum resources and inter-symbol interference. However, orthogonal frequency division multiplexing (OFDM) can effectively increase the transmission rate for its ability to improve spectrum efficiency and combat multi-path. It is a promising technology in wide band wireless systems. Moreover, dynamic resource allocation techniques can greatly improve the system spectrum efficiency by adaptive subcarrier and transmit power allocation according to the instantaneous channel state information (CSI). In frequency selective fading channel, the combination of OFDM and dynamic allocation can utilize the merits of both technologies and is attracting more and more interests.For multiuser OFDM systems, users share an OFDM system. Two classes of resource allocation schemes exist: 1) fixed resource allocation; 2) dynamic resource allocation. Two classes of optimization techniques have been proposed in dynamic multiuser OFDM allocation, namely: 1) margin adaptive (MA); 2) rate adaptive (RA). In this paper, we focus on rate adaptive dynamic resource allocation. In rate adaptive resource allocation, subcarrier and transmit power allocation are performed to maximize the overall data rate while achieving proportional fairness amongst users under a total power constraints. Though the sum capacity is distributed proportionally amongst users in [41], during performing power distribution among users, solving the set of functions provides the optimal power allocation scheme. The equations are, in general, nonlinear. Iterative methods, such as the Newton-Raphson, can be used to obtain the solution, with a certain amount of computational effort. After the above consideration, this paper proposes a simple power allocation algorithm and improves subcarrier allocation method. This algorithm reduces the computational cost of the system and increase system performance. Moreover, this paper applies genetic algorithm to optimization problems with its powerful search capability to replace the existing traditional methods, improves convergence of genetic algorithms and achieves the same proportional fairness amongst users.2. Multiuser allocation algorithm for RA optimization (1) This paper proposes a simple adaptive allocation algorithm. It is a low-complexity suboptimal algorithm that the subcarrier allocation and the power allocation are independently conducted. In the proposed algorithm, the subcarrier allocation is performed first under the assumption of an equal power distribution, and it introduces a concept of subcarrier use efficiency which enables every user to choose the most efficient subcarrier. Therefore, allocation result is improved. Then max-min average method is used to fulfill the power allocation among users to maintain proportional fairness. After the power allocation, the transmit power is distributed by water-filling policy or equal method for individual user to maximize the sum capacity.3. Multiuser allocation algorithm for RA optimization (2)A new adaptive allocation algorithm on the basis of Genetic strategy for power is developed. The subcarrier allocation method is the same as that of the first algorithm. Differently, during performing power allocation, the algorithm introduces genetic theory to achieve power distribution among users by natural selection and constant generation evolution to maintain proportional fairness. To speed up the convergence of the algorithm, we can make a record of initial power for each user, and add this to the initial population. Because proportional fairness is basically satisfied after subcarrier allocation, the'good'genes in it can be fully utilized from the beginning of the algorithm. Thus, the searching time of the algorithm is reduced.4. Simulation results and AnalysisSimulation results show that the proposed algorithm can offer balance and tradeoff between sum capacity and fairness. Furthermore, it can also satisfy differently required data rate compared to the max-min problem method[40]and the sum capacity is distributed more fairly among users than the sum capacity maximization method[39]. Compared to [41], system performance of the first proposed algorithm is better than that of [41]. Notice that this capacity gain is from subcarrier allocation. Because every user chooses the most efficient subcarrier which makes the total transmitted bit increased, the sum capacity is increased accordingly. The second proposed algorithm applies the theory of biological evolution for optimization techniques to solve nonlinear problems and achieves the same optimization object.