CDIT-Based Constrained Resource Allocation for Mobile WiMAX Systems

Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2009, Article ID 425367, 8 pages doi:10.1155/2009/425367 Research Article CDIT-Based Constrained Resource Allocation for Mobile WiMAX Systems Felix Brah, Jerome Louveaux, and Luc Vandendorpe Ecole Polytechnique de Louvain, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium Correspondence should be addressed to Felix Brah, Received 1 July 2008; Accepted 31 October 2008 Recommended by Ekram Hossain Adaptive resource allocation has been shown to provide substantial performance gain in OFDMA-based wireless systems, such as WiMAX, when full channel state information (CSI) is available at the transmitter. However, in some fading environments (e.g., fast fading), there may not be a feedback link sufficiently fast to convey the CSI to the transmitter. In this paper, we consider resource allocation strategies for downlink multiuser mobile WiMAX systems, where the transmitter has only the channel distribution information (CDI), but no knowledge of the instantaneous channel realization. We address the problem of subchannel assignment and power allocation, to maximize the ergodic weighted-sum rate under long-term fairness, minimum data rate requirement and power budget constraints. This problem is formulated as a nonlinear stochastic constrained optimization problem. We provide an analytical solution based on the Lagrange dual decomposition framework. The proposed method has a complexity of O(KM) for K users and M subchannels. Simulation results are provided to compare the performance of this method with other allocation schemes and to illustrate the tradeoff between maximized weighted-sum rate and the constraints. Copyright © 2009 Felix Brah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction The mobile version of the Worldwide Interoperability for Microwave Access (mobile WiMAX) is one of the solutions in the competition for wireless broadband applications in challenging mobile environments [1, 2]. The mobile WiMAX air interface is based on orthogonal frequency division multiple access (OFDMA) for improved performances in multipath environments. One of the future aspects of OFDMA is the subchannelization which allows to group a total number of subcarriers into subsets of subcarriers called subchannels [3]. The major advantage of subchannelization is the provision of frequency diversity. A byproduct of the subchannelization is that the need for knowledge of radio channel quality is reduced from per-subcarrier to per-subchannel resolution and resources are allocated on per-subchannel basis. There are three types of subchannelizations, namely, adaptive modulation and coding (AMC), partially used subchannelization (PUSC), and fully used subchannelization (FUSC). With AMC, the subchannels are composed of contiguous groups of subcarriers. With both PUSC and FUSC, the subchannels are composed of distributed subcarriers. For PUSC, the set of used subcarriers, that is, data and pilots, is first partitioned into subchannels, and then pilot subcarriers are allocated within each subchannel. For FUSC, the pilot tones are common for all subchannels and are allocated first and then the remaining subcarriers are divided into data subchannels. In general, AMC is well suited for stationary, portable, and low mobility applications, whereas PUSC and FUSC are the best alternatives for mobile applications. We employ FUSC in this work. This method uses all the subchannels and employs full-channel diversity by distributing the allocated subcarriers to subchannels using a permutation mechanism. Thanks to the frequency diversity provided by the FUSC, the performance degradation due to fast fading in mobile environments is minimized. Mobile WiMAX aimed at delivering broadband mobile services ranging from real-time interactive gaming, VoIP, and streaming media to nonreal-time web browsing and simple file transfers. Users have channels of different quality. With 2 EURASIP Journal on Wireless Communications and Networking classical best effort transmission, unfair resource allocation can lead to starvation of some users in bad channel conditions. Therefore, the achievement of fairness among users while satisfying users’ minimum rate requirements is an important issue. Most of the previous works on OFDMA resource allocation have considered only the case where instantaneous channel state (CSI) is available at the transmitter and various algorithms based on instantaneous CSI have been developed [4–14]. In [4], adaptive subcarriers assignment to minimize the total transmit power is investigated. The authors presented a heuristic algorithm, the so-called Hungarian algorithm, based on constructive assignment and iterative improvement. Following the Hungarian approach, [5] proposed an iterative algorithm for power minimization and bit loading. The algorithm is considered as suboptimal for adaptive modulation. To reduce the computational complexity, [6] proposed low complexity and computationally efficient bandwidth and power allocation algorithms to solve the problem of minimizing the total power consumption under bit error rate and transmission rate constraints. In [7], the performance of bandwidth-constrained power minimization and power minimization schemes in terms of outage probability and packet error rate under user data rate satisfaction are compared. It is shown that, in severe shadowing environment with both frequency selective and flat fading, the former scheme outperforms the later. Fairness issues in a wireline multiaccess channel have been taken into account in [8, 9]. The authors introduce the concept of balanced capacity to characterize the multiuser channel performance with total power constraints in [8] and they extend the concept to individual power constraints in [9]. This concept of balanced capacity is closely related to the one presented in [10] where a low complexity suboptimal algorithm that maximizes the sum capacity while maintaining proportional fairness among the users data rate is developed. In [11], suboptimal resource grids and power allocation algorithms to maximize the total throughput under user’s data rate requirement are presented. Rate-power allocation algorithms for expected mutual information maximization based on partial channel knowledge have been developed in [13]. In [14], the authors investigated the impact of imperfect channel information on OFDMA-based systems under fairness and minimum rate constraints. Instantaneous resource allocation strategies are suitable for quasistatic or slow fading environments. However, when the channel variations are fast, the transmitter may not be able to adapt to the instantaneous channel realization. Hence, CSI-aware resourc (...truncated)