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ahmedtaha
2011-11-08, 09:26 PM
hi all

Abstract
Broadband Wireless Access (BWA) is becoming one of the main driving forces in today’s wireless communications industry. Recently, the Third Generation Partnership Project (3GPP) initiated a standardization process, known as “3G Long-Term Evolution”, concerning the evolution of the existed third generation (3G) systems. By targeting data rates above 100 Mbps, 3G LTE will enable people to experience richer communications in terms of new type of services that will integrate voice, video and text. In the context of standardizing next generation wireless systems, efficient Radio Resource Management constitutes a key factor for achieving the performance targets.
Using the intrinsic characteristic of channel orthogonality, we emphasize on Orthogonal Frequency Division Multiple Access (OFDMA), which is the proposed modulation and multiple access method for 3G LTE wireless communication systems. Despite the fact that OFDM combats intra-cell interference inside a single cell, inter-cell interference due to frequency reuse exists and plays an important role in the multi-cell environment. The basic question which we try to answer in this thesis is how much we gain in terms of total throughput, when the available resources (frequency and power) are allocated in a coordinated way subject to power and rate requirement constraints. Specifically, we address the problem of joint channel and power allocation in a multi-cell multi-user OFDMA network for three different classes of services. Hence, we consider broadband or greedy services, narrowband services with a peak rate limitation and elastic services which can adopt their rate depending on the available resources. To the best of our knowledge, the problem of resource allocation for the last two types of services in a multi-cell setting has not been fully addressed in prior literature. For each case, we formulate the corresponding optimization problem which has in principle non-linear and non-convex features. As this problem is NP-hard, we focus on developing efficientheuristic algorithms which may provide optimal solutions for simple cases but are suboptimal in general.

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