Use Of Beamforming In Cross Layer Design For Wireless Communication Systems
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Use of Beamforming in Cross-layer Design for Wireless Communication Systems
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Release | : 2004 |
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Wireless systems that have traditionally been designed using a layered approach have seen a recent paradigm shift to a cross-layered approach where the interactions between two or more layers are considered explicitly in an integrated framework. This dissertation presents new methodologies that aim to improve the performance of wireless systems through consideration of cross-layer based design. The physical (PHY) and the medium access control (MAC) layers are the primary layers responsible for data transmission and user selection/control, respectively, in wireless systems. This dissertation begins with an analysis illustrating the use of multiple antennas and antenna arrays at. the PHY layer. A framework combining space-time block coding and beamforming for uplink in a wireless systems is considered for studying the trade-offs between antennas and antenna arrays at the receiver. Results indicate that in high noise environments the diversity achieved by using a large number of antennas combats bit error rate (BER) more efficiently than beamforming. On the other hand, in low noise environments beamforming plays an important role in reducing BER by minimizing the effect of interference from other co-channel users. Two approaches of cross-layer design that are currently available are the bottom-up and top-down approaches. The bottom-up approach uses the PHY laver information at the MAC and higher layers to make decisions that affect the system performance. Following a bottom-up approach, a new scheduling algorithm is designed that uses the channel state information and direction of arrival information of mobile users to efficiently schedule users for service. Both semi-analytical (based on the probability density and cumulative distribution functions) and numerical frameworks are used to compare the performance of this algorithm with the traditional round-robin and greedy scheduling algorithms. Both the numerical and semi-analytical frameworks which are shown to be cons.
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