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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/13540
Title: Robust Power Loading for the TDD MISO Downlink with Outage Constraints
Authors: Sohrabi, Foad
Advisor: Davidson, Tim
Department: Electrical and Computer Engineering
Keywords: Robust - Downlink - Power Loading - TDD;Signal Processing;Signal Processing
Publication Date: Oct-2013
Abstract: <p>We consider the problem of power allocation for the single-cell multiple-input single- output (MISO) downlink in a time division duplex (TDD) system. In such systems, the base station (BS) acquires information about the channel state during the training component of the uplink phase. The resulting estimation errors are modeled prob- abilistically, and the receivers specify quality-of-service (QoS) constraints in terms of a target signal-to-interference-and-noise ratio that is to be achieved with a given outage probability. For a fixed beamforming structure, we seek a power allocation that minimizes the transmission power required to satisfy the users’ QoS requests.</p> <p>The proposed approach to that problem begins with the observation that for TDD systems the channel estimation error at the base station can be modeled as being additive and Gaussian. Under that model, we obtain a precise deterministic characterization of the outage probability, and mildly conservative approximations thereof. Although the resulting deterministic optimization problems are not convex, we have been able to obtain good solutions using straightforward coordinate update algorithms. In fact, these solutions provide significantly better performance than the existing approaches, which are based on convex restrictions, because the proposed approximations are less conservative. By developing some approximations of the precise deterministic characterization of the outage probability, we develop algorithms that have good performance and much lower computational cost.</p>
URI: http://hdl.handle.net/11375/13540
Identifier: opendissertations/8377
9344
4613496
Appears in Collections:Open Access Dissertations and Theses

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