Probabilistic Quality-Of-Service Constrained Robust Transceiver Design in Multiple Antenna Systems

This dissertation, "Probabilistic Quality-of-service Constrained Robust Transceiver Design in Multiple Antenna Systems" by Xin, He, 何鑫, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In downlink multi-user multiple-input multiple-output (MU-MIMO) systems, different users, even multiple data streams serving one user, might require different quality-of-services (QoS). The transceiver should allocate resources to different users aiming at satisfying their QoS requirements. In order to design the optimal transceiver, channel state information is necessary. In practice, channel state information has to to be estimated, and estimation error is unavoidable. Therefore, robust transceiver design, which takes the channel estimation uncertainty into consideration, is important. For the previous robust transceiver designs, bounded estimation errors or Gaussian estimation errors were assumed. However, if there exists unknown distributed interference, the distribution of the channel estimation error cannot be modeled accurately a priori. Therefore, in this thesis, we investigate the robust transceiver design problem in downlink MU-MIMO system under probabilistic QoS constraints with arbitrary distributed channel estimation error. To tackle the probabilistic QoS constraints under arbitrary distributed channel estimation error, the transceiver design problem is expressed in terms of worst-case probabilistic constraints. Two methods are then proposed to solve the worst-case problem. Firstly, the Chebyshev inequality based method is proposed. After the worst-case probabilistic constraint is approximated by the Chebyshev inequality, an iteration between two convex subproblems is proposed to solve the approximated problem. The convergence of the iterative method is proved, the implementation issues and the computational complexity are discussed. Secondly, in order to solve the worst-case probabilistic constraint more accurately, a novel duality method is proposed. After a series of reformulations based on duality and S-Lemma, the worst-case statistically constrained problem is transformed into a deterministic finite constrained problem, with strong duality guaranteed. The resulting problem is then solved by a convergence-guaranteed iteration between two subproblems. Although one of the subproblems is still nonconvex, it can be solved by a tight semidefinite relaxation (SDR). Simulation results show that, compared to the non-robust method, the QoS requirement is satisfied by both proposed algorithms. Furthermore, among the two proposed methods, the duality method shows a superior performance in transmit power, while the Chebyshev method demonstrates a lower computational complexity. DOI: 10.5353/th_b4819952 Subjects: MIMO systems - Mathematical models Radio - Transmitter-receivers - Design and construction Probabilities