Interference mitigation using group decoding in multiantenna systems

fi=vertaisarvioitu|en=peerReviewed

Robust Symbol Level Precoding for Overlay Cognitive Radio Networks

This paper focuses on designing robust symbol-level precoding (SLP) in the downlink of an overlay cognitive radio (CR) network, where a primary base station (PBS) serving primary users (PUs) and a cognitive base station (CBS) serving cognitive users (CUs) share the same frequency band. When the PBS shares data and perfect channel state information (CSI) with the CBS, an SLP approach which minimizes the CR transmission power and satisfies symbol-wise Safety Margin (SM) constraints of both PUs and CUs, is obtained in a low-complexity quadratic formulation. Then for the case of imperfect CSI from the PBS to CBS, we propose robust SLP schemes. First, with a norm-bounded CSI error model to approximate uncertain channels at the PBS, we adopt the max-min philosophy to conservatively achieve robust SLP constraints. Second, we use the additive quantization noise model (AQNM) to describe the statistics of the quantized PBS CSI, and we employ a stochastic constraint to formulate the problem, where the SM constraints are converted to be deterministic. Simulation results show that the proposed robust SLP schemes help enable PUs to mitigate negative effect of the quantization noise and simultaneously offer CR transmission with significant improvements in energy efficiency compared to non-robust methods.Comment: 30 pages, 13 figures, journa

Robust transmit beamforming design using outage probability specification

Transmit beamforming (precoding) is a powerful technique for enhancing the channel capacity and reliability of multiple-input and multiple-output (MIMO) wireless systems. The optimum exploitation of the benefits provided by MIMO systems can be achieved when a perfect channel state information at transmitter (CSIT) is available. In practices, however, the channel knowledge is generally imperfect at transmitter because of the inevitable errors induced by finite feedback channel capacity, quantization and other physical constraints. Such errors degrade the system performance severely. Hence, robustness has become a crucial issue. Current robust designs address the channel imperfections with the worst-case and stochastic approaches. In worst-case analysis, the channel uncertainties are considered as deterministic and norm-bounded, and the resulting design is a conservative optimization that guarantees a certain quality of service (QoS) for every allowable perturbation. The latter approach focuses on the average performance under the assumption of channel statistics, such as mean and covariance. The system performance could break down when persistent extreme errors occur. Thus, an outage probability-based approach is developed by keeping a low probability that channel condition falls below an acceptable level. Compared to the aforementioned methods, this approach can optimize the average performance as well as consider the extreme scenarios proportionally. This thesis implements the outage-probability specification into transmit beamforming design for three scenarios: the single-user MIMO system and the corresponding adaptive modulation scheme as well as the multi-user MIMO system. In a single-user MIMO system, the transmit beamformer provides the maximum average received SNR and ensures the robustness to the CSIT errors by introducing probabilistic constraint on the instantaneous SNR. Beside the robustness against channel imperfections, the outage probability-based approach also provides a tight BER bound for adaptive modulation scheme, so that the maximum transmission rate can be achieved by taking advantage of transmit beamforming. Moreover, in multi-user MIMO (MU-MIMO) systems, the leakage power is accounted by probability measurement. The resulting transmit beamformer is designed based on signal-to-leakage-plus-noise ratio (SLNR) criteria, which maximizes the average received SNR and guarantees the least leakage energy from the desired user. In such a setting, an outstanding BER performance can be achieved as well as high reliability of signal-to-interference-plus-noise ratio (SINR). Given the superior overall performances and significantly improved robustness, the probabilistic approach provides an attractive alternative to existing robust techniques under imperfect channel information at transmitter