Interference estimation with applications to blind multiple-access communication over fading channels

Includes bibliographical references.We consider the detection of nonorthogonal multipulse signals on multiple-access fading channels. The generalized maximum-likelihood rule is employed to decode users whose complex fading gains are unknown. We develop geometrical interpretations for the resulting detectors and their corresponding asymptotic efficiencies. The generalized maximum-likelihood detection rule is then applied to find a matched subspace detector for the frequency-selective fading channel, under the assumption of a short coherence time (or long coherence time without the computational power to track the fading parameters). We propose blind implementations of these detectors for nonorthogonal multipulse signaling on both frequency-nonselective and frequency-selective multiple-access fading channels. These blind detectors extend the results of Wang and Poor to multipulse modulation and fast frequency selective fading. For comparison, the minimum mean-squared error decision rules for these channels are derived and blind implementations of their corresponding detectors are developed.This work was supported by the National Science Foundation under Contract ECS 9979400 and by the Office of Naval Research under Contracts N00014-89-J-1070 and N0014-00-1-0033

P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

We introduce a novel solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence to a reference image. Affine correspondences have traditionally been used to improve feature matching over wide baselines; however, little previous work has considered the use of such correspondences for absolute camera pose computation. The advantage of our approach (P1AC) is that it requires only a single correspondence in the minimal case in comparison to the traditional point-based approach (P3P) which requires at least three points. Our method removes the limiting assumptions made in previous work and provides a general solution that is applicable to large-scale image-based localization. Our evaluation on synthetic data shows that our approach is numerically stable and more robust to point observation noise than P3P. We also evaluate the application of our approach for large-scale image-based localization and demonstrate a practical reduction in the number of iterations and computation time required to robustly localize an image

Joint CFO Estimation and Data Detection in OFDM systems

Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique that is widely used in wireless broadband communication systems. The spectral e ciency of OFDM is very high since the subcarriers are spaced as closely as possible while maintaining orthogonality. However, one of the major problems with OFDM that can cause performance degradation is carrier frequency o set (CFO) which impairs the orthogonality among OFDM subcarriers, as a consequence, results in inter-subcarrier interference. In this thesis, an iterative algorithm for joint CFO estimation and data detection in OFDM systems over frequency selective channels is proposed. The proposed algorithm is performing both CFO estimation and data detection in the frequency domain based on the Expectation-Maximization (EM) algorithm. The proposed algorithm can achieve the same bit-error-rate (BER) performance as that of its time-domain counterpart with much lower complexity. Simulation results show that the proposed algorithm can converge after three iterations and an estimate of CFO can be obtained with high accuracy