We investigate the performance of several suboptimal multiuser detectors for rapidly time varying mobile radio channels. A modified Jakes model is used to simulate a realistic mobile fading channel. The use of the Kalman filter for this channel model is examined. We also analyze the performance of several noncoherent multiuser detectors. Analysis and simulation results indicate that the decorrelator is more robust than other considered multiuser detectors. I. Introduction Multiuser detection has the potential to reduce the Multi-Access Interference (MAI) and solve the near-far problem in the reverse link of a Code Division Multiple Access (CDMA) channel. Several suboptimal multiuser detection schemes with reasonable complexity have been studied. Among these schemes are the linear decorrelator [1], the multistage detector [2], the decision-feedback detector (DF) [3], the successive interference cancellation (SIC) [4], and parallel interference cancellation scheme (PIC) [4, 5]. Most evaluations of these multiuser detectors are performed under the ideal assumption of perfect channel estimation. This assumption is not valid in practice. The imperfect channel estimation degrades the performance of multiuser detectors since many multiuser detectors require channel estimates to cancel the MAI and/or to perform coherent reception. The main purpose of this paper is to compare robustness of these detectors with the Kalman channel estimators for realistic channels. Using the Kalman filter to estimate fading channel coefficients was first suggested in [6], and more work can be found in [7]. Most of this work modeled the fading channel as an auto-regressive (AR) process in order to apply the Kalman filter. In our work, we use a more realistic channel model-Jakes model [8] with modification of [9]. Combinations of Kalman filters and multiuser detectors were also studied previously. In [10], it was shown that a multiuser detector can be decoupled from a channel estimator, and [11] showed that the Kalman filter can be configured to estimate all the users' channel coefficient jointly or disjointedly. We adopt the disjoint estimation in our work. With the AR channel model, the Bit Error Rate (BER) analysis of the decorrelator, the DF, and the two-stage detector (2S) with the disjoint Kalman channel estimators can be found in [12, 13, 19, 21]. This paper differs from our previous work in the following aspects. (1) We use a more realistic channel model-the modified Jakes model, instead of a second order AR process to generate channel coefficients. (2) We test the use of Kalman filter to estimate a fading process which does not exactly obey the Gauss-Markov model. The signal model embedded in the Kalman filter is a second order AR process.
Cerebral visual impairments (CVIs) is an umbrella term that categorizes miscellaneous visual defects with parallel genetic brain disorders. While the manifestations of CVIs are diverse and ambiguous, molecular diagnostics stand out as a powerful approach for understanding pathomechanisms in CVIs. Nevertheless, the characterization of CVI disease cohorts has been fragmented and lacks integration. By revisiting the genome-wide and phenome-wide association studies (GWAS and PheWAS), we clustered a handful of renowned CVIs into five ontology groups, namely ciliopathies (Joubert syndrome, Bardet–Biedl syndrome, Alstrom syndrome), demyelination diseases (multiple sclerosis, Alexander disease, Pelizaeus–Merzbacher disease), transcriptional deregulation diseases (Mowat–Wilson disease, Pitt–Hopkins disease, Rett syndrome, Cockayne syndrome, X-linked alpha-thalassaemia mental retardation), compromised peroxisome disorders (Zellweger spectrum disorder, Refsum disease), and channelopathies (neuromyelitis optica spectrum disorder), and reviewed several mutation hotspots currently found to be associated with the CVIs. Moreover, we discussed the common manifestations in the brain and the eye, and collated animal study findings to discuss plausible gene editing strategies for future CVI correction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.