Carrier aggregation (CA) is one of the key features for LTE-Advanced. By means of CA, users gain access to a total bandwidth of up to 100 MHz in order to meet the IMT-Advanced requirements. The system bandwidth may be contiguous, or composed of several non-contiguous bandwidth chunks, which are aggregated. This paper presents a summary of the supported CA scenarios as well as an overview of the CA functionality for LTE-Advanced with special emphasis on the basic concept, control mechanisms, and performance aspects. The discussion includes definitions of the new terms primary cell (PCell) and secondary cell (SCell), mechanisms for activation and deactivation of CCs, and the new cross-CC scheduling functionality for improved control channel optimizations. We also demonstrate how CA can be used as an enabler for simple yet effective frequency domain interference management schemes. In particular, interference management is anticipated to provide significant gains in heterogeneous networks, envisioning intrinsically uncoordinated deployments of home base stations.
Abstract-This paper presents key parameters including the line-of-sight (LOS) probability, large-scale path loss, and shadow fading models for the design of future fifth generation (5G) wireless communication systems in urban macro-cellular (UMa) scenarios, using the data obtained from propagation measurements at 38 GHz in Austin, US, and at 2, 10, 18, and 28 GHz in Aalborg, Denmark. A comparison of different LOS probability models is performed for the Aalborg environment. Alpha-betagamma and close-in reference distance path loss models are studied in depth to show their value in channel modeling. Additionally, both single-slope and dual-slope omnidirectional path loss models are investigated to analyze and contrast their root-mean-square (RMS) errors on measured path loss values. While the results show that the dual-slope large-scale path loss model can slightly reduce RMS errors compared to its singleslope counterpart in non-line-of-sight (NLOS) conditions, the improvement is not significant enough to warrant adopting the dual-slope path loss model. Furthermore, the shadow fading magnitude versus distance is explored, showing a slight increasing trend in LOS and a decreasing trend in NLOS based on the Aalborg data, but more measurements are necessary to gain a better knowledge of the UMa channels at centimeter-and millimeter-wave frequency bands.
This paper proposes an end-to-end deep-learning based method for textindependent writer identification. In this approach, convolutional neural networks (CNNs) are trained initially to extract the local features which represent characteristics of individual handwriting in the whole and sub-regions of character images. We make randomly sampled tuples of images from the training set to train CNNs and aggregate the extracted local features of images from the tuples to form the global features. By alternating the images to make the tuples, we create a large number of training patterns required by textindependent writer identification as well as the training process of CNNs. Experiments on the JEITA-HP database of offline handwritten Japanese character patterns show the effectiveness of this approach to overcome the difficulties of gathering handwritten character patterns of the same categories as the specimens of the writer. When we can use 200 characters, the method realizes the accuracy of 99.97% to classify 100 writers. Even if we can only use 50 characters, the method achieves the accuracy of 92.8%, which shows the ability to retain the accuracy despite the more number of writers or the less number of characters for training. Moreover, we made experiments on the Firemaker database and the IAM database of offline handwritten English text. When we use one page per writer to train, the method exceeds the accuracy of 91.5% to classify 900 writers. This result, as well as results for other conditions, show better performance than the previously published best result using handcrafted features and clustering algorithms, which shows the effectiveness of the method also for handwritten English text.
A relatively recent idea of extending the benefits of MIMO systems to multiuser scenarios seems promising in the context of achieving high data rates envisioned for future cellular standards after 3G (3rd Generation). Although substantial research has been done on the theoretical front, recent focus is on making Multiuser Multiple-Input Multiple-Output (MUMIMO) practically realizable. This paper presents an overview of the different MU-MIMO schemes included/being studied in 3GPP standardization from LTE (long-term evolution) to LTE Advanced. MU-MIMO system concepts and implementation aspects have been studied here. Various low-complexity receiver architectures are investigated, and their performance assessed through link-level simulations. Appealing performance offered by low-complexity interference aware (IA) receivers is notably emphasized. Furthermore, system level simulations for LTE Release 8 are provided. Interestingly, it is shown that MU-MIMO only offers marginal performance gains with respect to single-user MIMO. This arises from the limited MU-MIMO features included in Release 8 and calls for improved schemes for the upcoming releases.
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.