15756929
Adaptive Orthonormal Random Beamforming and Multi-Beam Selection for Cellular Systems
Channel state information (CSI) at transmitter plays an important role for multiuser MIMO broadcast channels, but full CSI at transmitter is not available for many practical systems. Previous work has proposed orthonormal random beamforming (ORBF) [16] for MIMO broadcast channels with partial channel state information (CSI) feedback, and shown that ORBF achieves the optimal sum-rate capacity for a large number of users. However, for cellular systems with moderate number of users, i.e., no more than 64, ORBF only achieves slight performance gain. Therefore, we analyze the performance of ORBF with moderate number of users and total transmit power constraint and show that ORBF scheme is more efficient under low SNR. Then we propose an adaptive ORBF scheme that selects the number of random beams for simultaneous transmission according to the average signal-to-noise ratio (SNR). Moreover, a multi-beam selection (MBS) scheme that jointly selects the number and the subset of the multiple beams is proposed to further improve the system performance for low SNR cases. The simulation results show that the proposed schemes achieve significant performance improvement when the number of users is moderate.
34929506
Adaptive Receive Antenna Selection for Orthogonal Space-Time Block Codes with Imperfect Channel Estimation
For coherent detection, decoding Orthogonal Space-Time Block Codes (OSTBC) requires full channel state information at the receiver, which basically is obtained by channel estimation. However, in practical systems, channel estimation errors are inevitable and may degrade the system performance more as the number of antennas increases. This letter shows that, using fewer receive antennas can enhance the performance of OSTBC systems in presence of channel estimation errors. Furthermore, a novel adaptive receive antenna selection scheme, which adaptively adjusts the number of receive antennas, is proposed. Performance evaluation and numerical examples show that the proposed scheme improves the performance obviously.
13932512
Joint Transmit Rate, Power and Antenna Allocation for MIMO Systems with Multimedia Traffic
This paper proposes an adaptive transmission scheme for MIMO systems to provide different bit error rates and transmission rates for multimedia traffic. The adaptive transmission scheme allocates antennas, rate and power jointly according to the feedback information to satisfy the diverse QoS requirements of the multimedia traffic. Furthermore, an efficient search algorithm with low complexity is proposed for practical implementation. Simulation results show that the proposed scheme improves the spectral efficiency while guaranteeing the QoS requirements of multimedia traffic. Moreover, the proposed search algorithm achieves close optimal performance with great complexity reduction.
11873989
WLC09-6: Adaptive Orthonormal Random Beamforming for Cellular Downlink with Multiple Antennas
Channel state information (CSI) at transmitter plays an important role for multiuser MIMO broadcast channels, but full CSI at transmitter is not available for many practical systems. Previous work has proposed an orthonormal random beamforming (ORBF) [16] for MIMO broadcast channels with partial channel state information (CSI) feedback, and shown that ORBF achieves the optimal sum-rate capacity for a large number of users. However, for cellular systems with moderate number of users, i.e., no more than 64, ORBF only achieves slight performance gain. Therefore, we analyze the performance of ORBF with moderate number of users and total transmit power constraint and show that ORBF scheme is more efficient under low SNR. Then we propose an adaptive ORBF scheme that selects the number of random beams for simultaneous transmission according to the average SNR. Moreover, a multi-beam selection (MBS) scheme that jointly selects the number and the subset of the multiple beams is proposed to further improve the system performance for low SNR cases. The simulation results show that the proposed schemes achieve significant performance improvement when the number of users is moderate.
24105890
On the Impact of Carrier Frequency Offsets in OFDM/SDMA Systems
The combined OFDM/SDMA approach has raised lots of research interests recently as it appears to be quite suitable for future broadband wireless transmission. In order to build practical OFDM/SDMA systems, we need the performance evaluation of such systems under real-world conditions. The performance degradation exists of an OFDM/SDMA system due to carrier frequency offsets (CFO), because single-user plain OFDM is highly sensitive to CFO. However, the characteristic of the degradation is not clear. In this paper, the impact of CFO on the performance of an uplink OFDM/SDMA system is analyzed. Different from the existing analysis, we jointly consider the impact of CFO on channel estimation and multi-user signal detection. Analytical expressions are derived for the variance of channel estimation error and multi-user signal detection error in the presence of CFO, whose accuracies are validated by simulation results. Observations related to channel estimation accuracy and the interaction of the CFO effects from different users are also given.
35977333
Multi-Media Retrieval with Semantic Web: A Case Study in Airport Security Inspection Applications
Multi-media retrieval based on content meaning is an important issue. It has significant applications in the tracing of terrorists based on airport security inspection information. In this paper, we review the defects of traditional image retrieval technology. A semantic Web based image retrieval method is proposed, which is deployed into an airport security inspection information retrieval system. We present the techniques of domain ontology construction and semantic-Web annotation for the media. An image retrieval technique based on semantic annotation is suggested, and the design of a semantic information retrieval prototype system for airport security inspection application is described.
4300632
Keyword Extraction Using Support Vector Machine
This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.
46639537
Hierarchical Stereo Matching: From Foreground to Background
18767470
New event detection based on indexing-tree and named entity
New Event Detection (NED) aims at detecting from one or multiple streams of news stories that which one is reported on a new event (i.e. not reported previously). With the overwhelming volume of news available today, there is an increasing need for a NED system which is able to detect new events more efficiently and accurately. In this paper we propose a new NED model to speed up the NED task by using news indexing-tree dynamically. Moreover, based on the observation that terms of different types have different effects for NED task, two term reweighting approaches are proposed to improve NED accuracy. In the first approach, we propose to adjust term weights dynamically based on previous story clusters and in the second approach, we propose to employ statistics on training data to learn the named entity reweighting model for each class of stories. Experimental results on two Linguistic Data Consortium (LDC) datasets TDT2 and TDT3 show that the proposed model can improve both efficiency and accuracy of NED task significantly, compared to the baseline system and other existing systems.
1966273
Block-quantized kernel matrix for fast spectral embedding
Eigendecomposition of kernel matrix is an indispensable procedure in many learning and vision tasks. However, the cubic complexity <i>O</i>(<i>N</i><sup>3</sup>) is impractical for large problem, where <i>N</i> is the data size. In this paper, we propose an efficient approach to solve the eigendecomposition of the kernel matrix <i>W</i>. The idea is to approximate <i>W</i> with <i>W</i> that is composed of <i>m</i><sup>2</sup> constant blocks. The eigenvectors of <i>W</i>, which can be solved in <i>O</i>(<i>m</i><sup>3</sup>) time, is then used to recover the eigenvectors of the original kernel matrix. The complexity of our method is only <i>O</i>(<i>mN</i> + <i>m</i><sup>3</sup>), which scales more favorably than state-of-the-art low rank approximation and sampling based approaches (<i>O</i>(<i>m</i><sup>2</sup><i>N</i> + <i>m</i><sup>3</sup>)), and the approximation quality can be controlled conveniently. Our method demonstrates encouraging scaling behaviors in experiments of image segmentation (by spectral clustering) and kernel principal component analysis.