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.
266522
Multiuser MIMO Downlink Transmission Over Time-Varying Channels
The performance of multiuser MIMO downlink systems with block diagonalization (BD) relies on the channel state information (CSI) at the transmitter to a great extent. For time division duplex TDD systems, the transmitter estimates the CSI while receiving data at current time slot and then uses the CSI to transmit at the next time slot. When the wireless channel is time- varying, the CSI for transmission is imperfect due to the time delay between the estimation of the channel and the transmission of the data and severely degrades the system performance. In this paper, we propose a linear method to suppress the interferences among users and data streams caused by imperfect CSI at transmitter. The transmitter first sends pilot signals through a linear spatial preceding matrix so as to make possible that the receiver can estimate CSI of other users, and then the receiver exploits a linear prefllter to suppress the interference. The numerical results show that the proposed schemes achieve obvious performance enhancement in comparison to the scheme assuming perfect CSI at the transmitter.
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.
9235487
Queuing Analysis on MIMO Systems with Adaptive Modulation and Coding
The combined MIMO with adaptive modulation and coding (AMC) technology can provide high spectral efficiency and link robustness. While most existing adaptive algorithms focus on physical layer, cross-layer analysis on the queuing behavior of MIMO-AMC systems is necessary, but remains open. In this paper, under the conditions of unsaturated traffic and finite-length buffer, we investigate the queuing characters of two representative categories of MIMO systems, namely the BLAST system and the space-time block coding (STBC) system. We model the service processes of both STBC and BLAST coupled with AMC, which is the most challenging part of the queuing analysis. We observe a new tradeoff between diversity and multiplexing in terms of link layer packet loss rate and queuing delay, based on which we propose a cross-layer design of diversity-multiplexing switching scheme to optimize the QoS of the MIMO-AMC systems.
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.
2754791
Interference Suppression for Block Diagonalization MIMO Downlink Systems over Time-Varying Channels
The performance of multiuser MIMO downlink systems with block diagonalization (BD) relies on the channel state information (CSI) at the transmitter to a great extent. For time division duplex TDD systems, the transmitter estimates the CSI while receiving data at current time slot and then uses the CSI to transmit at the next time slot. When the wireless channel is time-varying, the CSI for transmission is imperfect due to the time delay between the estimation of the channel and the transmission of the data and severely degrades the system performance. In this paper, we propose a linear method to suppress the interferences among users and data streams caused by imperfect CSI at transmitter. The transmitter first sends pilot signals through a linear spatial precoding matrix so as to make possible that the receiver can estimate CSI of other users, and then the receiver exploits a linear prefilter to suppress the interference. The numerical results show that the proposed schemes achieve obvious performance enhancement in comparison to the BD scheme with imperfect CSI at the transmitter.
10595235
A Block Coprocessor for User Data Rate Improvements to GPRS Coding Scheme 4
The general packet radio service (GPRS) has been developed to allow packet data to be transported efficiently over an existing circuit-switched radio network, such as GSM. The main application of GPRS are in transporting Internet protocol (IP) datagrams from web servers (for telemetry or for mobile Internet browsers). Four GPRS baseband coding schemes are defined to offer a trade-off in requested data rates versus propagation channel conditions. However, data rates in the order of > 100 kbits/s are only achievable if the simplest coding scheme is used (CS-4) which offers little error detection and correction (EDC) (requiring excellent SNR) and the receiver hardware is capable of full duplex which is not currently available in the consumer market. A simple EDC scheme to improve the GPRS block error rate (BLER) performance is presented, particularly for CS-4, however gains in other coding schemes are seen. For every GPRS radio block that is corrected by the EDC scheme, the block does not need to be retransmitted releasing bandwidth in the channel and improving the user's application data rate. As GPRS requires intensive processing in the baseband, a viable field programmable gate array (FPGA) solution is presented in this paper.
4342705
Weighted Ontology-Based Search Exploiting Semantic Similarity
This paper is concerned with the problem of semantic search. By semantic search, we mean searching for instances from knowledge base. Given a query, we are to retrieve ‘relevant’ instances, including those that contain the query keywords and those that do not contain the keywords. This is contrast to the traditional approaches of generating a ranked list of documents that contain the keywords. Specifically, we first employ keyword based search method to retrieve instances for a query; then a proposed method of semantic feedback is performed to refine the search results; and then we conduct re-retrieval by making use of relations and instance similarities. To make the search more effective, we use weighted ontology as the underlying data model in which importances are assigned to different concepts and relations. As far as we know, exploiting instance similarities in search on weighted ontology has not been investigated previously. For the task of instance similarity calculation, we exploit both concept hierarchy and properties. We applied our methods to a software domain. Empirical evaluation indicates that the proposed methods can improve the search performance significantly.
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.
43377731
Software as a Service: An Integration Perspective
Software as a Service (SaaS) is gaining momentum in recent years with more and more successful adoptions. Though SaaS is delivered over Internet and charged on per-use basis, it is software application in essence. SaaS contains business data and logics which are usually required to integrate with other applications deployed by a SaaS subscriber. This makes Integration become one of the common requirements in most SaaS adoptions. In this paper, we analyze the key functional and non-functional SaaS integration requirements from an industry practitioner point of view; and summarize the SaaS integration patterns and existing offerings; then point out the gaps from both technology and tooling perspectives; finally we introduce a SaaS integration framework to address those gaps. Considering there is no much academic work on SaaS service modeling, we come up with a SaaS service description framework as an extension of Web Service description, so as to model SaaS unique features in a unified way. With the supported tooling and runtime platform, the framework can facilitate the SaaS integration lifecycle in a model-driven approach.
14078787
Maximum Margin Clustering Made Practical
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (MMC) is a recent approach that aims at extending large margin methods to unsupervised learning. However, its optimization problem is nonconvex and existing MMC methods all rely on reformulating and relaxing the nonconvex optimization problem as semidefinite programs (SDP). Though SDP is convex and standard solvers are available, they are computationally very expensive and only small data sets can be handled. To make MMC more practical, we avoid SDP relaxations and propose in this paper an efficient approach that performs alternating optimization directly on the original nonconvex problem. A key step to avoid premature convergence in the resultant iterative procedure is to change the loss function from the hinge loss to the Laplacian/square loss so that overconfident predictions are penalized. Experiments on a number of synthetic and real-world data sets demonstrate that the proposed approach is more accurate, much faster (hundreds to tens of thousands of times faster), and can handle data sets that are hundreds of times larger than the largest data set reported in the MMC literature.