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.
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.
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.
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.
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.
8110472
Methodology for Mode Selection in MIMO-OFDM System
Mode selection problem in MIMO-OFDM system is addressed. A general formulation is introduced to decompose the problem into two decoupled sub-problems: modulation and coding scheme (MCS) selection, and space-time processing (STP) optimization, each of which can be tackled more systematically. Mode selection algorithms can then be developed using a channel quality indicator (CQI) metric. The methodology is illustrated using the platform of IEEE 802.11n. The qualities of multiple CQI metrics are compared in terms of "coherency" and their performances are evaluated in terms of system goodput. This has been shown that the metric we proposed outperforms the other metrics.
2257586
Applying neighborhood consistency for fast clustering and kernel density estimation
Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine this idea with kernel density estimation based clustering, and derive the fast mean shift algorithm (FMS). FMS greatly reduces the complexity of feature space analysis, resulting satisfactory precision of classification. More importantly, we show that with FMS algorithm, we are in fact relying on a conceptually novel approach of density estimation, the fast kernel density estimation (FKDE) for clustering. The FKDE combines smooth and non-smooth estimators and thus inherits advantages from both. Asymptotic analysis reveals the approximation of the FKDE to standard kernel density estimator. Data clustering and image segmentation experiments demonstrate the efficiency of FMS.
42520367
Extending metadata with scenarios in adaptive distributed system
The traditional metadata cannot provide enough support for the adaptive distributed systems. In this paper, we propose a metadata extension that uses the scenarios as semantic aspect, describing the expected interaction sequence based on XML Schema. With such constraints, resource waste and further damages can be avoided by preventing the illegal interactions at runtime. This extension is more valid and user-friendly than the majority of the existing proposals, avoiding the potential inconsistency. A prototype has been developed to illustrate the applicability of the extended metadata. The experiments demonstrate that the prototype introduces an average overhead ratio of 6.4% in the inter-process interactions and only 0.27% in the remote interactions. The approach presented in this paper is suitable for the adaptive distributed systems and affords more guarantee of reliability.
7532408
Simplifying Mixture Models Through Function Approximation
The finite mixture model is widely used in various statistical learning problems. However, the model obtained may contain a large number of components, making it inefficient in practical applications. In this paper, we propose to simplify the mixture model by minimizing an upper bound of the approximation error between the original and the simplified model, under the use of the L 2 distance measure. This is achieved by first grouping similar components together and then performing local fitting through function approximation. The simplified model obtained can then be used as a replacement of the original model to speed up various algorithms involving mixture models during training (e.g., Bayesian filtering, belief propagation) and testing [e.g., kernel density estimation, support vector machine (SVM) testing]. Encouraging results are observed in the experiments on density estimation, clustering-based image segmentation, and simplification of SVM decision functions.
11313782
Efficient Heuristics for Solving Probabilistic Interval Algebra Networks
A probabilistic interval algebra (PIA) network is an interval algebra network with probabilities associated with the labels on an edge. The probabilities on each edge sum to 1. A solution is a consistent scenario where the product of the probabilities associated with each unique edge label is maximized. In this paper we investigate previous PIA network solution algorithms, and propose new ones. Our first algorithm is based on best first search and guarantees to output the optimal solution. However, this algorithm is only feasible for toy problems. We augment the algorithm with three heuristics. Although our proposed algorithm does not guarantee an optimal solution, it is very useful in practice. Good solutions can be generated quickly