2949829
On the design of MIMO block-fading channels with feedback-link capacity constraint
In this paper, we propose a combined adaptive power control and beamforming framework for optimizing multiple-input/multiple-output (MIMO) link capacity in the presence of feedback-link capacity constraint. The feedback channel is used to carry channel state information only. It is assumed to be noiseless and causal with a feedback capacity constraint in terms of maximum number of feedback bits per fading block. We show that the hybrid design could achieve the optimal MIMO link capacity, and we derive a computationally efficient algorithm to search for the optimal design under a specific average power constraint. Finally, we shall illustrate that a minimum mean-square error spatial processor with a successive interference canceller at the receiver could be used to realize the optimal capacity. We found that feedback effectively enhances the forward channel capacity for all signal-to-noise ratio (SNR) values when the number of transmit antennas (n/sub T/) is larger than the number of receive antennas (n/sub R/). The SNR gain with feedback is contributed by focusing transmission power on active eigenchannel and temporal power waterfilling . The former factor contributed, at most, 10log/sub 10/(n/sub T//n/sub R/) dB SNR gain when n/sub T/>n/sub R/, while the latter factor's SNR gain is significant only for low SNR values.
6717587
Capacity of memoryless channels and block-fading channels with designable cardinality-constrained channel state feedback
A coding theorem is proved for memoryless channels when the channel state feedback of finite cardinality can be designed. Channel state information is estimated at the receiver and a function of the estimated channel state is causally fed back to the transmitter. The feedback link is assumed to be noiseless with a finite feedback alphabet, or equivalently, finite feedback rate. It is shown that the capacity can be achieved with a memoryless deterministic feedback and with a memoryless device which select transmitted symbols from a codeword of expanded alphabet according to current feedback. To characterize the capacity, we investigate the optimization of transmission and channel state feedback strategies. The optimization is performed for both channel capacity and error exponents. We show that the design of the optimal feedback scheme is identical to the design of scalar quantizer with modified distortion measures. We illustrate the optimization using Rayleigh block-fading channels. It is shown that the optimal transmission strategy has a general form of temporal water-filling in important cases. Furthermore, while feedback enhances the forward channel capacity more effectively in low-signal-to noise ratio (SNR) region compared with that of high-SNR region, the enhancement in error exponent is significant in both high- and low-SNR regions. This indicates that significant gain due to finite-rate channel state feedback is expected in practical systems in both SNR regions.
2045443
A rank criterion for QAM space-time codes
Space-time coding has been studied extensively as a powerful error correction coding for systems with multiple transmit antennas. An important design goal is to maximize the level of space diversity that a code can achieve. Toward this goal, the only systematic algebraic coding theory so far is binary rank theory by Hammons and El Gamal (see ibid. vol. 46, p.524-42, 2000) for binary phase-shift keying (BPSK) modulated codes defined over binary field and quaternary phase-shift keying (QPSK) modulated codes defined over modulo four finite ring. To design codes with higher bandwidth efficiency, we develop an algebraic rank theory to ensure full space diversity for 2/sup 2k/ quadrature and amplitude modulated (QAM) codes for any positive integer k. The theory provides the most general sufficient condition of full space diversity so far. It includes the BPSK binary rank theory as a special case. Since the condition is over the same domain that a code is defined, the full space diversity code design is greatly simplified. The usefulness of the theory is illustrated in examples, such as analyses of existing codes, constructions of new space-time codes with better performance, including the full diversity space-time turbo codes.
14467341
On the design of space-time and space-frequency codes for MIMO frequency-selective fading channels
The authors introduced an algebraic design framework for space-time coding in flat-fading channels . We extend this framework to design algebraic codes for multiple-input multiple-output (MIMO) frequency-selective fading channels. The proposed codes strive to optimally exploit both the spatial and frequency diversity available in the channel. We consider two design approaches: The first uses space-time coding and maximum likelihood decoding to exploit the multi-path nature of the channel at the expense of increased receiver complexity. Within this time domain framework, we also propose a serially concatenated coding construction which is shown to offer a performance gain with a reasonable complexity iterative receiver in some scenarios. The second approach utilizes the orthogonal frequency division multiplexing technique to transform the MIMO multipath channel into a MIMO flat block fading channel. The algebraic framework is then used to construct space-frequency codes (SFC) that optimally exploit the diversity available in the resulting flat block fading channel. Finally, the two approaches are compared in terms of decoder complexity, maximum achievable diversity advantage, and simulated frame error rate performance in certain representative scenarios.
25530597
Optimal space-time scheduling for block fading channels with partial power feedbacks
The multi-user multiple-input-multiple-output (MIMO) space-time scheduling problem is the prime focus of the paper. Using multiple transmit (nT) and receive antennas (nR), it is well known that the link-level throughput will be increased linearly with respect to min[nT, nR] without increasing the bandwidth and power budget. However, optimizing the link-level performance of the MIMO system does not always imply achieving scheduling-level optimization. Therefore, the design optimization across the link layer and the scheduling layer is very important to fully exploit the temporal and spatial dimensions of the communication channel. In this paper, we address the design of the optimal space-time scheduler for the multi-user MIMO system based on an information theoretical approach. Since full knowledge of channel matrix at the transmitter requires a feedback channel capacity not scalable with respect to nT (number of transmit antennas at mobiles) and nR (number of receive antennas at base station), we will assume two partial feedback models.
29049246
Iterative detection for BLAST systems with an arbitrary number of receive antennas
Turbo-coded Vertical Bell Labs Layered Space-Time Architecture (V-BLAST) is a multiple-antenna system employing turbo codes and bit-interleaving to offer flexible rates and high-speed wireless data communications. The conventional detection techniques based on one-shot demodulation and decoding algorithms may suffer poor performance or simply fail when there are fewer receive antennas than transmit antennas or when the multiple-input-multiple-output (MIMO) channel exhibits strong spatial correlations. We propose employing an iterative demodulation and decoding algorithm that solves these problems and greatly extends the applicability of V-BLAST. Simulations demonstrate that iterative V-BLAST offers the best-known performance in a wide range of settings. Through complexity analysis, we show that (a) the incremental computing complexity is small, although additional memory is required, compared with the conventional one-shot maximum likelihood (ML) decoding method, and (b) an iterative algorithm employing generalized sphere decoding will be a low-complexity, memory-reduced, and high-performance solution. We also give extensions to the technique beyond simple V-BLAST. In addition, when feedback from the receiver to the transmitter is available, we illustrate how to adapt code rate, modulation, and the power distribution to take advantage of the channel knowledge. With a combination of the “best in class” receiver performance and the ability to decode V-BLAST transmissions with fewer receive antennas than transmit antennas, the techniques described here can be viewed as key “technology enablers” for BLAST/MIMO in next-generation systems.
15563828
Self-similarity and long range dependence on the internet: a second look at the evidence, origins and implications
In this paper we critically reexamine some of the long standing beliefs regarding self-similarity and long range dependence (LRD) on the Internet. Power law tails have been conjectured to be a cause of LRD. In this paper, we reexamine the claims regarding heavy tails. We first examine the generative models for the heavy tail phenomena, both in terms of the fragility of some proposed mechanisms to modeling perturbations as well as the weak statistical evidence for the mechanisms. Next, we take a look at some of the implications of LRD in key performance aspects of Internet algorithms. Finally, we present an alternative model explaining the LRD phenomena of Internet traffic. We argue that the multiple time-scale nature of the generation of traffic and transport protocols make the observation of LRD inevitable.
2748390
Ensemble structure of evolutionary artificial neural networks
Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolution can be introduced at various levels of ANNs. It can be used to evolve weights, architectures, and learning parameters and rules. The paper is concerned with the evolution of ANN architectures, where an evolutionary algorithm is used to evolve a population of ANNs. The current practice in evolving ANNs is to choose the best ANN in the last population as the final result. The paper proposes a novel approach to form the final result by combining all the individuals in the last generation in order to make best use of all the information contained in the whole population. This approach regards a population of ANNs as an ensemble of ANNs and use a method to combine them. We have used four simple methods in our computational studies. The first is the majority voting method. The second and third are linear combination methods over the whole population. The fourth is a linear combination method over a subset of the whole population. The near optimal subset is obtained by a genetic algorithm search. Our experiments have shown that all four methods have produced better results than those produced by the single best individual.
1463365
Approximating optimal spare capacity allocation by successive survivable routing
The design of survivable mesh based communication networks has received considerable attention in recent years. One task is to route backup paths and allocate spare capacity in the network to guarantee seamless communications services survivable to a set of failure scenarios. This is a complex multi-constraint optimization problem, called the spare capacity allocation (SCA) problem. This paper unravels the SCA problem structure using a matrix-based model, and develops a fast and efficient approximation algorithm, termed successive survivable routing (SSR). First, per-flow spare capacity sharing is captured by a spare provision matrix (SPM) method. The SPM matrix has a dimension the number of failure scenarios by the number of links. It is used by each demand to route the backup path and share spare capacity with other backup paths. Next, based on a special link metric calculated from SPM, SSR iteratively routes/updates backup paths in order to minimize the cost of total spare capacity. A backup path can be further updated as long as it is not carrying any traffic. Furthermore, the SPM method and SSR algorithm are generalized from protecting all single link failures to any arbitrary link failures such as those generated by Shared Risk Link Groups or all single node failures. Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.
14379760
Protein Quaternary Fold Recognition Using Conditional Graphical Models
Protein fold recognition is a crucial step in inferring biological structure and function. This paper focuses on machine learning methods for predicting quaternary structural folds, which consist of multiple protein chains that form chemical bonds among side chains to reach a structurally stable domain. The complexity associated with modeling the quaternary fold poses major theoretical and computational challenges to current machine learning methods. We propose methods to address these challenges and show how (1) domain knowledge is encoded and utilized to characterize structural properties using segmentation conditional graphical models; and (2) model complexity is handled through efficient inference algorithms. Our model follows a discriminative approach so that any informative features, such as those representative of overlapping or long-range interactions, can be used conveniently. The model is applied to predict two important quaternary folds, the triple β-spirals and double-barrel trimers. Cross-family validation shows that our method outperforms other state-of-the art algorithms.
17413518
Enhancing the Effectiveness of Clustering with Spectra Analysis
For many clustering algorithms, such as K-Means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters, that is, k, to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images, or biological data. In an effort to improve the effectiveness of clustering, we seek the answer to a fundamental question: How can we effectively estimate the number of clusters in a given data set? We propose an efficient method based on spectra analysis of eigenvalues (not eigenvectors) of the data set as the solution to the above. We first present the relationship between a data set and its underlying spectra with theoretical and experimental results. We then show how our method is capable of suggesting a range of k that is well suited to different analysis contexts. Finally, we conclude with further empirical results to show how the answer to this fundamental question enhances the clustering process for large text collections.