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.
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.
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.
53236276
From datalog rules to efficient programs with time and space guarantees
This paper describes a method for transforming any given set of Datalog rules into an efficient specialized implementation with guaranteed worst-case time and space complexities, and for computing the complexities from the rules. The running time is optimal in the sense that only useful combinations of facts that lead to all hypotheses of a rule being simultaneously true are considered, and each such combination is considered exactly once. The associated space usage is optimal in that it is the minimum space needed for such consideration modulo scheduling optimizations that may eliminate some summands in the space usage formula. The transformation is based on a general method for algorithm design that exploits fixed-point computation, incremental maintenance of invariants, and combinations of indexed and linked data structures. We apply the method to a number of analysis problems, some with improved algorithm complexities and all with greatly improved algorithm understanding and greatly simplified complexity analysis.
8252393
Queueing Network-Model Human Processor (QN-MHP): A computational architecture for multitask performance in human-machine systems
Queueing Network-Model Human Processor (QN-MHP) is a computational architecture that integrates two complementary approaches to cognitive modeling: the queueing network approach and the symbolic approach (exemplified by the MHP/GOMS family of models, ACT-R, EPIC, and SOAR). Queueing networks are particularly suited for modeling parallel activities and complex structures. Symbolic models have particular strength in generating a person's actions in specific task situations. By integrating the two approaches, QN-MHP offers an architecture for mathematical modeling and real-time generation of concurrent activities in a truly concurrent manner. QN-MHP expands the three discrete serial stages of MHP, of perceptual, cognitive, and motor processing, into three continuous-transmission subnetworks of servers, each performing distinct psychological functions specified with a GOMS-style language. Multitask performance emerges as the behavior of multiple streams of information flowing through a network, with no need to devise complex, task-specific procedures to either interleave production rules into a serial program (ACT-R), or for an executive process to interactively control task processes (EPIC). Using QN-MHP, a driver performance model was created and interfaced with a driving simulator to perform a vehicle steering, and a map reading task concurrently and in real time. The performance data of the model are similar to human subjects performing the same tasks.
18230
Indexable PLA for Efficient Similarity Search
Similarity-based search over time-series databases has been a hot research topic for a long history, which is widely used in many applications, including multimedia retrieval, data mining, web search and retrieval, and so on. However, due to high dimensionality (i.e. length) of the time series, the similarity search over directly indexed time series usually encounters a serious problem, known as the "dimensionality curse". Thus, many dimensionality reduction techniques are proposed to break such curse by reducing the dimensionality of time series. Among all the proposed methods, only Piecewise Linear Approximation (PLA) does not have indexing mechanisms to support similarity queries, which prevents it from efficiently searching over very large time-series databases. Our initial studies on the effectiveness of different reduction methods, however, show that PLA performs no worse than others. Motivated by this, in this paper, we re-investigate PLA for approximating and indexing time series. Specifically, we propose a novel distance function in the reduced PLA-space, and prove that this function indeed results in a lower bound of the Euclidean distance between the original time series, which can lead to no false dismissals during the similarity search. As a second step, we develop an effective approach to index these lower bounds to improve the search efficiency. Our extensive experiments over a wide spectrum of real and synthetic data sets have demonstrated the efficiency and effectiveness of PLA together with the newly proposed lower bound distance, in terms of both pruning power and wall clock time, compared with two state-of-the-art reduction methods, Adaptive Piecewise Constant Approximation (APCA) and Chebyshev Polynomials (CP).
58452091
A Systematic Incrementalization Technique and Its Application to Hardware Design
A transformation method based on incrementalization and value caching, generalizes a broad family of loop refinement techniques. This method and CACHET, an interactive tool supporting it, are presented. Though highly structured and automatable, better results are obtained with intelligent interaction, which provides insight and proofs involving term equality. Significant performance improvements are obtained in many representative program classes, including iterative schemes that characterize Today's hardware specifications. Incrementalization is illustrated by the derivation of a hardware-efficient nonrestoring square-root algorithm.
7524751
POMA: Prioritized Overlay Multicast in Ad Hoc Environments
Overlay Multicast networks in mobile ad-hoc environments have received much attention due to their increasing number of practical applications. In many applications, some participating nodes might be members of more than one overlay trees or may wish to build a temporary tree in order to perform certain important tasks. The priority of these trees can be defined by the importance of the service. For the success of such an application, it is necessary that nodes belonging to more than one tree are smart enough to ignore incoming messages from members in low priority trees while they are listening to members from a higher priority tree. In this paper, we present a prioritized overlay multicast in ad-hoc environments (POMA). POMA builds priority trees with certain nodes carrying important tasks in overlay networks, and rearranges low priority trees whenever some nodes temporarily move to a high priority network. We study the feasibility of POMA by identifying a suitable unicast routing protocol, exploring to use location information to build efficient trees, and studying the impact of density of wireless nodes to the system performance.