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.
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.
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.
6591453
Opportunistic fair scheduling over multiple wireless channels
Emerging spread spectrum high-speed data networks utilize multiple channels via orthogonal codes or frequency-hopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic channel model, any subset of users can be selected for data transmission at any time, albeit with different throughputs and system resource requirements. We first transform selection of the best users and rates from a complex general optimization problem into a decoupled and tractable formulation: a multiuser scheduling problem that maximizes total system throughput and a control-update problem that ensures long-term deterministic or probabilistic fairness constraints. We then design and evaluate practical schedulers that approximate these objectives.
14575854
Query Translation Disambiguation as Graph Partitioning
Resolving ambiguity in the process of query translation is crucial to cross-language information retrieval when only a bilingual dictionary is available. In this paper we propose a novel approach for query translation disambiguation, named "spectral query translation model". The proposed approach views the problem of query translation disambiguation as a graph partitioning problem. For a given query, a weighted graph is first created for all possible translations of query words based on the co-occurrence statistics of the translation words. The best translation of the query is then determined by the most strongly connected component within the graph. The proposed approach distinguishes from previous approaches in that the translations of all query words are estimated simultaneously. Furthermore, translation probabilities are introduced in the proposed approach to capture the uncertainty in translating queries. Empirical studies with TREC datasets have shown that the spectral query translation model achieves a relative 20% -50% improvement in cross-language information retrieval, compared to other approaches that also exploit word co-occurrence statistics for query translation disambiguation.
61733202
Parallel network RAM: effectively utilizing global cluster memory for large data-intensive parallel programs
Large scientific parallel applications demand large amounts of memory space. Current parallel computing platforms schedule jobs without fully knowing their memory requirements. This leads to uneven memory allocation in which some nodes are overloaded. This, in turn, leads to disk paging, which is extremely expensive in the context of scientific parallel computing. To solve this problem, we propose a new peer-to-peer solution called parallel network RAM. This approach avoids the use of disk and better utilizes available RAM resources. This approach will allow larger problems to be solved while reducing the computational, communication and synchronization overhead typically involved in parallel applications.
5743866
The powerrank web link analysis algorithm
The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm "the PowerRank algorithm", which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low "importance" score. In the second part, the global "importance" score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10%-30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm.
2728797
Generalized Rate Analysis for Media-Processing Platforms
In this paper we address the "rate analysis" problem for media-processing platforms consisting of multiple processor cores connected in a pipelined fashion. More precisely, we aim at determining fight bounds on the rates at which multimedia streams can be fed into such architectures. These bounds depend on architectural constraints (e.g. the available on-chip memory, bus arbitration policies, etc), as well as the application characteristics (e.g. application partitioning and mapping, workload rates generated by different tasks, etch The proposed framework for rate analysis can be used for fast design space exploration to determine how these bounds change with different architectural parameters, mapping of the application, or changing the QoS requirements associated with the input streams