Millimeter Wave Beam Alignment: Large Deviations Analysis and Design Insights

In millimeter wave cellular communication, fast and reliable beam alignment via beam training is crucial to harvest sufficient beamforming gain for the subsequent data transmission. In this paper, we establish fundamental limits in beam-alignment performance under both the exhaustive search and the hierarchical search that adopts multi-resolution beamforming codebooks, accounting for time-domain training overhead. Specifically, we derive lower and upper bounds on the probability of misalignment for an arbitrary level in the hierarchical search, based on a single-path channel model. Using the method of large deviations, we characterize the decay rate functions of both bounds and show that the bounds coincide as the training sequence length goes large. We go on to characterize the asymptotic misalignment probability of both the hierarchical and exhaustive search, and show that the latter asymptotically outperforms the former, subject to the same training overhead and codebook resolution. We show via numerical results that this relative performance behavior holds in the non-asymptotic regime. Moreover, the exhaustive search is shown to achieve significantly higher worst-case spectrum efficiency than the hierarchical search, when the pre-beamforming signal-to-noise ratio (SNR) is relatively low. This study hence implies that the exhaustive search is more effective for users situated further from base stations, as they tend to have low SNR.Comment: Author final manuscript, to appear in IEEE Journal on Selected Areas in Communications (JSAC), Special Issue on Millimeter Wave Communications for Future Mobile Networks, 2017 (corresponding author: Min Li

Faster optimal and suboptimal hierarchical search

In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This thesis makes three contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical search, called Switch. Empirical results suggest that Switch yields faster search than straightforward modifications of Switchback, such as weighting the heuristic. Finally, we propose a modification to our optimal algorithm that uses multiple additive abstractions in order to improve performance of both optimal and suboptimal hierarchical search on some domains

A hierarchical search for gravitational waves from supermassive black hole binary mergers

We present a method to search for gravitational waves from coalescing supermassive binary black holes in LISA data. The search utilizes the $\mathcal{F}$-statistic to maximize over, and determine the values of, the extrinsic parameters of the binary system. The intrinsic parameters are searched over hierarchically using stochastically generated multi-dimensional template banks to recover the masses and sky locations of the binary. We present the results of this method applied to the mock LISA data Challenge 1B data set.Comment: 11 pages, 2 figures, for GWDAW-12 proceedings edition of CQ

Extended hierarchical search (EHS) algorithm for detection of gravitational waves from inspiraling compact binaries

Pattern matching techniques like matched filtering will be used for online extraction of gravitational wave signals buried inside detector noise. This involves cross correlating the detector output with hundreds of thousands of templates spanning a multi-dimensional parameter space, which is very expensive computationally. A faster implementation algorithm was devised by Mohanty and Dhurandhar [1996] using a hierarchy of templates over the mass parameters, which speeded up the procedure by about 25 to 30 times. We show that a further reduction in computational cost is possible if we extend the hierarchy paradigm to an extra parameter, namely, the time of arrival of the signal. In the first stage, the chirp waveform is cut-off at a relatively low frequency allowing the data to be coarsely sampled leading to cost saving in performing the FFTs. This is possible because most of the signal power is at low frequencies, and therefore the advantage due to hierarchy over masses is not compromised. Results are obtained for spin-less templates up to the second post-Newtonian (2PN) order for a single detector with LIGO I noise power spectral density. We estimate that the gain in computational cost over a flat search is about 100.Comment: 6 pages, 6 EPS figures, uses CQG style iopart.cl

A Hierarchical Extension of the D ∗ Algorithm

In this paper a contribution to the practice of path planning using a new hierarchical extension of the D ∗ algorithm is introduced. A hierarchical graph is stratified into several abstraction levels and used to model environments for path planning. The hierarchical D∗ algorithm uses a downtop strategy and a set of pre-calculated trajectories in order to improve performance. This allows optimality and specially lower computational time. It is experimentally proved how hierarchical search algorithms and on-line path planning algorithms based on topological abstractions can be combined successfully

Large-scale Hierarchical Alignment for Data-driven Text Rewriting

We propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers. Our approach does not require a seed parallel corpus, but instead relies solely on hierarchical search over pre-trained embeddings of documents and sentences. We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on text simplification from the normal to the Simple Wikipedia. We show that pseudo-parallel sentences extracted with our method not only supplement existing parallel data, but can even lead to competitive performance on their own.Comment: RANLP 201

Detecting extreme mass ratio inspirals with LISA using time–frequency methods

The inspirals of stellar-mass compact objects into supermassive black holes are some of the most important sources for LISA. Detection techniques based on fully coherent matched filtering have been shown to be computationally intractable. We describe an efficient and robust detection method that utilizes the time–frequency evolution of such systems. We show that a typical extreme mass ratio inspiral (EMRI) source could possibly be detected at distances of up to ~2 Gpc, which would mean ~tens of EMRI sources can be detected per year using this technique. We discuss the feasibility of using this method as a first step in a hierarchical search

On-line path planning by heuristic hierarchical search

In this paper, the problem of path planning for robot manipulators with six degrees of freedom in an on-line provided three-dimensional environment is investigated. As a basic approach, the best-first algorithm is used to search in the implicit descrete configuration space. Collisions are detected in the Cartesian workspace by hierarchical distance computation based on the given CAD model. The basic approach is extended by three simple mechanisms and results in a heuristic hierarchical search. This is done by adjusting the stepsize of the search to the distance between the robot and the obstacles. As a first step, we show encouraging experimental results with two degrees of freedom for five typical benchmark problems