Bit-Error-Rate-Minimizing Channel Shortening Using Post-FEQ Diversity Combining and a Genetic Algorithm

In advanced wireline or wireless communication systems, i.e., DSL, IEEE 802.11a/g, HIPERLAN/2, etc., a cyclic prefix which is proportional to the channel impulse response is needed to append a multicarrier modulation (MCM) frame for operating the MCM accurately. This prefix is used to combat inter symbol interference (ISI). In some cases, the channel impulse response can be longer than the cyclic prefix (CP). One of the most useful techniques to mitigate this problem is reuse of a Channel Shortening Equalizer (CSE) as a linear preprocessor before the MCM receiver in order to shorten the effective channel length. Channel shortening filter design is a widely examined topic in the literature. Most channel shortening equalizer proposals depend on perfect channel state information (CSI). However, this information may not be available in all situations. In cases where channel state information is not needed, blind adaptive equalization techniques are appropriate. In wireline communication systems (such as DMT), the CSE design is based on maximizing the bit rate, but in wireless systems (OFDM), there is a fixed bit loading algorithm, and the performance metric is Bit Error Rate (BER) minimization. In this work, a CSE is developed for multicarrier and single-carrier cyclic prefixed (SCCP) systems which attempts to minimize the BER. To minimize the BER, a Genetic Algorithm (GA), which is an optimization method based on the principles of natural selection and genetics, is used. If the CSI is shorter than the CP, the equalization can be done by a frequency domain equalizer (FEQ), which is a bank of complex scalars. However, in the literature the adaptive FEQ design has not been well examined. The second phase of this thesis focuses on different types of algorithms for adapting the FEQ and modifying the FEQ architecture to obtain a lower BER. Simulation results show that this modified architecture yields a 20 dB improvement in BER

Why Philosophers Should Care About Computational Complexity

One might think that, once we know something is computable, how efficiently it can be computed is a practical question with little further philosophical importance. In this essay, I offer a detailed case that one would be wrong. In particular, I argue that computational complexity theory---the field that studies the resources (such as time, space, and randomness) needed to solve computational problems---leads to new perspectives on the nature of mathematical knowledge, the strong AI debate, computationalism, the problem of logical omniscience, Hume's problem of induction, Goodman's grue riddle, the foundations of quantum mechanics, economic rationality, closed timelike curves, and several other topics of philosophical interest. I end by discussing aspects of complexity theory itself that could benefit from philosophical analysis.Comment: 58 pages, to appear in "Computability: G\"odel, Turing, Church, and beyond," MIT Press, 2012. Some minor clarifications and corrections; new references adde

A Mobile Wireless Channel State Recognition Algorihm: Introduction, Definition, and Verification - Sensing for Cognitive Environmental Awareness

This research includes mobile wireless systems limited by time and frequency dispersive channels. A blind mobile wireless channel (MWC) state recognition (CSR) algorithm that detects hidden coherent nonselective and noncoherent selective processes is verified. Because the algorithm is blind, it releases capacity based on current channel state that traditionally is fixed and reserved for channel gain estimation and distortion mitigation. The CSR algorithm enables cognitive communication system control including signal processing, resource allocation/deallocation, or distortion mitigation selections based on channel coherence states. MWC coherent and noncoherent states, ergodicity, stationarity, uncorrelated scattering, and Markov processes are assumed for each time block. Furthermore, a hidden Markov model (HMM) is utilized to represent the statistical relationships between hidden dispersive processes and observed receive waveform processes. First-order and second-order statistical extracted features support state hard decisions which are combined in order to increase the accuracy of channel state estimates. This research effort has architected, designed, and verified a blind statistical feature recognition algorithm capable of detecting coherent nonselective, single time selective, single frequency selective, or dual selective noncoherent states. A MWC coherence state model (CSM) was designed to represent these hidden dispersive processes. Extracted statistical features are input into a parallel set of trained HMMs that compute state sequence conditional likelihoods. Hard state decisions are combined to produce a single most likely channel state estimate for each time block. To verify the CSR algorithm performance, combinations of hidden state sequences are applied to the CSR algorithm and verified against input hidden state sequences. State sequence recognition accuracy sensitivity was found to be above 99% while specificity was determined to be above 98% averaged across all features, states, and sequences. While these results establish the feasibility of a MWC blind CSR algorithm, optimal configuration requires future research to further improve performance including: 1) characterizing the range of input signal configurations, 2) waveform feature block size reduction, 3) HMM parameter tracking, 4) HMM computational complexity and latency reduction, 5) feature soft decision combining, 6) recursive implementation, 7) interfacing with state based mobile wireless communication control processes, and 8) extension to wired or wireless waveform recognition

Aeronautical engineering: A continuing bibliography with indexes, supplement 190

This bibliography lists 510 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1985

Robust transmit beamforming design using outage probability specification

Transmit beamforming (precoding) is a powerful technique for enhancing the channel capacity and reliability of multiple-input and multiple-output (MIMO) wireless systems. The optimum exploitation of the benefits provided by MIMO systems can be achieved when a perfect channel state information at transmitter (CSIT) is available. In practices, however, the channel knowledge is generally imperfect at transmitter because of the inevitable errors induced by finite feedback channel capacity, quantization and other physical constraints. Such errors degrade the system performance severely. Hence, robustness has become a crucial issue. Current robust designs address the channel imperfections with the worst-case and stochastic approaches. In worst-case analysis, the channel uncertainties are considered as deterministic and norm-bounded, and the resulting design is a conservative optimization that guarantees a certain quality of service (QoS) for every allowable perturbation. The latter approach focuses on the average performance under the assumption of channel statistics, such as mean and covariance. The system performance could break down when persistent extreme errors occur. Thus, an outage probability-based approach is developed by keeping a low probability that channel condition falls below an acceptable level. Compared to the aforementioned methods, this approach can optimize the average performance as well as consider the extreme scenarios proportionally. This thesis implements the outage-probability specification into transmit beamforming design for three scenarios: the single-user MIMO system and the corresponding adaptive modulation scheme as well as the multi-user MIMO system. In a single-user MIMO system, the transmit beamformer provides the maximum average received SNR and ensures the robustness to the CSIT errors by introducing probabilistic constraint on the instantaneous SNR. Beside the robustness against channel imperfections, the outage probability-based approach also provides a tight BER bound for adaptive modulation scheme, so that the maximum transmission rate can be achieved by taking advantage of transmit beamforming. Moreover, in multi-user MIMO (MU-MIMO) systems, the leakage power is accounted by probability measurement. The resulting transmit beamformer is designed based on signal-to-leakage-plus-noise ratio (SLNR) criteria, which maximizes the average received SNR and guarantees the least leakage energy from the desired user. In such a setting, an outstanding BER performance can be achieved as well as high reliability of signal-to-interference-plus-noise ratio (SINR). Given the superior overall performances and significantly improved robustness, the probabilistic approach provides an attractive alternative to existing robust techniques under imperfect channel information at transmitter

Improving our understanding of evolutionary persistence in an increasingly high CO2 world: Insight from marine polychaetes at a low pH vent system

The main aim of this thesis was to determine how marine metazoans might persist as ocean acidification (OA) conditions intensify. This was done using a combination of field surveys, field transplants and laboratory experiments with polychaetes from a site where volcanically-derived CO2 gas bubbles through the seafloor and drives the seawater pH down, resulting in a marine ecosystem representative of global OA projections for, or before, the year 2100. My first objective was to identify phenotypes, or traits, associated with OA tolerance (Chapter 2 and 5). To do this, I characterized the distribution of dominant calcifying polychaetes along natural pH gradients and used a comparative species recruitment trial to investigate life history traits underlying species’ OA tolerance, or vulnerability. I first found two dominant, closely related species of polychaete: Pileolaria militaris Claparède, 1870 and Simplaria sp. (Serpulidae, Spirorbinae). I then found that increased fecundity and rapid settlement are important traits in determining species’ abilities to persist in low pH environments (Chapter 2). Afterwhich, I investigated the life history traits of the non-calcifying polychaete, Platynereis dumerilii (Audouin & Milne Edwards, 1834), of one of the few species from the low pH site known to have broadcasting, pelagic development. I performed breeding experiments on P. dumerilii collected in both ambient and low pH sites and found that specimens from the low pH site were actually the direct developing brooder sister species, Platynereis massiliensis (Moquin-Tandon, 1869). By reanalayzing the distributions of both species at each site using genetic barcoding, I found clear evidence that direct development and brooding are dominant traits at low pH site, and for OA persistence (Chapter 5). My second objective was to use reciprocal transplant experiments to compare the relative importance of local adaptation and/or plasticity as potential mechanisms responsible for the differential tolerances of populations of the polychaete species Simplaria sp. to low pH. Laboratory transplants indicate that a local adaptation response occurred through genetic accommodation in the Simplaria sp. population from the low pH site. However, neither local adaptation nor plasticity appeared responsible for this species natural low pH persistence when assessed in situ (Chapter 3 & 4). My final objective was to create a framework using the polychaete vent model to identify other types of marine metazoans that are likely to be able to adapt to, and survive, under the predicted environmental conditions (Chapter 5). I overviewed the life history strategies of all dominant polychaetes in the low pH sites, and related trends in their life history strategies to those of other marine invertebrates. Brooding and direct development appear to be key traits for species likely to persist in future oceans pH. I conclude by summarizing how research regarding evolutionary responses may be advanced to add confidence to our projections of future marine metazoan responses

Humanist Narratology and the Suburban Ensemble Dramedy

What is a “humanistic drama”? Although we might describe narrative works as humanist, and references to the humanistic drama abound across a breadth of critical media, including film and literary theory, the parameters of these terms remain elliptical. My work attempts to clarify the narrative conditions of humanism. In particular, humanists ask how we use narrative texts to complicate our understanding of others, and question the ethics and efficacy of attempts to represent human social complexity in fiction. After historicising narrative humanism and situating it among related philosophies, I develop humanist hermeneutics as a method for reading fictive texts, and provide examples of such readings. I integrate literary Darwinism, anthropology, cognitive science and social psychology into a social narratology, which catalogues the social functions of narrative. This expansive study asks how we can unite the descriptive capabilities of social science with the more prescriptive ethical inquiry of traditional humanism, and aims to demonstrate their productive compatibility. From this groundwork, I then look at a cluster of humanistic film texts: the suburban ensemble dramedy, a phenomenon in millennial American cinema politicising the quotidian and the domestic. Popular works include The Kids Are All Right, Little Miss Sunshine, Little Children, Junebug, The Oranges, and what is arguably the inciting feature in a wave of such films entering production, American Beauty. I provide examples of humanist readings of these films at two levels: an overview of genre development as social phenomenon (including histories of suburban depiction onscreen, ensemble cinema and affective experimentation in recent American filmmaking), followed by a close reading of a progenitor text, Ron Howard's 1989 film Parenthood