Channel Precoding for STBCs from Generalized Pseudo Orthogonal Designs

Statistical information about the wireless channel can be used at the transmitter side to enhance the performance of MIMO systems. This paper addresses how the concept of channel precoding can be used to enhance the performance of STBCs from Generalized Pseudo Orthogonal Designs which were first introduced by Zhu and Jafarkhani. Such designs include some important classes of STBCs that are directly derivable from Quasi-Orthogonal Designs and Co-ordinate Interleaved Orthogonal Designs.

Optimality of orthogonal sequences with multi-dimensional signaling

The sum capacity on a symbol-synchronous CDMA system having processing gain N and supporting K power constrained users is achieved by employing any set of N orthogonal sequences if a few users are allowed to signal along multiple dimensions. Analogously, the minimum received power (energy-per-chip) on the symbolsynchronous CDMA system supporting K users that demand specified data rates is attained by employing any set of N orthogonal sequences. At most (N - 1) users need to be split and if there are no oversized users, these split users need to signal only in two dimensions each. These results show that sum capacity or minimum sum power can be achieved with minimal downlink signaling.

Degrees of freedom of relay aided MIMO X network with orthogonal components

We consider the one-way relay aided MIMO X fading Channel where there are two transmitters and two receivers along with a relay with M antennas at every node. Every transmitter wants to transmit messages to every other receiver. The relay broadcasts to the receivers along a noisy link which is independent of the transmitters channel. In literature, this is referred to as a relay with orthogonal components. We derive an upper bound on the degrees of freedom of such a network. Next we show that the upper bound is tight by proposing an achievability scheme based on signal space alignment for the same for M = 2 antennas at every node.

On the solution of bivariate population balance equations for aggregation: X-discretization of space for expansion and contraction of computational domain

A new structured discretization of 2D space, named X-discretization, is proposed to solve bivariate population balance equations using the framework of minimal internal consistency of discretization of Chakraborty and Kumar [2007, A new framework for solution of multidimensional population balance equations. Chem. Eng. Sci. 62, 4112-4125] for breakup and aggregation of particles. The 2D space of particle constituents (internal attributes) is discretized into bins by using arbitrarily spaced constant composition radial lines and constant mass lines of slope -1. The quadrilaterals are triangulated by using straight lines pointing towards the mean composition line. The monotonicity of the new discretization makes is quite easy to implement, like a rectangular grid but with significantly reduced numerical dispersion. We use the new discretization of space to automate the expansion and contraction of the computational domain for the aggregation process, corresponding to the formation of larger particles and the disappearance of smaller particles by adding and removing the constant mass lines at the boundaries. The results show that the predictions of particle size distribution on fixed X-grid are in better agreement with the analytical solution than those obtained with the earlier techniques. The simulations carried out with expansion and/or contraction of the computational domain as population evolves show that the proposed strategy of evolving the computational domain with the aggregation process brings down the computational effort quite substantially; larger the extent of evolution, greater is the reduction in computational effort. (C) 2011 Elsevier Ltd. All rights reserved.

Low-Delay, High-Rate Nonsquare Complex Orthogonal Designs

The maximal rate of a nonsquare complex orthogonal design for transmit antennas is 1/2 + 1/n if is even and 1/2 + 1/n+1 if is odd and the codes have been constructed for all by Liang (2003) and Lu et al. (2005) to achieve this rate. A lower bound on the decoding delay of maximal-rate complex orthogonal designs has been obtained by Adams et al. (2007) and it is observed that Liang's construction achieves the bound on delay for equal to 1 and 3 modulo 4 while Lu et al.'s construction achieves the bound for n = 0, 1, 3 mod 4. For n = 2 mod 4, Adams et al. (2010) have shown that the minimal decoding delay is twice the lower bound, in which case, both Liang's and Lu et al.'s construction achieve the minimum decoding delay. For large value of, it is observed that the rate is close to half and the decoding delay is very large. A class of rate-1/2 codes with low decoding delay for all has been constructed by Tarokh et al. (1999). In this paper, another class of rate-1/2 codes is constructed for all in which case the decoding delay is half the decoding delay of the rate-1/2 codes given by Tarokh et al. This is achieved by giving first a general construction of square real orthogonal designs which includes as special cases the well-known constructions of Adams, Lax, and Phillips and the construction of Geramita and Pullman, and then making use of it to obtain the desired rate-1/2 codes. For the case of nine transmit antennas, the proposed rate-1/2 code is shown to be of minimal delay. The proposed construction results in designs with zero entries which may have high peak-to-average power ratio and it is shown that by appropriate postmultiplication, a design with no zero entry can be obtained with no change in the code parameters.

On the solution of bivariate population balance equations for aggregation: Pivotwise expansion of solution domain

The solution of a bivariate population balance equation (PBE) for aggregation of particles necessitates a large 2-d domain to be covered. A correspondingly large number of discretized equations for particle populations on pivots (representative sizes for bins) are solved, although at the end only a relatively small number of pivots are found to participate in the evolution process. In the present work, we initiate solution of the governing PBE on a small set of pivots that can represent the initial size distribution. New pivots are added to expand the computational domain in directions in which the evolving size distribution advances. A self-sufficient set of rules is developed to automate the addition of pivots, taken from an underlying X-grid formed by intersection of the lines of constant composition and constant particle mass. In order to test the robustness of the rule-set, simulations carried out with pivotwise expansion of X-grid are compared with those obtained using sufficiently large fixed X-grids for a number of composition independent and composition dependent aggregation kernels and initial conditions. The two techniques lead to identical predictions, with the former requiring only a fraction of the computational effort. The rule-set automatically reduces aggregation of particles of same composition to a 1-d problem. A midway change in the direction of expansion of domain, effected by the addition of particles of different mean composition, is captured correctly by the rule-set. The evolving shape of a computational domain carries with it the signature of the aggregation process, which can be insightful in complex and time dependent aggregation conditions. (c) 2012 Elsevier Ltd. All rights reserved.

Orthogonal biofunctionalization of magnetic nanoparticles via ``clickable'' poly(ethylene glycol) silanes: a ``universal ligand'' strategy to design stealth and target-specific nanocarriers

The present work demonstrates a novel strategy to synthesize orthogonally bio-engineered magnetonanohybrids (MNPs) through the design of versatile, biocompatible linkers whose structure includes: (i) a robust anchor to bind with metal-oxide surfaces; (ii) tailored surface groups to act as spacers and (iii) a general method to implement orthogonal functionalizations of the substrate via ``click chemistry''. Ligands that possess the synthetic generality of features (i)-(iii) are categorized as ``universal ligands''. Herein, we report the synthesis of a novel, azido-terminated poly(ethylene glycol) (PEG) silane that can easily self-assemble on MNPs through hetero-condensation between surface hydroxyl groups and the silane end of the ligand, and simultaneously provide multiple clickable sites for high density, chemoselective bio-conjugation. To establish the universal-ligand-strategy, we clicked alkyl-functionalized folate onto the surface of PEGylated MNPs. By further integrating a near-infrared fluorescent (NIRF) marker (Alexa-Fluor 647) with MNPs, we demonstrated their folate-receptor mediated internalization inside cancer cells and subsequent translocation into lysosomes and mitochondria. Ex vivo NIRF imaging established that the azido-PEG-silane developed in course of the study can effectively reduce the sequestration of MNPs by macrophage organs (viz. liver and spleen). These folate-PEG-MNPs were not only stealth and noncytotoxic but their dual optical and magnetic properties aided in tracking their whereabouts through combined magnetic resonance and optical imaging. Together, these results provided a strong motivation for the future use of the ``universal ligand'' strategy towards development of ``smart'' nanohybrids for theragnostic applications.

Subspace Pursuit Embedded in Orthogonal Matching Pursuit

Orthogonal Matching Pursuit (OMP) is a popular greedy pursuit algorithm widely used for sparse signal recovery from an undersampled measurement system. However, one of the main shortcomings of OMP is its irreversible selection procedure of columns of measurement matrix. i.e., OMP does not allow removal of the columns wrongly estimated in any of the previous iterations. In this paper, we propose a modification in OMP, using the well known Subspace Pursuit (SP), to refine the subspace estimated by OMP at any iteration and hence boost the sparse signal recovery performance of OMP. Using simulations we show that the proposed scheme improves the performance of OMP in clean and noisy measurement cases.

Modulation Diversity for Spatial Modulation Using Complex Interleaved Orthogonal Design

In this paper, we propose modulation diversity techniques for Spatial Modulation (SM) system using Complex Interleaved Orthogonal Design (CIOD). Specifically, we show that the standard SM scheme can achieve a transmit diversity order of two by using the CIOD meant for two transmit antenna system without incurring any additional system complexity or bandwidth requirement. Furthermore, we propose a low-complexity maximum likelihood detector for our CIOD based SM schemes by exploiting the structure of the CIOD. We show with our simulation results that the proposed schemes offer transmit diversity order of two and give a better symbol error rate performance than the conventional SM scheme.

Maximum likelihood algorithms for joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing system

Maximum likelihood (ML) algorithms, for the joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error and channel impulse response is formulated. Cramer-Rao lower bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. The authors propose an ML algorithm for the estimation of synchronisation impairments and channel together, using the grid search method. To reduce the complexity of the joint grid search in the ML algorithm, a modified ML (MML) algorithm with multiple one-dimensional searches is also proposed. Further, a stage-wise ML (SML) algorithm using existing algorithms, which estimate less number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.

Construction of Block Orthogonal STBCs and Reducing Their Sphere Decoding Complexity

Construction of high rate Space Time Block Codes (STBCs) with low decoding complexity has been studied widely using techniques such as sphere decoding and non Maximum-Likelihood (ML) decoders such as the QR decomposition decoder with M paths (QRDM decoder). Recently Ren et al., presented a new class of STBCs known as the block orthogonal STBCs (BOSTBCs), which could be exploited by the QRDM decoders to achieve significant decoding complexity reduction without performance loss. The block orthogonal property of the codes constructed was however only shown via simulations. In this paper, we give analytical proofs for the block orthogonal structure of various existing codes in literature including the codes constructed in the paper by Ren et al. We show that codes formed as the sum of Clifford Unitary Weight Designs (CUWDs) or Coordinate Interleaved Orthogonal Designs (CIODs) exhibit block orthogonal structure. We also provide new construction of block orthogonal codes from Cyclic Division Algebras (CDAs) and Crossed-Product Algebras (CPAs). In addition, we show how the block orthogonal property of the STBCs can be exploited to reduce the decoding complexity of a sphere decoder using a depth first search approach. Simulation results of the decoding complexity show a 30% reduction in the number of floating point operations (FLOPS) of BOSTBCs as compared to STBCs without the block orthogonal structure.

Adaptive selection of search space in look ahead orthogonal matching pursuit

Compressive Sensing theory combines the signal sampling and compression for sparse signals resulting in reduction in sampling rate and computational complexity of the measurement system. In recent years, many recovery algorithms were proposed to reconstruct the signal efficiently. Look Ahead OMP (LAOMP) is a recently proposed method which uses a look ahead strategy and performs significantly better than other greedy methods. In this paper, we propose a modification to the LAOMP algorithm to choose the look ahead parameter L adaptively, thus reducing the complexity of the algorithm, without compromising on the performance. The performance of the algorithm is evaluated through Monte Carlo simulations.

Projection-based atom selection in orthogonal matching pursuit for compressive sensing

For compressive sensing, we endeavor to improve the atom selection strategy of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a least squares solution based atom selection method. From a set of promising atoms, the choice of an atom is performed through a new method that uses orthogonal projection along-with a standard matched filter. Through experimental evaluations, the effect of projection based atom selection strategy is shown to provide a significant improvement for the support set recovery performance, in turn, the compressive sensing recovery.

Bivariate frequency analysis of floods using a diffusion based kernel density estimator

Recent focus of flood frequency analysis (FFA) studies has been on development of methods to model joint distributions of variables such as peak flow, volume, and duration that characterize a flood event, as comprehensive knowledge of flood event is often necessary in hydrological applications. Diffusion process based adaptive kernel (D-kernel) is suggested in this paper for this purpose. It is data driven, flexible and unlike most kernel density estimators, always yields a bona fide probability density function. It overcomes shortcomings associated with the use of conventional kernel density estimators in FFA, such as boundary leakage problem and normal reference rule. The potential of the D-kernel is demonstrated by application to synthetic samples of various sizes drawn from known unimodal and bimodal populations, and five typical peak flow records from different parts of the world. It is shown to be effective when compared to conventional Gaussian kernel and the best of seven commonly used copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and Student's T) in estimating joint distribution of peak flow characteristics and extrapolating beyond historical maxima. Selection of optimum number of bins is found to be critical in modeling with D-kernel.