Design of MMSE filterbank precoder and equalizer for MIMO frequency selective channels

In this paper, we consider the problem of designing minimum mean squared error (MMSE) filterbank precoder and equalizer for multiple input multiple output (MIMO) frequency selective channels. We derive the conditions to be satisfied by the optimal precoder-equalizer pair, and provide an iterative algorithm for solving them. The optimal design is very general, in that it is not constrained by channel dimensions, channel order, channel rank, or the input constellation. We also discuss some pertinent difierences between the filterbank approach and the space-time approach to the design of optimal precoder and equalizer. Simulation results demonstrate that the proposed design performs better than the space-time systems while supporting a higher data rate.

Asymptotically optimal cooperative wireless networks without constellation expansion

In this work, we construct a unified family of cooperative diversity coding schemes for implementing the orthogonal amplify-and-forward and the orthogonal selection-decode-and-forward strategies in cooperative wireless networks. We show that, as the number of users increases, these schemes meet the corresponding optimal high-SNR outage region, and do so with minimal order of signaling complexity. This is an improvement over all outage-optimal schemes which impose exponential increases in signaling complexity for every new network user. Our schemes, which are based on commutative algebras of normal matrices, satisfy the outage-related information theoretic criteria, the duplex-related coding criteria, and maintain reduced signaling, encoding and decoding complexities

A Low ML-Decoding Complexity, Full-Diversity, Full-Rate MIMO Precoder

Precoding for multiple-input multiple-output (MIMO) antenna systems is considered with perfect channel knowledge available at both the transmitter and the receiver. For two transmit antennas and QAM constellations, a real-valued precoder which is approximately optimal (with respect to the minimum Euclidean distance between points in the received signal space) among real-valued precoders based on the singular value decomposition (SVD) of the channel is proposed. The proposed precoder is obtainable easily for arbitrary QAM constellations, unlike the known complex-valued optimal precoder by Collin et al. for two transmit antennas which is in existence for 4-QAM alone and is extremely hard to obtain for larger QAM constellations. The proposed precoding scheme is extended to higher number of transmit antennas on the lines of the E - d(min) precoder for 4-QAM by Vrigneau et al. which is an extension of the complex-valued optimal precoder for 4-QAM. The proposed precoder's ML-decoding complexity as a function of the constellation size M is only O(root M)while that of the E - d(min) precoder is O(M root M)(M = 4). Compared to the recently proposed X- and Y-precoders, the error performance of the proposed precoder is significantly better while being only marginally worse than that of the E - d(min) precoder for 4-QAM. It is argued that the proposed precoder provides full-diversity for QAM constellations and this is supported by simulation plots of the word error probability for 2 x 2, 4 x 4 and 8 x 8 systems.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

On a programmable signal processor for VLSI

This paper presents a method of designing a programmable signal processor based on a bit parallel matrix vector matrix multiplier (linear transformer). The salient feature of this design is that the efficiency of the direct vector matrix multiplier is improved and VLSI design is made much simpler by trading off the more expensive arithematic operation (multiplication) for 'cheaper' manipulation (addition/subtraction) of the data.

A PPCA based non-parametric modeling and retrieval of PD signal buried in excessive noise

The problem of on-line recognition and retrieval of relatively weak industrial signals such as partial discharges (PD), buried in excessive noise, has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) due to the overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, onsite, PD measurement is hardly possible in conventional frequency based DSP techniques. The observed PD signal is modeled as a linear combination of systematic and random components employing probabilistic principal component analysis (PPCA) and the pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and modeled instituting non-parametric methods, based on smooth FIR filters, and a maximum aposteriori probability (MAP) procedure employed therein, to estimate the filter coefficients. The classification of the pulses is undertaken using a simple PCA classifier. The methods proposed by the authors were found to be effective in automatic retrieval of PD pulses completely rejecting PI.

Towards formal verification of analog mixed signal designs using SPICE circuit simulation traces

An extension to a formal verification approach of hybrid systems is proposed to verify analog and mixed signal (AMS) designs. AMS designs can be formally modeled as hybrid systems and therefore lend themselves to the formal analysis and verification techniques applied to hybrid systems. The proposed approach employs simulation traces obtained from an actual design implementation of AMS circuit blocks (for example, in the form of SPICE netlists) to carry out formal analysis and verification. This enables the same platform used for formally validating an abstract model of an AMS design, to be also used for validating its different refinements and design implementation; thereby, providing a simple route to formal verification at different levels of implementation. The feasibility of the proposed approach is demonstrated with a case study based on a tunnel diode oscillator. Since the device characteristic of a tunnel diode is highly non-linear with a negative resistance region, dynamic behavior of circuits in which it is employed as an element is difficult to model, analyze and verify within a general hybrid system formal verification tool. In the case study presented the formal model and the proposed computational techniques have been incorporated into CheckMate, a formal verification tool based on MATLAB and Simulink-Stateflow Framework from MathWorks.

Unified Spectral Efficiency Analysis of Cellular Systems with Channel-Aware Schedulers

Spectral efficiency is a key characteristic of cellular communications systems, as it quantifies how well the scarce spectrum resource is utilized. It is influenced by the scheduling algorithm as well as the signal and interference statistics, which, in turn, depend on the propagation characteristics. In this paper we derive analytical expressions for the short-term and long-term channel-averaged spectral efficiencies of the round robin, greedy Max-SINR, and proportional fair schedulers, which are popular and cover a wide range of system performance and fairness trade-offs. A unified spectral efficiency analysis is developed to highlight the differences among these schedulers. The analysis is different from previous work in the literature in the following aspects: (i) it does not assume the co-channel interferers to be identically distributed, as is typical in realistic cellular layouts, (ii) it avoids the loose spectral efficiency bounds used in the literature, which only considered the worst case and best case locations of identical co-channel interferers, (iii) it explicitly includes the effect of multi-tier interferers in the cellular layout and uses a more accurate model for handling the total co-channel interference, and (iv) it captures the impact of using small modulation constellation sizes, which are typical of cellular standards. The analytical results are verified using extensive Monte Carlo simulations.

A Long Memory Process Based Parametric Modeling and Recognition of PD Signal

We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.

Determining optimum subband edges for signal compression

The coding gain in subband coding, a popular technique for achieving signal compression, depends on how the input signal spectrum is decomposed into subbands. The optimality of such decomposition is conventionally addressed by designing appropriate filter banks. The issue of optimal decomposition of the input spectrum is addressed by choosing the set of band that, for a given number of bands, will achieve maximum coding gain. A set of necessary conditions for such optimality is derived, and an algorithm to determine the optimal band edges is then proposed. These band edges along with ideal filters, achieve the upper bound of coding gain for a given number of bands. It is shown that with ideal filters, as well as with realizable filters for some given effective length, such a decomposition system performs better than the conventional nonuniform binary tree-structured decomposition in some cases for AR sources as well as images

Design and performance analysis of a signal detector based on suprathreshold stochastic resonance

This paper presents the design and performance analysis of a detector based on suprathreshold stochastic resonance (SSR) for the detection of deterministic signals in heavy-tailed non-Gaussian noise. The detector consists of a matched filter preceded by an SSR system which acts as a preprocessor. The SSR system is composed of an array of 2-level quantizers with independent and identically distributed (i.i.d) noise added to the input of each quantizer. The standard deviation sigma of quantizer noise is chosen to maximize the detection probability for a given false alarm probability. In the case of a weak signal, the optimum sigma also minimizes the mean-square difference between the output of the quantizer array and the output of the nonlinear transformation of the locally optimum detector. The optimum sigma depends only on the probability density functions (pdfs) of input noise and quantizer noise for weak signals, and also on the signal amplitude and the false alarm probability for non-weak signals. Improvement in detector performance stems primarily from quantization and to a lesser extent from the optimization of quantizer noise. For most input noise pdfs, the performance of the SSR detector is very close to that of the optimum detector. (C) 2012 Elsevier B.V. All rights reserved.

Queen signal should be honest to be involved in maintenance of eusociality: chemical correlates of fertility in Ropalidia marginata

Queens of many social insect species are known to maintain reproductive monopoly by pheromonal signalling of fecundity. Queens of the primitively eusocial wasp Ropalidia marginata appear to do so using secretions from their Dufour's glands, whose hydrocarbon composition is correlated with fertility. Solitary nest foundresses of R. marginata are without nestmates; hence expressing a queen signal can be redundant, since there is no one to receive the signal. But if queen pheromone is an honest signal inextricably linked with fertility, it should correlate with fertility and be expressed irrespective of the presence or absence of receivers of the signal, by virtue of being a byproduct of the state of fertility. Hence we compared the Dufour's gland hydrocarbons and ovaries of solitary foundresses with queens and workers of post-emergence nests. Our results suggest that queen pheromone composition in R. marginata is a byproduct of fertility and hence can honestly signal fertility. This provides important new evidence for the honest signalling hypothesis.

Carbon Nanotube based Composite Fibers for Strain Sensing, Signal Processing and Computing

Carbon nanotubes dispersed in polymer matrix have been aligned in the form of fibers and interconnects and cured electrically and by UV light. Conductivity and effective semiconductor tunneling against reverse to forward bias field have been designed to have differentiable current-voltage response of each of the fiber/channel. The current-voltage response is a function of the strain applied to the fibers along axial direction. Biaxial and shear strains are correlated by differentiating signals from the aligned fibers/channels. Using a small doping of magnetic nanoparticles in these composite fibers, magneto-resistance properties are realized which are strong enough to use the resulting magnetostriction as a state variable for signal processing and computing. Various basic analog signal processing tasks such as addition, convolution and filtering etc. can be performed. These preliminary study shows promising application of the concept in combined analog-digital computation in carbon nanotube based fibers. Various dynamic effects such as relaxation, electric field dependent nonlinearities and hysteresis on the output signals are studied using experimental data and analytical model.

Signal-Powered Low-Drop-Diode Equivalent Circuit for Full-Wave Bridge Rectifier

Piezoelectric-device-based vibration energy harvesting requires a rectifier for conversion of input ac to usable dc form. Power loss due to diode drop in rectifier is a significant fraction of the already low levels of harvested power. The proposed circuit is a low-drop-diode equivalent, which mimics a diode using linear region-operated MOSFET. The proposed diode equivalent is powered directly from input signal and requires no additional power supply for its control. Power used by the control circuit is kept at a bare minimum to have an overall output power improvement. Diode equivalent was used to replace the four diodes in a full-wave bridge rectifier, which is the basic full- wave rectifier and is a part of the more advanced rectifiers like switch-only and bias-flip rectifiers. Simulation in 130-nm technology and experiment with discrete components show that a bridge rectifier with the proposed diode provides a 30-169% increase in output power extracted from piezoelectric device, as compared to a bridge rectifier with diode-connected MOSFETs. The bridge rectifier with the proposed diode can extract 90% of the maximum available power from an ideal piezoelectric device-bridge rectifier circuit. Setting aside the constraint of power loss, simulations indicate that diode drop as low as 10 mV at 38 mu A can be achieved.

A Technique to Compute Smooth Amplitude, Phase, and Frequency Modulations From the Analytic Signal

Gabor's analytic signal (AS) is a unique complex signal corresponding to a real signal, but in general, it admits infinitely-many combinations of amplitude and frequency modulations (AM and FM, respectively). The standard approach is to enforce a non-negativity constraint on the AM, but this results in discontinuities in the corresponding phase modulation (PM), and hence, an FM with discontinuities particularly when the underlying AM-FM signal is over-modulated. In this letter, we analyze the phase discontinuities and propose a technique to compute smooth AM and FM from the AS, by relaxing the non-negativity constraint on the AM. The proposed technique is effective at handling over-modulated signals. We present simulation results to support the theoretical calculations.