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A practical, structurally simple, and scalable Gray-coded vectored hierarchical implementation is presented for 2^2n quadrature amplitude modulation with square constellation. The complexity of the vectored constellation mapping and the Gray codec is significantly reduced. The proposed modulator and demodulator algorithms are scalable, which make them favorable in adaptive and hierarchical modulations on software-defined radio platforms. The proposed algorithms are implemented on low-power Atmel SAM3X8E ARM Cortex-M3 microcontroller, as a test platform. [ABSTRACT FROM PUBLISHER]

The existence of multiple wireless networks with different radio access technologies and protocols makes the radio environment heterogeneous . In order to provide the best Quality of Service available from the active networks, and satisfy the concept of always best connected, one can take advantage of this heterogeneity by developing multi-mode terminals able to smartly switch from one interface to another. This switching process, known as vertical handover (VHO), requires some relevant metrics to be measured by the terminal in order to decide whether to trigger a VHO or not. Using multiple antennas, we propose to track the number of active sources and employ the results in CSMA/CA networks for VHO. The proposed algorithm is developed using a Markov chain model for sources enumeration at any given time. We also use a three state Markov model for CSMA/CA networks and show how this algorithm can be applied to recursively obtain two informative metrics about the channel state, namely the channel occupancy rate and the collision rate. Numerical simulations confirm that the proposed algorithm performs well for practical SNR values. The proposed algorithm relies on a physical layer sensing and requires no connection to the access point, no synchronization, no signal demodulation and no frame decoding. This particularity ensures a seamless handover with a time/energy economy. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we investigate the local PDF of natural signals in sparse domains. The statistical properties of natural signals are characterized more accurately in the sparse domains because the sparse domain coefficients have heavy-tailed distribution and have reduced correlation with adjacent coefficients. Our experiments on 3D data in 3D discrete complex wavelet transform domain show that a conditionally (given locally estimated variance and shape) independent Bessel K-form distribution (BKFD) locally fits the sparse domain's coefficients of natural signals, accurately. To justify this observation, we also investigate the PDF of the locally estimated variance and suggest a Gamma PDF for the locally estimated variance. Because commonly used sparse transformations are orthonormal, the PDF of the sparse domain coefficients must converge to Gaussian distribution by virtue of central limit theorem assuming that natural signals are locally wide sense stationary for small window sizes. Interestingly, we observe that the PDF of the normalized data (on the locally estimated variance) exhibit a Gaussian PDF, which confirms that the BKFD is an appropriate fit. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The exact maximum-likelihood (ML) detector for amplify-and-forward (AF) cooperative networks employing M-ary differential phase-shift keying (DPSK) in Rayleigh fading is derived in a single-integral form, which serves as a benchmark for differential AF networks. Two algorithms are then developed to reduce the complexity of the ML detector. Specifically, the first algorithm can eliminate a number of candidates in the ML search, while causing no loss of optimality of ML detection. In high signal-to-noise ratios (SNRs), this algorithm almost surely identifies a single candidate that amounts to the ML estimate of the signal. For low to medium SNRs with multiple candidates determined, we then derive an accurate closed-form approximation for the integral involved in the likelihood function, which only requires a five-sample evaluation per symbol candidate. Finally, combining these algorithms, we propose a closed-form approximate ML detector, which achieves an almost identical bit-error-rate (BER) performance to the exact ML detector at practical complexity. In particular, it is shown that the proposed approximate ML detector is far less complex than the well-known diversity combiner in high SNRs, while achieving approximately 1.7-dB gain in the \10^-5 BER when the relay is closer to the destination. [ABSTRACT FROM PUBLISHER]

An introduction is presented in which the editors discuss various reports within the issue on topics including relay performance bound, multiple-input multiple-output relay beamforming, and relay network coding.

In this paper, we study some relationships between the detection and estimation theories for a binary composite hypothesis test \cal H0 against \cal H1 and a related estimation problem. We start with a one-dimensional (1D) space for the unknown parameter space and one-sided hypothesis problems and then extend out results into more general cases. For one-sided tests, we show that the uniformly most powerful (UMP) test is achieved by comparing the minimum variance and unbiased estimator (MVUE) of the unknown parameter with a threshold. Thus for the case where the UMP test does not exist, the MVUE of the unknown parameter does not exist either. Therefore for such cases, a good estimator of the unknown parameter is deemed as a good decision statistic for the test. For a more general class of composite testing with multiple unknown parameters, we prove that the MVUE of a separating function (SF) can serve as the optimal decision statistic for the UMP unbiased test where the SF is continuous, differentiable, positive for all parameters under \cal H1 and is negative for the parameters under \cal H0. We then prove that the UMP unbiased statistic is equal to the MVUE of an SF. In many problems with multiple unknown parameters, the UMP test does not exist. For such cases, we show that if one detector between two detectors has a better receiver operating characteristic (ROC) curve,ref refid="fnote1"/ asterisk="no" id="fnote1"paraThe detection probability versus the false alarm probability for a test.para then using its decision statistic we can estimate the SF more \epsilon-accurately, in probability. For example, the SF is the signal-to-noise ratio (SNR) in some problems. These results motivate us to introduce new suboptimal SF-estimator tests (SFETs) which are easy to derive for many problems. Finally, we provide some practical examples to study the relationship between the decision statistic of a test and the estimator of its corresponding SF. [ABSTRACT FROM PUBLISHER]

In this letter, we develop a computationally efficient algorithm for the Maximum Likelihood (ML) sequences estimation (MLSE) of an M-ary Phase Shift keying (M-PSK) signal transmitted over a frequency non-selective slow fading channel with an unknown complex amplitude and an unknown variance additive white Gaussian noise. The proposed algorithm also provides the ML estimates of the complex amplitude and the noise variance that are critical in signal activity detection and demodulation in the modern cognitive communication receivers. We prove the optimality of the proposed algorithm and compare its performance via simulation with a recent suboptimal algorithm. [ABSTRACT FROM PUBLISHER]

The article discusses the identification of the Best Singular Mode (BSM) of a MIMO channel. In their study, the authors focused on two scenarios to detect the BSM of the channel including one that establishes initial communication link over the BSM assuming exchanged data is known at both sides and another where BSM is adaptively updated while real unknown data is exchanged. They found that the exchange of one symbol is enough to initialize algorithms in both nodes.

In this paper, we proposed novel noise reduction algorithms that can be used to enhance image quality in various medical imaging modalities such as magnetic resonance and multidetector computed tomography. The noisy captured 3-D data are first transformed by discrete complex wavelet transform. Using a nonlinear function, we model the data as the sum of the clean data plus additive Gaussian or Rayleigh noise. We use a mixture of bivariate Laplacian probability density functions for the clean data in the transformed domain. The MAP and minimum mean-squared error (MMSE) estimators allow us to efficiently reduce the noise. The employed prior distribution is mixture and bivariate, and thus accurately characterizes the heavy-tail distribution of clean images and exploits the interscale properties of wavelets coefficients. In addition, we estimate the parameters of the model using local information; as a result, the proposed denoising algorithms are spatially adaptive, i.e., the intrascale dependency of wavelets is also well exploited in the enhancement process. The proposed approach results in significant noise reduction while the introduced distortions are not noticeable as a result of accurate statistical modeling. The obtained shrinkage functions have closed form, are simple in implementation, and efficiently enhances data. Our experiments on CT images show that among our derived shrinkage functions usually BiLapGausMAP produces images with higher peak SNR. However, BiLapGausMMSE is preferred especially for CT images, which have high SNRs. Furthermore, BiLapRayMAP yields better noise reduction performance for low SNR MR datasets such as high-resolution whole heart imaging while BiLapGauMAP results in better performance in MR data with higher intrinsic SNR such as functional cine data. [ABSTRACT FROM AUTHOR]

In this paper, we design the training signal for a multi-input multi-output (MIMO) communication system in a colored medium. We assume that the known channel covariance matrix (CM) is a Kronecker product of a transmit channel CM and a receive channel CM. Similarly, the CM of the additive Gaussian noise is modeled by a Kronecker product of a temporal CM and a spatial CM. We maximize the differential entropy gained by receiver for a limited energy budget for training at the transmitter. Using, singular value decomposition of the involved CMs, we turn this problem into a convex optimization problem. We prove that the left and right singular vectors of the optimal training matrix are eigen-vectors of the channel transmit CM and the noise temporal CM. In general case, this problem can be solved numerically using efficient methods. The impact of the optimal training is more significant in environments with larger eigen-value spread. The expression of the optimal solution is interesting for some specific cases. For uncorrelated receive channel, the optimal training looks like water filling, i.e., more training power must be invested on the directions which. have more impact. For high signal-to-noise ratios (SNRs), any orthogonal training is optimal; this means that if large amount of energy is available, it must be invested uniformly in all directions. In low SNR scenarios where low amount of energy is available for channel training, all the energy must be allocated to the best mode of channel (which has the highest ratio of the received channel variance to the received noise power). [ABSTRACT FROM AUTHOR]

We consider an ad hoc wireless network consisting of d source-destination pairs communicating, in a pairwise manner, via R relaying nodes. The relay nodes wish to cooperate, through a decentralized beamforming algorithm, in order to establish all the communication links from each source to its respective destination. Our communication strategy consists of two steps. In the first step, all sources transmit their signals simultaneously As a result, each relay receives a noisy faded mixture of all source signals. In the second step, each relay transmits an amplitude- and phase-adjusted Version of its received signal. That is each relay multiply its received signal by a complex coefficient and retransmits the so-obtained signal. Our goal is to obtain these complex coefficients (beamforming weights) through minimization of the total relay transmit power while the signal-to-interference-plus-noise ratio (SINR) at the destinations are guaranteed to be above certain predefined thresholds. Although such a power minimization problem is not Convex, we use semidefinite relaxation to turn this problem into a semidefinite programming (SDP) problem. Therefore, we can efficiently solve the SDP problem using interior point methods. Our numerical examples reveal that for high network data rates, our space division multiplexing scheme requires significantly less total relay transmit power compared to other orthogonal multiplexing schemes, such as time-division multiple access schemes. [ABSTRACT FROM AUTHOR]