parallel detection of groupwise space time codes by predictive soft interference cancellation multiple input multiple output mimo techniques promise huge capacity increase in fading channels groupwise space time coding is a tradeoff between complexity and performance in this letter the structure of particular component space time code trellises is exploited using partial information from a viterbi decoder of the simultaneously decoded interfering component codes we have shown that by calculating the a posteriori probabilities of different states of the encoder at each time and predicting the conditional mean and covariance of interference for the next time index the system performance can be improved by soft interference cancellation the word error rate performance improvement for a system with four transmit and four receive antennas is 1 7 and 1 5 db for two space time codes with 8 and 16 states respectively in addition the parallel structure does not have a decoding delay as in successive interference cancellation and is highly modular for very large scale integration implementation

on the accuracy and resolvability of vector parameter estimates in this paper we address the problem of fundamental limitations on resolution in deterministic parameters estimation we introduce a definition of resolvability based on probability and incorporating a requirement for accuracy unlike most existing definitions indeed in many application the key problem is to obtain distributions of estimates that are not only distinguishable but also accurate and compliant with a required precision we exemplify the proposed definition with estimators that produce normal estimates as in the conditional model for which the gaussianity and efficiency of maximum likelihood estimators mles in the asymptotic region of operation in terms of signal to noise ratio and or in large number of snapshots is well established even for a single snapshot in order to measure the convergence in distribution we derive a simple test allowing to check whether the conditional mles operate in the asymptotic region of operation last we discuss the resolution of two complex exponentials with closely spaced frequencies and compare the results obtained with the ones provided by the various statistical resolution limit released in the open literature

characterization and computation of optimal distributions for channel coding this paper concerns the structure of capacity achieving input distributions for stochastic channel models and a renewed look at their computational aspects the following conclusions are obtained under general assumptions on the channel statistics i the capacity achieving input distribution is binary for low signal to noise ratio snr the proof is obtained on comparing the optimization equations that determine channel capacity with a linear program over the space of probability measures ii simple discrete approximations can nearly reach capacity even in cases where the optimal distribution is known to be absolutely continuous with respect to lebesgue measure iii a new class of algorithms is introduced based on the cutting plane method to iteratively construct discrete distributions along with upper and lower bounds on channel capacity it is shown that the bounds converge to the true channel capacity and that the distributions converge weakly to a capacity achieving distribution

capacity achieving distributions in gaussian multiple access channel with peak power constraints this paper addresses a two user gaussian multiple access channel mac under peak power constraints at the transmitters it is shown that generating the code books of both users according to discrete distributions with a finite number of mass points achieves the largest weighted sum rate in the network this verifies that any point on the boundary of the capacity region of a two user mac under peak power constraints at both transmitters is achieved by discrete distributions with a finite number of mass points although the capacity achieving distributions are not necessarily unique it is verified that only discrete distributions with a finite number of mass points can achieve a point on the boundary of the capacity region it is shown that there exist an infinite number of sum rate optimal points on the boundary of the capacity region in contrast to the gaussian mac with average power constraints we verify that time division td cannot achieve any of the sum rate optimal points in the gaussian mac with peak power constraints using the so called i mmse identity of guo et al the largest achievable sum rate by orthogonal code division ocd is characterized where it is shown that walsh hadamard spreading codes of length 2 are optimal in the symmetric case where the peak power constraints at both transmitters are identical we verify that ocd can achieve a sum rate that is strictly larger than the highest sum rate achieved by td finally it is demonstrated that there are values for the maximum peak power at the transmitters such that ocd can not achieve any of the sum rate optimal points on the boundary of the capacity region

greedy merge degrading has optimal power law consider a channel with a given input alphabet size and input distribution our aim is to degrade or upgrade it to a channel with at most l output letters channel q is degraded with respect to a channel w if q can be obtained from w by processing the output of w upgrading is the inverse relation this paper contains four main results the first result from which the paper title is derived deals with the so called greedy merge algorithm we derive an upper bound on the reduction in mutual information between input and output as a function of l this upper bound is within a constant factor of an algorithm independent lower bound thus we establish that greedy merge is optimal in the power law sense i e the power of l the other main results deal with upgrading the second result shows that a certain sequence of channels which was previously shown to be hard for degrading displays the same hardness in the context of upgrading that is suppose we are given such a channel and a corresponding input distribution if we upgrade degrade to a new channel with l output letters we incur an increase decrease in mutual information between input and output we show that a previously derived bound on the decrease in mutual information for the degrading case is also a lower bound on the increase for the upgrading case the third result is an efficient algorithm for optimal upgrading in the binary input case that is we are given a channel and an input distribution we must find an upgraded channel with l output letters for which the increase in mutual information is minimal we give a simple characterization of such a channel which implies an efficient algorithm the fourth result is an analog of the first result for the upgrading case when the input is binary that is we first present a sub optimal algorithm for the setting considered in the third result the main advantage of the sub optimal algorithm is that it is amenable to analysis we carry out the analysis and show that the increase incurred in mutual information is within a constant factor of the lower bound derived in the second result

multivariate signal parameter estimation under dependent noise from 1 bit dithered quantized data motivated by applications in sensor networks and communications we consider multivariate signal parameter estimation when only dithered 1 bit quantized samples are available the observation noise is taken to be a stationary strongly mixing process which covers a wide range of processes including autoregressive moving average arma models the noise is allowed to be gaussian or to have a heavy tail with possibly infinite variance an estimate of the signal parameters is proposed and is shown to be weakly consistent joint asymptotic normality of the parameters estimate is also established and the asymptotic mean and covariance matrices are identified

new approach for error compensation in coded v blast ofdm systems in this paper we investigate coded layered space time architectures for frequency selective fading multiple input multiple output orthogonal frequency division multiplexing ofdm channels by computing outage capacity formulas we will show that the capacity of the vertical bell labs layered space time v blast architecture can closely approach the shannon capacity in the frequency selective ofdm environment motivated by the capacity analysis we propose pragmatic approaches which preserve the optimality of the layered space time concept we present methods to prevent the error propagation from catastrophically affecting the signal detection in subsequent layers first we start with a comprehensive signal modeling which includes error propagation we derive an improved signal detector and describe the optimal soft bit log likelihood ratio value computation method by taking decision errors into account for soft input channel decoding then to further enhance the v blast performance we show that cancellation using decoded decisions from previous layers makes the decision errors almost completely disappear so that the layered space time architecture can approach the attainable channel capacity finally simulations confirm that the proposed schemes show a significant performance improvement over the conventional methods

semidefinite relaxation approaches to soft mimo demodulation for higher order qam signaling the soft demodulator is a key component of receivers that employ iterative demodulation and decoding for multiple input multiple output systems with binary or qpsk signaling the semidefinite relaxation sdr approach to list based soft demodulation provides a desirable balance between the performance and computational cost in this paper that approach is extended to systems with higher order qam signaling for the class of list sdr demodulators the key to the extension is a quadratic approximation of the a priori information and for the more computationally efficient class of single sdr demodulators the key to the extension is an algebraic expression derived herein for the probability that the sdr randomization procedure generates each candidate symbol to implement these demodulators customized primal dual and dual scaling algorithms are developed to solve the underlying semidefinite programs the performance of the proposed demodulators is compared with that of some existing demodulators by using error rate simulations the empirical distribution of the computational cost and exit chart analyses in our comparisons the proposed single sdr demodulator provides performance that comes close to that of the list sequential demodulator at a computational cost that is close to that of the minimum mean square error parallel soft interference canceller

decoding space time codes with blast architectures we introduce a coding decoding scheme matched to a vertical blast architecture a code has its words evenly split among the transmit antennas the subcodes so transmitted by each antenna are decoded in sequence to cancel the spatial interference while a final decoding step is performed on the whole code we also examine the behavior of zero forcing zf and minimum mean square error mmse blast by comparing their error probabilities with those resulting from optimum i e maximum likelihood ml processing since with vertical blast ordering of the columns of the channel gain matrix is crucial we also study the performance of algorithms intended to find an optimal or mildly suboptimal ordering

single bit oversampled a d conversion with exponential accuracy in the bit rate a scheme for simple oversampled analog to digital a d conversion using single bit quantization is presented the scheme is based on recording positions of zero crossings of the input signal added to a deterministic dither function this information can be represented in a manner such that the bit rate increases only logarithmically with the oversampling factor r the input band limited signal can be reconstructed from this information locally with o 1 r pointwise error resulting in an exponentially decaying distortion rate characteristic in the course of studying the accuracy of the proposed a d conversion scheme some new results are established about reconstruction of band limited signals from irregular samples using linear combination of functions with fast decay schemes for local interpolation of band limited signals from quantized irregular samples are also proposed

turbo space time processing to improve wireless channel capacity by deriving a generalized shannon capacity formula for multiple input multiple output rayleigh fading channels and by suggesting a layered space time architecture concept that attains a tight lower bound on the capacity achievable foschini see wireless pers commun vol 6 no 3 p 311 35 1998 has shown a potential enormous increase in the information capacity of a wireless system employing multiple element antenna arrays at both the transmitter and receiver the layered space time architecture allows signal processing complexity to grow linearly rather than exponentially with the promised capacity increase this paper includes two important contributions first we show that foschini s lower bound is in fact the shannon bound when the output signal to noise ratio snr of the space time processing in each layer is represented by the corresponding matched filter bound this proves the optimality of the layered space time concept second we present an embodiment of this concept for a coded system operating at a low average snr and in the presence of possible intersymbol interference this embodiment utilizes the already advanced space time filtering coding and turbo processing techniques to provide yet a practical solution to the processing needed performance results are provided for quasi static rayleigh fading channels with no channel estimation errors we see for the first time that the shannon capacity for wireless communications can be both increased by n times where n is the number of the antenna elements at the transmitter and receiver and achieved within about 3 db in average snr about 2 db of which is a loss due to the practical coding scheme we assume the layered space time processing itself is nearly information lossless

iterative detection in block orthogonal and spatially multiplexed multisensor signaling schemes the impact of iterative minimum mean square error mmse detection on multisensor signaling schemes in frequency selective media is considered the suitability of a decision feedback mmse df mmse front end relative to a linear mmse lmmse front end is examined using two case studies namely layered spatial multiplexing and block orthogonal space time signaling iterative mmse detection is observed to afford near optimal performance further in channels with large delay spreads a combination of the lmmse and df mmse front ends seems appropriate the former in earlier iterations and the latter in concluding iterations

quantum learning for neural associative memories abstract quantum information processing in neural structures results in an exponential increase of patterns storage capacity and can explain the extensive memorization and inferencing capabilities of humans an example can be found in neural associative memories if the synaptic weights are taken to be fuzzy variables in that case the weights update is carried out with the use of a fuzzy learning algorithm which satisfies basic postulates of quantum mechanics the resulting weight matrix can be decomposed into a superposition of associative memories thus the fundamental memory patterns attractors can be mapped into different vector spaces which are related to each other via unitary rotations quantum learning increases the storage capacity of associative memories by a factor of 2 n where n is the number of neurons

efficient approximation of channel capacities we propose an iterative method for approximately computing the capacity of discrete memoryless channels possibly under additional constraints on the input distribution based on duality of convex programming we derive explicit upper and lower bounds for the capacity the presented method requires o m 2 n sqrt log n varepsilon to provide an estimate of the capacity to within varepsilon where n and m denote the input and output alphabet size a single iteration has a complexity o m n we also show how to approximately compute the capacity of memoryless channels having a bounded continuous input alphabet and a countable output alphabet under some mild assumptions on the decay rate of the channel s tail it is shown that discrete time poisson channels fall into this problem class as an example we compute sharp upper and lower bounds for the capacity of a discrete time poisson channel with a peak power input constraint

mismatched decoding revisited general alphabets channels with memory and the wide band limit the mismatch capacity of a channel is the highest rate at which reliable communication is possible over the channel with a given possibly suboptimal decoding rule this quantity has been studied extensively for single letter decoding rules over discrete memoryless channels dmcs here we extend the study to memoryless channels with general alphabets and to channels with memory with possibly non single letter decoding rules we also study the wide band limit and in particular the mismatch capacity per unit cost and the achievable rates on an additive noise spread spectrum system with single letter decoding and binary signaling

reduced dimension map turbo blast detection the bell labs layered space time blast architecture is a simple and efficient multiantenna coding structure that can achieve high spectral efficiency many blast detectors require more receiver antennas than transmitter antennas we propose two novel turbo processing blast detectors that can operate in systems with fewer receiver antennas than transmitter antennas both detectors are based on the group detection strategy the first proposed detector the reduced dimension maximum a posteriori rdmap detector uses a dynamically formed group for each bit decision while the second proposed detector the group maximum a posteriori gmap uses a static grouping for both detectors a maximum a posteriori map decision is made using a group of transmitted symbols and the remaining signal contribution is treated as interference the interference is characterized as nonzero mean colored noise source that is whitened before a decision is made both proposed detectors are generalizations of the map detector and the turbo processing minimum mean squared error mmse detector in sellathurai and haykin and abe and matsumoto an uncoded bit error rate analysis for an independent rayleigh fading environment is also presented simulated results are presented which show that both the rdmap and gmap detectors have a performance improvement over the mmse detector especially in systems having an excess number of transmitter antennas

universal trellis codes for diagonally layered space time systems foschini s diagonally layered space time transmission system known as d blast is an architecture designed for a rayleigh fading environment using multiple element antenna arrays at both the transmit and receive sites to achieve very high spectral efficiencies in this paper we propose a simple coding technique for d blast that uses a single trellis code with finite traceback viterbi decoding we examine the performance of universal trellis codes that are designed to have a distance structure that is matched to the periodic signal to noise ratio snr variation of the channel created by d blast under the assumption that the channel is static during one burst but may change from burst to burst we show that a universal 64 state trellis code on a 2 spl times 2 d blast system with long enough block lengths displays universal behavior working on almost every 2 spl times 2 channel with at least the mutual information required by a standard 64 state awgn trellis code the only 2 spl times 2 channel where more mutual information is required is a certain rotation of the zero eigenvalue channel we also present 4 spl times 4 and 8 spl times 8 examples

the capacity of discrete time memoryless rayleigh fading channels we consider transmission over a discrete time rayleigh fading channel in which successive symbols face independent fading and where neither the transmitter nor the receiver has channel state information subject to an average power constraint we study the capacity achieving distribution of this channel and prove it to be discrete with a finite number of mass points one of them located at the origin we numerically compute the capacity and the corresponding optimal distribution as a function of the signal to noise ratio snr the behavior of the channel at low snr is studied and finally a comparison is drawn with the ideal additive white gaussian noise channel

effects of sampling and quantization on single tone frequency estimation the effects of sampling and quantization on frequency estimation for a single sinusoid are investigated the cramer rao bound for 1 bit quantization is derived and compared with the limit of infinite quantization it is found that 1 bit quantization gives a slightly worse performance however with a dramatic increase of variance at certain frequencies this can be avoided by using four times oversampling the effect of sampling when using nonideal antialiasing lowpass filters is therefore investigated through derivation of the cramer rao lower bounds finally fast estimators for 1 bit quantization in particular correlation based estimators are derived and their performance is investigated the paper is concluded with simulation results for four times oversampled 1 bit quantization

on the complexity distribution of sphere decoding we analyze the computational complexity distribution of sphere decoding sd for random infinite lattices in particular we show that under fairly general assumptions on the statistics of the lattice basis matrix the tail behavior of the sd complexity distribution is fully determined by the inverse volume of the fundamental regions of the underlying lattice particularizing this result to n x m n m i i d circularly symmetric complex gaussian lattice basis matrices we find that the corresponding complexity distribution is of pareto type with tail exponent given by n m 1 a more refined analysis reveals that the corresponding average complexity of sd is infinite for n m and finite for n m finally for i i d circularly symmetric complex gaussian lattice basis matrices we analyze sd preprocessing techniques based on lattice reduction such as the lll algorithm or layer sorting according to the v blast algorithm and regularization in particular we show that lattice reduction does not improve the tail exponent of the complexity distribution while regularization results in a sd complexity distribution with tails that decrease faster than polynomial

a barankin type lower bound on the estimation error of a hybrid parameter vector the barankin 1949 bound is a realizable lower bound on the mean square error mse of any unbiased estimator of a nonrandom parameter vector we present a barankin type bound which is useful in problems where there is a prior knowledge on some of the parameters to be estimated that is the parameter vector is a hybrid vector in the sense that some of its entries are deterministic while other are random variables we present a simple expression for a positive definite matrix which provides bounds on the covariance of any unbiased estimator of the nonrandom parameters and an estimator of the random parameters simultaneously we show that the barankin bound for deterministic parameters estimation and the bobrovsky zakai 1976 bound for random parameters estimation are special cases of our proposed bound

quantum theory the church turing principle and the universal quantum computer it is argued that underlying the church turing hypothesis there is an implicit physical assertion here this assertion is presented explicitly as a physical principle every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means classical physics and the universal turing machine because the former is continuous and the latter discrete do not obey the principle at least in the strong form above a class of model computing machines that is the quantum generalization of the class of turing machines is described and it is shown that quantum theory and the universal quantum computer are compatible with the principle computing machines resembling the universal quantum computer could in principle be built and would have many remarkable properties not reproducible by any turing machine these do not include the computation of non recursive functions but they do include quantum parallelism a method by which certain probabilistic tasks can be performed faster by a universal quantum computer than by any classical restriction of it the intuitive explanation of these properties places an intolerable strain on all interpretations of quantum theory other than everett s some of the numerous connections between the quantum theory of computation and the rest of physics are explored quantum complexity theory allows a physically more reasonable definition of the complexity or knowledge in a physical system than does classical complexity theory

performance analysis for channel estimation with 1 bit adc and unknown quantization threshold in this work the problem of signal parameter estimation from measurements acquired by a low complexity analog to digital converter adc with 1 bit output resolution and an unknown quantization threshold is considered single comparator adcs are energy efficient and can be operated at ultrahigh sampling rates for analysis of such systems a fixed and known quantization threshold is usually assumed in the symmetric case i e zero hard limiting offset it is known that in the low signal to noise ratio snr regime the signal processing performance degrades moderately by 2 pi text 1 96 db when comparing to an ideal infty bit converter due to hardware imperfections low complexity 1 bit adcs will in practice exhibit an unknown threshold different from zero therefore we study the accuracy that can be obtained with received data processed by a hard limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms to characterize the estimation performance of these nonlinear algorithms we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter in the low snr regime we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver as an application we consider the estimation of single input single output wireless channels with intersymbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms finally we comment on the extension to multiple input multiple output channel models

delayed decision feedback sequence estimation the authors delayed decision feedback sequence estimation ddfse for uncoded pam signals is considered estimates on the performance of the algorithm are given and simulation results are provided for several examples a more general form of ddfse applicable to coded modulation systems is also presented as an example detection of trellis coded qpsk quadrature phase shift keyed signals over intersymbol interference channels is discussed

efficient soft input soft output tree detection via an improved path metric tree detection techniques are often used to reduce the complexity of a posteriori probability app detection in multiantenna wireless communication systems in this paper we introduce an efficient soft input soft output tree detection algorithm that employs a new type of look ahead path metric in the process of branch pruning or sorting while conventional path metrics depend only on symbols on a visited path the new path metric accounts for unvisited parts of the tree in advance through an unconstrained linear estimator and adds a bias term that reflects the contribution of as yet undecided symbols by applying the linear estimate based look ahead path metric to an algorithm that selects the best paths for each level of the tree we develop a new soft input soft output tree detector called an improved soft input soft output algorithm iss ma based on an analysis of the probability of correct path loss we show that the improved path metric offers substantial performance gain over the conventional path metric we also demonstrate through simulations that the proposed iss ma can be a promising candidate for soft input soft output detection in high dimensional systems

improved linear soft input soft output detection via soft feedback successive interference cancellation we propose an improved minimum mean square error mmse vertical bell labs layered space time v blast detection technique called a soft input soft output and soft feedback siof v blast detector for turbo multi input multioutput turbo mimo systems we derive a symbol estimator by minimizing the power of the interference plus noise given a priori probabilities of undetected layer symbols and a posteriori probabilities for past detected layer symbols for a low complexity implementation an approximate siof algorithm is presented which allows for a time invariant realization of the symbol ordering and an mmse filtering process another implementation referred to as the iterative siof algorithm is introduced which decides on symbol detection order based on a posteriori symbol probabilities to improve the detection performance simulations performed on a space time bit interleaved coded modulation stbicm architecture over quasi static mimo fading channels demonstrate that the siof v blast detector provides performance gains over previous turbo blast detectors most notably when more transmit antennas are used

fading channels how perfect need perfect side information be the analysis of flat fading channels is often performed under the assumption that the additive noise is white and gaussian and that the receiver has precise knowledge of the realization of the fading process these assumptions imply the optimality of gaussian codebooks and of scaled nearest neighbor decoding here we study the robustness of this communication scheme with respect to errors in the estimation of the fading process we quantify the degradation in performance that results from such estimation errors and demonstrate the lack of robustness of this scheme for some situations we suggest the rule of thumb that in order to avoid degradation the estimation error should be negligible compared to the reciprocal of the signal to noise ratio snr

array shape calibration using sources in unknown locations part i far field sources this paper deals with source localization using a two dimensional array of sensors whose locations are not known precisely if only a single source is observed uncertainties in sensor location increase errors in source bearing and range by an amount which is independent of signal to noise ratio and which can easily dominate over all localization accuracy major performance gains could therefore result from successful calibration of array geometry the paper derives cramer rao bounds on calibration and source location accuracies achievable with far field sources whose bearings are not initially known the sources are assumed to radiate gaussian noise and to be spectrally disjoint of each other when the location of one sensor and the direction to a second sensor is known three noncollinear sources are sufficient to calibrate sensor positions with errors which decrease to zero as calibrating source strength or time bandwidth products tend to infinity the sole exception to this statement is a nominally linear array for which such calibration is not possible when one sensor location is known but no directional reference is available three noncollinear sources can determine array shape but there remains a residual error in angular orientation which is irremovable by the calibration procedure when no sensor locations are known a priori one adds to the residual error in rotation a translational component in the far field the latter should be unimportant in addition to the asymptotic results cramer rao bounds are computed for finite signal to noise ratios and observation times one finds that calibration permits significant reductions in localization errors for parameter values well within the practical range

resolution adaptive hybrid mimo architectures for millimeter wave communications in this paper we propose a hybrid analog digital beamforming architecture with resolution adaptive adcs for millimeter wave mmwave receivers with large antenna arrays we adopt array response vectors for the analog combiners and derive adc bit allocation ba solutions in closed form the ba solutions reveal that the optimal number of adc bits is logarithmically proportional to the rf chain s signal to noise ratio raised to the 1 3 power using the solutions two proposed ba algorithms minimize the mean square quantization error of received analog signals under a total adc power constraint contributions of this paper include 1 adc bit allocation algorithms to improve communication performance of a hybrid mimo receiver 2 approximation of the capacity with the ba algorithm as a function of channels and 3 a worst case analysis of the ergodic rate of the proposed mimo receiver that quantifies system tradeoffs and serves as the lower bound simulation results demonstrate that the ba algorithms outperform a fixed adc approach in both spectral and energy efficiency and validate the capacity and ergodic rate formula for a power constraint equivalent to that of fixed 4 bit adcs the revised ba algorithm makes the quantization error negligible while achieving 22 better energy efficiency having negligible quantization error allows existing state of the art digital beamformers to be readily applied to the proposed system

soft input soft output single tree search sphere decoding soft input soft output siso detection algorithms form the basis for iterative decoding the computational complexity of siso detection often poses significant challenges for practical receiver implementations in particular in the context of multiple input multiple output mimo wireless communication systems in this paper we present a low complexity siso sphere decoding algorithm based on the single tree search paradigm proposed originally for soft output mimo detection in studer soft output sphere decoding algorithms and vlsi implementation ieee j sel areas commun vol 26 no 2 pp 290 300 feb 2008 the new algorithm incorporates clipping of the extrinsic log likelihood ratios llrs into the tree search which results in significant complexity savings and allows to cover a large performance complexity tradeoff region by adjusting a single parameter furthermore we propose a new method for correcting approximate llrs resulting from sub optimal detectors which often significantly improves detection performance at low additional computational complexity

randomly spread cdma asymptotics via statistical physics this paper studies randomly spread code division multiple access cdma and multiuser detection in the large system limit using the replica method developed in statistical physics arbitrary input distributions and flat fading are considered a generic multiuser detector in the form of the posterior mean estimator is applied before single user decoding the generic detector can be particularized to the matched filter decorrelator linear minimum mean square error mmse detector the jointly or the individually optimal detector and others it is found that the detection output for each user although in general asymptotically non gaussian conditioned on the transmitted symbol converges as the number of users go to infinity to a deterministic function of a hidden gaussian statistic independent of the interferers thus the multiuser channel can be decoupled each user experiences an equivalent single user gaussian channel whose signal to noise ratio snr suffers a degradation due to the multiple access interference mai the uncoded error performance e g symbol error rate and the mutual information can then be fully characterized using the degradation factor also known as the multiuser efficiency which can be obtained by solving a pair of coupled fixed point equations identified in this paper based on a general linear vector channel model the results are also applicable to multiple input multiple output mimo channels such as in multiantenna systems

bayesian estimation in the presence of deterministic nuisance parameters part i performance bounds how accurately can one estimate a random parameter subject to unknown deterministic nuisance parameters the hybrid cramer rao bound hcrb provides an answer to this question for a restricted class of estimators the hcrb is the most popular performance bound on the mean square error mse for random parameter estimation problems which involve deterministic parameters the hcrb is useful when one is interested in both the random and the deterministic parameters and in the coupling between their estimation errors this bound refers to locally weak sense unbiased estimators with respect to w r t the deterministic parameters however if these parameters are nuisance it is unnecessary to restrict their estimation as unbiased this paper is the first of a two part study of bayesian parameter estimation in the presence of deterministic nuisance parameters it begins with a study on order relations between existing cramer rao cr type bounds of mean unbiased bayesian estimators then a new cr type bound is developed with no assumption of unbiasedness on the nuisance parameters alternatively lehmann s concept of unbiasedness is employed rather than conventional mean unbiasedness it is imposed on a risk that measures the distance between the estimator and the minimum mse mmse estimator which assumes perfect knowledge of the nuisance parameters in the succeeding paper asymptotic performances of some bayesian estimators with maximum likelihood based estimates for the nuisance parameters are investigated the proposed risk unbiased bound rub is proved to be asymptotically achieved by the mmse estimator with maximum likelihood estimates for the nuisance parameters while the existing cr type bounds are not necessarily achievable

capacity bounds via duality with applications to multiple antenna systems on flat fading channels a technique is proposed for the derivation of upper bounds on channel capacity it is based on a dual expression for channel capacity where the maximization of mutual information over distributions on the channel input alphabet is replaced with a minimization of average relative entropy over distributions on the channel output alphabet we also propose a technique for the analysis of the asymptotic capacity of cost constrained channels the technique is based on the observation that under fairly mild conditions capacity achieving input distributions escape to infinity the above techniques are applied to multiple antenna flat fading channels with memory where the realization of the fading process is unknown at the transmitter and unknown or only partially known at the receiver it is demonstrated that for high signal to noise ratio snr the capacity of such channels typically grows only double logarithmically in the snr to better understand this phenomenon and the rates at which it occurs we introduce the fading number as the second order term in the high snr asymptotic expansion of capacity and derive estimates on its value for various systems it is suggested that at rates that are significantly higher than the fading number communication becomes extremely power inefficient thus posing a practical limit on practically achievable rates upper and lower bounds on the fading number are also presented for single input single output siso systems the bounds coincide thus yielding a complete characterization of the fading number for general stationary and ergodic fading processes we also demonstrate that for memoryless multiple input single output miso channels the fading number is achievable using beam forming and we derive an expression for the optimal beam direction this direction depends on the fading law and is in general not the direction that maximizes the snr on the induced siso channel using a new closed form expression for the expectation of the logarithm of a noncentral chi square distributed random variable we provide some closed form expressions for the fading number of some systems with gaussian fading including siso systems with circularly symmetric stationary and ergodic gaussian fading the fading number of the latter is determined by the fading mean fading variance and the mean squared error in predicting the present fading from its past it is not directly related to the doppler spread for the rayleigh ricean and multiple antenna rayleigh fading channels we also present firm nonasymptotic upper and lower bounds on channel capacity these bounds are asymptotically tight in the sense that their difference from capacity approaches zero at high snr and their ratio to capacity approaches one at low snr

on the limits of communication with low precision analog to digital conversion at the receiver as communication systems scale up in speed and bandwidth the cost and power consumption of high precision e g 8 12 bits analog to digital conversion adc becomes the limiting factor in modern transceiver architectures based on digital signal processing in this work we explore the impact of lowering the precision of the adc on the performance of the communication link specifically we evaluate the communication limits imposed by low precision adc e g 1 3 bits for transmission over the real discrete time additive white gaussian noise awgn channel under an average power constraint on the input for an adc with k quantization bins i e a precision of log2 k bits we show that the input distribution need not have any more than k 1 mass points to achieve the channel capacity for 2 bin 1 bit symmetric quantization this result is tightened to show that binary antipodal signaling is optimum for any signal to noise ratio snr for multi bit quantization a dual formulation of the channel capacity problem is used to obtain tight upper bounds on the capacity the cutting plane algorithm is employed to compute the capacity numerically and the results obtained are used to make the following encouraging observations a up to a moderately high snr of 20 db 2 3 bit quantization results in only 10 20 reduction of spectral efficiency compared to unquantized observations b standard equiprobable pulse amplitude modulated input with quantizer thresholds set to implement maximum likelihood hard decisions is asymptotically optimum at high snr and works well at low to moderate snrs as well

channel optimized quantization with soft decision demodulation for space time orthogonal block coded channels we introduce three soft decision demodulation channel optimized vector quantizers covqs to transmit analog sources over space time orthogonal block stob coded flat rayleigh fading channels with binary phase shift keying bpsk modulation one main objective is to judiciously utilize the soft information of the stob coded channel in the design of the vector quantizers while keeping a low system complexity to meet this objective we introduce a simple space time decoding structure that consists of a space time soft detector followed by a linear combiner and a scalar uniform quantizer with resolution q the concatenation of the space time encoder modulator fading channel and space time receiver can be described by a binary input 2q output discrete memoryless channel dmc the scalar uniform quantizer is chosen so that the capacity of the equivalent dmc is maximized to fully exploit and capture the system s soft information by the dmc we next determine the statistics of the dmc in closed form and use them to design three covq schemes with various degrees of knowledge of the channel noise power and fading coefficients at the transmitter and or receiver the performance of each quantization scheme is evaluated for memoryless gaussian and gauss markov sources and various stob codes and the benefits of each scheme is illustrated as a function of the antenna diversity and soft decision resolution q comparisons to traditional coding schemes which perform separate source and channel coding operations are also provided

estimation of the autocorrelation function of complex gaussian stationary processes by amplitude clipped signals the normalized autocorrelation function of a gaussian process may be recovered from second order moments of their polarity through the arcsin law by analogy it is possible to calculate the normalized autocorrelation function of a circularly complex gaussian process from the knowledge of moments of its instantaneous phase in the present paper two estimators of the normalized autocorrelation function based on the phase only are presented their theoretical accuracy is evaluated and compared to the accuracy of the direct estimate

bayesian parameter estimation using single bit dithered quantization the bayesian parameter estimation problem using a single bit dithered quantizer is considered this problem arises e g for channel estimation under low precision analog to digital conversion adc at the receiver based on the bayesian cramer rao lower bound crlb bounds on the mean squared error are derived that hold for all dither strategies with strictly causal adaptive processing of the quantizer output sequence in particular any estimator using the binary quantizer output sequence is asymptotically in the sequence length at least 10 log10 2 1 96 db worse than the minimum mean squared error estimator using continuous observations for any dither strategy moreover dither strategies are designed that are shown by simulation to closely approach the derived lower bounds

low precision a d conversion for maximum information rate in channels with memory analog to digital converters that maximize the information rate between the quantized channel output sequence and the channel input sequence are designed for discrete time channels with intersymbol interference additive noise and for independent and identically distributed signaling optimized scalar quantizers with regions achieve the full information rate of log2 bits per channel use with a transmit alphabet of size at infinite signal to noise ratio these quantizers however are not necessarily uniform quantizers low precision scalar and two dimensional analog to digital converters are designed at finite signal to noise ratio and an upper bound on the information rate is derived simulation results demonstrate the effectiveness of the designed quantizers over conventional quantizers the advantage of the new quantizers is further emphasized by an example of a channel for which a slicer with a single threshold at zero and a carefully optimized channel input with memory fail to achieve a rate of one bit per channel use at high signal to noise ratio in contrast to memoryless binary signaling and an optimized quantizer

control design of uncertain quantum systems with fuzzy estimators an approach of control design using fuzzy estimators fes is proposed for quantum systems with uncertainties two types of quantum control problems are considered 1 control of a pure state quantum system in the presence of uncertainties and 2 control design of quantum systems with initial mixed states and uncertainties for the first type of tasks a partial feedback control scheme with an fe is presented to design controllers in this scheme an fe is trained to estimate the quantum state for feedback control of a quantum system and controlled projective measurement is used to assist in controlling the system for the second type of quantum control tasks a probabilistic fuzzy estimator pfe is trained to estimate the quantum state for control design of a quantum system with an initial mixed state and a corresponding control algorithm is proposed to design a control law that drives the system from the mixed state to a target pure state two examples of two spin 1 2 systems are also presented and analyzed to demonstrate the process of control design and potential applications of the proposed approach

optimum space time processors with dispersive interference unified analysis and required filter span we consider optimum space time equalizers with unknown dispersive interference consisting of a linear equalizer that both spatially and temporally whitens the interference and noise followed by a decision feedback equalizer or maximum likelihood sequence estimator we first present a unified analysis of the optimum space time equalizer and then show that for typical fading channels with a given signal to noise ratio snr near optimum performance can be achieved with a finite length equalizer expressions are given for the required filter span as a function of the dispersion length number of cochannel interferers number of antennas and snr which are useful in the design of practical near optimum space time equalizers

trellis search based soft input soft output mimo detector algorithm and vlsi architecture in this paper we propose a trellis search based soft input soft output detection algorithm and its very large scale integration vlsi architecture for iterative multiple input multiple output mimo receivers we construct a trellis diagram to represent the search space of a transmitted mimo signal with the trellis model we evenly distribute the workload of candidates searching among multiple trellis nodes for parallel processing the search complexity is significantly reduced because the number of candidates is greatly limited at each trellis node by leveraging the trellis structure we develop an approximate log map algorithm by using a small list of largest exponential terms to compute the llr log likelihood ratio values the trellis search based detector has a fixed complexity and is very suitable for parallel vlsi implementation as a case study we have designed and synthesized a trellis search based soft input soft output mimo detector for a 4 4 16 qam system using a 1 08 v tsmc 65 nm technology the detector can achieve a maximum throughput of 1 7 gb s with a core area of 1 58 mm2

phase quantized block noncoherent communication analog to digital conversion adc is a key bottleneck in scaling dsp centric receiver architectures to multigigabit s speeds recent information theoretic results obtained under ideal channel conditions perfect synchronization no dispersion indicate that low precision adc 1 4 bits could be a suitable choice for designing such high speed systems in this work we study the impact of employing low precision adc in a carrier asynchronous system specifically we consider transmission over the block noncoherent additive white gaussian noise channel and investigate the achievable performance under low precision output quantization we focus attention on an architecture in which the receiver quantizes only the phase of the received signal this has the advantage of being implementable without automatic gain control using multiple 1 bit adcs preceded by analog multipliers for standard uniform phase shift keying psk modulation we study the structure of the transition density of the phase quantized block noncoherent channel several results based on the symmetry inherent in the channel model are provided to characterize this transition density low complexity procedures for computing the channel information rate and for block demodulation are obtained using these results numerical computations are performed to compare the performance of quantized and unquantized systems for different quantization precisions and different block lengths with qpsk modulation it is observed for example that for snr larger than 2 3 db 8 bin phase quantization of the received signal recovers about 80 85 of the mutual information attained with unquantized observations while 12 bin phase quantization recovers more than 90 of the unquantized mutual information dithering the constellation is shown to improve the performance in the face of drastic quantization

on the complexity of sphere decoding in digital communications sphere decoding has been suggested by a number of authors as an efficient algorithm to solve various detection problems in digital communications in some cases the algorithm is referred to as an algorithm of polynomial complexity without clearly specifying what assumptions are made about the problem structure another claim is that although worst case complexity is exponential the expected complexity of the algorithm is polynomial herein we study the expected complexity where the problem size is defined to be the number of symbols jointly detected and our main result is that the expected complexity is exponential for fixed signal to noise ratio snr contrary to previous claims the sphere radius which is a parameter of the algorithm must be chosen to ensure a nonvanishing probability of solving the detection problem this causes the exponential complexity since the squared radius must grow linearly with problem size the rate of linear increase is however dependent on the noise variance and thus the rate of the exponential function is strongly dependent on the snr therefore sphere decoding can be efficient for some snr and problems of moderate size even though the number of operations required by the algorithm strictly speaking always grows as an exponential function of the problem size

quantized massive mu mimo ofdm uplink coarse quantization at the base station bs of a massive multi user mu multiple input multiple output mimo wireless system promises significant power and cost savings coarse quantization also enables significant reductions of the raw analog to digital converter data that must be transferred from a spatially separated antenna array to the baseband processing unit the theoretical limits as well as practical transceiver algorithms for such quantized mu mimo systems operating over frequency flat narrowband channels have been studied extensively however the practically relevant scenario where such communication systems operate over frequency selective wideband channels is less well understood this paper investigates the uplink performance of a quantized massive mu mimo system that deploys orthogonal frequency division multiplexing ofdm for wideband communication we propose new algorithms for quantized maximum a posteriori channel estimation and data detection and we study the associated performance quantization tradeoffs our results demonstrate that coarse quantization e g four to six bits depending on the ratio between the number of bs antennas and the number of users in massive mu mimo ofdm systems entails virtually no performance loss compared with the infinite precision case at no additional cost in terms of baseband processing complexity

least squares quantization in pcm it has long been realized that in pulse code modulation pcm with a given ensemble of signals to handle the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall it has been shown by panter and dite that in the limit as the number of quanta becomes infinite the asymptotic fractional density of quanta per unit voltage should vary as the one third power of the probability density per unit voltage of signal amplitudes in this paper the corresponding result for any finite number of quanta is derived that is necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy the optimization criterion used is that the average quantization noise power be a minimum it is shown that the result obtained here goes over into the panter and dite result as the number of quanta become large the optimum quautization schemes for 2 b quanta b 1 2 cdots 7 are given numerically for gaussian and for laplacian distribution of signal amplitudes

a variational inference framework for soft in soft out detection in multiple access channels we propose a unified framework for deriving and studying soft in soft out siso detection in multiple access channels using the concept of variational inference the proposed framework may be used in multiple access interference mai intersymbol interference isi and multiple input multiple output mimo channels without loss of generality we will focus our attention on turbo multiuser detection to facilitate a more concrete discussion it is shown that with some loss of optimality variational inference avoids the exponential complexity of a posteriori probability app detection by optimizing a closely related but much more manageable objective function called variational free energy in addition to its systematic appeal there are several other advantages to this viewpoint first of all it provides unified and rigorous justifications for numerous detectors that were proposed on radically different grounds and facilitates convenient joint detection and decoding utilizing the turbo principle when error control codes are incorporated second efficient joint parameter estimation and data detection is possible via the variational expectation maximization em algorithm such that the detrimental effect of inaccurate channel knowledge at the receiver may be dealt with systematically we are also able to extend bpsk based siso detection schemes to arbitrary square qam constellations in a rigorous manner using a variational argument

on quasi isometry of threshold based sampling the problem of isometry for threshold based sampling such as integrate and fire or send on delta is addressed while for uniform sampling the parseval theorem provides isometry and makes the euclidean metric canonical there is no analogy for threshold based sampling the relaxation of the isometric postulate to quasi isometry however allows the discovery of the underlying metric structure of threshold based sampling this paper characterizes this metric structure making hermann weyl s discrepancy measure canonical for threshold based sampling the usefulness of the approach is demonstrated by means of a novel adaptive threshold based sampling scheme which applied on electrocardiography signals allows a substantial reduction of the number of samples while maintaining nearly the same snr after reconstruction

near optimum tree search detection schemes for bit synchronous multiuser cdma systems over gaussian and two path rayleigh fading channels simple reduced tree search detection schemes of the breadth first type are applied to suboptimal joint multiuser detection in bit synchronous code division multiple access cdma systems over both gaussian and two path rayleigh fading channels it is pointed out that in contrast to the case of the optimal multiuser detector the choice of the receiver filter severely influences the performance of suboptimal multiuser detectors simulation results supported by analysis show that breadth first tree search algorithms using a decorrelating noise whitening receiver filter perform better than similar receivers which solely use a matched filter mf for virtually all nonsingular spreading code sets studied most of the code sets are randomly generated the m and t algorithm detectors based on decorrelating noise whitening filter wf outputs can achieve near optimum performance at a very low complexity compared to the optimal detector although the proposed detectors are more complex than some known suboptimum detectors furthermore the use of combining techniques is considered for a two path rayleigh fading channel and a semi synchronous cdma structure is proposed it is shown that if maximum ratio combining mrc is employed the decorrelating noise wf still exists the corresponding suboptimal combining detector with a decorrelating noise wf outperforms a similar noncombining detector

reduced complexity time domain equalization for turbo mimo systems a reduced complexity time domain equalization scheme for wideband turbo multiple input multiple output turbo mimo systems is presented this scheme called iterative trellis search equalization is based on a modified version of the m bahl cocke jelinek raviv m bcjr algorithm applied to a suitably chosen trellis representation of the wideband mimo channel process exploiting the properties of quadrature amplitude modulation qam signal constellations with block partitionable labels this modified m bcjr algorithm has complexity per bit that is independent of the constellation size and polynomial in the number of transmit antennas and channel memory results from computer simulations show that the new scheme successfully mitigates intersymbol interference even if only a very small fraction of trellis state transitions is considered it is also demonstrated that asynchronous transmission of the spatially multiplexed symbol streams can result in considerable performance improvement compared to synchronous mimo systems

the joint map ml criterion and its relation to ml and to extended least squares the joint maximum a posteriori maximum likelihood jmap ml estimation criterion can serve as an alternative to the maximum likelihood ml criterion when estimating parameters from an observed data vector whenever another unobserved data vector is involved rather than maximize the probability of the observed data with respect to the parameters jmap ml maximizes the joint probability of the observed and unobserved data with respect to both the unknown parameters and the unobserved data in this paper we characterize the relation between the ml and jmap ml estimates in the gaussian case and provide insight into the apparent bias of jmap ml although jmap ml is an inconsistent estimator we show that with short data records it is often preferable to ml in terms of both bias and variance we also identify jmap ml as a special case of the deterministic extended least squares xls criterion we indicate a general relation between a possible maximization algorithm for jmap ml and the well known estimation maximization em algorithm

geometric programming duals of channel capacity and rate distortion we show that the lagrange dual problems of the channel capacity problem with input cost and the rate distortion problem are simple geometric programs upper bounds on channel capacity and lower bounds on rate distortion can be efficiently generated from their duals for channel capacity the geometric programming dual characterization is shown to be equivalent to the minmax kullback leibler kl characterization in csiszar et al 1981 for rate distortion the geometric programming dual is extended to rate distortion with two sided state information a duality by mapping is then given between the lagrange dual problems of channel capacity with input cost and rate distortion which resolves several apparent asymmetries between their primal problems in the familiar form of mutual information optimization problems both the primal and dual problems can be interpreted in a common framework of free energy optimization from statistical physics

capacity analysis for continuous alphabet channels with side information part i a general framework capacity analysis for channels with side information at the receiver has been an active area of interest this problem is well investigated for the case of finite alphabet channels however the results are not easily generalizable to the case of continuous alphabet channels due to analytical difficulties inherent with continuous alphabets in the first part of this two part paper we address an analytical framework for capacity analysis of continuous alphabet channels with side information at the receiver for this purpose we establish novel necessary and sufficient conditions for weak continuity and strict concavity of the mutual information these conditions are used in investigating the existence and uniqueness of the capacity achieving measures furthermore we derive necessary and sufficient conditions that characterize the capacity value and the capacity achieving measure for continuous alphabet channels with side information at the receiver

iterative decoding of binary block and convolutional codes iterative decoding of two dimensional systematic convolutional codes has been termed turbo de coding using log likelihood algebra we show that any decoder can be used which accepts soft inputs including a priori values and delivers soft outputs that can be split into three terms the soft channel and a priori inputs and the extrinsic value the extrinsic value is used as an a priori value for the next iteration decoding algorithms in the log likelihood domain are given not only for convolutional codes but also for any linear binary systematic block code the iteration is controlled by a stop criterion derived from cross entropy which results in a minimal number of iterations optimal and suboptimal decoders with reduced complexity are presented simulation results show that very simple component codes are sufficient block codes are appropriate for high rates and convolutional codes for lower rates less than 2 3 any combination of block and convolutional component codes is possible several interleaving techniques are described at a bit error rate ber of 10 sup 4 the performance is slightly above or around the bounds given by the cutoff rate for reasonably simple block convolutional component codes interleaver sizes less than 1000 and for three to six iterations

mutual information and minimum mean square error in gaussian channels this paper deals with arbitrarily distributed finite power input signals observed through an additive gaussian noise channel it shows a new formula that connects the input output mutual information and the minimum mean square error mmse achievable by optimal estimation of the input given the output that is the derivative of the mutual information nats with respect to the signal to noise ratio snr is equal to half the mmse regardless of the input statistics this relationship holds for both scalar and vector signals as well as for discrete time and continuous time noncausal mmse estimation this fundamental information theoretic result has an unexpected consequence in continuous time nonlinear estimation for any input signal with finite power the causal filtering mmse achieved at snr is equal to the average value of the noncausal smoothing mmse achieved with a channel whose snr is chosen uniformly distributed between 0 and snr

the reconstruction of analog signals from the sign of their noisy samples the reconstruction of a continuous time signal s t from the sign of its deliberately contaminated samples is considered sequential generally nonlinear estimates of s t are established and their performance is studied error bounds and convergence rates are derived the signal s t need not be band limited the convergence rates obtained here are faster than those obtained previously for nonsequential estimates the degradation in the reconstruction of the signal due to transmission over a noisy channel is also investigated and bounds on the additional error are obtained

fundamentals of statistical signal processing estimation theory minimum variance unbiased estimation cramer rao lower bound linear models general minimum variance unbiased estimation best linear unbiased estimators maximum likelihood estimation least squares method of moments the bayesian philosophy general bayesian estimators linear bayesian estimators kalman filters summary of estimators extension for complex data and parameters

at low snr asymmetric quantizers are better we study the capacity of the discrete time gaussian channel when its output is quantized with a 1 bit quantizer we focus on the low signal to noise ratio snr regime where communication at very low spectral efficiencies takes place in this regime a symmetric threshold quantizer is known to reduce channel capacity by a factor of 2 i e to cause an asymptotic power loss of approximately 2 db here it is shown that this power loss can be avoided by using asymmetric threshold quantizers and asymmetric signaling constellations to avoid this power loss flash signaling input distributions are essential consequently 1 bit output quantization of the gaussian channel reduces spectral efficiency threshold quantizers are not only asymptotically optimal at every fixed snr a threshold quantizer maximizes capacity among all 1 bit output quantizers the picture changes on the rayleigh fading channel in the noncoherent case a 1 bit output quantizer causes an unavoidable low snr asymptotic power loss in the coherent case however this power loss is avoidable provided that we allow the quantizer to depend on the fading level

new results on deterministic cramer rao bounds for real and complex parameters the cramer rao bounds crb is a lower bound of great interest for system analysis and design in the asymptotic region high signal to noise ratio snr and or large number of snapshots as it is simple to calculate and it is usually possible to obtain closed form expressions the first part of the paper is a generalization to complex parameters of the barankin rationale for deriving mse lower bounds that is the minimization of a norm under a set of linear constraints with the norm minimization approach the study of fisher information matrix fim singularity constrained crb and regularity conditions become straightforward corollaries of the derivation the second part provides new results useful for system analysis and design a general reparameterization inequality the equivalence between reparameterization and equality constraints and an explicit relationship between parameters unidentifiability and fim singularity

unveiling turbo codes some results on parallel concatenated coding schemes a parallel concatenated coding scheme consists of two simple constituent systematic encoders linked by an interleaver the input bits to the first encoder are scrambled by the interleaver before entering the second encoder the codeword of the parallel concatenated code consists of the input bits to the first encoder followed by the parity check bits of both encoders this construction can be generalized to any number of constituent codes parallel concatenated schemes employing two convolutional codes as constituent codes in connection with an iterative decoding algorithm of complexity comparable to that of the constituent codes have been previously shown to yield remarkable coding gains close to theoretical limits they have been named and are known as turbo codes we propose a method to evaluate an upper bound to the bit error probability of a parallel concatenated coding scheme averaged over all interleavers of a given length the analytical bounding technique is then used to shed some light on some crucial questions which have been floating around in the communications community since the proposal of turbo codes

a general framework for transmission with transceiver distortion and some applications a general theoretical framework is presented for analyzing information transmission over gaussian channels with memoryless transceiver distortion which encompasses various nonlinear distortion models including transmit side clipping receive side analog to digital conversion and others the framework is based on the so called generalized mutual information gmi and the analysis in particular benefits from the setup of gaussian codebook ensemble and nearest neighbor decoding for which it is established that the gmi takes a general form analogous to the channel capacity of undistorted gaussian channels with a reduced effective signal to noise ratio snr that depends on the nominal snr and the distortion model when applied to specific distortion models an array of results of engineering relevance is obtained for channels with transmit side distortion only it is shown that a conventional approach which treats the distorted signal as the sum of the original signal part and a uncorrelated distortion part achieves the gmi for channels with output quantization closed form expressions are obtained for the effective snr and the gmi and related optimization problems are formulated and solved for quantizer design finally super nyquist sampling is analyzed within the general framework and it is shown that sampling beyond the nyquist rate increases the gmi for all snr values for example with binary symmetric output quantization information rates exceeding one bit per channel use are achievable by sampling the output at four times the nyquist rate

bayesian estimation in the presence of deterministic nuisance parameters part ii estimation methods one of the fundamental issues of estimation theory is the presence of deterministic nuisance parameters while in the bayesian paradigm the model parameters are random introduction of deterministic nuisance parameters into the model exceeds the bayesian framework to the hybrid framework in this type of scenarios the conventional bayesian estimators are not valid as they assume knowledge of the deterministic nuisance parameters this paper is the second of a two part study of bayesian parameter estimation in the presence of deterministic nuisance parameters in part i a new cramer rao cr type bound on the mean square error mse for bayesian estimation in the presence of deterministic nuisance parameters was established based on the concept of risk unbiasedness the proposed bound was named risk unbiased bound rub this paper presents properties of asymptotic uniform mean and risk unbiasedness of some bayesian estimators 1 the minimum mse mmse or maximum a posteriori probability map estimators with maximum likelihood ml estimates substituting the deterministic parameters named ms ml and map ml respectively and 2 joint map and ml estimator named jmap ml furthermore an asymptotic performance analysis of the ms ml and map ml estimators is presented these estimators are shown to asymptotically achieve the rub while the existing cr type bounds can be achieved only in distinct cases simulations verify these results for the problem of blind separation of nonstationary sources it is shown that unlike existing cr type bounds the rub is asymptotically tight

near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs in massive multiple input multiple output mimo systems it may not be power efficient to have a pair of high resolution analog to digital converters adcs for each antenna element in this paper a near maximum likelihood nml detector for uplink multiuser massive mimo systems is proposed where each antenna is connected to a pair of one bit adcs i e one for each real and imaginary component of the baseband signal the exhaustive search over all the possible transmitted vectors required in the original maximum likelihood ml detection problem is relaxed to formulate an ml estimation problem then the ml estimation problem is converted into a convex optimization problem which can be efficiently solved using the solution the base station can perform simple symbol by symbol detection for the transmitted signals from multiple users to further improve detection performance we also develop a two stage nml detector that exploits the structures of both the original ml and the proposed one stage nml detectors numerical results show that the proposed nml detectors are efficient enough to simultaneously support multiple uplink users adopting higher order constellations e g 16 quadrature amplitude modulation since our detectors exploit the channel state information as part of the detection an ml channel estimation technique with one bit adcs that shares the same structure with our proposed nml detector is also developed the proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive mimo system

quantization of binary input discrete memoryless channels the quantization of the output of a binary input discrete memoryless channel to a smaller number of levels is considered an algorithm which finds an optimal quantizer in the sense of maximizing mutual information between the channel input and quantizer output is given this result holds for arbitrary channels in contrast to previous results for restricted channels or a restricted number of quantizer outputs in the worst case the algorithm complexity is cubic m 3 in the number of channel outputs m optimality is proved using the theorem of burshtein della pietra kanevsky and nadas for mappings which minimize average impurity for classification and regression trees

a discrete queue based model for capturing memory and soft decision information in correlated fading channels a discrete binary input 2q ary output communication channel with memory is introduced to judiciously capture both the statistical memory and the soft decision information of a time correlated discrete fading channel dfc used with antipodal signaling and soft output quantization of resolution q the discrete channel which can be explicitly described via its binary input process and a 2q ary noise process is shown to be symmetric thus admitting a simple expression for its capacity when its noise is stationary ergodic it is observed that considerable capacity gains can be achieved due to the channel s memory and the use of as few as 2 bits for soft decision over interleaving the channel to render it memoryless and hard decision demodulation q 1 the 2q ary noise process is next modeled via a queue based qb ball sampling mechanism to produce a mathematically tractable stationary ergodic markovian noise source the dfc is fitted by the qb noise model via an iterative procedure that minimizes the kullback leibler divergence rate between the dfc and qb noise sources modeling results measured in terms of channel noise correlation function and capacity reveal a good agreement between the two channels for a broad range of fading conditions

notes on the tightness of the hybrid cramer rao lower bound in this paper we study the properties of the hybrid cramer rao bound hcrb we first address the problem of estimating unknown deterministic parameters in the presence of nuisance random parameters we specify a necessary and sufficient condition under which the hcrb of the nonrandom parameters is equal to the cramer rao bound crb in this case the hcrb is asymptotically tight in high signal to noise ratio snr or in large sample scenarios and therefore useful this condition can be evaluated even when the crb cannot be evaluated analytically if this condition is not satisfied we show that the hcrb on the nonrandom parameters is always looser than the crb we then address the problem in which the random parameters are not nuisance in this case both random and nonrandom parameters need to be estimated we provide a necessary and sufficient condition for the hcrb to be tight furthermore we show that if the hcrb is tight it is obtained by the maximum likelihood maximum a posteriori probability ml map estimator which is shown to be an unbiased estimator which estimates both random and nonrandom parameters simultaneously optimally in the minimum mean square error sense

subspace beamforming for near capacity mimo performance a subspace beamforming method is presented that decomposes a multiple input multiple output mimo channel into multiple pairs of subchannels the pairing is done based on singular values such that similar channel capacity is obtained between different subchannel pairs this new capacity balancing concept is key to achieving high performance with low complexity we apply the subspace idea to geometric mean decomposition gmd and maximum likelihood ml detection the proposed subspace gmd scheme requires only two layers of detection decoding regardless of the total number of subchannels thus alleviating the latency issue associated with conventional gmd we also show how the subspace concept makes the optimization of ml beamforming and ml detection itself feasible for any k timesk mimo system simulation results show that subspace beamforming performs nearly as well as optimum gmd performance and to within only a few decibels of the shannon bound

the equivalence of two unified solutions for optimum space time processing ariyavisitakul et al ieee trans commun vol 47 p 1073 83 1999 july provided a unified analysis of optimum space time processors based on the following two analytical diversity receiver models 1 a general model with a linear filter on each diversity branch and 2 a matched filter model with a bank of matched filters on each branch followed by common filter closed form results were given for each receiver model in terms of the minimum mean square error or maximum signal to noise ratio solutions for different types of equalizers including a linear equalizer le a decision feedback equalizer dfe and a maximum likelihood sequence estimator mlse although we implied that the two receiver models lead to the same optimum solutions this requires some proof that is not directly obtainable from the results we presented we therefore provide such a proof in the present paper for the completeness of the overall unified analysis

performance analysis of deliberately clipped ofdm signals we analyze the performance of the clipped orthogonal frequency division multiplexing ofdm system in terms of peak power reduction capability and degradation of channel capacity the clipping is performed on the baseband ofdm signals with and without oversampling followed by the ideal low pass filter first the effect of the envelope clipping on the peak to average power ratio papr and the instantaneous power of the band limited ofdm signal is studied we then discuss the channel capacity of the oversampled and clipped ofdm signals over the additive white gaussian noise and ideally interleaved rayleigh fading channels the capacity is calculated based on the assumption that the distortion terms caused by the clipping are gaussian it is shown that the snr penalty due to the clipping can be considerably alleviated by using optimal coding and reducing the information data rate the results are justified by the simulation results using near optimal turbo codes

on binary quantizer for maximizing mutual information we consider a channel with a binary input x being corrupted by a continuous valued noise that results in a continuous valued output y an optimal binary quantizer is used to quantize the continuous valued output y to the final binary output z to maximize the mutual information i x z we show that when the ratio of the channel conditional density r y frac p y y x 0 p y y x 1 is a strictly increasing or decreasing function of y then a quantizer having a single threshold can maximize mutual information furthermore we show that an optimal quantizer possibly with multiple thresholds is the one with the thresholding vector whose elements are all the solutions of r y r for some constant r 0 in addition we also characterize necessary conditions using fixed point theorem for the optimality and uniqueness of a quantizer based on these conditions we propose an efficient procedure for determining all locally optimal quantizers and thus a globally optimal quantizer can be found our results also confirm some previous results using alternative elementary proofs

lattice reduction based approximate map detection with bit wise combining and integer perturbed list generation for iterative detection and decoding idd in multiple input multiple output mimo systems the log likelihood ratio llr of each coded bit can be found by an optimal bit wise maximum a posteriori probability map detector however since this map detector requires a prohibitively high computational complexity low complexity suboptimal detectors are desirable in this paper lattice reduction lr based mimo detection is investigated to derive a low complexity detector that can achieve near map performance for idd in order to approximate llr values incorporating the extrinsic information provided by a soft input soft output siso decoder bit wise lr based minimum mean square error mmse filters are derived furthermore in order to minimize the performance degradation due to quantization or rounding errors in the lr based detection a low complexity integer perturbed list generation method is proposed where no tree search is used by taking advantage of a near orthogonal channel basis obtained by lr through a complexity analysis and simulations it is shown that the proposed approach achieves near optimal performance while the complexity is comparable with that of the mmse soft cancellation method which is known to be computationally efficient as a bit wise detector a parallel implementation of the proposed method would be straightforward which lowers the detection delay

soft decision demodulation design for covq over white colored and isi gaussian channels in this work the design of a q bit scalar and vector soft decision demodulator for gaussian channels with binary phase shift keying modulation is investigated the demodulator is used in conjunction with a soft decision channel optimized vector quantization covq system the covq is constructed for an expanded q 1 discrete channel consisting of the concatenation of the modulator the gaussian channel and the demodulator it is found that as the demodulator resolution q increases the capacity of the expanded channel increases resulting in an improvement of the covq performance consequently the soft decision demodulator is designed to maximize the capacity of the expanded channel three gaussian channel models are considered as follows 1 additive white gaussian noise channels 2 additive colored gaussian noise channels and 3 gaussian channels with intersymbol interference comparisons are made with a hard decision covq systems b covq systems which utilize interleaving and c an unquantized q spl infin soft decision decoder proposed by skoglund and hedelin 1999 it is shown that substantial improvements can be achieved over covq systems which utilize hard decision demodulation and or channel interleaving the performance of the proposed covq system is comparable with the system by skoglund and hedelin though its computational complexity is substantially less

parallelization of a fuzzy control algorithm using quantum computation quantum computation is proposed for the parallelization of a fuzzy logic control flc algorithm quantum computation speeds up the fuzzy inference since serial operations between matrices of large dimensionality are now replaced by a one step quantum addition or a quantum subtraction the unitarity properties of the algorithm prove that the flc stands for a simulator of a quantum computing machine

iterative tree search detection for mimo wireless systems this paper presents a reduced complexity soft input soft output detection scheme called iterative tree search detection for multiple input multiple output wireless communication systems employing turbo processing at the receiver in this scheme a reduced search space is selected with the aid of the m algorithm and qam signal constellations with block partitionable labels are used in order to make the detection complexity per bit almost independent of the modulation order as well as asymptotically linear in the number of transmit antennas results from computer simulations are presented which demonstrate the capability of the scheme to approach optimal performance at considerably reduced complexity

signal parameter estimation using 1 bit dithered quantization motivated by the estimation of spatio temporal events with cheap simple sensors we consider the problem of estimation of a parameter thetas of a signal s x thetas corrupted by noise assuming that only 1 bit precision dithered quantized samples are available an estimate that does not require the knowledge of the dither signal and the noise distribution is proposed and it is analyzed in detail under variety of nonidealities the consistency and asymptotic normality of the estimate is established for deterministic and random sampling imprecise knowledge of sampling locations gaussian and non gaussian noise with possibly infinite variance a wide class of dither distributions and under erroneous transmission of the binary observations via binary symmetric channels bscs it is also shown that if approximation to the log likelihood equation in the full precision case yields a good estimate then there is a corresponding good estimate based on 1 bit dithered samples the proposed estimate requires no more computation than the maximum likelihood estimate for the full precision case and suffers only a logarithmic rate loss compared to the full precision case when uniform dithering is used it is shown that uniform dithering leads to the best rate among a broad class of dither distributions a condition under which no dithering leads to a better estimate is also given

soft quasi maximum likelihood detection for multiple antenna wireless channels the paper addresses soft maximum likelihood ml detection for multiple antenna wireless communication channels we propose a soft quasi ml detector that maximizes the log likelihood function by deploying a semi definite relaxation sdr given perfect channel state information at the receiver the quasi ml sdr detector closely approximates the performance of the optimal ml detector in both coded and uncoded multiple input multiple output mimo channels with quadrature phase shift keying qpsk modulation and frequency flat rayleigh fading the complexity of the quasi ml sdr detector is much less than that of the optimal ml detector thus offering more favorable performance complexity characteristics in contrast to the existing sphere decoder the new quasi ml detector enjoys guaranteed polynomial worst case complexity the two detectors exhibit quite comparable performance in a variety of ergodic qpsk mimo channels but the complexity of the quasi ml detector scales better with increasing number of transmit and receive antennas especially in the region of low signal to noise ratio snr

on channel capacity per unit cost memoryless communication channels with arbitrary alphabets where each input symbol is assigned a cost are considered the maximum number of bits that can be transmitted reliably through the channel per unit cost is studied it is shown that if the input alphabet contains a zero cost symbol then the capacity per unit cost admits a simple expression as the maximum normalized divergence between two conditional output distributions the direct part of this coding theorem admits a constructive proof via stein s lemma on the asymptotic error probability of binary hypothesis tests single user multiple access and interference channels are studied

a reduced complexity tree search detection algorithm for mimo systems in this correspondence we propose a simplified qr decomposition m algorithm for coded multiple input multiple output systems based on the idea of selective branch extension the proposed algorithm results in significant complexity reductions without observable performance losses especially for large modulation orders where complexity reduction is most important

a new class of iterative equalizers for space time bicm over mimo block fading multipath awgn channel this paper addresses the issue of advanced equalization methods for space time communications over multiple input multiple output block fading channel with intersymbol interference instead of resorting to conventional multiuser detection techniques based on the straightforward analogy between antennas and users we adopt a different point of view and separate time equalization from space equalization thus introducing a higher degree of freedom in the overall space time equalizer design time domain equalization relies on minimum mean square error criterion and operates on multidimensional modulation symbols whose individual components can be detected in accordance with another criterion in particular when the optimum maximum a posteriori criterion is chosen substantial performance gains over conventional space time turbo equalization have been observed for different transmission scenarios at the price of an increased albeit manageable computational complexity