We show that the probability distribution of the error exponent in i.i.d. code ensembles over classical-quantum (CQ) channels with arbitrary output states accumulates above a threshold that is strictly larger than the CQ random coding exponent (RCE) at low rates, while coinciding with it at rates close to the mutual information of the channel. This result, combined with the work by Dalai [1] and the recent ones by Renes [2] and Li and Yang [3], implies that the ensemble distribution of error exponents concentrates around the CQ RCE in the high rate regime. Moreover, in the same rate regime the threshold we derive coincides with the ensemble-average of the exponent, that is, the typical random coding (TRC) exponent [4].

In addressing the challenge posed by noise in actual quantum devices, the application of quantum error mitigation techniques becomes essential. These techniques are resource-efficient, making them viable for implementation in noisy intermediate-scale quantum devices, unlike the resource-intensive quantum error correction codes. A prominent example among these techniques is Clifford Data Regression, which employs a supervised learning approach. This work explores two variants of this technique, both of which add a non-trivial set of gates to the original circuit. The first variant leverages copies of the original circuit, whereas the second approach adds a layer of 1-qubit rotations.

We define a new family of codes for symmetric classical-quantum channels and establish their optimality. To this end, we extend the classical notion of generalized perfect and quasi-perfect codes to channels defined over some finite dimensional complex Hilbert output space. The resulting optimality conditions depend on the channel considered and on an auxiliary state defined on the output space of the channel. For certain$N$-qubit classical-quantum channels, we show that codes based on a generalization of Bell states are quasi-perfect and, therefore, they feature the smallest error probability among all codes of the same blocklength and cardinality.

A polar coding scheme is proposed for the Wiretap Broadcast Channel with two legitimate receivers and one eaves-dropper. We consider a model in which the transmitter wishes to to reliably send different confidential and private messages to the different legitimate receivers, and the confidential message must also be (strongly) secured from the eavesdropper. There are two different inner-bounds on the achievable region of this model in the literature. Both are characterized by using Marton's coding and the only difference between them is the decoding strategy: one is characterized by using joint decoding, while the other uses successive decoding. In this paper we present a polar coding scheme that achieves the larger inner-bound, and we show that polar-based joint decoding is crucial for this purpose.

We study the communication capabilities of a quantum channel under the most general channel model known as the one-shot model. Unlike classical channels that can only be used to transmit classical information (bits), a quantum channel can be used for transmission of classical information, quantum information (qubits) and simultaneous transmission of classical and quantum information. In this work, we investigate the one-shot capabilities of a quantum channel for simultaneously transmitting bits and qubits. This problem was studied in the asymptotic regime for a memoryless channel where a regularized characterization of the capacity region was reported. It is known that the transmission of private classical information is closely related to the problem of quantum information transmission. We resort to this idea and find achievable and converse bounds on the simultaneous transmission of the public and private classical information. Then shifting the classical private rate to the quantum information rate leads to a rate region for simultaneous transmission of classical and quantum information. In the case of asymptotic i.i.d. setting, our one-shot result is evaluated to the known results in the literature. Our main tools used in the achievability proofs are position-based decoding and convex-split lemma.

The two-receiver broadcast channel with primary and third party receivers is studied. The sender wishes to reliably communicate a common (or public) message to both receivers as well as individualized and confidential messages to the primary receiver only. The third party receiver must be kept completely ignorant of the confidential message but there are no secrecy requirements associated to the individualized message. A trade-off arises between the rates of the three messages: when one of the rates is high, the other rates may need to back off to guarantee the reliable transmission of all three messages. In addition, the confidentiality requirement implies availability of local randomness at the transmitter in order to implement a stochastic encoding. This article studies the trade-off between the rates of the common, individualized and confidential messages as well as that of the local randomness in the one-shot regime of a quantum broadcast channel. We provide an achievability region, by proving a conditional version of the convex-split lemma combined with the position-based decoding, as well as a (weak) converse region. We study the asymptotic behaviour of our bounds and recover several well-known asymptotic results in the literature, including simultaneous transmission of classical and quantum information.

A polar coding scheme is proposed for the Wiretap Broadcast Channel with two legitimate receivers and one eavesdropper. We consider a model in which the transmitter wishes to send a private and a confidential message that must be reliably decoded by the receivers, and the confidential message must also be (strongly) secured from the eavesdropper. The coding scheme aims to use the optimal rate of randomness and does not make any assumption regarding the symmetry or degradedness of the channel. This paper extends previous work on polar codes for the wiretap channel by proposing a new chaining construction that allows to reliably and securely send the same confidential message to two different receivers. This construction introduces new dependencies between the random variables involved in the coding scheme that need to be considered in the secrecy analysis.

The 2-receiver broadcast channel with primary and third-party receivers is studied. The messages are classified into public, private and confidential. The messages in the public class are messages intended for both receivers. The private messages are intended for the primary receiver with no secrecy requirements imposed upon them. And the confidential messages are aimed exclusively to the primary receiver such that they must not be accessible to the other receiver. The encoder performs the necessary encryption by virtue of local randomness whose rate is assumed to be limited. We find an achievability region on the trade-off between the rates of the three messages and the source of randomness in the one-shot regime of a quantum broadcast channel.

We aim to study the optimal rates of transmission of public and private classical information over a quantum channel in the most general channel model. To this end, we discuss a scenario in which a quantum channel is being used only once, i.e., one-shot regime is considered. A quantum channel can be used to send classical information (bits) either publicly or privately and for either case, one-shot bounds have been reported in the literature. This paper investigates the one-shot capacity capabilities of a quantum channel for simultaneous transmission of public and private information. We derive an achievable rate region in the form of a tradeoff between public and private rates. We also provide converse bounds assessing the tightness of our achievable rates. Our main tools used in the achievability proofs are position-based decoding and convex-split lemma.

Most performance measures of pilot-assisted multiple-input multiple-output systems are functions of the linear precoder and the pilot sequence. A framework for the optimization of these two parameters is proposed, based on a matrix-valued generalization of the concept of effective signal-to-noise ratio (SNR) introduced in the famous work by Hassibi and Hochwald. Our framework aims to extend the work of Hassibi and Hochwald by allowing for transmit-side fading correlations, and by considering a class of utility functions of said effective SNR matrix, most notably including the well-known capacity lower bound used by Hassibi and Hochwald. We tackle the joint optimization problem by recasting the optimization of the precoder (resp. pilot sequence) subject to a fixed pilot sequence (resp. precoder) into a convex problem. Furthermore, we prove that joint optimality requires that the eigenbases of the precoder and pilot sequence be both aligned along the eigenbasis of the channel correlation matrix. We finally describe how to wrap all studied subproblems into an iteration that converges to a local optimum of the joint optimization.

As shown by Médard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input X with power P and by upper-bounding the conditional entropy h(X|Y, Ĥ) by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating X from (Y, Ĥ). We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input X as the sum of two independent Gaussian variables X1and X2and by applying Médard's lower bound first to bound the mutual information between X1and Y while treating X2as noise, and by applying it a second time to the mutual information between X2and Y while assuming X1to be known, we obtain a capacity lower bound that is strictly larger than Médard's lower bound. We then generalize this approach to an arbitrary number L of layers, where X is expressed as the sum of L independent Gaussian random variables of respective variances Pℓ, ℓ = 1, ... , L summing up to P. Among all such rate-splitting bounds, we determine the supremum over power allocations Pℓand total number of layers L. This supremum is achieved for L →∞ and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error H - Ĥ tends to zero as the SNR tends to infinity.

The envisioned smart grid aims to improve the interaction between the supply- and the demand-side of the electricity network, resulting in a great optimization potential. In this paper, we propose a holistic-based, distributed day-ahead demand-side management method that is suitable for energy markets subject to an external regulation. Here, active subscribers solve the nonconvex problem of deriving the bidding strategies that minimize their overall expected monetary expense and simultaneously optimize eventual dispatchable energy generation and storage strategies. We show that, when such users collaborate, they achieve greater saving with respect to the corresponding user-oriented, selfish optimization. In this setting, we propose a cooperative, distributed, and iterative algorithm providing the optimal bidding, production, and storage strategies of the users, along with its convergence properties.

A well-established capacity lower bound of multiple-input multiple-output (MIMO) single-user fading channels operating with imperfect receiver-side channel-state information (CSI) is improved using a simple rate-splitting and successive-decoding scheme. The potential improvement is shown to increase with the number of allowed decoding steps (layers) to such extent that the best layering strategy is approached in the limit as the number of layers tends to infinity. We give a general analytic expression of this limit, which constitutes a new capacity lower bound that is sharper than the conventional bound. Using large random matrix theory, we derive an asymptotic approximation of this novel bound, which is shown via numerical simulation to be highly accurate over the whole range of signal-to-noise ratios.

We study the maximization of an achievable ergodic rate expression of a multiple-input single-output (MISO) channel where both ends are cognizant of the same erroneous estimate of the current fading state, yet they have complete knowledge of channel statistics. A training procedure estimates each channel state by means of dedicated pilot symbols whose sum energy is fixed, while for data transmission a fixed average energy per channel access (i.e., average power) is available. The optimization consists, on one hand, in finding the optimal beamforming strategy and temporal power control policy, and on the other hand, in optimally constructing the pilot sequence according to the channel's correlation structure.

Following the seminal work by Zheng and Tse on the diversity and multiplexing tradeoff (DMT) of multiple-input multiple-output (MIMO) channels, in this paper, we introduce the array gain to investigate the fundamental relation between transmission rate and reliability in MIMO systems. The array gain gives information on the power offset that results from exploiting channel state information at the transmitter or as a consequence of the channel model. Hence, the diversity, multiplexing, and array gain (DMA) analysis is able to cope with the limitations of the original DMT and provide an operational meaning in the sense that the DMA gains of a particular system can be directly translated into a parameterized characterization of its associated outage probability performance. In this paper, we derive the best DMA gains achievable by any scheme employing isotropic signaling in uncorrelated Rayleigh, semicorrelated Rayleigh, and uncorrelated Rician block-fading MIMO channels. We use these results to analyze the effect of important channel parameters on the outage performance at different points of the DMT curve.

Following the seminal work of Zheng and Tse on the diversity and multiplexing tradeoff (DMT) of MIMO channels, in this paper we introduce the array gain to investigate the fundamental relation between transmission rate and reliability in MIMO systems. The array gain gives information on the power offset that results from exploiting channel state information at the transmitter or as a consequence of the channel model. Hence, the diversity, multiplexing, and array gain (DMA) analysis can be directly translated into a parameterized characterization of its associated outage probability performance. In this paper we derive the fundamental DMA tradeoff achievable by any scheme in uncorrelated Rayleigh, semicorrelated Rayleigh, and uncorrelated Rician block-fading MIMO channels. We use these results to analyze the effect of important channel parameters in the outage performance at different points of the DMT curve.

For single-user MIMO channels with partial re ceiver CSI, we study the difference between a lower bound and two alternative upper bounds of the mutual information achieved with Gaussian codebooks. These differences are termed bound gaps Δ and δ, respectively. The latter may serve to derive a capacity bound gap. In contrast to previous studies, we assume that the channel estimation error statistics are not given a priori, but depend on the parameters of a training routine, in which a pilot sequence is transmitted, and where the channel realization is linearly estimated. Under these conditions, we successively determine analytic upper and lower bounds on the mutual information bound gap Δ. We further study the asymptotic behavior of said bound gaps for high SNR and a large number of antennas. This allows us to prove, for example, that for MISO channels and a certain class of semicorrelated MIMO channels, when the training and transmit power levels are equal, the capacity is approached to within min(NT, NR) bits by the capacity bounds, where NTand NRstand for the number of transmit and receive antennas, respectively.

For single-user, multiple-input multiple-output (MIMO) channels with Rayleigh fading correlated at the transmitter side, and where the receiver only has partial channel knowledge in form of an MMSE channel estimate, we study the joint optimization of the linear precoder and the pilot (training) sequence under the constraint of prescribed transmit power and training energy budgets. Although this joint problem is generally not convex itself, we can show that the two marginal problems of optimizing either the pilot sequence or the precoder when the other variable is fixed, are convex. Furthermore, we characterize the jointly optimal transmit and training directions. Finally, we propose a full characterization of the Pareto efficient joint power loading strategies for the case of two transmit antennas, and illustrate the behavior of the jointly optimal solution.

Following the seminal work of Zheng and Tse on the diversity and multiplexing tradeoff (DMT) of MIMO channels, in this paper we introduce the array gain to further investigate the fundamental relation between transmission rate and reliability in MIMO systems. The array gain gives information on the power offset that results from exploiting channel state information at the transmitter or, simply, because of the channel model. Hence, the diversity, multiplexing, and array gain (DMA) tradeoff is able to cope with the limitations of the original DMT and provide with operational meaning in the sense that the DMA tradeoff of a particular system can be directly translated into a parameterized characterization of its associated outage probability performance. As a first step towards this objective, we present in this paper the fundamental DMA tradeoff achievable by any scheme in uncorrelated Rayleigh block-fading MIMO channels.

The computation of the channel capacity of discrete memoryless channels is a convex problem that can be efficiently solved using the Arimoto-Blahut (AB) iterative algorithm. However, the extension of this algorithm to the computation of capacity regions of multiterminal networks is not straightforward since it gives rise to non-convex problems. In this context, the AB algorithm has only been successfully extended to the calculation of the sum-capacity of the discrete memoryless multiple-access channel (DMAC). Thus, the computation of the whole capacity region still requires the use of computationally demanding search methods. In this paper, we first give an alternative reformulation of the capacity region of the DMAC which condenses all the non-convexities of the problem into a single rank-one constraint. Then, we propose efficient methods to compute outer and inner bounds on the capacity region of the two-user DMAC by solving a relaxed version of the problem and projecting its solution onto the original feasible set. Targeting numerical results, we first take a randomization approach. Focusing on analytical results, we study projection via minimum divergence, which amounts to the marginalization of the relaxed solution. In this case we derive sufficient conditions and necessary and sufficient conditions for the bounds to be tight. Furthermore, we are able to show that the class of channels for which the marginalization bounds match exactly the capacity region includes all the two-user binary-input deterministic DMACs as well as other non-deterministic channels. In general, however, both methods are able to compute very tight bounds as shown for various examples.

The performance characterization of decentralized wireless networks with uncoordinated sender-destination pairs motivates the study of the totally asynchronous interference channel with single-user receivers. Since this channel is not information stable, its capacity region is determined resorting to information density, although more amenable single-letter inner and outer bounds are provided as well. Aiming at numerical evaluation of the achievable rates, we subsequently concentrate on the inner bound for the Gaussian case. We show that taking Gaussian inputs is not the best choice in general and derive analytical conditions under which other input distributions may be optimal. Essentially, these conditions require the channel to be interference-limited. Finally, the existence of such non-Gaussian distributions with superior performance is validated numerically in different scenarios.

In this paper we present a general formulation that unifies the probabilistic characterisation of Hermitian random matrices with a specific structure. Based on a unified expression for the joint pdf, we obtain (i) the joint cdf, (ii) the marginal cdf's, and (iii) the marginal pdf's of the ordered eigenvalues, where (ii) and (iii) follow as simple particularizations of (i). Our formulation is shown to include the distribution of some common MIMO channel models such as the uncorrelated and semicorrelated Rayleigh, and the uncorrelated Rician fading MIMO channel, although it is not restricted only to these. Hence, we provide a solid framework for the simultaneous analytical performance analysis of MIMO systems under different channel models. As an example of application, we obtain the exact outage probability of a spatial multiplexing MIMO system transmitting through the strongest channel eigenmodes.

While the capacity region of the discrete memoryless broadcast channel is in general unknown, it admits a computable single-letter characterization when it is degraded. In this case, we pose its computation as an optimization problem and analyze its structure. We show that the computation of the capacity region of the two-user discrete memoryless degraded broadcast channel can be characterized as a difference of convex optimization problem, a non-convex problem in general. For this problem, which cannot be solved optimally in polynomial time, we obtain necessary conditions for optimality which substantially reduce the set of potential capacity-achieving candidate distributions. As an application of this result, the capacity region of the BEC-BSC degraded broadcast channel is derived by maximizing the achievable rates over this set of reduced dimensionality.

Following the seminal work of Zheng and Tse, this paper investigates the fundamental diversity and multiplexing tradeoff of multiple-input-multiple-output (MIMO) systems in which knowledge of the channel state at both sides of the link is employed to transmit independent data streams through the channel eigenmodes. First, the fundamental diversity and multiplexing tradeoff of each of the individual substreams is obtained and this result is then used to derive a tradeoff optimal scheme for rate allocation along channel eigenmodes. The tradeoff of spatial multiplexing is finally compared to the fundamental tradeoff of the MIMO channel and to the one of both space only codes and V-BLAST which do not require channel state information (CSI) at the transmit side.

The problem of maximizing the spectral efficiency of an OFDMA broadcast channel is considered under the practical restriction of single antenna terminals. Given a total transmit power constraint and (possibly) different per-user quality of service (QoS) requirements, a subcarrier assignment and joint power and rate allocation algorithm is proposed to maximize a weighted sum of the users' rates (assuming continuous values for the rates). The proposed solution, which turns out to be a simple rate waterfilling, is very flexible since it can accommodate different scheduling criteria by tuning the users' weights involved in the maximization procedure. The output of the algorithm is quantized so as to restrict the rates to be practical values of squared QAM modulations. For small-sized systems, the comparison of the proposed algorithm with the optimal but impractical brute force search is possible and the results show excellent performance at a much lower complexity load.

Linear MIMO transceivers (composed of a linear precoder at the transmitter and a linear equalizer at the receiver) are a low-complexity approach to optimize the spectral efficiency and/or the reliability of the communication, when perfect channel state information is available at both sides of the link. The design of linear transceivers has been extensively studied in the literature with a variety of cost functions. In this paper we focus on the minimum BER design, and show that the common practice of fixing a priori the number of transmitted data symbols per channel use inherently limits the diversity gain of the system. Finally, we propose a minimum BER linear precoding scheme that achieves the full diversity of the MIMO channel.

The computation of the channel capacity of discrete memoryless channels is a convex problem that can be efficiently solved using the Arimoto-Blahut (AB) iterative algorithm. However, the extension of this algorithm to the computation of capacity regions of multiterminal networks is not straightforward since its computation gives rise to non-convex problems. In this context, the AB algorithm has been only successfully extended to the calculation of the sum-capacity of the discrete memoryless multiple-access channel. However, the computation of the capacity region still requires the use of computationally demanding random search algorithms or brute force (full search) methods. In this paper, we first give an alternative reformulation of the problem that identifies the non-convexity as a rank-one constraint. We then propose an efficient algorithm to compute outer and inner bounds on the capacity region by relaxing the original problem and then by projecting the relaxed solution onto the original space variable via a minimum divergence criterion. There exists a class of channels for which the proposed algorithm can be shown to compute exactly the capacity region. As an illustration, we analyze two particular channels, the binary adder MAC and the binary switching MAC, in detail. In the general case, the algorithm is able to compute very tight bounds as shown by simulation.

Following the pioneering work of Zheng and Tse, this paper derives the diversity and spatial multiplexing gain tradeoff for MIMO systems employing a multiple beamforming scheme. This result is obtained looking at the channel performance limits when both the SNR and the transmission rate tend to infinity, such that the relation between the rate and the capacity is constant. Assuming a uniform power allocation among eigenbeams, the optimal rate allocation policy in the sense of the best diversity and multiplexing tradeoff is obtained

The work considers the problem of channel estimation in the iterative reception of pilot-aided signals in DS-CDMA systems. The performance of classical training-based schemes is severely degraded in highly frequency selective channels due to the code-multiplexing of traffic and pilot signals. Thus, estimation algorithms that rely on the presence of the pilot signal, but also consider the information signal structure, are preferred. We present a Bayesian channel estimation algorithm for turbo receivers that effectively exploits the available soft information about the symbols (to model the traffic signal) in order to improve the channel estimation iteratively. Simulation results in realistic frequency selective test cases reveal only a moderate degradation compared to the perfect channel knowledge case.

We present a general performance analysis of space-time block coding (STBC) with fixed and variable data rate for wireless communications. The performance is measured in terms of the tradeoff curves between diversity and spatial multiplexing gains, and in terms of symbol error rate (SER) for QAM constellations. Effective data rate and asymptotic approximation of SER expressions for fixed and adaptive modulation are found.

This work considers a high rate, multiple input multiple output (MIMO) systems using multiple codes, as well as channel coding and space time (ST) coding. The transmitter consists of a channel encoder followed by parallel linear dispersion codes (LDC) ST encoders using different spreading codes. The iterative receiver consists of a soft input and soft output (SISO) demodulator, followed by a SISO detector. Simulation results in realistic frequency selective third generation partnership project test cases reveal good performance even for high rate HSDPA services.

The paper analyzes the asymptotic capacity per receive antenna in a multiple input multiple output (MIMO) system with fading correlation at either the transmitter or the receiver. The objective is the derivation of a closed form solution for the asymptotic capacity under fading correlation when the number of transmit and receive antennas increases without bound at the same rate. To do that, we consider a particular correlation model that yields a closed form expression for the asymptotic density of the channel eigenvalues. We proposed the correlation model (Mestre, X. et al., IEEE JSAC, 2003) to analyze the asymptotic capacity of a correlated MIMO system under a uniform power allocation strategy. We extend those results to the closed-loop configuration under waterfilling power allocation. The asymptotic expressions provide some new interesting insights into the different influence of fading correlation on the channel capacity.

This paper investigates the asymptotic uniform power allocation capacity of frequency nonselective multiple-input multiple-output channels with fading correlation at either the transmitter or the receiver. We consider the asymptotic situation, where the number of inputs and outputs increase without bound at the same rate. A simple uniparametric model for the fading correlation function is proposed and the asymptotic capacity per antenna is derived in closed form. Although the proposed correlation model is introduced only for mathematical convenience, it is shown that its shape is very close to an exponentially decaying correlation function. The asymptotic expression obtained provides a simple and yet useful way of relating the actual fading correlation to the asymptotic capacity per antenna from a purely analytical point of view. For example, the asymptotic expressions indicate that fading correlation is more harmful when arising at the side with less antennas. Moreover, fading correlation does not influence the rate of growth of the asymptotic capacity per receive antenna with high Eb/N/sub 0/.

This paper proposes a spatial filtering technique for the reception of pilot-aided multi-rate multi-code DS/CDMA systems such as WCDMA. These systems introduce a code-multiplexed pilot sequence that can be used for the estimation of the filter weights, but the presence of the traffic signal (transmitted at the same time as the pilot sequence) corrupts that estimation up to the point that it might render the filter completely useless. The traffic and pilot signals are designed to be orthogonal, but the frequency selectivity of the channel degrades this orthogonality in the received signal. Here we propose a semi-blind technique that eliminates the self-noise caused by the code-multiplexing. We derive analytically the asymptotic performance of both the training-only and the semi-blind techniques and compare it with the actual simulated performance. It is shown, both analytically and via simulation, that high gains can be achieved with respect to training-only based techniques.

This paper analyzes the influence of channel estimation errors on the performance of linear multiuser receivers. Assuming randomized codes and asymptotically high spreading factors and noise power, we show that the performance of the decorrelating and the minimum mean squared error (MMSE) receivers tend to the same limit in terms of signal-to-noise ratio and bit error rate (but not in terms of mean squared error). Using these results and assuming Gaussian-distributed channel estimators, we derive two simple approximations to the bit error rate and compare them with the actual values via simulation.

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) with high spreading factors, where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. Assuming randomized training and code sequences, we derive and compare the asymptotic covariances of the training-only (TO), semi-blind conditional ML (CML) and semi-blind Gaussian ML (GML) channel estimators.

We analyze different space-time architectures for the TDD mode of UTRA when employing multi-element antenna arrays at both the transmit and receive sites. We compare space-time codes that do not require channel knowledge at the transmitter with a beamforming scheme, which exploits channel reciprocity available in the TDD mode to construct a beamvector at the transmitter.

It is well known that employing multielement antenna arrays at both transmit and receive sites is capable of enormous theoretical capacity over wireless communications systems. In this paper we analyze different decoding strategies for BLAST architectures, combining interference cancellation and decoding in a turbo-like fashion.

Multi-input multi-output (MIMO) channels arising from the use of multi-element antenna (MEA) systems both in transmission and reception have been shown to support a considerable amount of bit rate. The information-theoretic capacity of such channels is severely affected by the spatial correlation. In this paper, we evaluate the ergodic and outage capacity of typical MIMO channels appearing in UMTS indoor scenarios. The frequency-selectivity and the spatial correlation of the MIMO channel are taken into account using realistic models obtained from field measurements performed within the IST project METRA (http://www.ist-metra.org). For capacity assessment, we use the transmission schemes considered by the 3GPP for UMTS. In particular, we analyze the cases of having and not having channel state information (CSI) at the transmitter, and also the case in which beamforming is used for transmission.

The problem of joint beamforming and channel estimation for multi-rate multi-code systems is addressed. Usual schemes perform this filtering/estimation operation making use of a training sequence time-multiplexed with the transmitted data. However if pilot and traffic signals are transmitted simultaneously using distinct code allocation-as it is the case in recent standards such as cdma2000 or WCDMA-these schemes tend to fail. This paper proposes semi-blind techniques to overcome the uplink auto-interfering effects of such systems. It is shown that the semi-blind approach yields substantially better performance results thanks to the implicit modeling of the unknown traffic data.

The use of spatial diversity at the receiver front-end together with a sequence detector implies a joint design problem of the spatial combiner and the sequence detector impulse response. This joint design is usually faced under the constraint that the impulse response of the sequence detector is matched to the channel and combiner response. This procedure maximizes the signal to noise ratio at the input of the detector but, as it is well known, this does not guarantee a minimum probability of error, which is more related to the so-called effective signal to noise ratio. This work presents a procedure that, starting from a simple structure for the space-time receiver aims directly at the maximization of the effective signal to noise ratio, yet preserving all the features of the spatial processor in terms of co-channel and high order intersymbol interference rejection.

In this paper, two probabilistic adaptive algorithms for jointly detecting active users in a DS-CDMA system are compared. The first one, which is based on the theory of hidden Markov models (HMM) is proposed within the CDMA scenario and compared with the previously developed Viterbi-based algorithm. Both techniques are completely blind in the sense that no knowledge of the signature sequences, channel state information or training sequences is required for any user. After convergence, an estimate of the signature of each user convolved with its physical channel impulse response (CIR), and estimated data sequences are provided. This CIR estimate can then be used to switch to any decision-directed (DD) adaptation scheme. Performance of the algorithms is verified with simulations as well as with experimental data from an underwater acoustics (UWA) environment. In both cases, performance is found to be highly satisfactory, showing the near-far resistance of the analyzed algorithms.

An algorithm for estimating and suppressing intercell interference in a hybrid C/TDMA system is presented. A scheme consisting in two coupled algorithms running in parallel has been chosen, with a joint detection block to detect data from the users of interest and a probabilistic algorithm relying on the theory of hidden Markov models to perform blind interference cancellation. Observation through an array of sensors is also considered. In order to assess performance of the proposed algorithm, a set of computer simulations adopting the FRAMES proposal for the UMTS air-interface is provided.

The objective of this paper is to introduce a fourth-order cost function of the displaced frame difference (DFD) capable of estimating motion even for small regions or blocks. Using higher than second-order statistics is appropriate in case the image sequence is severely corrupted by additive Gaussian noise. Some results are presented and compared to those obtained from the mean kurtosis and the mean square error of the DFD.

Multiuser detection in code division multiple access systems usually requires either knowledge of the transmitted signature sequences and channel state information or use of a known training sequence for adaptation. We develop a scheme that can be employed for the joint adaptive blind multiuser identification and detection in asynchronous CDMA systems. This scheme relies on a multiuser Viterbi algorithm that incorporates an adaptive estimation of the overall channel impulse responses, given by the convolution of the signature sequences of the users and corresponding physical channels impulse responses. Once the overall channel responses are estimated, the blind multiuser detection algorithm performs like the maximum-likelihood sequence estimator. Results are provided to illustrate the convergence of the blind multiuser approach, near-far resistance and sensitivity to the algorithm initialization.

This article studies the formulation of new members of the Cohen-Posch (1985) class of positive time-frequency energy distributions. Members of this class are always positive functionals and satisfy the marginal constraints. Therefore, they can be properly interpreted as distributions. We considered the minimization of cross-entropy measures with respect to different priors or "a priori" distributions and the case of no prior or maximum entropy, and concluded with the necessity to introduce new marginal constraints. New mixed in time and frequency constraints are derived based on a "direction invariance" criterion on the time-frequency plane that are directly related to the Fractional Fourier Transform. The implications of this new constrains in the positive class are analyzed from an information theoretic perspective. We conclude that the new constraints provide enough information to fully determine the resulting distribution and thus, no "a priori" distribution is necessary.>

A general class of higher-order time-frequency representations, including Wigner higher-order spectra (WHOS), has been defined and studied recently as an extension of bilinear time-frequency distributions in terms of instantaneous higher-order moments of the signal. The analysis of mono- and multicomponent signals is considered using higher-order based time-frequency distributions. A computationally feasible implementation of the Wigner bispectrum and trispectrum (WHOS in the third and fourth order domain) is proposed considering one slice of the multifrequency space. The problem of cross-terms cancellation is addressed, and reduced interference distributions are defined as an extension of the Choi-Williams distribution.>

The Wigner higher-order spectra (WHOS) are defined as extensions of the Wigner distribution (WD) to higher-order statistics domains. A general class of time-frequency higher-order spectra is also defined in terms of arbitrary higher-order moments of the signal as generalizations of the Cohen's general class of time-frequency representations. For signal processing applications, discrete time and frequency WHOS distributions are introduced and shown to be implemented with two fast-Fourier-transform-based algorithms. One application in which the Wigner bispectrum is applied for the detection of transient signals embedded in noise is presented. The Wigner bispectrum is compared with the WD and simulation results are given.>