Search

A new forecasting technique called the extended structural model (ESM) is presented. This technique is derived from the basic structural model (BSM) by the introduction of extra parameters that were assumed to be 1 in the BSM. The ESM is constructed from the training sequence using standard Kalman filter recursions, and then, the extra parameters are estimated to minimize the mean absolute percentage error (MAPE) of the validation sequence. The model is evaluated by the prediction of the total number of minutes of wireless airtime per month on the Bell Canada network. The ESM shows an improvement in the MAPE of the test sequence over the BSM, seasonal autoregressive integrated moving average (ARIMA), and generalized random walk models on the series considered in this paper. The improved prediction can significantly reduce the cost for wireless service providers who need to accurately predict future wireless spectrum requirements. Index Terms--Autoregressive integrated moving average (ARIMA), basic structural model (BSM), Kalman filter, mean absolute percentage error (MAPE).

In this paper, we study the energy-efficient distributed estimation problem for a wireless sensor network where a physical phenomena that produces correlated data is sensed by a set of spatially distributed sensor nodes and the resulting noisy observations are transmitted to a fusion center via noise-corrupted channels. We assume a Gaussian network model where (i) the data samples being sensed at different sensors have a correlated Gaussian distribution and the correlation matrix is known at the fusion center, (ii) the links between the local sensors and the fusion center are subject to fading and additive white Gaussian noise (AWGN), and the fading gains are known at the fusion center, and (iii) the central node uses the squared error distortion metric. We consider two different distortion criteria: (i) individual distortion constraints at each node, and (ii) average mean square error distortion constraint across the network. We determine the achievable power-distortion regions under each distortion constraint. Taking the delay constraint into account, we investigate the performance of an uncoded transmission strategy where the noisy observations are only scaled and transmitted to the fusion center. At the fusion center, two different estimators are considered: (i) the best linear unbiased estimator (BLUE) that does not require knowledge of the correlation matrix, and (ii) the minimum mean-square error (MMSE) estimator that exploits the correlations. For each estimation method, we determine the optimal power allocation that results in a minimum total transmission power while satisfying some distortion level for the estimate (under both distortion criteria). The numerical comparisons between the two schemes indicate that the MMSE estimator requires less power to attain the same distortion provided by the BLUE and this performance gap becomes more dramatic as correlations between the observations increase. Furthermore, comparisons between power-distortion region achieved by the theoretically optimum system and that achieved by the uncoded system indicate that the performance gap between the two systems becomes small for low levels of correlation between the sensor observations. If observations at all sensor nodes are uncorrelated, the uncoded system with MMSE estimator attains the theoretically optimum system performance. Index Terms--Distributed estimation, wireless sensor network, power-distortion region, MMSE, BLUE.

We present a new method (MMSE-Threshold) for peak/average power reduction. This technique is derived from a constellation shaping algorithm, where the constellation points with lower average energy are selected from a larger set of points. There are multiple choices available to select the points with lower peak energy for a given sequence of data bits, and this flexibility is used to reduce the peak to average power ratio (PAPR). Subsequently, this selection algorithm, which is formulated in terms of a zero-one quadratic problem, is optimized by the semidefinite programming algorithm (SDPA). Simulation results show that the PAPR of SDPA is noticeably better than MMSE-Threshold, while the complexity of MMSE-Threshold is smaller than that of SDPA. MMSE-Threshold is compared with alternative techniques reported in the literature. We show that, while we obtain a PAPR that is similar or better than those of the compared techniques, the complexity of MMSE-Threshold is low. In addition, the MMSE-Threshold method results in about l dB shaping gain (reduction in the average energy) with less than 1% rate loss for PAPR reduction. Index Terms--Constellation shaping, OFDM, optimization, PAPR, semidefinite programming algorithm.

A simple signaling method for broadcast channels with multiple-transmit multiple-receive antennas is proposed. In this method, for each user, the direction in which the user has the maximum gain is determined. The best user in terms of the largest gain is selected. The corresponding direction is used as the modulation vector (MV) for the data stream transmitted to the selected user. The algorithm proceeds in a recursive manner where in each step, the search for the best direction is performed in the null space of the previously selected MVs. It is demonstrated that with the proposed method, each selected MV has no interference on the previously selected MVs. Dirty-paper coding is used to cancel the remaining interference. For the case that each receiver has one antenna, the presented scheme coincides with the known scheme based on Gram--Schmidt orthogonalization (QR decomposition). To analyze the performance of the scheme, an upper bound on the cumulative distribution function (CDF) of each subchannel is derived which is used to establish the diversity order and the asymptotic sum-rate of the scheme. It is shown that using fixed rate codebooks, the diversity order of the jth data stream, 1 [less than or equal to] j [less than or equal to] M, is equal to N(M - j + 1)(K - j + 1), where M, N, and K indicate the number of transmit antennas, the number of receive antennas, and the number of users, respectively. Furthermore, it is proven that the throughput of this scheme scales as M log log(K) and asymptotically (K [right arrow] [infinity]) tends to the sum-capacity of the multiple-input multiple-output (MIMO) broadcast channel. The simulation results indicate that the achieved sum-rate is close to the sum-capacity of the underlying broadcast channel. Index Terms--Dirty-paper coding, multiple-antenna arrays, multiple-input multiple-output (MIMO) broadcast channels, multiuser diversity, multiuser systems, QR decomposition, space-division-multiple access.

In this paper, a downlink communication system, in which a base station (BS) equipped with M antennas communicates with N users each equipped with K receive antennas, is considered. An efficient suboptimum algorithm is proposed for selecting a set of users in order to maximize the sum-rate throughput of the system, in a Rayleigh-fading environment. For the asymptotic case when N tends to infinity, the necessary and sufficient conditions in order to achieve the maximum sum--rate throughput, such that the difference between the achievable sum--rate and the maximum value approaches zero, is derived. The complexity of our algorithm is investigated in terms of the required amount of feedback from the users to the BS, as well as the number of searches required for selecting the users. It is shown that the proposed method is capable of achieving a large portion of the sum-rate capacity, with a very low complexity. Index Terms--Asymptotic analysis, multiple-input multipleoutput (MIMO) broadcast channel, sum--rate capacity, user selection, zero-forcing beamforming.

Diversity order is an important measure for the performance of communication systems over multiple-input-multiple-output (MIMO) fading channels. In this correspondence, we prove that in MIMO multiple-access systems (or MIMO point-to-point systems with V-BLAST transmission), lattice-reduction-aided decoding achieves the maximum receive diversity (which is equal to the number of receive antennas). Also, we prove that the naive lattice decoding (which discards the out-of-region decoded points) achieves the maximum diversity. Index Terms--Lattice decoding, LLL lattice-basis reduction, multiple-input-multiple-output (MIMO) fading channels, recieve diversity, V-BLAST.

A new viewpoint for adopting the lattice reduction in communication over multiple-input multiple-output (MIMO) broadcast channels is introduced. Lattice basis reduction helps us to reduce the average transmitted energy by modifying the region which includes the constellation points. The new viewpoint helps us to generalize the idea of lattice-reduction-aided (LRA) precoding for the case of unequal-rate transmission, and obtain analytic results for the asymptotic behavior (signal-to-noise ratio (SNR) [right arrow] [infinity]) of the symbol error rate for the LRA precoding and the perturbation technique. Also, the outage probability for both cases of fixed-rate users and fixed sum rate is analyzed. It is shown that the LRA method, using the Lenstra-Lenstra-Lovasz (LLL) algorithm, achieves the optimum asymptotic slope of symbol error rate (called the precoding diversity). Index Terms--Lattice precoding, Lenstra-Lenstra-Lovasz (LLL) lattice-basis reduction, multiple-input multiple-output (MIMO) broadcast channel, MIMO fading channel.

In this paper, a new method for performance evaluation of turbo-like codes is presented. This is based on estimating the probability density function of the bit log-likelihood-ratio using higher order statistics. We do not restrict ourselves to any specific model for the pdf and try to estimate it directly using a cumulant matching method. Numerical results show a close agreement between the proposed method and the simulations. The complexity of this method is similar to the Monte-Carlo simulation with the advantage of providing similar accuracy using significantly fewer samples. Index Terms--Channel coding, communication system performance, error analysis, gaussian channels, higher order statistics.

In multiple-input multiple-output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this problem is known to be NP-hard. In this paper, a quasi-ML algorithm based on semi-definite programming (SDP) is proposed. We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models. The proposed relaxation models are also used for soft output decoding in MIMO systems. Index Terms--Closest lattice point, computational complexity, decoding, lattice basis reduction, lattice decoding, multiple-input multiple-output (MIMO) systems, quasi-maximum likelihood, semi-definite programming (SDP). Digital Object Identifier 10.1109/TIT.2007.907472

A network of n communication links, operating over a shared wireless channel, is considered. Fading is assumed to be the dominant factor affecting the strength of the channels between transmitter and receiver terminals. It is assumed that each link can be active and transmit with a constant power P or remain silent. The objective is to maximize the throughput over the selection of active links. By deriving an upper bound and a lower bound, it is shown that in the case of Rayleigh fading: (i) the maximum throughput scales like log n; (ii) the maximum throughput is achievable in a distributed fashion. The upper bound is obtained using probabilistic methods, where the key point is to upper bound the throughput of any random set of active links by a chi-squared random variable. To obtain the lower bound, a decentralized link activation strategy is proposed and analyzed. Index Terms--Decentralized link activation, fading channel, scaling law, throughput, wireless network.

The closed-loop transmit diversity technique is used to improve the performance of the downlink channel in multiple-input multiple-output (MIMO) communication systems. The wideband code-division multiple-access standard endorsed by the Third Generation Partnership Project (3GPP) adopts a downlink closed-loop scheme based on partial channel state information (CSI) known as mode 1. The information is fed back from the mobile unit to the base station through a low-rate uncoded feedback bit stream. In this paper, several reconstruction techniques are introduced to improve the performance of mode 1 of 3GPP in the presence of feedback error by taking advantage of the redundancy available in the bit stream of the CSI. We propose a number of algorithms for the reconstruction of beamforming weights at the base station with the constraint of a constant transmit power. The performance is examined within a simulated 3GPP framework. It is demonstrated that the proposed algorithms have substantial gain over the conventional method for low, moderate, and high mobile speeds. It is also shown that the proposed algorithms can substitute the antenna weight verification process done at the mobile unit in many cases. The proposed approach is applicable to other feedback schemes as well. Index Terms--Antenna weight verification (AV), channel feedback, channel state information (CSI), closed-loop transmit diversity, downlink communication, frequency-division-duplex (FDD) wideband code-division multiple access (WCDMA), joint source-channel coding, mode 1 of the Third Generation Partnership Project (3GPP).

This paper introduces a linear space-time precoding scheme for downlink transmission in a multiple-transmit single-receive antenna code-division multiple-access (CDMA) system. The proposed scheme relies on using space--time spreading sequences that are constructed from the time-domain user signatures combined with spatial channel information that corresponds to multiple transmit antennas. Numerical results show that the proposed scheme significantly reduces the multiple-access interference in both flat- and multipath-fading channels. At the same time, it achieves the maximum possible diversity order that is offered by the combination of multiple transmit antennas and multipath fading. To further improve the performance in overloaded CDMA systems, the proposed linear precoding method is combined with a nonlinear operation known as vector perturbation (based on applying modulo operation to minimize the transmit energy). Throughout the paper, we assume that 1) the channel state information is known at the transmitter, 2) the modulation is binary phase-shift keying, and 3) the receivers are simply based on linear filters that are matched to the time-domain user signatures. We use a recent work of Reynolds et al. as a baseline for comparison. Numerical results show that our proposed method outperforms that of Reynolds et al. in both flat and multipath channels. Index Terms--Code-division multiple-access (CDMA) systems, linear precoding, modulo operation, multiple-access interference (MAI) cancellation, multiple antenna systems, space-time spreading sequences, vector perturbation.

This work studies certain properties of the probability density function (pdf) of the bit log-likelihood ratio (LLR) for binary linear block codes over a memoryless channel with discrete input and discrete or continuous output. We prove that under a set of mild conditions, the pdf of the bit LLR of a specific bit position is independent of the transmitted codeword. It is also shown that the pdf of a given bit LLR when the corresponding bit takes the values of zero and one are symmetric with respect to each other (reflection of one another with respect to the vertical axis). For the case of channels with binary input, a sufficient condition for two bit positions to have the same pdf is presented. Index Terms--Bit decoding, block codes, geometrically uniform, log-likelihood ratio (LLR), probability density function (pdf), regular channel, symmetric channel.

Joint source-channel coding is an effective approach for the design of bandwidth efficient and error resilient communication systems with manageable complexity. An interesting research direction within this framework is the design of source decoders that exploit the residual redundancy for effective signal reconstruction at the receiver. Such source decoders are expected to replace the traditionally heuristic error concealment units that are elements of most multimedia communication systems. In this paper, we consider the reconstruction of signals encoded with a multistage vector quantizer (MSVQ) and transmitted over a noisy communications channel. The MSVQ maintains a moderate complexity and, due to its successive refinement feature, is a suitable choice for the design of layered (progressive) source codes. An approximate minimum mean squared error source decoder for MSVQ is presented, and its application to the reconstruction of the linear predictive coefficient (LPC) parameters in mixed excitation linear prediction (MELP) speech codec is analyzed. MELP is a low-rate standard speech codec suitable for bandwidth-limited communications and wireless applications. Numerical results demonstrate the effectiveness of the proposed schemes. Index Terms--Error concealment, joint source-channel coding, linear predictive coding (LPC), line spectral frequencies (LSFs), Markov model, MELP, minimum mean squared error (MMSE) estimation, multistage vector quantization (MSVQ), residual redundancy, source decoding.

In this paper, a method is proposed to construct spectrally efficient unitary space-time codes for high-rate differential communications over multiple-antenna channels. Unlike most of the known methods which are designed to maximize the diversity product (the minimum determinant distance), our objective is to increase the spectral efficiency. The simulation results indicate that for high spectral efficiency and for more than one receive antenna, the new method significantly outperforms the existing alternatives. In the special case of two transmit antennas, which is the main focus of this paper, the relation between the proposed code and the Alamouti scheme helps us to provide an efficient maximum-likelihood (ML) decoding algorithm. Also, we demonstrate that similar ideas can be applied to designing codes for more than two transmit antennas. As an example, we present a construction for 4-by-4 unitary constellations which has a good performance, compared with the other known codes. Index Terms--Alamouti code, differential space-time (ST) coding, Lie groups, multiple-antenna systems, unitary constellations.

This paper describes a new approach to fixed-rate entropy-constrained vector quantization (FEVQ) for stationary memoryless sources where the structure of codewords are derived from a variable-length scalar quantizer. We formulate the quantization search operation as a zero-one integer-optimization problem, and show that the resulting integer program can be closely approximated by solving a simple linear program. The result is a Lagrange formulation which adjoins the constraint on the entropy (codeword length) to the distortion. Unlike the previously known methods with a fixed Lagrange multiplier, we use an iterative algorithm to optimize the underlying objective function, while updating the Lagrange multiplier until the constraint on the overall rate is satisfied. The key feature of the new method is the substantial reduction in the number of iterations in comparison with previous related methods. In order to achieve some packing gain, we combine the process of trellis-coded quantization with that of FEVQ. This results in an iterative application of the Viterbi algorithm on the underlying trellis for selecting the Lagrange multiplier. Numerical results are presented which demonstrate substantial improvement in comparison with the alternative methods reported in the literature. Index Terms--Entropy coding, linear programming, source coding, trellis-coded quantization, vector quantization.

In this paper, the problem of reducing the peak-toaverage-power ratio (PAPR) in an orthogonal frequency-division multiplexing system is considered. We design a cubic constellation, called the Hadamard constellation, whose boundary is along the bases defined by the Hadamard matrix in the transform domain. Then, we further reduce the PAPR by applying the selective-mapping technique. The encoding method, following the method introduced in the work of Kwok, is derived from a decomposition known as the Smith normal form. This new technique offers a PAPR that is significantly lower than those of the best-known techniques without any loss in terms of energy and/or spectral efficiency, and without any side information being transmitted. Moreover, it has a low computational complexity. Index Terms--Hadamard constellation, orthogonal frequency-division multiplexing (OFDM), peak-to-average-power ratio (PAPR), selective mapping (SLM), Smith normal form (SNF).

A technique is presented to reduce the multiple-access interference in the forward link of a direct-sequence code-division multiple-access system. For each symbol period, an energy-constrained nonlinear transformation is applied at the transmitter output to minimize the mean-squared error at the receiver, subject to a constraint on the peak transmitted energy. The proposed algorithm can be implemented with existing optimization techniques that solve the quadratic trust-region problem. It is shown that in the presence of forward error-correction codes, the proposed method results in a significant gain over earlier known techniques at the cost of a modest increase in computational complexity at the base station. Another advantage of the proposed precoder is that it caps the peak energy (while earlier methods cap the average energy), requiring less sophisticated power amplifiers. Index Terms--Code-division multiple access (CDMA), interference cancellation, multiple-access channels, quadratic optimization, transmitter precoding, trust region.

In this paper, we address the application of adaptive modulation and coding (AMC) for 3rd-generation (3G) wireless systems. We propose a new method for selecting the appropriate modulation and coding schemes (MCS) according to the estimated channel condition. In this method, we take a statistical decision making approach to maximize the average throughput while maintaining an acceptable frame error rate (FER). We use a first order finite state Markov model to approximate the time variations of the average channel signal to noise ratio (SNR) in subsequent frames. The MCS is selected in each state of this Markov model (among the choices proposed in the 3G standards proposals) to maximize the statistical average of the throughput in that state. Using this decision making approach, we also propose a simplified Markov model with fewer parameters, which is suitable in systems where changes in the fading characteristics need to be accounted for in an adaptive fashion. Numerical results are presented showing that both of our models substantially outperform the conventional techniques that use a memoryless threshold based decision making. Index Terms--Adaptive modulation and coding (AMC), first-order finite-state Markov model, 3rd-generation (3G) code division multiple access (CDMA), lognormal shadowing, spectral efficiency, turbo coding.

In this paper, deliberate level clipping and turbo coding are combined to achieve an orthogonal frequency division multiplexing (OFDM) transmission system with a low peak-to-average power ratio (PAR) and a good performance. Using the linear approximation technique based on the minimum mean square error (MMSE) criterion, we first modify the metric computation for the turbo decoding in order to consider the distortion effects of the nonlinearity, caused by the Cartesian clipper. Also, this paper introduces a modified turbo decoder which simultaneously performs the data estimation and signal reconstruction. In other words, the turbo decoder iteratively recovers the clipped signal by using the estimated data, and then improves the data estimation by using the newly recovered signal. Numerical results are presented showing an improvement in the performance of the OFDM transmission system over the nonlinear channel, an increase in the efficiency of the high power amplifier (HPA), and/or an expansion of the transmitter coverage area. Index Terms--Cartesian clipper, minimum mean square error (MMSE), nonlinear device, orthogonal frequency division multiplexing (OFDM), peak-to-average power ratio (PAR), turbo code.

A new method of combined source-channel coding for the scalar quantization of a discrete memoryless source is presented, which takes advantage of the reliability information produced by a soft-output channel decoder. Numerical results are presented for a memoryless Gaussian source in conjunction with turbo code showing up to 1-dB improvement in the end-to-end distortion with respect to a traditional channel optimized scalar quantizer. The results include a Gaussian source designed using closed-form expression without the need for a training sequence, as well as image pixels using a training sequence. Furthermore, certain issues related to the effect of the channel mismatch and spectral efficiency of the system are studied. It is shown that the increase in distortion due to a channel mismatch can be substantially reduced by using an adaptive receiver. Index Terms--Quantization, soft-output decoding, turbo code.

Performance evaluation of maximum-likelihood (ML) soft-decision-decoded binary block codes is usually carried out using bounding techniques. Many tight upper bounds on the error probability of binary codes are based on the so-called Gallager's first bounding technique (GFBT). The tangential sphere bound (TSB) of Poltyrev which has been believed for many years to offer the tightest bound developed for binary block codes is an example. Within the framework of the TSB and GFBT, we apply a new method referred to as the "added-hyper-plane" (AHP) technique, to the decomposition of the error probability. This results in a bound developed upon the application of two stages of the GFBT with two different Gallager regions culminating in a tightened upper bound beyond the TSB. The proposed bound is simple and only requires the spectrum of the binary code. Additive white Gaussian noise (AWGN) channel, block codes, bounds, decoding error probability, distance spectrum, Gallager bounds, linear binary block codes, maximum-likelihood (ML) decoding, probability of error, union bound, upper bounds.

The error probability of maximum-likelibood (NIL) soft-decision decoded binary block codes rarely accepts nice closed forms. In addition, for long codes, ML decoding becomes prohibitively complex. Nevertheless, bounds on the performance Of ML decoded systems provide insight into the effect of system parameters on the overall system performance as well as a measure of goodness of the subaptimum decoding methods used in practice. Using the so-coiled Galinger's first bounding technique (involving a so-called Gallager region) and within the framework of tangential sphere bound (TSB) of Poltyrov, we develop a general bound referred to as the generalized TSB (GTSB). The Gollager region is chosen to be a general hyper-surface of revolution (HSR) which is optimized to tighten the bound. The search for the optimal Gallager region is a classical problem dating back to Gallager's thesis in the early 1960s. For the random coding case, Gallager provided the optimal solution in a closed form while for the nonrandom case the problem has been an active area of research in information theory for many years. We prove that for a sphere code, the optimal HSR within the proposed GTSB is a hyper-cone. This will climax to the TSB of Poltyrev, one of the tightest bounds ever developed for binary block codes, and therefore terminates the search for a better Gallager region in the groundwork of the GTSB. Index Terms--Block codes, Gallager bounds, maximum-likelihood (ML) decoding, union bound, upper bounds.

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth-efficient way to combat noisy channel degradations. This redundancy can be employed to either assist the channel decoder for improved performance or design better source decoders. In this work, a family of solutions for the asymptotically optimum minimum mean-squared error (MMSE) reconstruction of a source over memoryless noisy channels is presented when the redundancy in the source encoder output stream is exploited in the form of a [gamma]-order Markov model ([gamma] [greater than or equal to] 1) and a delay of [delta], [delta] > 0, is allowed in the decoding process. It is demonstrated that the proposed solutions provide a wealth of tradeoffs between computational complexity and the memory requirements. A simplified MMSE decoder which is optimized to minimize the computational complexity is also presented. Considering the same problem setup, several other maximum a posteriori probability (MAP) symbol and sequence decoders are presented as well. Numerical results are presented which demonstrate the efficiency of the proposed algorithms. Index Terms--Forward-backward recursion, joint source-channel coding, maximum a posteriori probability (MAP) detection, Markov sources, minimum mean-squared error (MMSE) estimation, residual redundancies, source decoding.

In this paper, we consider the problem of decoding a predictively encoded signal over a noisy channel when there is a residual redundancy (captured by a [lambda]-order Markov model) in the sequence of transmitted data. Our objective is to minimize the mean-squared error (MSE) in the reconstruction of the original signal (input to the predictive source coder). The problem is formulated and solved through minimum mean-squared error (MMSE) decoding of a sequence of samples over a memory-less noisy channel. The related previous works include several maximum a posteriori (MAP) and MMSE-based decoders. The MAP-based approaches are suboptimal when the performance criterion is the MSE. On the other hand, the previously known MMSE-based approaches are suboptimal, since they are designed to efficiently reconstruct the data samples received (the prediction residues) rather than the original signal. The proposed scheme is set up by modeling the source-coder-produced symbols and their redundancy with a trellis structure. Methods are presented to optimize the solutions in terms of complexity. Numerical results and comparisons are provided, which demonstrate the effectiveness of the proposed techniques. Index Terms--Differential pulse code modulation (DPCM), forward-backward recursion, joint source-channel coding, Markov sources, maximum a posteriori (MAP) detection, minimum mean-squared error (MMSE) estimation, predictive quantization, residual redundancies, source decoding.

This paper presents new techniques to improve the performance of a fixed-rate entropy-coded trellis-coded quantizer (FE-TCQ) in transmission over a noisy channel. In this respect, we first present the optimal decoder for a fixed-rate entropy-coded vector quantizer (FEVQ). We show that the optimal decoder for the FEVQ can be a maximum likelihood decoder where a trellis structure is used to model the set of possible code words and the Viterbi algorithm is subsequently applied to select the most likely path through this trellis. In order to add quantization packing gain to the FEVQ, we take advantage of a trellis-coded quantization (TCQ) scheme. To prevent error propagation, it is necessary to use a block structure obtained through a truncation of the corresponding trellis. To perform this task in an efficient manner, we apply the idea of tail biting to the trellis structure of the underlying TCQ. It is shown that the use of a tail-biting trellis significantly reduces the required block length with respect to some other possible alternatives known for trellis truncation. This results in a smaller delay and also mitigates the effect of error propagation in signaling over a noisy channel. Finally, we present methods and numerical results for the combination of the proposed FEVQ soft decoder and a tail-biting TCQ. These results show that, by an appropriate design of the underlying components, one can obtain a substantial improvement in the overall performance of such a fixed-rate entropy-coded scheme. Index Terms--Combined source and channel coding, error propagation, fixed-rate entropy-coded vector quantization (FEVQ), trellis-coded quantization (TCQ), Viterbi algorithm.

An analytical method for approximate performance evaluation of binary linear block codes using an additive white Gaussian noise channel model with binary phase-shift keying modulation is presented. We focus on the probability density function of the bit log-likelihood ratio (LLR), which is expressed in terms of the Gram-Charlier series expansion. This expansion requires knowledge of the statistical moments of the bit LLR. We introduce an analytical method for calculating these moments. This is based on some recursive calculations involving certain weight enumerating functions of the code. It is proved that the approximation can be as accurate as desired, if we use enough terms in the Gram--Charlier series expansion. Numerical results are provided for some examples, which demonstrate close agreement with simulation results. Index Terms--Additive white Gaussian noise (AWGN) channel, binary phase-shift keying (BPSK), bit decoding, bit-error probability (BEP), block codes, log-likelihood ratio (LLR), weight distribution.

We present a method for maximum likelihood decoding of the (48, 24, 12) quadratic residue code. This method is based on projecting the code onto a subcode with an acyclic Tanner graph, and representing the set of coset leaders by a trellis diagram. This results in a two level coset decoding which can be considered a systematic generalization of the Wagner rule. We show that unlike the (24, 12, 8) Golay code, the (48, 24, 12) code does not have a Pless-construction which has been an open question in the literature. It is determined that the highest minimum distance of a (48, 24) binary code having a Pless-construction is 10, and up to equivalence there are three such codes. Index Terms--Linear block codes, maximum-likelihood decoding, trellis diagram.

A number of different iterative decoding methods are proposed for the multiuser interference cancelation in a code-division multiple-access system where turbo-codes are utilized for forward error correction (FEC). In the proposed methods, the individual users are decoded separately with the operation of iterative interference cancelation being mixed with the iterative decoding of turbo-code. This results in a modest increase in the overall complexity as compared to a conventional single-user receiver utilizing turbo-code for FEC. Numerical results are presented showing that the proposed iterative decoders show an improvement in the bit error rate performance and/or a reduction in the computational complexity as compared to similar previously known methods reported in [2] and [3]. Index Terms--Code-division multiple access (CDMA), iterative decoding, multiunder interference cancellation.

The best-known achievable rate region for the two-user Gaussian interference channel corresponds to the Han-Kobayashi scheme. However, mathematical expressions that characterize the Han-Kobayashi rate region are complicated. This complexity hinders a comprehensive understanding of the rate region. For instance, when interference is weak, the maximum achievable sum-rate of the Han-Kobayashi scheme has been unknown. This paper studies the sum-rate of the Han-Kobayashi scheme with Gaussian inputs and fully characterizes the maximum achievable sum-rate, when no time sharing is used. The optimal power-splitting variables and the corresponding maximum achievable sum-rate are explicitly expressed in closed forms. With the same approach, the maximum weighted sum-rate is expressed that characterizes the boundary of the Han-Kobayashi region without time sharing. Moreover, when time sharing is used, the boundary is expressed in terms of the upper concave envelope of a function of transmitters’ powers. [ABSTRACT FROM AUTHOR]

Most coding schemes proposed for the interference channel take advantage of joint decoding to enlarge rate region. However, decoding complexity escalates considerably when joint decoding is used. This paper studies the achievable sum-rate of the two-user Gaussian interference channel when joint decoding is replaced by successive decoding. First, the strong interference class is examined, and it is proved that if transmitters’ powers satisfy certain conditions, successive decoding is optimal and achieves the sum-capacity. The number of the required splits, the amount of power allocated to each split, and the order of decoding at receivers are explicitly determined. Second, the weak interference class is examined. A novel rate-splitting scheme is proposed that does not use joint decoding. The number of required splits and the amount of power allocated to each split are expressed in closed forms. It is shown that, for a wide range of transmitters’ powers, this scheme achieves the sum-rate of the Gaussian Han-Kobayashi scheme. Moreover, it is proved that the difference between the sum-rate of this scheme and that of the Gaussian Han-Kobayashi scheme is bounded, for all values of transmitters’ powers. [ABSTRACT FROM AUTHOR]

In this paper, capacity bounds for the two-user interference channels with cooperative receivers via conferencing links of finite capacities are investigated. Capacity results known for these communication scenarios are limited to a very few special cases of the one-sided channels. One of the challenges in obtaining capacity limits of such cooperative networks is how to establish efficient capacity outer bounds for them. In this paper, by applying new techniques, novel capacity outer bounds are established for the interference channels with conferencing receivers. Using the outer bounds, several new capacity results are proved for interesting channels with unidirectional cooperation in strong and mixed interference regimes. A conferencing link (between receivers) may be utilized to provide a receiver with “information about its desired signal” or “information about its interfering signal”. It is demonstrated that both strategies can be helpful to achieve capacity. Finally, for the case of Gaussian interference channel with conferencing receivers, it is argued that our outer bound is strictly tighter than the previous one derived by Wang and Tse. [ABSTRACT FROM AUTHOR]

Delay, guaranteeing causality, is inevitable in cooperative communication systems. Traditionally, delay granularity has been limited to one symbol; however, channel delay is in fact governed by channel memory and can be shorter. For example, the delay requirement in orthogonal frequency-division multiplexing, captured in the cyclic prefix, is typically much shorter than the symbol itself. This perspective is used to study the two-user Gaussian interference channel with full-duplex transmitters. By superimposing the signal from the other node onto its own signal, each transmitter cancels the interference at its receiver. Among other results, it is proved that under a mild condition, the maximum multiplexing gain of this channel is in fact two, rather than the limit of one, previously shown under the traditional constraint of causal delay. Further, the optimal power allocation among orthogonal sub-carriers, which maximizes the achievable sum-rate, is shown to be a generalization of the well-known water filling. Simulation results are included to demonstrate the improvement in the achievable sum-rate when full-duplex transmitters are used. [ABSTRACT FROM AUTHOR]