Achilleas Anastasopoulos

Likelihood ratio tests for modulation classification

We discuss modulation classification (MC) algorithms that are based on the decision theoretic approach, where the MC problem is viewed as a multiple-hypothesis testing problem. In particular, a random-phase AWGN channel is considered and possible solutions to this hypothesis testing problem are reviewed. We present two novel algorithms and we compare their performance with existing ones for a variety of modulation pairs. Simulation results show that these new algorithms can offer a significant performance gain for classification of dense, non-constant envelope constellations.

Adaptive soft-input soft-output algorithms for iterative detection with parametric uncertainty

The soft-input soft-output (SISO) module is the basic building block for established iterative detection (ID) algorithms for a system consisting of a network of finite state machines. The problem of performing ID for systems having parametric uncertainty has received relatively little attention in the open literature. Previously proposed adaptive SISO (A-SISO) algorithms are either based on an oversimplified channel model, or have a complexity that grows exponentially with the observation length N (or the smoothing lag D). In this paper, the exact expressions for the soft metrics in the presence of parametric uncertainty modeled as a Gauss-Markov process are derived in a novel way that enables the decoupling of complexity and observation length. Starting from these expressions, a family of suboptimal (practical) algorithms is motivated, based on forward/backward adaptive processing with linear complexity in N. Previously proposed A-SISO algorithms, as well as existing adaptive hard decision algorithms are interpreted as special cases within this framework. Using a representative application-joint iterative equalization-decoding for trellis-based codes over frequency-selective channels-several design options are compared and the impact of parametric uncertainty on previously established results for ID with perfect channel state information is assessed.

Adaptive iterative detection for phase tracking in turbo-coded systems

The problem of performing iterative detection (ID)-a technique originally introduced for the decoding of turbo codes-for systems having parametric uncertainty has received relatively little attention in the open literature. In this paper, the problem of adaptive ID (AID) for serial and parallel concatenated convolutional codes (SCCCs and PCCCs or turbo codes) in the presence of carrier-phase uncertainty is examined. Based on the theoretical framework of Anastasopoulos and Chugg, (see Proc. Int. Conf. Communications, p.177-181, 1999). and Colavolpe, Ferrari and Raheli (see IEEE Trans. Commun., vol.48, p.1488-98, 2000), adaptive soft inverse (ASI) algorithms are developed for two commonly used blocks in turbo codes, leading to the adaptive soft-input soft-output (A-SISO) and the adaptive soft demodulator (A-SODEM) algorithms. Based on these algorithms, practical AID receivers are presented. Several design options are proposed and compared and the impact of parametric uncertainty on previously established results for iterative detection with perfect channel state information (CSI) is assessed.

A comparison between the sum-product and the min-sum iterative detection algorithms based on density evolution

Recently, density evolution techniques have been used to predict the performance of iterative decoders utilizing the sum-product belief propagation algorithm. We extend this analysis to the min-sum algorithm for binary codes. Using two representative applications, i.e., low-density parity-check (LDPC) codes and repeat accumulate (RA) codes, the sum-product and min-sum algorithms are compared. The results demonstrate a performance degradation of 0.27-1.03 dB for the min-sum algorithm, which confirms earlier simulation results. However, it is shown that a small modification to the min-sum algorithm results in an approximate sum-product algorithm, which performs at least as well as the original sum-product algorithm when finite message precision is considered.

Polynomial-complexity noncoherent symbol-by-symbol detection with application to adaptive iterative decoding of turbo-like codes

The problem of generating symbol-by-symbol soft decision metrics (SbSSDMs) in the presence of unknown channel parameters is considered. The motivation for this work lies in its application to iterative decoding of high-performance turbo-like codes, transmitted over channels that introduce unknown parameters in addition to Gaussian noise. Traditional methods for the exact evaluation of SbSSDMs involve exponential complexity in the sequence length. A class of problems is identified for which the SbSSDMs can be exactly evaluated with only polynomial complexity with respect to the sequence length. Utilizing the close connection between symbol-by-symbol and sequence detection, it is also shown that for the aforementioned class of problems, detection of an uncoded data sequence in the presence of unknown parameters can be performed with polynomial complexity. The applicability of this technique is demonstrated by considering the problem of iterative detection of low-density parity-check codes in the presence of unknown and time-varying carrier-phase offset. Finally, based on the proposed exact schemes, an ultra-fast approximate algorithm for performing joint iterative decoding and phase estimation is derived that is well suited for hardware implementation.

Capacity Achieving LDPC Codes Through Puncturing

The performance of punctured LDPC codes under maximum-likelihood (ML) decoding is studied in this paper via deriving and analyzing their average weight distributions (AWDs) and the corresponding asymptotic growth rate of the AWDs. In particular, we prove that capacity-achieving codes of any rate and for any memoryless binary-input output-symmetric (MBIOS) channel under ML decoding can be constructed by puncturing some original LDPC codes with small enough rate. Moreover, we prove that the gap to capacity of all the punctured codes can he the same as the original codes with a small enough rate. Conditions under which puncturing results in no rate loss with asymptotically high probability are also given in the process. These results show high potential for puncturing to be used in designing capacity-achieving codes, and also be used in rate-compatible coding under any MBIOS channel.

Pilot-symbol-assisted coded transmission over the block-noncoherent AWGN channel

In this paper, pilot-symbol-assisted transmission in conjunction with high-performance coding over the block-independent noncoherent additive white Gaussian noise channel is investigated. Several approximate iterative receivers are proposed, which either perform carrier-phase estimation separately from detection, or joint carrier-phase estimation/decoding in an iterative fashion. The performance of the proposed receivers is analyzed using density evolution. The power allocation to the pilot symbol is quantified, and it is shown that an optimal allocation scheme exists that minimizes the overall information bit signal-to-noise ratio required for error-free communication. This optimal power allocation, which could be utilized in code design, is found to be sensitive to the channel coherence interval, as well as to the particular receiver used. In addition, a simple upper bound on the performance of any receiver that performs joint iterative carrier-phase estimation and detection, is derived. The obtained results are compared with the simulated performance of the proposed receivers.

Optimal Joint Detection/Estimation in Fading Channels With Polynomial Complexity

The problem of sequence detection in frequency-nonselective/time-selective fading channels, when channel state information (CSI) is not available at the transmitter and receiver, is considered in this paper. The traditional belief is that exact maximum-likelihood sequence detection (MLSD) of an uncoded sequence over this channel has exponential complexity in the channel coherence time. Thus, for slowly varying channels, i.e., channels having coherence time on the order of the sequence length, the complexity appears to be exponential in the sequence length. In the first part of this work, it is shown that exact MLSD can be computed with only polynomial worst case complexity in the sequence length regardless of the operating signal-to-noise ratio (SNR) for equal-energy signal constellations. By establishing a relationship between the aforementioned complexity and the rank of the correlation matrix of the fading process, an understanding of how complexity of the optimal MLSD receiver varies as the channel dynamics change is provided. In the second part of this paper, the problem of decoding turbo-like codes in frequency-nonselective/time-selective fading channels without receiver CSI is examined. Using arguments similar to the ones used for the MLSD case, it is shown that the exact symbol-by-symbol soft-decision metrics (SbSSDMs) implied by the min-sum algorithm can be evaluated with polynomial worst case complexity in the sequence length regardless of SNR for equal-energy signal constellations. Finally, by simplifying some key steps in the polynomial-complexity algorithm, a family of fast, approximate algorithms is derived, which yield near-optimal performance

Iterative Detection for Channels With Memory

In this paper, we present an overview on the design of algorithms for iterative detection over channels with memory. The starting point for all the algorithms is the implementation of soft-input soft-ouput maximum a posteriori (MAP) symbol detection strategies for transmissions over channels encompassing unknown parameters, either stochastic or deterministic. The proposed solutions represent effective ways to reach this goal. The described algorithms are grouped into three categories: i) we first introduce algorithms for adaptive iterative detection, where the unknown channel parameters are explicitly estimated; ii) then, we consider finite-memory iterative detection algorithms, based on ad hoc truncation of the channel memory and often interpretable as based on an implicit estimation of the channel parameters; and iii) finally, we present a general detection-theoretic approach to derive optimal detection algorithms with polynomial complexity. A few illustrative numerical results are also presented.

Iterative equalization/decoding of TCM for frequency-selective fading channels

The severity of frequency-selective fading channels necessitates the combining of multiple diversity sources to achieve acceptable performance. Traditional techniques often perform the combining of different sources of diversity separately which may result in a significant performance degradation (uncoded systems, for example, outperform TCM codes when used in an interleaved frequency-selective fading channel with separate decoding and equalization). It was demonstrated that soft decision equalization techniques are necessary and sufficient for the application of TCM techniques over such channels. This enabling feature was obtained with relatively simple, low-latency, non-iterative algorithms. We investigate the applicability of more complex iterative equalization/decoding algorithms. Several configurations are examined using simulation and further improvements are demonstrated.