Tianbin Wo

Superposition modulation: myths and facts

Traditionally, digital modulation schemes are bijective (i.e., the signal constellation points are disjunct), and the mapping is unique. Only recently it has been discovered that non-bijective modulation schemes may outperform bijective modulation schemes when employed in conjunction with suitable channel coding and iterative processing. In this in-depth tutorial, we clarify myths and facts about non-bijective modulation. Emphasis is on superposition modulation (SM). Without active signal shaping, SM outperforms bit-interleaved coded modulation with PSK or square QAM modulation at even lower receiver complexity.

Performance Analysis of Maximum-Likelihood Semiblind Estimation of MIMO Channels

Iterative channel estimation and data detection is a useful method to improve the channel estimation quality without sacrificing the bandwidth efficiency. Since both the known training symbols (non-blind) and the unknown data symbols (blind) are used for channel estimation, corresponding techniques are referred to as semiblind. If the channel estimator and data detector are both optimal in the sense of maximum-likelihood criterion, we may call the algorithm as maximum-likelihood (ML) semiblind channel estimation (SBCE). This paper deals with ML-SBCE for frequency-flat multi-input multi-output systems with focus on the channel estimation mean squared error (MSE) analysis. Through semi-analytical efforts, we showed that ML-SBCE is biased at low SNR and tends to be unbiased at high SNR. The reasons of biasing are the erroneous data detection and the correlation between the noise and the detection errors. Besides, we showed that the MSE performance of ML-SBCE is also influenced by the noise-error correlation. Based on these analyses, possibilities to compensate the biasing as well as improve the MSE performance is pointed out

Graph-Based Soft Channel and Data Estimation for MIMO Systems with Asymmetric LDPC Codes

In this paper, we propose an iterative soft channel estimation and data detection algorithm based on a factor graph. Channel coefficients as well as data symbols are treated as variable nodes and are all estimated in a low-complexity element-wise manner. Applying asymmetric LDPC codes, this algorithm is able to deliver ambiguity-free outputs for MIMO systems with or without training symbols. Training symbols are inherently utilized as a type of a priori information. This algorithm thoroughly relaxes the troublesome constraints on training design in the sense that an arbitrary (even zero) number of training symbols can be placed at arbitrary positions within a data burst.

A Simple Iterative Gaussian Detector for Severely Delay-Spread MIMO Channels

In this paper, a low-complexity high-performance detection algorithm for multiple input multiple output (MIMO) channels with severe delay spread is proposed. This algorithm performs iterative data detection over factor graphs which apply an independence approximation as well as a Gaussian approximation. It is shown that this algorithm achieves a near-optimum BER performance for coded MIMO systems with severe delay spread. The computational complexity of this algorithm is strictly linear in the number of transmit antennas, the number of receive antennas, and the number of non-zero channel coefficients.

A Graph-Based Iterative Gaussian Detector for Frequency-Selective MIMO Channels

In this paper, we propose a high-performance low-complexity data detector for frequency-selective multi-input multi-output (MIMO) channels. This detector applies the principles of factor graph and Gaussian approximation in modeling interference. Compared to the available algorithms based on Gaussian approximation, the proposed detector goes one step further by applying an independence approximation, which reduces the complexity significantly while incurring only a marginal performance loss. The amount of operations needed is polynomial in the number of receive antennas, the number of transmit antennas, and the channel memory length

A universal coding approach for superposition mapping

Superposition mapping (SM) is a modulation technique that uses linear superposition to produce Gaussian-like data symbols. Communication systems employing SM have a theoretical potential to approach the Gaussian channel capacity without using active signal shaping. This paper tackles several critical issues of SM and provides corresponding solutions. We point out that repetition coding is often an indispensable part for SM systems, a topic which has been obscured for a long time. More importantly, the bandwidth efficiency limit of SM systems with equal power allocation of about 2 bits per symbol per dimension can be eliminated by using irregular repetition codes. Following this cognition, we propose a universal coding approach, called low-density hybrid-check (LDHC) coding, for SM systems with arbitrary power allocation.

Iterative processing for superposition mapping

Superposition mapping (SM) is a modulation technique which loads bit tuples onto data symbols simply via linear superposition. Since the resulting data symbols are often Gaussian-like, SM has a good theoretical potential to approach the capacity of Gaussian channels. On the other hand, the symbol constellation is typically nonbijective and its characteristic is very different from that of conventional mapping schemes like QAM or PSK. As a result, its behavior is also quite different from conventional mapping schemes, particularly when applied in the framework of bit-interleaved coded modulation. In this paper, a comprehensive analysis is provided for SM, with particular focus on aspects related to iterative processing.

Low-complexity Gaussian detection for MIMO systems

For single-carrier transmission over delay-spread multi-input multi-output (MIMO) channels, the computational complexity of the receiver is often considered as a bottleneck with respect to (w.r.t.) practical implementations. Multi-antenna interference (MAI) together with intersymbol interference (ISI) provides fundamental challenges for efficient and reliable data detection. In this paper, we carry out a systematic study on the interference structure of MIMO-ISI channels, and sequentially deduce three different Gaussian approximations to simplify the calculation of the global likelihood function. Using factor graphs as a general framework and applying the Gaussian approximation, three low-complexity iterative detection algorithms are derived, and their performances are compared by means of Monte Carlo simulations. After a careful inspection of their merits and demerits, we propose a graph-based iterative Gaussian detector (GIGD) for severely delay-spread MIMO channels. The GIGD is characterized by a strictly linear computational complexity w.r.t. the effective channel memory length, the number of transmit antennas, and the number of receive antennas. When the channel has a sparse ISI structure, the complexity of the GIGD is strictly proportional to the number of nonzero channel taps. Finally, the GIGD provides a near-optimum performance in terms of the bit error rate (BER) for repetition encoded MIMO systems.

Graph-Based Soft Channel Estimation for Fast Fading Channels

In this paper, a graph-based soft iterative receiver (GSIR) for fast fading channels is investigated. Soft channel estimation as well as soft-output data detection are jointly accomplished via Bayesian inference over a general factor graph. The key feature is a transfer node which enables information flow from one channel node to adjacent channel nodes. The performance characteristics of this receiver is investigated via an EXIT chart analysis and simulation results. Particular emphasis is on a proper channel code design. The algorithm is universally applicable to arbitrary (bijective and non-bijective) modulation formats and can easily be extended to multi-dimensional channel estimation.

Graph-based soft iterative receiver for higher-order modulation

In this paper, we propose a graph-based soft iterative receiver (GSIR) for higher-order modulation. The performance characteristics of this kind of receiver is investigated via an EXIT chart analysis. Interpolation helps to provide reliable channel estimates for the whole data burst at the initialization stage. Additionally, our proposed receiver algorithm is compared with various interpolation-based initialization methods by Monte-Carlo simulations.