Tianbin Wo

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—Superposition mapping (SM) is a modulation technique which converts bit tuples into symbols via linear super-position instead of bijective mapping. Since the resulting signal is often Gaussian-like, it has a good theoretical potential to approach the capacity of Gaussian channels. This paper discusses available SM schemes in a systematic manner, with(More)
— Iterative channel estimation and data detection is a very 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(More)
— 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(More)
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(More)
— 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(More)
This paper deals with joint data detection and channel estimation for frequency-selective multiple-input multiple-output (MIMO) systems with focus on the analysis of the channel estimator. First, we present a scheme alternating between joint Viterbi detection and least squares channel estimation and analyze its performance in terms of unbiasedness. Since in(More)
— The task of channel estimation in a frequency-selective multi-input multi-output (MIMO) system presents a real challenge for conventional training-based solutions, since the huge number of channel coefficients demand a huge number of training symbols, which will significantly reduce the system bandwidth-efficiency. As a possible countermeasure, we propose(More)
Conventionally, the uncertainties of channel coefficients are neglected, that is the estimated values of channel coefficients are taken as the true values in the stage of data detection. In the communications community, it is still an open question how to take into account the channel uncertainty for data detection/decoding, especially in a low-complexity(More)