Defeng David Huang

A Concise Representation for the Soft-in Soft-out LMMSE Detector

An alternative simple derivation for the well-known soft-in soft-out LMMSE (linear minimum mean square error) detector in a turbo system is presented. The derivation leads to a concise representation for the LMMSE detector in terms of the extrinsic means and extrinsic variances of the data symbols, and provides new perspectives for its implementation.

Expectation propagation approach to joint channel estimation and decoding for OFDM systems

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K|A| + K log2 K), where |A| denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the known-channel bound.

Linear Combining of Nonlinear Preprocessors for OFDM-Based Power-Line Communications

Nonlinear preprocessors, including conventional clipping, blanking, joint blanking/clipping, and deep clipping, have been employed to mitigate the impulsive noise in orthogonal frequency division multiplexing-based power-line communications. Those nonlinear preprocessors are characterized by one or two thresholds, which are optimized to achieve an optimum output signal-to-noise ratio (SNR). In this paper, we aim to further improve the output SNR by linearly combining two nonlinear preprocessors. Both analytical and simulation results show that the proposed method yields better output SNR and symbol/bit error rate performance than the individual ones.

Spectrum Sensing Using Weighted Covariance Matrix in Rayleigh Fading Channels

Covariance-based detection is a low-complexity blind spectrum sensing scheme that exploits spatial and/or temporal correlations of primary signals. However, its performance severely degrades with the decrease of signal correlations. In this work, a weighted-covariance-based detector is proposed by introducing data-aided weights to the covariance matrix. The false alarm probability, decision threshold, and detection probability are analyzed in the low signal-to-noise ratio (SNR) regime, and their approximate analytical expressions are derived based on the central limit theorem. The analyses are verified through simulations. Experiments with simulated multiple-antenna signals and field measurement digital television signals show that the proposed weighted detection can significantly outperform the original covariance-based detection.

Throughput Analysis of Opportunistic Feedback for Downlink Multiuser Diversity with Capture Effect

An opportunistic feedback protocol has been proposed in the literature to achieve multiuser diversity for the downlink transmission of a multiuser wireless system. In this letter, through theoretical analysis and extensive simulations, it is shown that the capture effect can have a significant impact on system throughput. It is also shown that, to achieve optimal throughput, the number of minislots used for opportunistically finding a user in the protocol should not be too large due to the overhead induced by the control packets.

Low Complexity Iterative MMSE-PIC Detection for Medium-Size Massive MIMO

In medium-size massive MIMO systems, the minimum mean-square-error parallel interference cancellation (MMSE-PIC)-based soft-input soft-output (SISO) detector is often used due to its relatively low complexity and good bit error rate (BER) performance. The computational complexity of MMSE-PIC for detecting a block of data is dominated by the computation of a Gram matrix and a matrix inversion. They have computational complexity of O(K2M) and O(K3), respectively, where K is the number of uplink users with one transmit antenna each and M is the number of receive antennas at the base station. In this letter, by using an L (typically L ≤ 3) terms of Neumann series expansion to approximate the matrix inversion, we reduce the total computational complexity to O(LK M). Compared with alternative algorithms, which focus on reducing the complexity of the matrix inversion only, the proposed method can also avoid calculating the Gram matrix explicitly and thus significantly reducing the total complexity.

Expectation propagation based iterative group wise detection for large-scale multiuser MIMO-OFDM systems

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms.

Soft-In Soft-Out Detection Using Partial Gaussian Approximation

This paper concerns the soft-in soft-out detection in a coded communication system, where the transmitted symbols are discrete valued, and the exact a posteriori probability (APP) detection often involves prohibitive complexity. By using the properties of Gaussian functions, an approximate approach to the APP detection is devised with the idea that, in the computation of the APP of each symbol, the remaining symbols are distinguished based on their contributions to the APP of the concerned symbol, and the symbols with less contributions are approximated as (continuous) Gaussian variables [hence the name partial Gaussian approximation (PGA)] to reduce the computational complexity. The connection between the PGA detector and the reduced dimension maximum a posteriori detector (RDMAP) is investigated. It is shown that, PGA is equivalent to RDMAP, but it has a complexity much lower than that of RDMAP, i.e., PGA can be regarded as an efficient implementation of RDMAP. In addition, the application of PGA in intersymbol interference (ISI) channel equalization is also investigated. We show that PGA allows further significant complexity reduction by exploiting the circulant structure of the system transfer matrix, which makes PGA very attractive in handling severe ISI channels with large memory length.

Reweighted smoothed l0-norm based DOA estimation for MIMO radar

The DOA estimation problem for monostatic MIMO radar is considered.A reweighted smoothed l0-norm minimization framework with a reweighted continuous function is designed for DOA estimation.The proposed method is about two orders of magnitude faster than conventional l1-norm minimization based DOA algorithms.The proposed method provides better angle estimation performance than l1-SVD, reweighted l1-SVD, RV l1-SRACV, RD-Capon and RD-ESPRIT algorithms. In this paper, a reweighted smoothed l0-norm algorithm is proposed for direction-of-arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar. The proposed method firstly performs the vectorization operation on the covariance matrix, which is calculated from the latest received data matrix obtained by a reduced dimensional transformation. Then a weighted matrix is introduced to transform the covariance estimation errors into a Gaussian white vector, and the proposed method further constructs the other reweighted vector to enhance sparse solution. Finally, a reweighted smoothed l0-norm minimization framework with a reweighted continuous function is designed, based on which the sparse solution is obtained by using a decreasing parameter sequence and the steepest ascent algorithm. Consequently, DOA estimation is accomplished by searching the spectrum of the solution. Compared with the conventional l1-norm minimization based methods, the proposed reweighted smoothed l0-norm algorithm significantly reduces the computation time of DOA estimation. The proposed method is about two orders of magnitude faster than the l1-SVD, reweighted l1-SVD and RV l1-SRACV algorithms. Meanwhile, it provides higher spatial angular resolution and better angle estimation performance. Simulation results are used to verify the effectiveness and advantages of the proposed method.

A Factor Graph Approach to Exploiting Cyclic Prefix for Equalization in OFDM Systems

In OFDM systems, cyclic prefix (CP) insertion and removal enables the use of a set of computationally efficient single-tap equalizers at the receiver. Due to the extra transmission time and energy, the CP causes a loss in both spectrum efficiency and power efficiency. On the other hand, as a repetition of part of the data, the CP brings extra information and can be exploited for detection. Therefore, instead of discarding the CP observation as in the conventional OFDM system, we utilize all the received signals in a soft-input soft-output equalizer of a turbo equalization OFDM system. First, the models for both the CP part and the non-CP part of observation are presented in a Forney-style factor graph (FFG). Then based on the computation rules of the FFG and the Gaussian message passing (GMP) technique, we develop an equalization algorithm. With proper approximation, the complexity of the proposed algorithm is reduced to \changedmathcal O(2RNlog2N+4RGlog2G+2RG) per data block for R iterations, where N is the length of the data block and G is equal to P+L-1 with P the length of the CP and L the maximum delay spread of the channel. To justify the performance improvement, SNR analysis is provided. Simulation results show that the proposed approach achieves a significant gain over the conventional approach and the turbo equalization system converges within two iterations.