Carrson C. Fung

Estimation of Two-Dimensional Frequencies Using Modified Matrix Pencil Method

The problem of multiple two-dimensional (2-D) sinusoidal frequency estimation is considered. A modified matrix pencil method is proposed to simultaneously estimate the frequency pairs in the signal, thereby bypassing the computationally expensive pairing operation as seen in the literature. Simulation results show that the accuracy of the estimates for each frequency from our technique is better than or comparable to that of existing methods and the variance of the estimates are close to the Crameacuter-Rao bound. Simulation results also show that our method provides accurate and consistent frequency estimation results that other methods cannot provide with less or comparable computational complexity

Robust Training Sequence Design for Spatially Correlated MIMO Channel Estimation

A robust superimposed training sequence design is proposed for spatially correlated multiple-input-multiple-output (MIMO) channel estimation. The proposed scheme does not require accurate knowledge of the spatial correlation matrix, and it is shown to outperform previously proposed robust correlated MIMO channel estimators, such as relaxed minimum mean square error (RMMSE) and least-square RMMSE. Since the training sequence is overlaid into the data stream, the spectral efficiency of the system is higher than those that use time-multiplexed pilots. A solution for the sequence can easily be obtained by using a projection on convex-set-based iterative algorithm that is guaranteed to converge as long as the training sequence matrix is initialized to have full rank. Furthermore, it is shown that the proposed scheme is identical to the RMMSE-based schemes when the MIMO channel is spatially uncorrelated. The computational complexity of the proposed algorithm is also illustrated.

Estimation of 2-dimensional frequencies using modified matrix pencil method

A modified matrix pencil method is proposed to simultaneously estimate the frequency pairs in multiple 2-D sinusoidal signals to bypass the computationally expensive pairing operation as seen in the literature. Simulation results show that the accuracy of the estimates for each frequency from our technique is better than or comparable to that of existing methods and the variance of the estimates are close to the Cramer Rao bound with lower computational complexity.

Robust interference channel transmission using sparsity enhanced mismatch model

Herein a maximin-SNR robust precoder design is proposed for MIMO cognitive radio transmission when inaccurate channel estimate is present. The proposed scheme exploits the structural property of the statistical transmit covariance matrix to increase the degree of freedom in the mismatch model related to the channel matrix. Simulation results show that the proposed scheme outperforms previously proposed methods in terms of SER under certain conditions. The performance gain is obtained at the cost of increased interference level toward the primary user, but without violating the average interference power constraint. Analytical and simulation results are given to support the efficacy of the proposed scheme. Furthermore, analysis is carried out to differentiate the performance given by the lower bound method and S-Procedure method.

Interference Suppression for OFDM Systems With Insufficient Guard Interval Using Null Subcarriers

Herein proposed is a new frequency domain equalizer (FEQ) to suppress channel induced interference such as ICI and ISI, co-channel interference, and overlaid systems interference. Unlike earlier schemes, this proposed algorithm requires no temporal oversampling nor the use of more than one receive antenna. All the above is achieved by exploiting null subcarriers (a.k.a. virtual/unused/unmodulated subcarriers) inherent in standard multicarrier systems, and by a generalized sidelobe cancellation (GSC) like scheme. This proposed method can offer superior bit error rate over earlier methods as well as simpler computations over another GSC-like scheme.

Sparsity Enhanced Mismatch Model for Robust Spatial Intercell Interference Cancelation in Heterogeneous Networks

Performance of precoder-based spatial intercell interference cancelation in heterogeneous networks is often hampered due to lack of accurate channel state information. Performance can be augmented by modifying the design of the precoder to incorporate the channel estimate mismatch by using deterministic and probabilistic mismatch models. Previously proposed models either have been deemed too conservative (deterministic) or are prone to error due to inaccuracy in the probability distribution function and corresponding parameters (stochastic). A new deterministic mismatch model is proposed herein in an attempt to alleviate these problems. Different from all previously proposed deterministic models, the proposed model, called sparsity enhanced mismatch model (SEMM), exploits the inherent sparse characteristics of MIMO interference channels. The SEMM has two variants, i.e., SEMM (angular) and SEMM (eigenmode). The SEMM incorporates a basis expansion model to bring forth the inherent sparsity, which exists in MIMO interference channels. In the context of precoder design for heterogeneous network, it is analytically shown, and by simulation, the proposed mismatch models enable the aggressor-transmitter (A-Tx) to allocate more transmission power to the sparse elements of the interfering link so that performance in the communicating link is enhanced compared with conventional norm ball mismatch model.

Robust Training Sequence Design for Spatially Correlated MIMO Channel Estimation Using Affine Precoder

A robust superimposed training sequence design is proposed for spatially correlated MIMO channel estimation. The proposed scheme does not require accurate knowledge about the spatial correlation matrix and it is shown to outperform robust correlated MIMO channel estimators such as relaxed MMSE (RMMSE) and least-squares-RMMSE (LSRMMSE). Since the training sequence is overlaid into the data stream, spectral efficiency of the system is higher than those that use time-multiplex pilots. A solution for the sequence can be obtained easily by using an iterative algorithm which is guaranteed to converge as long as the training sequence matrix is initialized to have full rank.

Packetized video transmission for OFDM wireless systems with dynamic ordered subcarrier selection algorithm

In this paper, we proposed a dynamic ordered subcarrier selection algorithm (DOSSA) for OFDM based video transmission system. The proposed scheme is shown to achieve lower bit error rate (BER) than the previously proposed OSSA by first selecting a fraction of the subcarriers with highest channel gain. The content information is then exploited in order to extend the OSSA to achieve unequal error protection (UEP) for packets of different importance. Simulation results show that system that utilizes the proposed scheme can achieve higher PSNR, especially at low SNR, compared to those that use the equal error protection (EEP) OSSA.

Sparsity Enhanced Mismatch Model for Robust Intercell Interference Management in Heterogeneous Networks With Doubly-Selective Fading Channels

Transmission over doubly-selective fading (DSF) interference channel often relies on the use of robust precoder due to a lack of accurate channel state information, with performance often depending on the conservativeness of the mismatch model. Previously proposed mismatch models either have been deemed too conservative (deterministic models) or are prone to error due to inaccuracy in the probability density function (pdf) and corresponding parameters (stochastic models). A deterministic mismatch model called Sparsity Enhanced Mismatch Model - Reverse discrete prolate spheroidal sequence, or SEMMR, is proposed herein in an attempt to alleviate this problem. Different from all previously deterministic models, the proposed model exploits the inherent sparse characteristics of DSF interference channels which lead to a two-stage robust transceiver design that outperforms precoding only strategy incorporating conventional norm ball mismatch model (NBMM). The inherent sparsity in the channel is brought forth by modeling the channel using a basis expansion model (BEM) where discrete prolate spheroidal sequence (DPSS) is used as a basis. Analytical and simulation results are provided to validate the performance gains of the SEMMR transceiver over the NBMM precoder.

Robust Transmission for Heterogeneous Networks with Cognitive Small Cells

Transmission over flat-fading multiple-input and multiple-output (MIMO) and doubly selective fading single-input and single-output (SISO) interference channels often relies on the use of a robust precoder due to a lack of accurate channel state information, with performance often depending on the conservativeness of the mismatch model. In the literature, cognitive small cells using such a transmission scheme have been proposed for use in heterogeneous networks, which can provide increased cell density with minimum effect from interference. Cognitive sensing and transmission are keys in enabling cognitive small cells. Inaccuracy in sensing can lead to performance degradation during transmission. An overview about sensing technologies will be briefly provided in this chapter. An extensive exposition of robust transmission techniques will then follow, which utilize a new channel mismatch model called sparsity- enhanced mismatch model (SEMM). Previously proposed mismatch models either have been deemed too conservative (deterministic models) or are prone to error due to inaccuracy in the probability density function (pdf) and corresponding parameters (stochastic models). The SEMM, and its derivative SEMM-Reverse discrete prolate spheroidal sequence (SEMMR), will be shown herein in an attempt to alleviate this problem. Different from all previously proposed deterministic models, the SEMM and SEMMR exploit the inherent sparse characteristics of MIMO and doubly selective fading interference channels that lead to the SEMM precoder and SEMMR transceiver, which outperform previously proposed precoding’s only strategy of incorporating the conventional norm ball mismatch model (NBMM).