Wei-Ping Zhu

Relay-Selection Improves the Security-Reliability Trade-Off in Cognitive Radio Systems

We consider a cognitive radio (CR) network consisting of a secondary transmitter (ST), a secondary destination (SD) and multiple secondary relays (SRs) in the presence of an eavesdropper, where the ST transmits to the SD with the assistance of SRs, while the eavesdropper attempts to intercept the secondary transmission. We rely on careful relay selection for protecting the ST-SD transmission against the eavesdropper with the aid of both single-relay and multi-relay selection. To be specific, only the “best” SR is chosen in the single-relay selection for assisting the secondary transmission, whereas the multi-relay selection invokes multiple SRs for simultaneously forwarding the STs transmission to the SD. We analyze both the intercept probability and outage probability of the proposed single-relay and multi-relay selection schemes for the secondary transmission relying on realistic spectrum sensing. We also evaluate the performance of classic direct transmission and artificial noise based methods for the purpose of comparison with the proposed relay selection schemes. It is shown that as the intercept probability requirement is relaxed, the outage performance of the direct transmission, the artificial noise based and the relay selection schemes improves, and vice versa. This implies a trade-off between the security and reliability of the secondary transmission in the presence of eavesdropping attacks, which is referred to as the security-reliability trade-off (SRT). Furthermore, we demonstrate that the SRTs of the single-relay and multi-relay selection schemes are generally better than that of classic direct transmission, explicitly demonstrating the advantage of the proposed relay selection in terms of protecting the secondary transmissions against eavesdropping attacks. Moreover, as the number of SRs increases, the SRTs of the proposed single-relay and multi-relay selection approaches significantly improve. Finally, our numerical results show that as expected, the multi-relay selection scheme achieves a better SRT performance than the single-relay selection.

A Semiblind Channel Estimation Approach for MIMO–OFDM Systems

In this paper, a very efficient semiblind approach that uses a training-based least square criterion along with a blind constraint is proposed for multiple-input-multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) channel estimation. The blind constraint is derived from the linear prediction of the received MIMO-OFDM signal and is used in conjunction with a weighting factor in the semiblind cost function. An appealing scheme for the determination of the weighting factor is presented as a part of the proposed approach. A perturbation analysis of the proposed method is conducted to justify the superiority of the semiblind solution and to obtain a closed-form expression for the mean square error (MSE) of the blind constraint, further facilitating the calculation of the weighting factor. The proposed method is validated through computer simulation-based experimentations, showing a very high estimation accuracy of the semiblind solution in terms of the MSE of the channel estimate.

A Joint Source and Relay Power Allocation Scheme for a Class of MIMO Relay Systems

A joint power allocation (PA) scheme for a class of MIMO relay systems is presented in this paper. Based on the maximization of the capacity or minimization of the mean-square error (MSE), two joint PA optimization problems are first formulated. Since the cost functions thus obtained are in general not convex, a tight lower bound of the capacity and a similar upper bound of the MSE are derived, and employed to modify the two cost functions so as to obtain a convex optimization problem. It is confirmed through computer simulations that the proposed PA scheme outperforms the existing method in terms of both the capacity and the MSE of MIMO relay systems.

Semiblind Sparse Channel Estimation for MIMO-OFDM Systems

In this paper, a semiblind algorithm is presented for the estimation of sparse multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) channels. An analysis of the second-order statistics of the signal that was received through a sparse MIMO channel is first conducted, showing that the correlation matrices of the received signal can be expressed in terms of the most significant taps (MSTs) of the sparse channel. This relationship is used to derive a blind constraint for the effective channel vector that corresponds to the MST position. The blind constraint is then combined with the training-based least square criterion to develop a semiblind approach for the estimation of MSTs of the sparse channel. A signal perturbation analysis of the proposed approach is conducted, showing that the new semiblind solution is not subject to the signal perturbation error when the sparse channel is a decimated version of a full finite impulse response channel. Furthermore, the proposed sparse semiblind algorithm has been extended for the estimation of channels in the upsampling domain for MIMO-OFDM systems with pulse shaping. A number of computer-simulation-based experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semiblind approach.

Carrier Frequency Offset Estimation and I/Q Imbalance Compensation for OFDM Systems

Two types of radio-frequency front-end imperfections, that is, carrier frequency offset and the inphase/quadrature (I/Q) imbalance are considered for orthogonal frequency division multiplexing (OFDM) communication systems. A preamble-assisted carrier frequency estimator is proposed along with an I/Q imbalance compensation scheme. The new frequency estimator reveals the relationship between the inphase and the quadrature components of the received preamble and extracts the frequency offset from the phase shift caused by the frequency offset and the cross-talk interference due to the I/Q imbalance. The proposed frequency estimation algorithm is fast, efficient, and robust to I/Q imbalance. An I/Q imbalance estimation/compensation algorithm is also presented by solving a least-square problem formulated using the same preamble as employed for the frequency offset estimation. The computational complexity of the I/Q estimation scheme is further reduced by using part of the short symbols with a little sacrifice in the estimation accuracy. Computer simulation and comparison with some of the existing algorithms are conducted, showing the effectiveness of the proposed method.

Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems

In this paper, a very efficient semi-blind approach for the detection of most significant taps (MSTs) in sparse orthogonal frequency-division multiplexing (OFDM) channel estimation is developed. The least square (LS) estimation problem of sparse OFDM channels is first formulated, showing that the key to sparse channel estimation lies in the detection of the MSTs. An in-depth study of the second-order statistics of the signal received through a noise-free sparse OFDM channel reveals the sparsity and other properties of the correlation functions of the received signal. These properties lead to a direct relationship between the positions of the MSTs of the sparse channel and the most significant lags of the correlation functions, which is then used in conjunction with a pilot-assisted LS estimation to detect the MSTs in a semi-blind fashion. It os also shown that the new MST detection algorithm can be extended for the estimation of multiple-input–multiple-output (MIMO)–OFDM channels. A number of computer-simulation-based experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semi-blind approach.

A limited feedback precoding system with hierarchical codebook and linear receiver

In this paper, the conventional Grassmannian codebook for precoding is first analyzed, showing that the performance loss caused by linear receivers was not taken into account. To tackle the performance loss issue, a novel hierarchical codebook consisting of a Grassmannian subcodebook and a perturbation subcodebook is then proposed for precoding systems with linear receivers. A two-step codeword selection scheme that uses the product of two codewords selected from the subcodebooks as the precoder is also presented. Our analysis shows that the perturbation subcodebook is able to compensate for the performance loss from linear receivers. Compared with the Grassmannian codebook, the superiority of the proposed codebook in terms of search complexity as well as throughput/ BER is further confirmed by computer simulations.

Compensation of Loudspeaker Nonlinearity in Acoustic Echo Cancellation Using Raised-Cosine Function

The nonlinearity of a power amplifier or loudspeaker in a large-signal situation gives rise to a nonlinear distortion of acoustic signal. A conventional acoustic echo canceller using linear adaptive filters is not able to eliminate the nonlinear echo component. In this brief, a novel nonlinear echo cancellation technique is presented by using a nonlinear transformation in conjunction with a conventional linear adaptive filter. The nonlinear transformation is derived from a raised-cosine function and is exploited to compensate for the nonlinearity of a loudspeaker. The transformation parameters are updated using the normalized least mean square algorithm according to the unknown nonlinear characteristic of the loudspeaker. Computer simulations show that the proposed method yields, in general, a satisfactory cancellation performance while having a very low computational complexity

Joint 2-D DOA Estimation via Sparse L-shaped Array

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of one uniform linear array (ULA) and one sparse linear array (SLA). The shift-invariance property of the ULA is used to estimate the elevation angles with low computational burden. The signal subspace is constructed by the cross-covariance matrix (CCM) of the received data without implementing eigendecomposition. The source waveforms are then obtained by the estimated elevation angles, which together with each sensor of the SLA, considered as a linear regression model, is used to estimate the azimuth angle by the modified total least squares (MTLS) technique. Our new algorithm yields correct parameter pairs without requiring the computationally expensive pairing operation, and therefore, has at least two advantages over the previous L-shaped array based algorithms: less computational load and better performance due to the use of SLA and CCM. Expressions for the asymptotic mean-squared error (MSE) of the 2-D DOA estimates are derived. Simulation results show that our method provides accurate and consistent 2-D DOA estimation results that could not be obtained by the existing methods with comparable computational complexity.

A closed-form solution to the least-square design problem of 2-D linear-phase FIR filters

In this paper, the least-square design problem of a general two-dimensional (2-D) linear-phase FIR filter with an arbitrary magnitude response is studied. By minimizing the frequency-domain error function and exploiting some of the properties of the functions and matrices associated with the design problem, a novel closed-form solution is developed. The solution is expressed in terms of the desired magnitude specifications and is eventually presented as an explicit expression for the impulse response of the filter to be designed, making a very fast evaluation of the filters coefficients possible. It is also shown that when this solution is used to design filters that have centrosymmetric and quadrantally symmetric magnitude responses, some further computational savings can be achieved. Several design examples illustrating the effectiveness of the proposed solution are considered.