This paper proposes a novel interference cancellation technique that prevents radio receivers from degrading due to periodic interference signals caused by electromagnetic waves emitted from high power circuits. The proposed technique cancels periodic interference signals in the frequency domain, even if the periodic interference signals drift in the time domain. We propose a drift estimation based on a super resolution technique such as ESPRIT. Moreover, we propose a sequential drift estimation to enhance the drift estimation performance. The proposed technique employs a linear filter based on the minimum mean square error criterion with assistance of the estimated drifts for the interference cancellation. The performance of the proposed technique is confirmed by computer simulation. The proposed technique achieves a gain of more than 40dB at the higher frequency part in the band. The proposed canceler achieves such superior performance, if the parameter sets are carefully selected. The proposed sequential drift estimation relaxes the parameter constraints, and enables the proposed cancellation to achieve the performance upper bound.

Numerous variable tap-length algorithms can be found in some literature and few strategies are derived from a basic theoretical formula. Thus, some algorithms lack of theoretical depth and their performance are unstable. In view of this point, the novel variable tap-length algorithm which is based on the mixed error cost function is presented in this letter. By analyzing the mixed expectation of the prior and the posterior error, the novel variable tap-length strategy is derived. The proposed algorithm has a more valid proximity to the optimal tap-length and a good convergence ability by the performance analysis. It can solve many deficiencies comprising large fluctuations of the tap-length, the high complexity and the weak steady-state ability. Simulation results demonstrate that the proposed algorithm equips good performance.

This paper expands our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems to support multi modulus signals. The original proposal applies the channel state information (CSI) aided blind adaptive array (BAA) interference suppression after the beamspace preprocessing and the decision feedback channel estimation (DFCE). BAA is based on the constant modulus algorithm (CMA) which can fully exploit the degree of freedom (DoF) of massive antenna arrays to suppress both inter-user interference (IUI) and inter-cell interference (ICI). Its effectiveness has been verified under the extensive pilot contamination constraint. Unfortunately, CMA basically works well only for constant envelope signals such as QPSK and thus the proposed scheme should be expanded to cover QAM signals for more general use. This paper proposes to apply the multi modulus algorithm (MMA) and the minimum mean square error weight derivation based on data-aided sample matrix inversion (MMSE-SMI). It can successfully realize interference suppression even with the use of multi-level envelope signals such as 16QAM with satisfactorily outage probability performance below the fifth percentile.

This paper proposes a sampling strategy for bandlimited graph signals over perturbed graph, in which we assume the edge between any pair of the nodes may be deleted randomly. Considering the mismatch between the true graph and the presumed graph, we derive the mean square error (MSE) of the reconstructed bandlimited graph signals. To minimize the MSE, we propose a greedy-based algorithm to obtain the optimal sampling set. Furthermore, we use Neumann series to avoid the pseudo-inverse computing. An efficient algorithm with low-complexity is thus proposed. Finally, numerical results show the superiority of our proposed algorithms over the other existing algorithms.

In this letter, a switching detection scheme based on a channel condition number for the MIMO-OFDM system is proposed. The switching algorithm operates by selecting one of three detection schemes of QRD-M, LR-aided MMSE-DFE, and LR-aided MMSE. The switching detection uses the threshold based on the switching algorithm according to the channel condition number. From the simulation results, the proposed detection scheme shows error detection performance and computational complexity in accordance with the threshold for switching detection.

In this paper, an iterative robust minimum-mean square error (MMSE) receiver for space-time block coding (STBC) is proposed to mitigate the performance degradations caused by channel state information (CSI) errors. The proposed scheme estimates an instantaneous covariance matrix of the effective noise, which includes additive white Gaussian noise and the effect of CSI errors. For this estimation, multiple solution candidate vectors are selected based on the distances between the MMSE estimate of the solution and the constellation points, and their a-posteriori probabilities are utilized to execute the estimation of the covariance matrix. To improve the estimation accuracy, the estimated covariance matrix is updated iteratively. Simulation results show that proposed robust receiver achieves substantial performance gains in terms of bit error rates as compared to conventional receiver schemes under CSI errors.

In this paper, an iterative optimal method is proposed to design the prototype filters for a fast filter bank (FFB) with low complexity, aiming to control the optimum ripple magnitude tolerance of each filter according to the overall specifications. This problem is formulated as an optimization problem for which the total number of multiplications is to be minimized subject to the constrained ripple in the passband and stopband. In the following, an iterative solution is proposed to solve this optimization problem for the purpose of obtaining the impulse response coefficients with low complexity at each stage. Simulations are conducted to verify the performance of the proposed scheme and show that compared with the original method, the proposed scheme can reduce about 24.24% of multiplications. In addition, the proposed scheme and the original method provide similar mean square error (MSE) and the mean absolute error (MAE) of the frequency response.

In this letter, we propose a new distance estimation method based on statistical models of a Received Signal Strength (RSS) at the receiver. The conventional distance estimator estimates the distance between the transmitter and the receiver based on the statistical average of the RSS when the receiver obtains instantaneous RSS and an estimate of the hyperparameters which consists of the path loss exponent and so on. However, it is well-known that instantaneous RSS does not always correspond to the average RSS because the RSS varies in accordance with a statistical model. Although the statistical model has been introduced for the hyperparameters estimation and the localization system, the conventional distance estimator has not yet utilized it. We introduce the statistical model to the distance estimator whose expected value of the estimate corresponds to true distance. Our theoretical analysis establishes that the proposed distance estimator is preferable to the conventional one in order to improve accuracy in the expected value of the distance estimate. Moreover, we evaluate the Mean Square Error (MSE) between true distance and the estimate. We provide evidence that the MSE is always proportional to the square of the distance if the estimate of the hyperparameters is ideally obtained.

Implicit feedback is an approach that utilizes uplink channel state information (CSI) for downlink transmit beamforming on multiple-input multiple-output (MIMO) systems, relying on over-the-air channel reciprocity. The implicit feedback improves throughput efficiency because overhead of CSI feedback for change of over-the-air channel responses is omitted. However, it is necessary for the implicit feedback to calibrate circuitry responses that uplink CSI includes, because actual downlink and uplink channel responses do not match due to different transmit and receive circuitry chains. This paper presents our proposed calibration scheme, weighted-combining calibration (WCC); it offers improved calibration accuracy. In WCC, an access point (AP) calculates multiple calibration coefficients from ratios of downlink and uplink CSI, and then combines coefficients with minimum mean square error (MMSE) weights. The weights are derived using a linear approximation in the high signal to noise power ratio (SNR) regime. Analytical mean square error (MSE) of calibration coefficients with WCC and calibration schemes for comparison is expressed based on the linear approximation. Computer simulations show that the analytical MSE matches simulated one if the linear approximation holds, and that WCC improves the MSE and signal to interference plus noise power ratio (SINR). Indoor experiments are performed on a multiuser MIMO system with implicit feedback based on orthogonal frequency division multiplexing (OFDM), built using measurement hardware. Experimental results verify that the channel reciprocity can be exploited on the developed multiuser MIMO-OFDM system and that WCC is also effective in indoor environments.

In this paper, an adaptive expansion strategy (AES) is proposed for multiple-input/multiple-output (MIMO) detection in the presence of circular signals. By exploiting channel properties, the AES classifies MIMO channels into three types: excellent, average and deep fading. To avoid unnecessary branch-searching, the AES adopts single expansion (SE), partial expansion (PE) and full expansion (FE) for excellent channels, average channels and deep fading channels, respectively. In the PE, the non-circularity of signal is exploited, and the widely linear processing is extended from non-circular signals to circular signals by I (or Q) component cancellation. An analytical performance analysis is given to quantify the performance improvement. Simulation results show that the proposed algorithm can achieve quasi-optimal performance with much less complexity (hundreds of flops/symbol are saved) compared with the fixed-complexity sphere decoder (FSD) and the sphere decoder (SD).

The estimation of the power spectral density (PSD) of noise is crucial for retrieving speech in noisy environments. In this study, we propose a novel method for estimating the non-white noise PSD from noisy speech on the basis of a generalized gamma distribution and the minimum mean square error (MMSE) approach. Because of the highly non-stationary nature of speech, deriving its actual spectral probability density function (PDF) using conventional modeling techniques is difficult. On the other hand, spectral components of noise are more stationary than those of speech and can be represented more accurately by a generalized gamma PDF. The generalized gamma PDF can be adapted to optimally match the actual distribution of the noise spectral amplitudes observed at each frequency bin utilizing two real-time updated parameters, which are calculated in each frame based on the moment matching method. The MMSE noise PSD estimator is derived on the basis of the generalized gamma PDF and Gaussian PDF models for noise and speech spectral amplitudes, respectively. Combined with an improved Weiner filter, the proposed noise PSD estimate method exhibits the best performance compared with the minimum statistics, weighted noise estimation, and MMSE-based noise PSD estimation methods in terms of both subjective and objective measures.

In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.

Novel joint motion-compensated interpolation using eight-neighbor block motion vectors (8J-MCI) is presented. The proposed method uses bi-directional motion estimation (BME) to obtain the motion vector field of the interpolated frame and adopts motion vectors of the interpolated block and its 8-neighbor blocks to jointly predict the target block. Since the smoothness of the motion vector filed makes the motion vectors of 8-neighbor blocks quite close to the true motion vector of the interpolated block, the proposed algorithm has the better fault-tolerancy than traditional ones. Experiments show that the proposed algorithm outperforms the motion-aligned auto-regressive algorithm (MAAR, one of the state-of-the-art frame rate up-conversion (FRUC) schemes) in terms of the average PSNR for the test image sequence and offers better subjective visual quality.

In this paper, we propose a genetic algorithm (GA) based equalization approach for direct sequence ultra-wideband (DS-UWB) wireless communication systems, where the GA is combined with a RAKE receiver to combat the inter-symbol interference (ISI) due to the frequency selective nature of UWB channels for high data rate transmission. The proposed GA based equalizer outperforms significantly the RAKE and the RAKE-minimum mean square error (MMSE) receivers according to results obtained from intensive simulation work. The RAKE-GA receiver also provides bit-error-rate (BER) performance very close to that of the optimal RAKE-maximum likelihood detection (MLD) approach, while offering a much lower computational complexity.

This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.

In this paper, the issue of carrier frequency offset (CFO) compensation in interleaved orthogonal frequency division multiple access (OFDMA) uplink system is investigated. To mitigate the effect of multiple access interference (MAI) caused by CFOs of different users, a new parallel interference cancellation (PIC) compensation algorithm is proposed. This scheme uses minimum mean square error (MMSE) criterion to obtain the estimation of interference users, then circular convolutions are employed to restore MAI and compensate CFO. To tackle the complexity problem of circular convolutions, an efficient MAI restoration and cancellation method is developed. Simulations illustrate the good performance and low computational complexity of the proposed algorithm.

In this letter, a new joint precoding and decoding design scheme for multiuser MIMO downlink is proposed which dispenses with iterative operations and can achieve better performance. This scheme introduces zero-force processing into minimum mean square error (MMSE) design scheme to avoid iterative operations. We derived closed-form precoders and decoders and transmit power allocation strategy of proposed design scheme, validated performance of proposed design scheme by computer simulation. The simulation results show that the proposed design scheme can achieve better bit error rate (BER) and sum capacity performance compared to an existing non-iterative design scheme.

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square (VEDNSS LMS) algorithm. Adopting VEDNSS LMS provides fast convergence at early stages of adaptation while ensuring small final misadjustment. An analysis of convergence and steady-state performance for zero-mean Gaussian inputs is provided. Simulation results comparing the proposed algorithm to existing algorithms indicate its superior performance under various noise and frequency environments.

Phase noise (PHN) can cause the common phase error (CPE) and the inter-carrier interference (ICI), both of which impair the accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems. In this letter, we build a new signal model parameterized by the channel impulse response, the CPE and the ICI. Based on this model, we derive the maximum likelihood estimator (MLE) and the minimum mean square error estimator (MMSEE). Simulation results show that the proposed schemes significantly improve the performance of OFDM systems in the presence of PHN.

This letter presents a new mathematical expression for the excess mean-square error (EMSE) of the affine projection (AP) algorithm. The proposed expression explicitly shows the proportional relationship between the EMSE and the condition number of the input signals.