A novel quantum calculus-based complex least mean square algorithm (q-CLMS)

An adaptive beam former is a device, which is able to steer and modifies an array's beam pattern in order to enhance the reception of a desired signal, while simultaneously suppressing interfering signals through complex weight selection. However, the weight selection is a critical task to get the low Side Lobe Level (SLL) and Low Beam Width. One needs to have a low SLL and low beam width to reduce the antenna's energy radiation/reception ability in unintended directions. The weights can be chosen to minimize the SLL and to place nulls at certain angles. The convergence of the array output towards desired signal is also very important for a good signal processing tool of an adaptive beam former. A vast number of possible window functions are available to calculate the weights for Smart Antennas. From the analysis of many of these algorithms, it is observed that there is a compromise between HPBW and SLL. But in case of smart antennas, both of these parameters must have low values to get good performance. In our earlier work it is proposed that Complex Least Mean Square (CLMS) and Augmented Complex Least Mean Square ( ACLMS) algorithms gives low beam width and side lobe level in noisy environment. Another neural algorithm Adaptive Amplitude Non Linear Gradient Decent algorithm (AANGD) has the advantage of more number of control parameters over CLMS and ACLMS algorithms. In this paper the hybrid of CLMS and AANGD is presented and this novel hybrid algorithm has outperformed the hybrid algorithm of CLMS and ACLMS in the aspect of convergence towards the desired signal.

A smart antenna system combines multiple antenna elements with a signal processing capability to optimize its radiation and/or reception pattern automatically in response to the signal environment through complex weight selection. The weight selection process to get suitable Array factor with low Half Power Beam Width (HPBW) and Side Lobe Level (SLL) is a complex method. The aim of this task is to design a new approach for smart antennas to minimize the noise and interference effects from external sources with least number of iterations. This paper presents Heuristics based adaptive step size Complex Least Mean Square (CLMS) model for Smart Antennas to speedup convergence. In this process Benveniste and Mathews algorithms are used as heuristics with CLMS and the improvement of performance of Smart Antenna System in terms of convergence rate and array factor are discussed and compared with the performance of CLMS and Augmented CLMS (ACLMS) algorithms.

In this paper we investigate the effect of observation quality information (OQI) on the performance of a special class of adaptive networks known as distributed incremental least-mean square (DILMS) algorithm. To this aim we consider two different cases: (1) a homogeneous environment where all the nodes have the same observation noise variance (ONV) and (2) an inhomogeneous environment, where different nodes have different ONVs. In the first case we show that, for the same steady-state error, the DILMS algorithm has faster convergence rate in comparison with a non-cooperative scheme. In the second case, we first show that regardless of what ONVs are, the steady-state curves of mean-square deviation, excess mean-square error and mean square error (MSE) in each node are monotonically increasing functions of step-size parameter. Then, to use the OQI, we reformulate the parameter estimation as a constrained optimization problem with MSE criterion as the cost function and ONVs as the constraints. Using the Robbins-Monro method to solve the resultant problem, a new algorithm (which we call noise-constrained incremental LMS algorithm) is obtained which has faster convergence rate than the existing incremental LMS algorithm. Simulation results are also provided to clarify the performance of proposed algorithm.

An increasing number of elderly­­­­ and disabled people urge the need for a health care monitoring system which has the capabilities for analyzing patient health care data to avoid preventable deaths. Medical Telemetry is becoming a key tool in assisting patients living remotely where a “Real-time Remote Critical Health Care Monitoring System” (RRCHCMS) can be utilized for the same. The RRCHCMS is capable of receiving and transmitting data from a remote location to a location that has the capability to diagnose the data and affect decision making and further providing assistance to the patient. During the cardiac analysis, several artifacts solidly affect the ST segment, humiliate the signal quality, frequency resolution, and results in large amplitude signals in ECG that simulate PQRST waveform and cover up the miniature features that are useful for clinical monitoring and diagnosis. In this paper, several leaky based adaptive filter structures for cardiac signal improvement are discussed. The Circular Leaky Least Mean Square (CLLMS) algorithm being the steepest drop strategy for dropping the mean squared error gives a better result in comparison with the Least Mean Square (LMS) algorithm. To enlarge the filtering ability some variants of LMS, Normalized Least Mean Square (NLMS), CLLMS, Variable Step Size CLLMS (VSS-CLLMS) algorithms are used in both time domain (TD) and frequency domain (FD). At last, we applied this algorithm on cardiac signals occurred due to MIT-BIH database. The performance of CLLMS algorithm is better compared to LLMS counterparts in conditions of Signal to Noise Ratio Improvement (SNRI), Excess Mean Square Error (EMSE) and Misadjustment (MSD). When compared to all other algorithms VSS-CLLMS gives superior SNRI. These values are 13.5616dB and 13.7592dB for Baseline Wander (BW) and Muscle Artifact (MA) removal.

A perspective on CLMS as a deficient length augmented CLMS: Dealing with second order noncircularity

An adaptive beam former is a device, which is able to steer and modify an array's beam pattern in order to enhance the reception of a desired signal, while simultaneously suppressing interfering signals through complex weight selection.However, the weight selection is a critical task to get the low Side Lobe Level (SLL) and Low Beam Width.It needs to have a low SLL and low beam width to reduce the antenna's radiation/reception ability in unintended directions.The weights can be chosen to minimize the SLL and to place nulls at certain angles.A vast number of possible window functions that are available to provide the weights to be used in Smart Antennas.This paper presents various traditional windowing techniques such as Binomial, Kaiser-Bessel, Blackman, Gaussian, and so on for computing weights for adaptive beam forming and also neural based methods like, Least Mean Square (LMS), Complex LMS (CLMS) [5], and Augmented CLMS (ACLMS) [1] algorithms.This paper discusses about various observations on signal processing techniques of Smart Antennas, that compromise between SLL and beam width (Directivity), to improve the base station capacity in Cellular and Mobile Communications and also the performance analysis of CLMS and ACLMS in terms of SLL and beam width, error convergence rate.

Analysis and simulations to compare the standard least mean square (LMS) algorithm to the noncanonical LMS (NCLMS) algorithm are presented. The NCLMS algorithm is implemented using a variant of the standard FIR, called the non-canonical FIR (NCFIR). Simulation results are given which show a reduced excess mean square error (EMSE) level and an improved performance in an impulsive noise environment for the NCLMS over the conventional LMS algorithm.

Quantized augmented complex least-mean square algorithm: Derivation and performance analysis

The authors present analysis and simulations of an LMS (least mean square) based adaptive filtering algorithm called the NCLMS (non-canonical LMS). Rather than using the standard FIR (finite impulse response) filter as for the LMS algorithm, a modified structure called the NCFIR (non-canonical FIR) is used. The NCFIR allows a faster VLSI implementation than the conventional FIR. A comparison of the performances of the NCLMS and conventional LMS algorithm is presented for an inverse system modeling application. Simulation results are given which show a reduced EMSE (excess mean square error) level and an improved performance in an impulsive noise environment for the NCLMS over the LMS algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In remote health care monitoring, the extraction of high resolution cardiac signals is an important task. For this the cardiac signal (CS) needed to be enhanced. Among various filtering techniques, the adaptive noise cancellation (ANC) is a promising methodology. In adaptive filtering least mean square (LMS) algorithm is the fundamental enhancement algorithm. However, it suffers with slow convergence and weight drift problem in non-stationery environment. In order to improve the performance of ANC this paper proposes to implement an ANC methodology based on variable step size on the normalization of fundamental LMS algorithm for CS enhancement. Based on such strategy this research implements ANC using hybrid algorithm called variable normalized LMS (VNLMS) algorithm. Further, to improve the convergence characteristics, to filter the ability and to minimize computational complexity some versions of VNLMS algorithms are implemented too. Finally, the proposed ANCs are tested using real CS obtained from MIT-BIH data base. The performance evaluation is carried based on signal to noise ratio improvement (SNRI) and excess mean square error (EMSE).

This paper presents a simple and easy implementable Least Mean Square (LMS) type approach for frequency estimation of three phase power system in an unbalanced condition. The proposed LMS type algorithm is based on a second order recursion for the complex voltage derived from Clarke’s transformation which is proved in the paper. The proposed algorithm is real adaptive filter with real parameter (not complex) which can be efficiently implemented by DSP. In unbalanced situations, simulation experiments show the advantages and drawbacks of the proposed algorithm in comparison to Complex LMS (CLMS) and Augmented Complex LMS (ACLMS) methods. Index Terms-Frequency estimation, LMS algorithm, adaptive filter, power systems.

Analysis of incremental LMS adaptive algorithm over wireless sensor networks with delayed-links

The adaptive filtering algorithms based on the arctangent cost function framework have shown robustness against impulsive noise. In this brief, the standard least mean square (LMS) algorithm under this framework is concentrated on, which is called the arctangent LMS (ATLMS) algorithm. The steady-state excess mean square error (EMSE) and mean square deviation (MSD) of the ATLMS algorithm are analyzed using the energy conservation relation. In stationary environment, both Gaussian and non-Gaussian situations are discussed. The closed-form expressions of the steady-state EMSE and MSD are obtained using Taylor’s expansion. In non-stationary environment, a first-order random-walk model is used for modeling the time-varying optimal weight. Theoretical steady-state performance and the optimal step size are derived. Simulation results under different noise environments verify the validity of our theoretical findings.

A robust and stable variable step-size design for the least-mean fourth algorithm using quotient form

The Complex Block Least Mean Square (LMS) technique is widely used in adaptive filtering applications because of its simplicity and efficiency from a theoretical and implementation standpoint. However, the limitations of the Complex Block LMS technique are slow convergence and dependence on the proper choice of the stepsize or convergence factor. Moreover, its performance degrades significantly in time-varying environments. In this paper, a novel adaptive LMS technique named the Complex Block Conjugate LMS algorithm, CBC-LMS, is presented. Based on the Conjugate Gradient Principle, the proposed technique searches orthogonal directions to update the filter coefficients instead of the negative gradient directions used in the Complex Block LMS algorithm. In addition, the CBC-LMS algorithm derives optimal stepsizes to adjust the adaptive system coefficients at each iteration. As a result, the developed method overcomes the inherent limitations of the existing Complex Block LMS algorithm. The performance of the CBC-LMS technique is tested in wireless channel estimation and equalization applications, using both computer simulations and laboratory experiments. Furthermore, the developed technique is compared to the Complex Block LMS method and a recently proposed method, which is called Complex Optimal Block Adaptive LMS (OBA-LMS). The experimental and simulation results confirm that the proposed CBC-LMS technique achieves faster convergence with comparable accuracy and reduced computational complexity, relative to the existing techniques.