Performance of Beamforming for Smart Antenna using Traditional LMS Algorithm for Various Parameters

This paper surveys recent advances related to sparse least mean square (LMS) algorithms. Since standard LMS algorithm does not take advantage of the sparsity information about the channel being estimated, various sparse LMS algorithms that are aim at outperforming standard LMS in sparse channel estimation are discussed. Sparse LMS algorithms force the solution to be sparse by introducing a sparse penalty to the standard LMS cost function. Under the reasonable conditions on the training datas and parameters, sparse LMS algorithms are shown to be mean square stable, and their mean square error performance and convergence rate are better than standard LMS algorithm. We introduce the sparse algorithms under Gaussian noises model. The simulation results presented in this work are useful in comparing sparse LMS algorithms against each other, and in comparing sparse LMS algorithms against standard LMS algorithm.

We describe the application of a genetic algorithm (GA) to the problem of parameter optimization for an adaptive finite impulse response (FIR) filter combining genetic algorithm (GA) and least mean square (LMS) algorithm. For system identification problem, LMS algorithm computes the filter coefficients and GA search the optimal step-size adaptively. Because step-size influences on the stability and performance, so it is necessary to apply method that can control it. The simulation results of the GA were compared to the traditional LMS algorithm. We obtained that genetic algorithm was clearly superior (in accuracy) in most cases.

Smart antenna is the most efficient leading innovation for maximum capacity and improved quality and coverage.Efficient utilization of limited radio frequency spectrum is only possible to use smart/adaptive antenna system.Smart antenna radiates not only narrow beam towards desired users exploiting signal processing capability but also places null towards interferers, thus optimizing the signal quality and enhancing capacity.Smart antennas exploit space diversity to help provide high data rates, increased channel capacity, and improved quality of service.A systematic comparison of the performance of different Adaptive Algorithms for beam forming for Smart Antenna System has been extensively studied in this research work.Simulation results revealed that training sequence algorithms like Recursive Least Squares (RLS) and Least Mean Squares (LMS) are best for beam forming (to form main lobes) towards desired user but they have limitations towards interference rejection.While Constant Modulus Algorithm (CMA) has satisfactory response towards beam forming and it gives better outcome for interference rejection, but Bit Error Rate (BER) is maximum in case of single antenna element in CMA.The effect of changing step size for LMS algorithm has also been studied.

The transversal adaptive filter using the least mean square (LMS) algorithm of Widrow and Ropf (1976) has been widely used mainly due to its relative ease of implementation. The major drawback of this time-domain LMS (TDLMS) algorithm is that as the eigenvalue spread of the input autocorrelation matrix increases, the convergence speed of the algorithm decreases. This led to the transform domain adaptive filters where the input signals are orthogonalized. Normalized frequency domain LMS algorithms (NFDLMS) are known to be faster than the time domain implementations. However, in some implementations with low signal to return noise ratio, NFDLMS algorithms can have stability problems. The stability problem can be solved by weighting the normalization gain /spl mu/. We perform computer simulations for the telephone echo channel and show that the modified /spl mu/-weighted NFDLMS algorithm is 8 times faster than the time-domain LMS (TDLMS) algorithm and more stable than the NFDLMS algorithm over a wide range of signal to noise ratios.

Objectives: In case of medical emergency, an ambulance uses siren (which produces noise up to 120 dB) to alert the traffic on the road to give way. However, siren noise is troublesome to the patient lying inside. This calls for noise control inside the ambulance. Methods/Statistical Analysis: The passive noise control methods are not feasible and economically inviable so active noise control (ANC) method is applied. The ANC is electro-acoustic technique in which the noise signal is acquired and noise with opposite phase is produced and overlapped on input noise to suppress the same. The weights are updated using adaptive algorithms so as to reduce the error in ANC system. In present work, least mean square (LMS) and filtered- reference least mean square (FxLMS) algorithm based ANC systems are applied and compared for ambulance siren noise reduction. MATLAB is used for simulating the ANC systems. Findings: Both the algorithms are compared on the basis of residual noise and smoothened ensemble average square error (SEASE). From the results, it is found that both algorithms LMS and FxLMS are good in noise reduction. The noise reduction for FxLMS based ANC system is 28 dB whereas it is 25 dB for LMS based ANC system, which indicates the superiority of FxLMS algorithm over LMS algorithm. The results are obtained analytically as well as indicated by power spectral density (PSD) plots of the residual noise. The comparison of SEASE for both algorithms also indicates better performance for FxLMS algorithm. The ANC system based on FxLMS algorithm is more stable as it considers the secondary path which includes practical components such as ADC, DAC, etc. Applications/Improvements: The results indicate that FxLMS algorithm based on ANC system can be implemented on the hardware using DSP processor. It can be further used for the other applications of noise reduction such as server noise, vacuum cleaner, etc. Keywords: Active Noise Control, Ambulance Siren Noise, FxLMS Algorithm, LMS

A major complaint of individuals with normal hearing and hearing impairments is a reduced ability to understand speech in a noisy environment. This paper is proposed to remove the noise from the speech signal in various real time environments and to make the hearing impaired to understand the information by transforming speech in to text format. The quality of audio signal can be improved by filtering the degraded speech signal through adaptive filters. For Noise cancellation widrow & hoff’s Least Mean Square (LMS) algorithms are being used for simplicity in implementation. The LMS algorithm was simple in it's derivation and robust in a number of applications but it also have limitation in selection of a certain values such as step size and results in computational complexity. Hence to overcome all the limitations and to present an enhanced speech signal, a novel approach named VSSNDLMS algorithm is proposed. The performance of algorithms like Variable Step Size Normalised LMS , Normalised Differential LMS with proposed VSSNDLMS with different input signals are analysed in this paper . Finally, through simulation results the proposed VSSNDLMS algorithm converges fastly with minimum mean square error and it is useful in predicting the adaptive filter performance of various algorithms and the implementation indicate the improvement in quality of the speech signal and it appears to be favourable for hearing impaired. Keywords: VSSNDLMS, LMS Algorithm, Noise Cancellation, Speech Processing, Adaptive Filter

The smart antenna adaptive algorithms achieve the best weight vector for beam forming by iterative means. Whether the algorithm is good depends on the convergence rate and steady state error. Beam forming is directly determined by the two factors. The performance of the traditional LMS algorithm is analyzed in this paper. Then a new variable step size algorithm is proposed and is applied to beam forming with the software Matlab. The simulation result indicates that the algorithm improved could achieve faster convergence and lower steady state error.

Simulation of active noise reduction using least mean squares (LMS) algorithm was carried out to support the development of the ANC (active noise control) prototype, which will be applied to a four-wheeled medium class gasoline-fueled Multi-Purpose Vehicle (MPV) with 2000-2400 cc engines. In this simulation, the LMS is applied to a set of synthetic and field data, to focus on the LMS being successfully applied to all field data, ignoring the delay time between errors and references microphone, and obtaining the optimal LMS step-size and order. Based on the simulation, positive result is shown by the LMS algorithm, where the best result, the superposition of the noise and antinoise signal attenuates and reaches convergence in about 20 ms. In addition, changes in the value of LMS orders do not significantly influence LMS predictions. On the other hand, the change in the step-size value is quite influential on the prediction, where the smaller the step-size, the longer the superposition time to reach convergence.

In the radio observation based on the large single antenna, in order to reduce the interference of navigation signal L 2 from observation of the red-shifted H I spectral line at L-band, we used the an auxiliary channel and normalized LMS algorithm, and proposed new evaluating indicators deduced by theoretical method. Finally, we verified the suppression performance and the indicators' utility by multiple simulations and statistical method.

Accurate tracking of the GPS signal in a base band correlator is critical for the positioning accuracy. However, the performance of the received signal is degraded by the presence of a multipath signal. This paper proposes the tracking of the GPS signal in the presence of the multipath using a RAKE receiver with multiple fingers. Each finger in the RAKE receiver is modelled to track the direct and respective multipath signals using the Least Mean Square (LMS) algorithm. The LMS algorithm provides an estimate of the multipath signal, and the RAKE combiner suitably combines the navigation data from the individual finger with the estimated multipath value and provides the final navigation data. A discussion on the use of the LMS for the carrier and code tracking is also presented.

Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.

Many researchers and practitioners make heavy use of the least mean squares (LMS) algorithm as an efficient adaptive filter suitable for a multitude of problems. Despite being versatile and efficient, a drawback of this algorithm is that the adaptation rate, i.e. step‐size, has to be chosen very carefully in order to get the desired result (optimum compromise between fast adaptation and low steady state error). This choice was simplified by the invention of the normalised LMS, which bounds the step‐size and guarantees convergence. However, the optimum choice of the normalisation becomes non‐trivial if the system to be approximated is part of a bigger, non‐trivial model, e.g. cascaded filters or linear paths followed by nonlinearities. Such cases usually require approximations or worst‐case estimates in order to yield a normalised update algorithm, which might result in sub‐optimal performance. To counteract this problem, a new class of LMS algorithms which automatically choose their own normalisation terms, the so‐called self normalising LMS, is introduced. The simulations show that this new algorithm not only outperforms state‐of‐the‐art solutions in terms of steady state performance in a cascaded filter scenario but also converges just as fast as all other considered algorithms.

Gradient optimization p-norm-like constraint LMS algorithm for sparse system estimation

Adaptive digital filters (ADFs) are, in general, the most sophisticated and resource intensive components of modern digital signal processing (DSP) and communication systems. Improvements in performance or the complexity of ADFs can have a significant impact on the overall size, speed, and power properties of a complete system. The least mean square (LMS) algorithm is a popular algorithm for coefficient adaptation in ADF because it is robust, easy to implement, and a close approximation to the optimal Wiener-Hopf least mean square solution. The main weakness of the LMS algorithm is the slow convergence, especially for non Markov-1 colored noise input signals with high eigenvalue ratios (EVRs). Since its introduction in 1993, the turbo (supercharge) principle has been successfully applied in error correction decoding and has become very popular because it reaches the theoretical limits of communication capacity predicted 5 decades ago by Shannon. The turbo principle applied to LMS ADF is analogous to the turbo principle used for error correction decoders: First, an "interleaver" is used to minimize crosscorrelation, secondly, an iterative improvement which uses the same data set several times is implemented using the standard LMS algorithm. Results for 6 different interleaver schemes for EVR in the range 1-100 are presented.

A novel method of the computation of the Discrete Fractional Cosine Transform (DFRCT) based on adaptive Least Mean Square (LMS) algorithm is proposed. The weight vectors are adjusted or adapted in accord with the least mean square error between the “desired response” and the reconstructed signal. It can be proved that the weight vector is in relation to the previous N-point DFRCT by choosing the iterative index properly. Theoretical analysis and simulation results show that the performance of the proposed algorithm is very close to that of Pei <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> , and also it is simple in computation.