A Robust Zero-point Attraction LMS Algorithm on Near Sparse System Identification

The newly proposed $l_1$ norm constraint zero-point attraction Least Mean Square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse system identification. However, ZA-LMS has less advantage against standard LMS when the system is near sparse. Thus, in this paper, firstly the near sparse system modeling by Generalized Gaussian Distribution is recommended, where the sparsity is defined accordingly. Secondly, two modifications to the ZA-LMS algorithm have been made. The $l_1$ norm penalty is replaced by a partial $l_1$ norm in the cost function, enhancing robustness without increasing the computational complexity. Moreover, the zero-point attraction item is weighted by the magnitude of estimation error which adjusts the zero-point attraction force dynamically. By combining the two improvements, Dynamic Windowing ZA-LMS (DWZA-LMS) algorithm is further proposed, which shows better performance on near sparse system identification. In addition, the mean square performance of DWZA-LMS algorithm is analyzed. Finally, computer simulations demonstrate the effectiveness of the proposed algorithm and verify the result of theoretical analysis.Comment: 20 pages, 11 figure

Acoustic Echo Cancellation and their Application in ADF

In this paper, we present an overview of the principal, structure and the application of the echo cancellation and kind of application to improve the performance of the systems. Echo is a process in which a delayed and distorted version o the original sound or voice signal is reflected back to the source. For the acoustic echo canceller much and more study are required to make the good tracking speed fast and reduce the computational complexity. Due to the increasing the processing requirement, widespread implementation had to wait for advances in LSI, VLSI echo canceller appeared. DOI: 10.17762/ijritcc2321-8169.150513

Precoded Chebyshev-NLMS based pre-distorter for nonlinear LED compensation in NOMA-VLC

Visible light communication (VLC) is one of the main technologies driving the future 5G communication systems due to its ability to support high data rates with low power consumption, thereby facilitating high speed green communications. To further increase the capacity of VLC systems, a technique called non-orthogonal multiple access (NOMA) has been suggested to cater to increasing demand for bandwidth, whereby users' signals are superimposed prior to transmission and detected at each user equipment using successive interference cancellation (SIC). Some recent results on NOMA exist which greatly enhance the achievable capacity as compared to orthogonal multiple access techniques. However, one of the performance-limiting factors affecting VLC systems is the nonlinear characteristics of a light emitting diode (LED). This paper considers the nonlinear LED characteristics in the design of pre-distorter for cognitive radio inspired NOMA in VLC, and proposes singular value decomposition based Chebyshev precoding to improve performance of nonlinear multiple-input multiple output NOMA-VLC. A novel and generalized power allocation strategy is also derived in this work, which is valid even in scenarios when users experience similar channels. Additionally, in this work, analytical upper bounds for the bit error rate of the proposed detector are derived for square $M$-quadrature amplitude modulation.Comment: R. Mitra and V. Bhatia are with Indian Institute of Technology Indore, Indore-453552, India, Email:[email protected], [email protected]. This work was submitted to IEEE Transactions on Communications on October 26, 2016, decisioned on March 3, 2017, and revised on April 25, 2017, and is currently under review in IEEE Transactions on Communication

Developing an Enhanced Adaptive Antenna Beamforming Algorithm for Telecommunication Applications

As a key enabler for advanced wireless communication technologies, smart antennas have become an intense field of study. Smart antennas use adaptive beamforming algorithms which allow the antenna system to search for specific signals even in a background of noise and interference. Beamforming is a signal processing technique used to shape the antenna array pattern according to prescribed criteria. In this thesis, a comparative study is presented for various adaptive antenna beamforming algorithms. Least mean square (LMS), normalized least mean square (NLMS), recursive least square (RLS), and sample matrix inversion (SMI) algorithms are studied and analyzed. The study also considers some possible adaptive filter combinations and variations, such as: LMS with SMI weights initialization, and combined NLMS filters with a variable mixing parameter. Furthermore, a new adaptive variable step-size normalized least mean square (VSS-NLMS) algorithm is proposed. Sparse adaptive algorithms, are also studied and analyzed, and two-channel estimations sparse algorithms are applied to an adaptive beamformer, namely: proportionate normalized least-mean-square (PNLMS), and lp norm PNLMS (LP-PNLMS) algorithms. Moreover, a variable step size has been applied to both of these algorithms for improved performance. These algorithms are simulated for antenna arrays with different geometries and sizes, and results are discussed in terms of their convergence speed, max side lobe level (SLL), null depths, steady-state error, and sensitivity to noise. Simulation results confirm the superiority of the proposed VSS-NLMS algorithms over the standard NLMS without the need of using combined filters. Results also show an improved performance for the sparse algorithms after applying the proposed variable step size