(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 10, 2017 440 | Page www.ijacsa.thesai.org Integrated Framework to Study Efficient Spectral Estimation Techniques for Assessing Spectral Efficiency Analysis Kantipudi MVV Prasad Research Scholar Visvesvaraya Technological University Belgaum, India Dr. H.N. Suresh Prof., Dept. of Instrumentation Bangalore Institute Technology, Bangalore, India Abstract—The advanced network applications enable software driven spectral analysis of non-stationary signal or processes which precisely involves domain analysis with the purpose of decomposing a complex signal coefficients into simpler forms. However, the proper estimation of power coefficients over frequency components of a random signal leads to provide very useful information required in various fields of study. The complex design constraints associated with conventional parametric models such as Dynamic Average Model, Autoregressive MA, etc. for multidimensional spectral estimation using adaptive filters leads to a situation where higher computational complexities generate significant overhead on the systems. Therefore, the proposed study aims to formulate an efficient framework intended to derive a fast algorithm for processing Adaptive Capon and Phase Estimator (APES). The proposed method is applied to a non-stationary signal which is random. Further, the adaptive estimation of power spectra along with more accurate spectral efficiency has been identified in case of APES. An extensive performance evaluation followed by a comparative analysis has been performed by obtaining the values from different spectral estimation techniques, such as APES, PSC, ASC, and CAPON. Moreover, the framework ensures that unlike others, APES is subjected to attain superior signal quality regarding Power Spectral Density (PSD) and Signal to Noise Ratio (SNR) while achieving very less amount of Mean Square Error (MSE). It also exhibits comparatively low convergence speed and computational complexity as compared to its legacy versions. Keywords—Amplitude and phase estimation; ASC; capon spectral estimator; spectral estimation; PSC I. INTRODUCTION Spectral analysis of signals is the measurement of power spectral components further analyzed to investigate the frequency coefficients of a random signal. The power distribution over a non-stationary signal eases the computation of frequency components. However, the large scope of its applicability extended into various fields of study for software-driven electronic devices including Speech Analysis, Medicine, RADAR, and SONAR communications, etc. The prime reason lies in the fact that the frequency content of an observed signal can provide very useful information in the fields like multidimensional intelligence Naval and military communications [1], [2]. A data independent method namely Periodogram was initially developed by the author named Arthur Schuster with the purpose of estimating spectral coefficients of a non-stationary signal efficiently. The numerical computing method which is applied to a synthetic signal has adopted the concept of Fourier transform followed by efficient utilization of FFT algorithm [3]. However, the algorithm is claimed to have a limited scope of applications due to various factors such as poor resolution and high side lobe problems. This situation further leads to a scenario, where retrieval of significant information by analyzing signal coefficients becomes entirely unfeasible. An in-depth investigational study gives an insight into the fact that the conventional data-dependent (adaptive) methods for both non-parametric and parametric approaches attain superior performance efficiency in comparison with the conventional data independent methods like Periodogram. Adaptive data dependent methodologies are also claimed to achieve optimal computational cost. The applicability of data- adaptive approaches further leads to improve the spectrum quality of a signal significantly and helps to retrieve more information under study. Therefore, it has gained the interest among more researchers to explore its applicability towards mitigating issues of spectral estimation. These advantages have led to increasing interest in data-adaptive approaches towards the problem of spectral estimation. The proposed study thereby formulated a novel framework to access the performance efficiency of the conventional APES technique and determine the quality signal concerning PSD and computational complexity perspectives [4]. The study also gives insight into the in-depth performance analysis of conventional PSC, ASC and Capon estimation methods while improving the SNR as well as reducing the MSE of a non- stationary process. The experimental outcomes precisely exhibit the performance efficiency of the APES method on evaluating spectral correlation (SC) and effective spectral growth regarding SNR and PSD [5]. The paper is organized in a way where Section II discusses the essentials of the spectrum estimation followed by existing survey highlighted in Section III. However, Section IV discusses the conceptual framework for APES spectrum estimation. Finally, Section V discusses the outcomes of the study followed by Section VI that discusses conclusion and future work.