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A Fast Converging Channel Estimation Algorithm for Wireless Sensor Networks

A Fast Converging Channel Estimation Algorithm for Wireless Sensor Networks

ABSTRACT :

A set-membership affine projection algorithm is proposed which can estimate a complex-valued channel matrix using a set of complex-valued pilots in the presence of additive white Gaussian noise (AWGN). It is shown that the algorithm converges faster than the well-known set-membership normalized least mean square algorithm (SM-NLMS) while it resolves the high steady-state error value and the complexity issues in the regular affine projection algorithm. The fast convergence of the proposed algorithm means that a shorter training sequence inside each data block is required which in turn improves the effective bit-rate. This fast convergence is more pronounced when the pilot vectors are highly correlated, which can occur in wireless sensor networks (WSNs) due to the power consumption and complexity constraints on the pilot-orthogonality management. We incorporated the set-membership filtering framework into our study to reduce the computational complexity of the algorithm and preserve energy in the WSNs. Other studies have shown the superiority of the adaptive filtering algorithms, and in particular the NLMS algorithm, over other alternatives in various signal processing areas in WSNs, therefore, our proposed algorithm is a powerful substitute for a variety of algorithms. In other words, the implementation of various signal processing algorithms for different purposes can be replaced with the implementation of the proposed multi-task algorithm. In this work, we combine the results of our previous studies and prove the convergence of the algorithm. Furthermore, the steady-state analysis in the output mean-square error (MSE) is presented for two cases of pilot signals, and in the conducted simulations, the MSE performance of the algorithm is compared with the regular affine projection algorithm and the SM-NLMS algorithm.

 

EXISTING SYSTEM :

The same situation holds for channel estimation. Channel estimation is a fundamental part of many communication systems. However, different communication systems have different constraints.

This makes the implementation of some algorithms difficult or sometimes impossible. Furthermore, WSNs have various types depending on their applications.

Sensors can be plugged-in or powered by batteries. They can be also powered by various energy scavengers such as photovoltaic cells or wind energy harvesters. Similar to the power constraints, limitations might also exist for memory and life-time.

 

PROPOSED SYSTEM :

The implementation of various signal processing algorithms for different purposes can be replaced with the implementation of the proposed multi-task algorithm. In this work, we combine the results of our previous studies and prove the convergence of the algorithm.

Furthermore, the steady-state analysis in the output mean-square error (MSE) is presented for two cases of pilot signals, and in the conducted simulations, the MSE performance of the algorithm is compared with the regular affine projection algorithm and the SM-NLMS algorithm.

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