No abstract

Abstract-In this work, we propose a method based

Abstract-In this paper, we have addressed three major problems of uniform linear array in case of a sensor failure at any position. We assume that sensor position is known. The problems include increase in sidelobe levels, displacement of nulls and diminishing of null depth. The desired null depth is achieved by making the weight of symmetrical counterpart element passive. Genetic algorithm (GA) along with pattern search (PS) is used for reduction of sidelobe levels, and adjustment of nulls. Fitness function minimizing the error between the desired and estimated beam pattern along with null constraints is used. Simulation results for diversified scenarios have been given to demonstrate the validity and performance of the proposed algorithm.

Abstract:In this paper, we propose a structure for independent null steering by decoupling complex weights. It has the advantage of independent steering of (N − 1) nulls for a linear array having N elements and the complex weights are decoupled such that each weight corresponds to a particular null. For this purpose, the position of each zero of the array factor on the unit circle in the complex plane is controlled by the corresponding weight in the structure. It means that if a single jammer changes its position, only a single corresponding complex weight has to be recalculated, which reduces computational complexity. Keywords: null steering, linear array, array factor, progressive phase Classification: Science and engineering for electronics References[1] H. Krim and M. Viberg, "Two decades of array signal processing research,"

In rhis work a iiew technique is presented f a . blind chunnel rynaliza~ion. Must of the existing techniques perfor-m channel esrimatior? in firs1 phuse and eqzrdizotion in second phase. The algorithm pr.e.~enred hew provides not on1,v the direct blind equalizntion of fhe chcmnel oiitprrts but also provides the w h a l e d channe( in parallel. This technique utilizes three layered Artificial Neural Networks (ANN) model accompanied with learning algorithm for updufing of the weights. This learni*rg algorithm iriilizes the EiiclideaH distance error as well as the statistics to be moiiiraiiied ai three different lajrers of' ANN. The weights between first two layers provide an eqwlizatioiz matri.y and oufpiif of second layer gives estimrrte of s o w c e symbok. The weights between second and rhird layer provide channel esfimate.

In this paper, we propose a method based on evolutionary computations for joint estimation of amplitude, Direction of Arrival and range of near field sources. We use memetic computing in which the problem starts with a global optimizer and ends up with a local optimizer for fine tuning. For this, we use Genetic algorithm and Simulated annealing as a global optimizer while Interior Point Algorithm as a rapid local optimizer. We set up Mean Square Error as a fitness evaluation function which defines an error between actual and estimated signal. This fitness function is optimum and is derived from Maximum likelihood principle. It requires only single snapshot to converge and does not require any permutations to link it with the angles found in the previous snapshot as in some other methods. The efficiency and reliability of the proposed scheme is tested on the basis of Monte-Carlo simulations and its inclusive statistical analysis.

No abstract

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.