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Implementing an efficient frequency reuse (FR) plan is significantly important to meet the demand on high data rates and the required quality of service for 5G. In this paper, we use the direction of arrival algorithms and the correlator to determine the directions of the desired user and the interferers in the cell. Then, we use the beamformer to produce a beam towards the desired user and nulls in the direction of the interferers. Moreover, we implement the synthesizer to smartly form the desired beam shape and make the nulls deeper. We take the advantage of the smart antennas, beamforming capabilities, and the radiation pattern synthesizing techniques to build up an efficient FR plan for 5G. In addition, we develop a formula for calculating the signal to interference and noise ratio (SINR) in terms of the desired and the interferers directions. Our objective is to maintain the SINR at the minimum levels required for data calls with accepted quality while reducing the beam sizes, and hence increase the FR factor. Our simulation results show that with a uniform linear antenna of 11 elements, we can achieve the desirable SINR levels using beams of 10 • width, which raises the FR factor from 1 to 18 and subsequently increases the number of mobile users by 18 times.

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In cellular networks, the performance of the adaptive beamforming algorithms is severely degraded by the presence of the interfering signals. In this paper, we introduce a beamforming based algorithm for 5G applications named Direction Finding for Beamforming and Synthesizing (DFBS). This algorithm combines the Direction of Arrival (DOA), adaptive beamforming, and the radiation pattern synthesizing. The proposed algorithm uses the DOA technique to feed the adaptive beamforming algorithms with estimations of the desired user direction, desired user signal, and the interfering signals with their directions as initial values. In addition, we use the adaptive beamforming process to supply the radiation pattern synthesizing algorithms with an initial radiation pattern, and the required positions of nulls. At the beamformer output, we evaluate our proposed mechanism in terms of the error convergence, tracking capabilities, and the obtained radiation pattern characteristics. At the synthesizer output, we carry out analysis in terms of the convergence speed and the resultant radiation pattern attributes to investigate the efficiency of the proposed algorithm. The simulation results show that, our proposed algorithm has significantly fast convergence, reliable tracking capabilities, and radiation patterns with very low Side Lobe Levels (SLL).2010 MSC: 00-01, 99-00