研究目的
To propose a self optimization beamforming null control (SOBNC) scheme for improving the signal to interference plus noise ratio (SINR) in wireless communication systems by automatically controlling the number of nulls in the antenna pattern using an adaptive-network based fuzzy inference system (ANFIS) to maintain SINR above a threshold.
研究成果
The ANFIS-based SOBNC effectively selects the optimum number of nulls to maintain receiver SINR above the threshold (10 dB) with minimal distortion to the antenna pattern. It outperforms MMSE-based adaptive nulling and fixed null schemes by improving SINR gain, especially in environments with varying SINR and angular spread, making it suitable for modern wireless systems like Wi-Fi, LTE, and LTE-A.
研究不足
The study is based on simulations and may not account for all real-world channel variations or hardware imperfections. The ANFIS training requires extensive data, and the method is specific to the modeled scenarios (e.g., SC-FDMA signals and GBSBM channel). The number of nulls is limited to 1, 3, or 5, which might not cover all possible interference conditions.
1：Experimental Design and Method Selection:
The study uses a simulation-based approach to evaluate the SOBNC scheme. It involves modeling wireless communication systems with multipath fading channels, employing the Geometrically Based Single Bounce Macro-cell (GBSBM) channel model, and using the generalized multiple signal classification (GMUSIC) algorithm for direction of arrival (DOA) and angular spread (AS) estimation. The ANFIS is applied for adaptive null control.
2：Sample Selection and Data Sources:
Simulations are conducted with SC-FDMA signals (used in LTE and LTE-A uplink) as desired and interference signals. Parameters include bandwidth of 10 MHz, subcarrier spacing of 15 KHz, FFT/IFFT size of 1024, and preamble length of 320. Scatter radii of 0.5 km, 0.75 km, and 1 km are used to vary AS, with a distance of 10 km between base station and mobile station.
3：Scatter radii of 5 km, 75 km, and 1 km are used to vary AS, with a distance of 10 km between base station and mobile station.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: A uniform linear array (ULA) with 16 antenna elements and half-wavelength inter-element spacing is simulated. A 30 dB Chebyshev window is applied. No specific physical equipment is mentioned; the study is computational.
4：Experimental Procedures and Operational Workflow:
The process includes channel estimation using pilots, DOA and AS estimation via GMUSIC, SINR measurement, and ANFIS-based selection of the number of nulls (1, 3, or 5). Weight vectors for beamforming are calculated using covariance matrix methods. Simulations run over 500 snapshots for each scenario.
5：5). Weight vectors for beamforming are calculated using covariance matrix methods. Simulations run over 500 snapshots for each scenario.
Data Analysis Methods:
5. Data Analysis Methods: Performance is evaluated using root mean square error (RMSE) of SINR estimation and comparison of output SINR with thresholds. The ANFIS is trained with data from simulations to optimize premise and consequence parameters.
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