Adaptive Algorithms and Beamforming Algorithms for Smart Antennas in Software Defined Radio

by sdruav.com | Sep 28, 2025 | Articles

Adaptive Algorithms and Beamforming Algorithms for Smart Antennas in Software Defined Radio
Adaptive algorithms are the core research focus of smart antennas and are generally categorized into non-blind algorithms and blind algorithms.

Non-blind algorithms require reference signals (such as pilot sequences or pilot channels). In this case, the receiver knows what was transmitted. These algorithms determine or iteratively adjust the weight coefficients according to a specific criterion to maximize the correlation between the smart antenna's output and the known input. Commonly used correlation criteria include MMSE (Minimum Mean Square Error), LMS (Least Mean Square), and LS (Least Squares).

Blind algorithms, on the other hand, do not require the transmitter to send known pilot signals. They typically exploit inherent characteristics of the modulated signal that are independent of the specific information bits, such as constant modulus, subspace properties, finite symbol sets, or cyclostationarity. The algorithms adjust the weight coefficients so that the output satisfies these properties. Common examples are various gradient-based algorithms using different constraints.

Compared to blind algorithms, non-blind algorithms generally have smaller errors and faster convergence speeds but consume certain system resources. Combining the two leads to semi-blind algorithms. These use a non-blind algorithm to determine the initial weights and then employ a blind algorithm for tracking and adjustments. This approach combines the advantages of both and aligns with practical communication systems, where pilot symbols are not transmitted continuously but are time-multiplexed with the corresponding traffic channels.

The goal of beamforming is to achieve the optimal combination and distribution of baseband signals based on system performance indicators. Specifically, the main tasks of beamforming are to compensate for signal fading and distortion caused by path loss and multipath effects during wireless propagation, while also reducing co-channel interference between users.

Software Defined Radio systems universally implement beam synthesis digitally, known as Digital Beamforming (DBF). This allows for the updating of adaptive algorithms through software design, increasing system flexibility without changing the hardware configuration.

Depending on the different stages of the beamforming process, the implementation of smart antennas can be divided into two approaches:
1. Element Space Processing: This method directly samples the received signals from each antenna element, applies weighting, and then combines them to form the array output. This aims the main lobe of the antenna pattern towards the user's signal direction of arrival. All elements in the antenna array participate in the adaptive adjustment.

2. Beam Space Processing: This approach essentially involves a two-stage process. The first stage applies fixed weighting to the signals from each element and sums them to form beams pointing in different directions. The second stage performs adaptive weighting adjustment and synthesis on the outputs of the first stage. This scheme does not calculate weight coefficients for all elements for global optimization but only performs adaptive processing on a subset of beams. Its characteristics include lower computational complexity, faster convergence, and good beam pattern preservation.

Adaptive Algorithms and Beamforming Algorithms for Smart Antennas in Software Defined Radio

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