LLMS adaptive beamforming algorithm implemented with finite precision

This paper studies the influence of the use of finite wordlength on the operation of the LLMS adaptive beamforming algorithm. The convergence behavior of LLMS algorithm, based on the minimum mean square error (MSE), is analyzed for operation with finite precision. Computer simulation results verify that a wordlength of eight bits is sufficient for the LLMS algorithm to achieve performance close to that provided by full precision. Based on the simulation results, it is shown that the LLMS algorithm outperforms least mean square (LMS) in addition to other earlier algorithms, such as, modified robust variable step size (MRVSS) and constrained stability LMS (CSLMS)

Low complexity robust adaptive beamformer based on parallel RLMS and Kalman RLMS

International audienceTo ease spectral congestion and enhance frequency reuse, researchers are targeting smart antenna systems using spatial multiplexing and adaptive signal processing techniques. Moreover, the accuracy and efficiency of such systems is highly dependent on the adaptive algorithms they employ. A popular, adaptive beamforming algorithm, widely used in smart antennas, is the Recursive Least Square (RLS) algorithm. While, the classical RLS implementation achieves high convergence, it still suffers from its inability to track the target of interest. Recently, a new adaptive algorithm called Recursive Least Square - Least Mean Square (RLMS) which employs a RLS stage followed by a Least Mean Square (LMS) algorithm stage and separated by an estimate of the array image vector, i.e. steering vector, has been proposed. RLMS outperforms previous RLS and LMS variants, with superior convergence and tracking capabilities, at the cost of a moderate increase in computational complexity. In this paper, an enhanced, low complexity parallel version of the cascade RLMS is presented by eliminating the need for computing the array image vector cascading stage. Hence, For an antenna of N elements our strategy can reduce the complexity of the system by 20N multiplications, 6N additions and 2N divisions. Moreover, a new Kalman based parallel RLMS (RKLMS) method is also proposed, where the LMS stage is replaced by a Kalman implementation of the classical LMS, and compared under low Signal to Interference plus Noise ratios (SINR). Simulation results show identical performance for the parallel RLMS, cascaded RLMS at 10dB and superior performance and robustness for the RKLMS on low SINR cases up to -10dB