Adaptive beamformer

About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.

A Class of Constrained Adaptive Beamforming Algorithms Based on Uniform Linear Arrays

Abstract: A new class of adaptive beamforming algorithms is proposed based on a uniformly spaced linear array by constraining its weight vector to a specific conjugate symmetric form. The method is applied to the well-known reference signal based (RSB) beamformer and the linearly constrained minimum variance (LCMV) beamformer as two implementation examples. The effect of the additional constraint is equivalent to adding a second step in the derived adaptive algorithm. However, a difference arises for the RSB case since no direction-of-arrival (DOA) information of the desired signal is available, which leads to a two-stage structure for incorporating the imposed constraint. Compared to the traditional algorithms, the proposed ones can achieve a faster convergence speed and a higher steady state output signal-to-interference-plus-noise ratio, given the same stepsize.

Enhanced connection acquisition using an array antenna

Abstract: An array antenna is utilized to enhance the adaptive acquisition capability of a communication connection with one or more wireless subscribers. Subscribers who are located outside the omnidirectional range of the array antenna are acquired by using adaptive beamforming techniques to create an acquisition beam dedicated to acquiring new connections with wireless subscribers. The acquisition beam may sweep through the coverage of the array antenna seeking subscribers who lie beyond the omni range of the array antenna, but fall within the acquisition range using adaptive beamforming.

Deep Learning Based Hotspot Prediction and Beam Management for Adaptive Virtual Small Cell in 5G Networks

Abstract: To meet the extremely stringent but diverse requirements of 5G, cost-effective network deployment and traffic-aware adaptive utilization of network resources are becoming essential. In this paper, a hotspot prediction based virtual small cell (VSC) operation scheme is adopted to improve both the cost efficiency and operational efficiency of 5G networks. This paper focuses on how to predict the hotspots by using deep learning, and then demonstrates how the predictions can be leveraged to support adaptive beamforming and VSC operation. We first leverage the feature extraction capabilities of deep learning and exploit use of a long short-term memory (LSTM) neural network to achieve hotspot prediction for the potential formation of the VSCs. To support the operation of VSCs, large-scale antenna array enabled hybrid beamforming is adaptively adjusted for highly directional transmission to cover these hotspot-based VSCs. Within each VSC, an appropriate user equipment is selected as a cell head to collect the intra-cell traffic in the unlicensed band and relays the aggregated traffic to the macro-cell base station by using the licensed band. Our simulation results illustrate that the proposed LSTM-based method can extract spatial and temporal traffic features of hotspot with higher accuracy, compared with some existing deep and non-deep learning approaches. Numerical results also show that VSCs with hotspot prediction and hybrid beamforming can improve the energy efficiency dramatically with flexible deployment and low latency, compared with the scenario of the convolutional fixed small cells.

Dual-Microphone Speech Dereverberation using a Reference Signal

Abstract: Speech signals recorded with a distant microphone usually contain reverberation, which degrades the fidelity and intelligibility of speech, and the recognition performance of automatic speech recognition systems. In this paper we propose a speech dereverberation system which uses two microphones. A generalized sidelobe canceller (GSC) type of structure is used to enhance the desired speech signal. The GSC structure is used to create two signals. The first signal is the output of a standard delay and sum beamformer, and the second signal is a reference signal which is constructed such that the direct speech signal is blocked. We propose to utilize the reverberation which is present in the reference signal to enhance the output of the delay and sum beamformer. The power envelope of the reference signal and the power envelope of the output of the delay and sum beamformer are used to estimate the residual reverberation in the output of the delay and sum beamformer. The output of the delay and sum beamformer is then enhanced using a spectral enhancement technique. The proposed method only requires an estimate of the direction of arrival of the desired speech source. Experiments using simulated room impulse responses are presented and show significant reverberation reduction while keeping the speech distortion low.

Semi-Adaptive Beamforming for OFDM Based Hybrid Terrestrial-Satellite Mobile System

Abstract: Adaptive interference mitigation requires significant resources due to recursive processing. Specific to satellite systems, interference mitigation by employing adaptive beamforming at the gateway or at the satellite both have associated problems. While ground based beamforming reduces the satellite payload complexity, it results in added feeder link bandwidth requirements, higher gateway complexity and suffers from feeder link channel degradations. On the other hand, employing adaptive beamforming onboard the satellite gives more flexibility in case of variation in traffic dynamics and also for changing of beam patterns. However, these advantages come at the cost of additional complexity at the satellite. In pursuit of retaining the benefits of onboard beamforming and to reduce the complexity associated with adaptive processing, we here propose a novel semi-adaptive beamformer for a Hybrid Terrestrial-Satellite Mobile System. The proposed algorithm is a dual form of beamforming that enables adaptive and non-adaptive processing to coexist via a robust gradient based switching mechanism. We present a detailed complexity analysis of the proposed algorithm and derive bounds associated with its power requirements. In the scenarios studied, results show that the proposed algorithm consumes up to 98% less filter computing power as compared to full-adaptive case without compromising on system performance.