广播频道的宽波束设计

IF 4.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal Processing Pub Date : 2024-08-13 DOI:10.1109/TSP.2024.3440353
Cheng Du;Yi Jiang
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引用次数: 0

摘要

在大规模多输入多输出(MIMO)蜂窝通信系统中,用于普通信息广播的传统波束扫频方案可提供较高的波束成形增益,但由于波束较窄,需要的时隙过多。为了在保持足够波束增益的同时缩短波束扫描时间,本文重点研究了设计波束宽度可调的宽波束。首先,通过对阶跃啁啾模拟信号进行过采样,我们构建了一个新的序列族,称为广义阶跃啁啾(GSC)序列,它具有简单的闭式表达式,其中一些参数可以调整,以灵活地调整波束宽度和粗化相位分辨率。单极化天线均匀矩形阵列(URA)的波束成形矩阵可视为两个 GSC 序列的外积。其次,通过利用双极化天线 URA 的全自由度,我们使用数值算法进一步减少了通带中的功率变化。通过利用问题的特定结构,可以大大加快算法的速度。这两种方案都可以使用具有有限分辨率的模拟移相器网络(PSN)来实现。
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Broad Beam Designs for Broadcast Channels
In a massive multi-input multi-output (MIMO) cellular communication system, the conventional beam-sweeping scheme for common message broadcasting provides high beamforming gain but requires too many time slots due to the narrowness of the beams. To reduce the beam sweeping time while maintaining a sufficient beamforming gain, this paper focuses on designing broad beams with tunable beamwidths. First, by over-sampling a step-chirp analog signal, we construct a novel sequence family termed the generalized step-chirp (GSC) sequence with a simple closed-form expression, in which some parameters can be tuned to flexibly adjust the beamwidth and coarsen the phase resolution. The beamforming matrix of a uniform rectangular array (URA) of single–polarized antennas can be taken as the outer product of two GSC sequences. Second, by exploiting the full degree of freedom of URA of dual-polarized antennas, we further reduce the power variations in the passband by using a numerical algorithm. The algorithm can be drastically accelerated by exploiting the specific structure of the problem. Both schemes can be implemented using an analog phase shifter network (PSN) with finite resolution.
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来源期刊
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing 工程技术-工程:电子与电气
CiteScore
11.20
自引率
9.30%
发文量
310
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
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