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Large Scale Antenna Systems Made Practical: Advanced Signal Processing for Compact Deployments [LSAS-SP]

大规模天线系统变得实用：用于紧凑部署的高级信号处理 [LSAS-SP]

基本信息

批准号：
EP/M014150/1
负责人：
Christos Masouros
金额：
$ 34.03万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM014150%2F1
关键词：
Large Scale Antenna Systems Made

项目摘要

This project investigates signal processing techniques for practical and realistic implementations of large-scale antenna systems (LSAS) for energy- and spectral- efficient wireless communication. It is expected that the energy bill for cellular networks will double by 2015 and therefore there is a growing concern to reduce the associated operational expenditure (OPEX) along with the global CO2 emission in all fields of communications. The physical layer of wireless communication is a core building block of the telecommunication system chain and the ever-increasing Quality-of-Service demands directly reflect on the performance requirements of the relevant signal processing techniques. The physical limitations of wireless propagation form the bottleneck of physical layer transmission. Multiple Input Multiple Output (MIMO) systems have proven particularly useful in circumventing this bottleneck by providing an increased number of data streams in the physical channel. Small scale MIMO systems are currently part of communication standards and commercial designs. LSAS are envisaged for the next generations of wireless systems, to capitalise on the utilisation of multiple antennas, and deliver the transmission rates required for future communications in a power-efficient manner. LSAS involve several critical benefits:- The transmit power is split to many low power antennas, of the order of milliWatts.- Hence, the design of the radio frequency (RF) front-end components is simplified as low cost power amplifiers can be deployed. - LSAS designs can be extremely robust in that the failure of one or a few of the antenna units would not appreciably affect the system. - In terms of signal processing, by scaling up the dimensions of MIMO low complexity user detection and precoding become close-to-optimal. - In information theoretic terms, as the numbers of antennas grow infinitely large, the statistics of the MIMO channel tend to deterministic functions. and associated challenges:- The massive amount of RF chains required to feed the hundreds of antennas poses an important practical challenge in their deployment, - With the increase of spatial dimensions the complexity of even the simplest signal processing techniques increases significantly - The massive antenna arrays must be deployed in the limited physical space that is available in both base stations and mobile devices. This creates two main effects which become particularly relevant in LSAS: spatial correlation due to the proximity of the antennas as signal sources and mutual coupling due to the proximity of the antennas as electrical components. - For large numbers of antennas pilot sequences for channel estimation have to be reused between adjacent cells. Channel State Information (CSI) provisioning becomes a significant burden and the performance of LSAS becomes limited by the resulting inter-cell interference (pilot contamination problem). This project tackles the issue of large scale antenna deployment by a) information theoretical analysis with realistic modelling, b) signal processing and CSI acquisition devoted to power efficiency and c) analogue-digital beamforming designs and reduced RF-chain solutions aimed at power- and cost- effective implementations. The project aims to achieve power-efficient transmission by large scale antenna systems based on two key disruptive concepts: a) using analogue beamforming using the principles of Electrically-Steerable Parasitic Array Radiators (ESPAR) based LSAS and b) exploiting constructive interference. In addition, this project re-examines the anticipated benefits of LSAS from the viewpoint of realistic deployments of the antenna arrays in limited physical space which are prone to increased correlation and coupling between the densely deployed antennas. We aim at a thorough and pragmatic investigation of the benefits of LSAS for Green Communications, and their practical implementation solutions.

本计画主要研究信号处理技术，应用于大规模天线系统（LSAS）的实际应用，以达到高能量及频谱效率的无线通讯。预计到2015年，蜂窝网络的能源账单将翻一番，因此，人们越来越关注减少所有通信领域中的相关运营支出（OPEX）沿着全球CO2排放。无线通信的物理层是电信系统链的核心构建块，并且不断增长的服务质量需求直接反映在相关信号处理技术的性能要求上。无线传播的物理限制形成了物理层传输的瓶颈。多输入多输出（MIMO）系统已经证明在通过在物理信道中提供增加数量的数据流来规避该瓶颈方面特别有用。小规模MIMO系统目前是通信标准和商业设计的一部分。 LSAS被设想用于下一代无线系统，以利用多个天线的使用，并以节能的方式提供未来通信所需的传输速率。LSAS涉及几个关键的好处：-发射功率被分割到许多毫瓦量级的低功率天线。因此，射频（RF）前端组件的设计被简化，因为可以部署低成本功率放大器。- LSAS设计可以是非常稳健的，因为一个或几个天线单元的故障不会明显影响系统。- 在信号处理方面，通过按比例增加MIMO的维度，低复杂度的用户检测和预编码变得接近最优。- 在信息论的术语中，当天线的数量无限大时，MIMO信道的统计趋于确定性函数。以及相关的挑战：-需要大量的RF链来馈送数百个天线，这对它们的部署提出了重要的实际挑战。-随着空间维度的增加，即使是最简单的信号处理技术的复杂性也显著增加。-必须在基站和移动的设备中可用的有限物理空间中部署大量的天线阵列。这产生了两个主要的效应，这在LSAS中变得特别相关：由于天线作为信号源的接近而产生的空间相关性以及由于天线作为电气组件的接近而产生的互耦合。- 对于大量的天线，用于信道估计的导频序列必须在相邻小区之间重用。信道状态信息（CSI）提供成为显著的负担，并且LSAS的性能受到所产生的小区间干扰（导频污染问题）的限制。该项目通过以下方式解决大规模天线部署问题：a）具有现实建模的信息理论分析，B）致力于功率效率的信号处理和CSI采集，以及c）旨在实现功率和成本效益的模拟-数字波束成形设计和减少RF链解决方案。该项目旨在通过基于两个关键颠覆性概念的大规模天线系统实现功率高效传输：a）使用基于LSAS的电可控寄生阵列辐射器（ESPAR）原理的模拟波束成形，以及B）利用相长干扰。此外，该项目从天线阵列在有限物理空间中的实际部署的角度重新审视了LSAS的预期效益，这些物理空间易于增加密集部署的天线之间的相关性和耦合。我们的目标是对LSAS在绿色通信中的好处及其实际实施解决方案进行全面而务实的调查。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Energy-Efficient Trajectory Design of a Multi-IRS Assisted Portable Access Point

DOI：
10.1109/tvt.2022.3202953
发表时间：
2022-09
期刊：
IEEE Transactions on Vehicular Technology
影响因子：
6.8
作者：
N. Babu;M. Virgili;M. Al-jarrah;Xiaoye Jing;E. Alsusa;P. Popovski;Andrew J. Forsyth;C. Masouros;C. Papadias
通讯作者：
N. Babu;M. Virgili;M. Al-jarrah;Xiaoye Jing;E. Alsusa;P. Popovski;Andrew J. Forsyth;C. Masouros;C. Papadias

A Memory-Efficient Learning Framework for Symbol Level Precoding With Quantized NN Weights