Jintao Wang, Chengzhi Ma, Shiqi Gong, Xi Yang, Shaodan Ma
{"title":"Joint Beamforming Optimization and Mode Selection for RDARS-aided MIMO Systems","authors":"Jintao Wang, Chengzhi Ma, Shiqi Gong, Xi Yang, Shaodan Ma","doi":"arxiv-2401.11205","DOIUrl":null,"url":null,"abstract":"Considering the appealing distribution gains of distributed antenna systems\n(DAS) and passive gains of reconfigurable intelligent surface (RIS), a flexible\nreconfigurable architecture called reconfigurable distributed antenna and\nreflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two\nspecial cases and maintains the advantages of distributed antennas while\nreducing the hardware cost by replacing some active antennas with low-cost\npassive reflecting surfaces. In this paper, we present a RDARS-aided uplink\nmulti-user communication system and investigate the system transmission\nreliability with the newly proposed architecture. Specifically, in addition to\nthe distribution gain and the reflection gain provided by the connection and\nreflection modes, respectively, we also consider the dynamic mode switching of\neach element which introduces an additional degree of freedom (DoF) and thus\nresults in a selection gain. As such, we aim to minimize the total sum\nmean-square-error (MSE) of all data streams by jointly optimizing the receive\nbeamforming matrix, the reflection phase shifts and the channel-aware placement\nof elements in the connection mode. To tackle this nonconvex problem with\nintractable binary and cardinality constraints, we propose an inexact block\ncoordinate descent (BCD) based penalty dual decomposition (PDD) algorithm with\nthe guaranteed convergence. Since the PDD algorithm usually suffers from high\ncomputational complexity, a low-complexity greedy-search-based alternating\noptimization (AO) algorithm is developed to yield a semi-closed-form solution\nwith acceptable performance. Numerical results demonstrate the superiority of\nthe proposed architecture compared to the conventional fully passive RIS or\nDAS. Furthermore, some insights about the practical implementation of RDARS are\nprovided.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"122 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Information Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2401.11205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Considering the appealing distribution gains of distributed antenna systems
(DAS) and passive gains of reconfigurable intelligent surface (RIS), a flexible
reconfigurable architecture called reconfigurable distributed antenna and
reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two
special cases and maintains the advantages of distributed antennas while
reducing the hardware cost by replacing some active antennas with low-cost
passive reflecting surfaces. In this paper, we present a RDARS-aided uplink
multi-user communication system and investigate the system transmission
reliability with the newly proposed architecture. Specifically, in addition to
the distribution gain and the reflection gain provided by the connection and
reflection modes, respectively, we also consider the dynamic mode switching of
each element which introduces an additional degree of freedom (DoF) and thus
results in a selection gain. As such, we aim to minimize the total sum
mean-square-error (MSE) of all data streams by jointly optimizing the receive
beamforming matrix, the reflection phase shifts and the channel-aware placement
of elements in the connection mode. To tackle this nonconvex problem with
intractable binary and cardinality constraints, we propose an inexact block
coordinate descent (BCD) based penalty dual decomposition (PDD) algorithm with
the guaranteed convergence. Since the PDD algorithm usually suffers from high
computational complexity, a low-complexity greedy-search-based alternating
optimization (AO) algorithm is developed to yield a semi-closed-form solution
with acceptable performance. Numerical results demonstrate the superiority of
the proposed architecture compared to the conventional fully passive RIS or
DAS. Furthermore, some insights about the practical implementation of RDARS are
provided.