Active RIS Assisted Rate-Splitting Multiple Access Network: Spectral and Energy Efficiency Tradeoff

被引:51
|
作者
Niu, Hehao [1 ]
Lin, Zhi [2 ,3 ]
An, Kang [1 ]
Wang, Jiangzhou [4 ]
Zheng, Gan [5 ]
Al-Dhahir, Naofal [6 ]
Wong, Kai-Kit [7 ]
机构
[1] Natl Univ Def Technol, Res Inst 63, Nanjing 210007, Peoples R China
[2] Natl Univ Def Technol, Coll Elect Engn, Hefei 230037, Peoples R China
[3] Macau Univ Sci & Technol, Sch Comp Sci & Engn, Macau, Peoples R China
[4] Univ Kent, Sch Engn, Canterbury CT2 7NZ, England
[5] Univ Warwick, Sch Engn, Coventry CV4 7AL, England
[6] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75080 USA
[7] UCL, Dept Elect & Elect Engn, London WC1E 6BT, England
基金
中国国家自然科学基金;
关键词
Optimization; Measurement; Wireless networks; Precoding; Power demand; NOMA; Hardware; Rate-splitting multiple access; RIS; active load; tradeoff; resource efficiency; MULTIUSER MISO SYSTEMS; WIRELESS NETWORKS; RATE MAXIMIZATION; POWER-CONTROL; SUM-RATE; INTELLIGENT; TRANSMISSION; OPTIMIZATION; ALLOCATION; SECURE;
D O I
10.1109/JSAC.2023.3240718
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks.
引用
收藏
页码:1452 / 1467
页数:16
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