English

Intelligent Reflecting Surface based Passive Information Transmission: A Symbol-Level Precoding Approach

Signal Processing 2022-01-25 v2

Abstract

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes.

Keywords

Cite

@article{arxiv.2007.14738,
  title  = {Intelligent Reflecting Surface based Passive Information Transmission: A Symbol-Level Precoding Approach},
  author = {Rang Liu and Ming Li and Qian Liu and A. Lee Swindlehurst and Qingqing Wu},
  journal= {arXiv preprint arXiv:2007.14738},
  year   = {2022}
}

Comments

14 pages, 11 figures, major revision

R2 v1 2026-06-23T17:29:24.390Z