RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access

Abstract

Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)-which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments-is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs).In this paper we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo-RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance.

Large Intelligent Surface-Assisted Nonorthogonal Multiple Access for 6G Networks: Performance Analysis

“…Apart from the extended communication range, LIS offers additional advantages to massive IoT networks: i) Physical layer security can be enhanced in IoT devices by destructively combining the reflected beams from the LIS to prevent signals' leakage to illegal devices [28]. ii) Wireless power transfer in massive IoT networks is a prominent application for LIS.…”

Electromagnetic Model of Reflective Intelligent Surfaces

“…The metasurface unit cells are loaded with active components in order to control the phase and the amplitude of the reflection coefficient. The unit cells can be tuned such that signals bouncing off a RIS are combined constructively to increase signal in the position of the intended receiver or destructively to avoid leaking signals to undesired receivers [16]. The modeling, analysis and design of RISs for application to wireless networks is a multidisciplinary research topic covering communication theory, computer science and electromagnetism.…”

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

“…One solution to this problem is deploying RIS into umMTC systems since it can enhance the signal transmission by passive beamforming. Specifically, the work in [134] exploited the sum mean squared error criterion to design the active and passive beamformers jointly for an RIS-aided massive access system. However, the above joint beamforming design requires the CSI and the channel estimation for such a system needs a larger training overhead than conventional massive connectivity systems because the passive RIS cannot perform active transmitting/receiving.…”