Max-Min SINR Optimization for RIS-Aided Uplink Communications With Green Constraints

Subhash, Athira; Kammoun, Abla; Elzanaty, Ahmed; Kalyani, Sheetal; Al-Badarneh, Yazan H.; Alouini, Mohamed‐Slim

doi:10.1109/lwc.2023.3244519

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…MMSE Precoder/Combiner. Following the steps in [17], the solution for the above problem is given by the minimum mean squared error (MMSE):…”

Section: B Combiner (Rx-ul) Designmentioning

confidence: 99%

Performance of Massive MIMO aided by Reflective Intelligent Surfaces

Flaiban,

Abrao

2024

Preprint

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Reflective intelligent surfaces (RIS) are large passive panels with hundreds or thousands of passive reflective meta- surface sensors, with the ability to electronically adjust the angle- of-departure (AoD) of the incident signal but not to apply any amplification and signal processing to the radio frequency signal. This technology is up-and-coming for networks beyond 5G (B5G). We evaluate the performance gains, including SINR and outage probability (OP) of RIS-aided massive MIMO systems regard- ing the conventional massive MIMO systems, highlighting the potential of beamforming performance and RIS element phase- shift optimization, aiming to deal with dense urban obstacles scenarios and high density of mobile users. This work aims to investigate the behavior of a RIS-aided MIMO system under different channel scenarios, considering the Rician factor.

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“…MMSE Precoder/Combiner. Following the steps in [17], the solution for the above problem is given by the minimum mean squared error (MMSE):…”

Section: B Combiner (Rx-ul) Designmentioning

confidence: 99%

Performance of Massive MIMO aided by Reflective Intelligent Surfaces

Flaiban,

Abrao

2024

Preprint

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“…In [28], the RIS phases were optimized to minimize the total uplink exposure of users. On the other hand, the work in [29] and [30] considered designing the RIS phases to minimize the maximum exposure (min-max problem) with instantaneous and statistical channel state information, respectively. The authors in [31] applied probabilistic shaping to minimize the average EMF exposure while ensuring a target throughput.…”

Section: A Related Workmentioning

confidence: 99%

Joint Uplink and Downlink EMF Exposure: Performance Analysis and Design Insights

Chen

Elzanaty

Kishk

et al. 2023

IEEE Trans. Wireless Commun.

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Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rates in the 5th-generation cellular networks (5G). However, a non-negligible amount of the population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population's exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than 20 BSs/km 2 .

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“…Several studies have been conducted on solving the challenges of beamforming and phase shift design in an RISaided multi-user MIMO scenario with different objectives [4]- [14] such as maximization of the sum rate [4], [5], spectral efficiency [6] and energy efficiency [7], [8] or minimization Fig. 1: The system model of the transmission power [9], max-min SINR problem [10], [11], and the exposure to electromagnetic fields [12], [13]. However, the proposed solutions involve intricate algorithms and straightforward designs are not presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

MU-Massive MIMO With Multiple RISs: SINR Maximization and Asymptotic Analysis

Aghashahi

Zeinalpour-Yazdi

Tadaion

et al. 2023

IEEE Wireless Commun. Lett.

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In this letter, we investigate the signal-tointerference-plus-noise-ratio (SINR) maximization problem in a multi-user massive multiple-input-multiple-output (massive MIMO) system enabled with multiple reconfigurable intelligent surfaces (RISs). We examine two zero-forcing (ZF) beamforming approaches for interference management namely BS-UE-ZF and BS-RIS-ZF that enforce the interference to zero at the users (UEs) and the RISs, respectively. Then, for each case, we resolve the SINR maximization problem to find the optimal phase shifts of the elements of the RISs. Also, we evaluate the asymptotic expressions for the optimal phase shifts and the maximum SINRs when the number of the base station (BS) antennas tends to infinity. We show that if the channels of the RIS elements are independent and the number of the BS antennas tends to infinity, random phase shifts achieve the maximum SINR using the BS-UE-ZF beamforming approach. The simulation results illustrate that by employing the BS-RIS-ZF beamforming approach, the asymptotic expressions of the phase shifts and maximum SINRs achieve the rate obtained by the optimal phase shifts even for a small number of the BS antennas.

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Max-Min SINR Optimization for RIS-Aided Uplink Communications With Green Constraints

Cited by 12 publications

References 19 publications

Performance of Massive MIMO aided by Reflective Intelligent Surfaces

Performance of Massive MIMO aided by Reflective Intelligent Surfaces

Joint Uplink and Downlink EMF Exposure: Performance Analysis and Design Insights

MU-Massive MIMO With Multiple RISs: SINR Maximization and Asymptotic Analysis

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