Dynamic Blockage Pre-Avoidance using Reconfigurable Intelligent Surfaces

et al. 2022

Preprint

Self Cite

In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum intelligent reconfigurable surface (IRS) phase-shift vector for reducing the feedback overhead. The main idea consists of factorizing the IRS phase-shift vector as a Kronecker product of smaller vectors, namely factors. The proposed phase-shift model allows the network to trade-off between achievable data rate and feedback reduction by controling the factorization parameters. Our simulations show that the proposed phase-shift factorization drastically reduces the feedback overhead, while improving the data rate in some scenarios, compared to the state-of-the-art schemes.

Section: Proposed Feedback-aware Methodsmentioning

confidence: 99%

“…By applying the tensorization operator, defined as T {•}, we can form the tensor Y = T {y} ∈ C N1×N2×...×N P . The mapping of elements from y to Y is defined as Y n1,n2,...,n P = y n1+(n2−1)N1+...+(n P −1)N P −1 •••N2N1 , (5) where n p = {1, . .…”

Section: A Tensorizationmentioning

confidence: 99%

IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation

Sokal¹,

Gomes²,

et al. 2022

Preprint

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“…I NTELLIGENT reflecting surface (IRS) is a promising technology to beyond 5G wireless networks, which may offer high spectral efficiency, while improving the energy efficiency/reliability and reducing the end-to-end latency and cost [1], [2]. An IRS is a two-dimensional array structure composed of a large number of passive (or semi-passive) software-controlled reconfigurable scattering elements, whose electromagnetic response can be dynamically adjusted [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Semi-Blind Joint Channel and Symbol Estimation in IRS-Assisted Multi-User MIMO Networks

Araújo¹,

Gomes²,

et al. 2022

Preprint

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Intelligent reflecting surface (IRS) is a promising technology for beyond 5G wireless communications. In fully passive IRS-assisted systems, channel estimation is challenging and should be carried out only at the base station or at the terminals since the elements of the IRS are incapable of processing signals.In this letter, we formulate a tensor-based semi-blind receiver that solves the joint channel and symbol estimation problem in an IRS-assisted multi-user multiple-input multiple-output system. The proposed approach relies on a generalized PARATUCK tensor model of the signals reflected by the IRS, based on a two-stage closed-form semi-blind receiver using Khatri-Rao and Kronecker factorizations. Simulation results demonstrate the superior performance of the proposed semi-blind receiver, in terms of the normalized mean squared error and symbol error rate, as well as a lower computational complexity, compared to recently proposed parallel factor analysis-based receivers.

“…Another advantage of an IRS with fully passive elements is that the power consumption is concentrated at the controller. This makes the IRS a more attractive technology in terms of energy efficiency compared to alternative technologies, e.g., amplify-and-forward and decode-and-forward relays [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Reducing the Control Overhead of Intelligent Reconfigurable Surfaces Via a Tensor-Based Low-Rank Factorization Approach

Sokal¹,

Gomes²,

et al. 2022

Preprint

Self Cite

Passive intelligent reconfigurable surfaces (IRS) are becoming an attractive component of cellular networks due to their ability of shaping the propagation environment and thereby improving the coverage. While passive IRS nodes incorporate a great number of phase-shifting elements and a controller entity, the phase-shifts are typically determined by the cellular base station (BS) due to its computational capability. Since the fine granularity control of the large number of phase-shifters may become prohibitive in practice, it is important to reduce the control overhead between the BS and the IRS controller. To this end, in this paper we propose a low-rank approximation of the near-optimal phase-shifts, which would incur prohibitively high communication overhead on the BS-IRS controller links. The key idea is to represent the potentially large IRS phase-shift vector using a low-rank tensor model. This is achieved by factorizing a tensorized version of the IRS phase-shift vector, where each component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, which can be obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Our simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase-shifts, but with a drastically reduced communication overhead.