Demystifying the Power Scaling Law of Intelligent Reflecting Surfaces and Metasurfaces

Intelligent reflecting surfaces (IRSs) have recently attracted the attention of communication theorists as a means to control the wireless propagation channel. It has been shown that the signal-to-noise ratio (SNR) of a single-user IRS-aided transmission increases as $N^2$, with $N$ being the number of passive reflecting elements in the IRS. This has been interpreted as a major potential advantage of using IRSs, instead of conventional Massive MIMO (mMIMO) whose SNR scales only linearly in $N$. This paper shows that this interpretation is incorrect. We first prove analytically that mMIMO always provides higher SNRs, and then show numerically that the gap is substantial; a very large number of reflecting elements is needed for an IRS to obtain SNRs comparable to mMIMO.Comment: To appear at IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019, 5 pages, 4 figure

Demystifying the Power Scaling Law of Intelligent Reflecting Surfaces and Metasurfaces

Intelligent reflecting surfaces (IRSs) have recently attracted the attention of communication theorists as a means to control the wireless propagation channel. It has been shown that the signal-to-noise ratio (SNR) of a single-user IRS-aided transmission increases as N2, with N being the number of passive reflecting elements in the IRS. This has been interpreted as a major potential advantage of using IRSs, instead of conventional Massive MIMO (mMIMO) whose SNR scales only linearly in N. This paper shows that this interpretation is incorrect. We first prove analytically that mMIMO always provides higher SNRs, and then show numerically that the gap is substantial; a very large number of reflecting elements is needed for an IRS to obtain SNRs comparable to mMIMO

Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces

The use of large arrays might be the solution to the capacity problems in wireless communications. The signal-to-noise ratio (SNR) grows linearly with the number of array elements $N$ when using Massive MIMO receivers and half-duplex relays. Moreover, intelligent reflecting surfaces (IRSs) have recently attracted attention since these can relay signals to achieve an SNR that grows as $N^2$, which seems like a major benefit. In this paper, we use a deterministic propagation model for a planar array of arbitrary size, to demonstrate that the mentioned SNR behaviors, and associated power scaling laws, only apply in the far-field. They cannot be used to study the regime where $N\to\infty$. We derive an exact channel gain expression that captures three essential near-field behaviors and use it to revisit the power scaling laws. We derive new finite asymptotic SNR limits but also conclude that these are unlikely to be approached in practice. We further prove that an IRS-aided setup cannot achieve a higher SNR than an equal-sized Massive MIMO setup, despite its faster SNR growth. We quantify analytically how much larger the IRS must be to achieve the same SNR. Finally, we show that an optimized IRS does not behave as an "anomalous" mirror but can vastly outperform that benchmark.Comment: Published in IEEE Open Journal of the Communications Society, 18 pages, 11 figures. Typo in Eq. (64) has been correcte

On the use of programmable metasurfaces in vehicular networks

Metasurface-based intelligent reflecting surfaces constitute a revolutionary technology which can serve the purpose of alleviating the blockage problem in mmwave communication systems. In this work, we consider the hypersurface paradigm complementing the software defined metasurface with an embedded controller network in order to facilitate the dissemination of reconfiguration directives to unit cell controllers. For the first time, we describe the methodology with which to characterize the workload within this embedded network in the case of the metasurface tracking multiple users and we use a vehicular communications setting to showcase the methodology. Beyond that, we demonstrate use cases of the workload analysis. We show how the workload characterization can guide the design of information dissemination schemes achieving significant reduction in the network traffic. Moreover, we show how the workload, as a measure of the consumed power, can be used in designing energy efficient communication protocols through a multi-objective optimization problem maximizing the achieved utilization while at the same time minimizing the workload incurred.Peer ReviewedPostprint (author's final draft

Processing Distribution and Architecture Tradeoff for Large Intelligent Surface Implementation

The Large Intelligent Surface (LIS) concept has emerged recently as a new paradigm for wireless communication, remote sensing and positioning. It consists of a continuous radiating surface placed relatively close to the users, which is able to communicate with users by independent transmission and reception (replacing base stations). Despite of its potential, there are a lot of challenges from an implementation point of view, with the interconnection data-rate and computational complexity being the most relevant. Distributed processing techniques and hierarchical architectures are expected to play a vital role addressing this while ensuring scalability. In this paper we perform algorithm-architecture codesign and analyze the hardware requirements and architecture trade-offs for a discrete LIS to perform uplink detection. By doing this, we expect to give concrete case studies and guidelines for efficient implementation of LIS systems.Comment: Presented at IEEE ICC 202

Using MetaPrisms for Performance Improvement in Wireless Communications

In this paper, we put forth the idea of metaprism, a passive and non-reconfigurable metasurface acting as a metamirror with frequency-dependent reflecting properties within the signal bandwidth. We show that, with an appropriate design of the metaprism, it is possible to control that each data stream in an orthogonal frequency division multiplexing (OFDM) system is reflected in the desired direction without the need for control channels and channel state information (CSI) estimation between the base station and the metaprism, but simply by correctly assigning subcarriers to users. Furthermore, the metaprism can also be designed so that it focuses the signal towards a specific position depending on the subcarrier, provided that it is in the near-field, with consequent path-loss reduction. A critical discussion is also presented about the path-loss gain obtainable from metaprisms and, more generally, from metasurfaces. The numerical results show that this solution is surprisingly effective in extending the coverage in areas experiencing severe non line-of-sight (NLOS) channel conditions, thus making it a very appealing alternative to reconfigurable metasurfaces when low-cost, no energy consumption, and backward compatibility with existing wireless standards are required.Comment: 30 pages, 10 figures, Submitted to IEEE Trans. on Wireless Communication