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Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart

Ying-Chang Liang, Ruizhe Long, Qianqian Zhang, Jie Chen, Hei Victor Cheng, Huayan Guo

arXiv:1906.06578v1cs.IT

TL;DR

LISA addresses the rising power, spectrum, and implementation costs of increasingly dense active-radio networks by making wireless environments programmable through reflection. The paper surveys reflective-radio fundamentals, LISA technology, applications, and open challenges, reporting SNR scaling with M^2 and practical advantages over active relays.

  • Problem

    Growing deployments of base stations and wireless terminals make power, spectrum, and implementation costs of conventional active-radio systems increasingly prohibitive.

  • Method

    The paper reviews reflective-radio principles, LISA signal and channel models, multi-user transmission, implementations, applications, and associated research challenges.

  • Results

    LISA's received SNR scales with M^2 because multipath signals combine constructively without additional noise, while reflective relays offer ultra-low power consumption, no self-interference, and no added thermal noise.

  • Takeaways & Limitations

    LISA can intentionally program wireless environments and support applications including reflective massive MIMO, wireless power transfer, cognitive radio, physical-layer security, and symbiotic radio networks.

  • Takeaways & Limitations

    LISA research still has critical limitations, challenges, and open problems requiring substantial effort before its benefits become a reality.

Abstract

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Large intelligent surface/antennas (LISA), a two-dimensional artificial structure with a large number of reflective-surface/antenna elements, is a promising reflective radio technology to construct programmable wireless environments in a smart way. Specifically, each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties, such as negative refraction, perfect absorption, and anomalous reflection, thus the wireless environments can be software-defined according to various design objectives. In this paper, we introduce the reflective radio basics, including backscattering principles, backscatter communication, and reflective relay, and the fundamentals and implementations of LISA technology. Then, we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks. Finally, the limitations, challenges, and open issues associated with LISA for future wireless applications are discussed.

I. INTRODUCTION

Reflective radio uses electromagnetic scattering to reduce the power, spectrum, and implementation burdens of active-radio systems. The paper traces backscatter-based technologies toward LISA, which uses many reflective elements to improve reflective-link performance and supports programmable wireless environments.

  • Motivation: Active-radio systems rely on power-hungry and costly components, while growing deployments increase power, spectrum, and implementation demands.The motivation is the search for reflective-radio alternatives based on electromagnetic scattering principles.
  • Reflective radio technologies: Traditional backscatter communication uses a dedicated RF emitter, whereas ambient backscatter communication reuses RF signals from sources such as TV towers, cellular base stations, and WiFi access points.Ambient backscatter therefore does not require a dedicated RF emitter.
  • Technology evolution: Reflective relays assist primary transmissions, and large reflective arrays evolved into LISA, a two-dimensional structure containing many reflective-radio elements.LISA is the paper’s central technology and is presented alongside BSC, AmBC, reflective relay, and symbiotic radio.
  • LISA overview: LISA achieves a significant SNR gain when the number of reflective-radio elements becomes large.The paper reviews LISA fundamentals, implementation, applications, limitations, and future research directions.
  • System model: A general reflective-radio model contains a transmitter, reflective device, and receiver, with the device either sending its own messages or assisting the transmitter-to-receiver link.The received signal comprises direct-link and backscattered components.

A. Principles of backscattering

Backscattering decomposes the scattered field into structural-mode and antenna-mode components. Varying the reflective device’s load impedance changes its reflection coefficient, allowing information modulation or control of the incident signal’s phase.

  • Field decomposition: Green’s decomposition separates the backscattered field into structural-mode and antenna-mode scattering components.The decomposition is applied to a basic backscattering circuit with load impedance Z_i and fixed antenna impedance Z_a.
  • Structural-mode scattering: Structural-mode scattering depends on the reflective device antenna’s geometry and the electromagnetic properties of its material.Its value can be measured when the load impedance matches the conjugate of the antenna impedance.
  • Antenna-mode scattering: Antenna-mode scattering is mainly affected by the load impedance through the reflection coefficient Γ_i.The reflection coefficient is the controllable quantity linking the load impedance to antenna-mode scattering.
  • Control mechanism: Changing the reflective device’s load impedance adjusts the backscattered signal’s reflection coefficient, enabling information modulation or desired phase changes.This tunability provides the basic mechanism underlying reflective-radio operation.

B. Traditional Backscatter Communication

Traditional backscatter communication modulates a dedicated continuous-wave carrier by switching the reflective device’s load impedance. Ambient backscatter removes the dedicated emitter requirement but faces unknown ambient signals and direct-link interference.

  • Traditional BSC: Traditional BSC transmits reflective-device messages by varying the load impedance and thereby modulating reflection coefficients over a dedicated continuous-wave signal.The receiver recovers the device information from the reflection states when the transmitted carrier is fixed and known.
  • Backscatter modulation: Binary modulation uses two reflection states, while higher-order schemes such as QAM require more discrete load-impedance states.The reflection-state set must correspond to the desired signal constellation.
  • Ambient backscatter: Ambient backscatter communication exploits existing RF signals from primary systems, eliminating the need for a dedicated RF emitter and enabling more flexible network deployment.It also naturally shares spectrum with the primary communication system.
  • Ambient-backscatter limitations: Ambient backscatter receivers decode the reflective-device symbol in the presence of an unknown ambient signal and suffer from direct-link interference.The resulting coverage range and transmission rate are limited.
  • Symbiotic radio: A cooperative receiver can jointly recover primary and reflective-device signals, motivating symbiotic radio for mutually beneficial coexistence with cellular communications.When the reflective-device symbol duration is much longer than the primary signal’s, backscatter may enhance the primary transmission.

D. Reflective Relay

Reflective relays use a reflected path to assist transmission when direct links are unfavorable. LISA extends this approach with many tunable elements, enabling stronger links and programmable wireless-environment functions.

  • Reflective relay concept: Reflective relays provide an alternate transmission path when obstacles block the direct link and degrade user QoS.The relay backscatters the incident signal toward the receiver.
  • Reflective relay concept: The reflective link becomes especially weak at high carrier frequencies because received backscatter power decreases with frequency to the fourth power.This motivates deploying many reflective-radio elements at the relay.
  • LISA extension: LISA comprises many passive meta-atoms with unusual electromagnetic properties and can steer incident RF signals toward desired directions.Examples include negative reflection or refraction, perfect absorption, and anomalous reflection.
  • LISA extension: Modern LISA can redirect beams to arbitrary angles and tune them electronically in real time, allowing integration into walls, ceilings, and buildings.These capabilities support software-defined control of wireless environments.
  • LISA applications: LISA-based reflective relays can enhance QoS while using ultra-low power, avoiding self-interference and adding no thermal noise to the forwarded signal.The paper compares this reflection mechanism with active amplify-and-forward relays.
  • LISA applications: Beyond relaying, LISA can support interference cancellation for spectrum sharing and physical-layer security, and focus electromagnetic power for energy harvesting.These applications exploit the large number of elements and the surface aperture.

A. Performance Gain Analysis for LISA

LISA performance depends on how its reflection coefficients are modeled and controlled. With channel-aware phase alignment, many elements combine coherently, producing an SNR scaling advantage over conventional antenna combining.

  • System model: The LISA downlink model considers a transmitter, an M-element LISA, and a receiver, with each element contributing through forward and backward channels.The reflection coefficients determine how the element contributions combine at the receiver.
  • Reflection assumptions: Three reflection-coefficient assumptions are considered: continuous amplitude and phase, constant amplitude with continuous phase, and constant amplitude with discrete phase.These assumptions range from idealized control to practical quantized phase control.
  • Reflection assumptions: Discrete phase control is more practical because hardware limitations make continuous reflection-coefficient control costly.Q denotes the number of quantized reflection-coefficient values.
  • System model: With CSI, LISA can set each reflection phase opposite to the composite channel phase so backscattered signals add coherently at the receiver.The model assumes unit reflection-amplitude magnitude in this case.
  • Performance gain: The received SNR scales as M^2 with the number of reflective-radio elements, compared with linear M scaling for conventional MRT and MRC.This gain results from constructive multipath addition without additional noise.

B. Implementation of LISA

LISA uses electronically tunable scatterers and supporting control hardware to shape reflection amplitudes, phases, and beam patterns. Implementations combine tunable materials or devices with a controller, DAC board, and leakage-suppressing backplane.

  • Tunable elements: LISA electronically tunes scatterers using devices such as varactor diodes, PIN switches, ferroelectric devices, and MEMS switches.At high frequencies, tunable materials include ferroelectric films, liquid crystals, and graphene.
  • Design rationale: LISA combines the low-cost, high-gain features of reflective antennas with the fast, adaptive beam-forming capabilities of antenna arrays.This combination motivates electronically shaped, adaptive reflection surfaces.
  • System implementation: A typical implementation includes the LISA, a DAC board for amplitude and phase control, a copper backplane, and a micro-controller linked to the access point.The access-point link may use wired or wireless communication.
  • Tunable elements: Reflective-element designs can use electronically controlled relay switches, varactor diodes, MEMS, or liquid-crystal metasurfaces to alter resonance, phase, and amplitude.These alternatives provide different mechanisms for electronic reflection control.
  • Reflective-element design: A PIN diode switches between On and Off states, while element dimensions can be designed to produce a 180° phase difference.Bias-voltage variation can provide more phase states, and variable resistors can control reflected amplitude.
  • Reflective-element design: Ideal designs seek independent amplitude and phase control, while metasurface development is expected to enable more phase and amplitude levels.The paper notes successful designs with independent control and ongoing materials progress.

IV. APPLICATIONS OF LISA

LISA programs the wireless environment by re-scattering incident electromagnetic waves, enabling transmission enhancement and interference suppression across wireless applications. Its MISO downlink use requires joint beamforming and reflection optimization, while cascaded-channel estimation creates substantial pilot overhead.

  • IV. APPLICATIONS OF LISA: LISA re-scatters incident electromagnetic waves with properties such as negative refraction, perfect absorption, and anomalous reflection.These programmable properties support enhancing desired signals while suppressing undesired signals.
  • IV. APPLICATIONS OF LISA: In MISO downlink transmission, LISA assists a multi-antenna base station in serving a mobile user through a programmable reflective link.The system includes one multi-antenna BS, one LISA, and one mobile user, followed by signal modeling and channel-estimation analysis.
  • IV. APPLICATIONS OF LISA: Each LISA element combines received multipath signals and forwards them by reflection, forming a dyadic backscatter channel between the BS and user.The composite channel depends on the BS-user, LISA-user, and BS-LISA channel responses.
  • IV. APPLICATIONS OF LISA: The central design problem jointly optimizes the BS transmit beamforming vector and LISA reflection coefficients for different objectives.Prior work used semidefinite relaxation with iterative optimization and later manifold optimization to address rank-relaxation loss more efficiently.
  • IV. APPLICATIONS OF LISA: Channel acquisition is difficult because the dyadic backscatter channel is cascaded and contains many reflective-radio elements.A two-stage protocol estimates the direct channel with the LISA off, then activates elements successively; its pilot overhead becomes prohibitive as the element count grows.

B. Downlink Transmission in Multi-user Case

The single-user LISA-assisted downlink extends to multiple users served by a multi-antenna base station and one LISA. Research formulates non-convex rate and efficiency objectives under individual user-quality constraints and solves them with alternating optimization methods.

  • B. Downlink Transmission in Multi-user Case: The multi-user system comprises a multi-antenna BS, one LISA, and K single-antenna mobile users.It extends the single-user MISO downlink model to multiple users.
  • B. Downlink Transmission in Multi-user Case: In downlink unicast, the BS transmits independent signals using user-specific beamforming vectors, whereas multicast sends one common signal to all users.The transmitted signal is modeled as s = Σ_k w_kx_k for unicast and s = wx for multicast.
  • B. Downlink Transmission in Multi-user Case: Multi-user studies optimize sum rate, energy efficiency, or spectral efficiency subject to each user’s individual QoS requirement.The resulting optimization problems are non-convex.
  • B. Downlink Transmission in Multi-user Case: Majorization-minimization-based alternating maximization and fractional programming were applied to solve the formulated non-convex downlink problems.The latter technique decouples the weighted sum-rate optimization problem.

C. LISA-Assisted Wireless Power Transfer System

LISA can provide an assisting link for wireless power transfer and has also been studied in cognitive radio networks to improve primary and secondary transmissions. These designs use channel estimation, beamforming, and interference-aware passive reflection.

  • C. LISA-Assisted Wireless Power Transfer System: In wireless power transfer, LISA assists power transfer from the BS to an energy receiver.A channel-estimation protocol without prior CSI knowledge and near-optimal closed-form beamforming expressions were developed to maximize received power at intended users.
  • D. LISA-Assisted Cognitive Radio Network: In cognitive radio networks, LISA is deployed to enhance both primary and secondary transmissions by suppressing inter-system interference.The model includes primary and secondary transmitters and users connected through direct and LISA-assisted channels.
  • D. LISA-Assisted Cognitive Radio Network: When primary and secondary channels are highly correlated, conventional secondary-transmitter beamforming struggles to protect the primary link while maintaining secondary efficiency.LISA provides additional virtual links through carefully designed passive beamforming, and an experimental testbed verified its effectiveness in cognitive radio networks.

E. LISA-Assisted Physical Layer Security

LISA-assisted physical-layer security uses a programmable virtual link to help confidential data reach a legitimate receiver while avoiding eavesdroppers. The reviewed work formulates constrained secrecy-rate optimization and develops an efficient non-convex optimization algorithm.

  • E. LISA-Assisted Physical Layer Security: The secret-communication system uses LISA to create a virtual safe link from the BS to a legitimate receiver in the presence of multiple eavesdroppers.The model includes direct and LISA-assisted channels to both legitimate and eavesdropping receivers.
  • E. LISA-Assisted Physical Layer Security: The secrecy rate is defined as the nonnegative part of the legitimate receiver’s advantage over the eavesdropper.The notation is [a]+ = max(0, a).
  • E. LISA-Assisted Physical Layer Security: When legitimate and eavesdropping channels toward the BS are highly correlated, beamforming alone cannot reliably guarantee secret communication.LISA can route the confidential stream around the eavesdropper toward the legitimate receiver, potentially improving secrecy rate.
  • E. LISA-Assisted Physical Layer Security: Prior work maximized the minimum secrecy rate among multiple eavesdroppers under practical continuous and discrete reflection-coefficient constraints.An alternating-optimization and path-following algorithm was developed for the resulting non-convex problem.

A. Channel Characterization

Integrating LISA introduces channel-modeling and channel-estimation difficulties alongside deployment, optimization, coordination, AI, and security challenges. The paper frames these issues as open problems requiring accurate physical models, suitable placement, intelligent control, and effective protection.

  • A. Channel Characterization: Accurate channel models must reflect LISA’s physical effects, including channel correlation and metasurface-induced reflection.These effects complicate assessment of actual system performance.
  • A. Channel Characterization: Separating the two constituent channels is difficult when LISA cannot receive signals, although their separate knowledge is important for transmission assistance.Estimating the complete multiplicative channel is relatively easier than recovering its two components.
  • B. LISA Deployment: LISA deployment must determine both where to place surfaces and how many units to deploy, particularly when transmitter–LISA and LISA–receiver LoS paths are available.Appropriate deployment can significantly improve system performance.
  • C. Network Optimization and Resource Allocation: LISA changes network optimization and resource allocation because it can assist users with poor channel gains without requiring other channels or access points.Multiple LISAs in residential areas also raise the open problem of coordinating their operation.
  • D. Artificial Intelligence: AI control may be centralized at the base station or distributed across LISA agents that learn through interaction to pursue a global optimum.The paper presents both cell-level control and independent-agent decision making as possible approaches.
  • E. Security and Privacy: LISA creates security and privacy risks because adversaries may disrupt surfaces or users may provide false feedback to manipulate their behavior.The paper identifies effective security and privacy policies as an urgent research problem.
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