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Symbiotic Radio: A New Communication Paradigm for Passive Internet-of-Things

Ruizhe Long, Huayan Guo, Gang Yang, Ying-Chang Liang, Rui Zhang

arXiv:1810.13068v1cs.IT

TL;DR

Symbiotic radio integrates a backscatter device with a primary communication system to address limited radio spectrum and direct-link interference affecting BD performance. The scheme jointly assists and decodes primary and BD transmissions, enabling BD transmission while improving the primary system’s achievable rate through the BD’s scattering.

  • Problem

    Limited radio spectrum bottlenecks IoT, while severe direct-link interference can degrade backscatter-device performance.

  • Method

    The paper proposes symbiotic radio, integrating a backscatter device with a primary system whose transmitter assists both transmissions and whose receiver decodes both signals.

  • Results

    The proposed scheme enables backscatter-device transmission and improves the primary system’s achievable rate by exploiting the BD’s scattering.

  • Takeaways & Limitations

    Integrating backscatter with the primary communication system supports BD transmission while enhancing the primary transmission rate.

Abstract

from arXiv · show

In this paper, a novel technique, called symbiotic radio (SR), is proposed for passive Internet-of-Things (IoT), in which a backscatter device (BD) is integrated with a primary transmission. The primary transmitter is designed to assist the primary and BD transmissions, and the primary receiver decodes the information from the primary transmitter as well as the BD. We consider a multiple-input single-output (MISO) SR and the symbol period for BD transmission is designed to be either the same as or much longer than that of the primary system, resulting in parasitic or commensal relationship between the primary and BD transmissions. We first derive the achievable rates for the primary system and the BD transmission. Then, we formulate two transmit beamforming optimization problems, i.e., the weighted sum-rate maximization problem and the transmit power minimization problem, and solve these non-convex problems by applying semi-definite relaxation technique. In addition, a novel transmit beamforming structure is proposed to reduce the computational complexity of the solutions. Simulation results show that when the BD transmission rate is properly designed, the proposed SR not only enables the opportunistic transmission for the BD via energy-efficient passive backscattering, but also enhances the achievable rate of the primary system by properly exploiting the additional signal path from the BD.

I. INTRODUCTION

The paper proposes symbiotic radio (SR), a passive IoT scheme integrating backscatter with a primary transmission while sharing spectrum and the primary receiver. It develops SR models, rate analyses, beamforming optimizations, and a lower-complexity beamforming structure.

  • Traditional IoT transmitters use costly, power-consuming active RF components, motivating spectrum- and energy-efficient communication technologies.
  • Ambient backscatter avoids active RF components but can suffer severe direct-link interference, degrading BD transmission performance.
  • SR integrates a backscatter device with a primary transmission, with the primary transmitter and receiver supporting and decoding both transmissions.
  • Unlike conventional ambient backscatter, SR shares the primary system’s radio spectrum, RF source, and receiver.
  • The MISO SR model uses joint transmit beamforming, enabling BD opportunistic transmission and potentially improving primary rate through an additional BD signal path.
  • The paper derives PSR and CSR achievable rates, formulates weighted sum-rate and transmit-power problems, applies SDR, and proposes a lower-complexity beamforming structure.

II. SYSTEM MODEL

The system comprises a multi-antenna primary transmitter, single-antenna primary receiver, and single-antenna backscatter device. The BD modulates the incident primary signal, while SR operation distinguishes PSR and CSR by their symbol periods.

  • The SR model contains a PT with M > 1 antennas, a single-antenna PR, and a single-antenna BD.
  • The PT beamforms primary information while enabling the BD to transmit information to the PR by varying its reflection coefficient.
  • SR shares both the primary system’s spectrum and receiver, unlike schemes that only share an ambient RF source.
  • The backscatter-link channel is modeled as the product of the PT-to-BD forward channel and the BD-to-PR backward channel.
  • The model assumes block flat-fading channels, TDD operation, and perfect PT/PR CSI for direct and backscatter links.
  • PSR sets Ts = Tc, whereas CSR sets Tc = NTs with N ≫ 1; the BD reflection parameter α ∈ [0, 1] controls backscatter power.

A. PSR Setup

In the PSR setup, the primary receiver first decodes the primary signal while treating the BD signal as interference, then uses SIC to decode the BD signal. Achievable rates are derived for both transmissions.

  • A. PSR Setup: The AWGN has zero mean and power σ2, and the relevant rate expression is monotonic and concave in its argument.
  • A. PSR Setup: The direct-link signal is typically stronger because backscatter experiences double attenuation and additional power loss.
  • A. PSR Setup: The PR first decodes s(n) with the BD signal treated as background noise, then applies SIC to detect c(n).The primary signal is decoded before interference cancellation and BD detection.
  • A. PSR Setup: The primary-system data rate is obtained from the SINR for decoding s(n) at the PR.
  • A. PSR Setup: The BD rate is derived from the SNR for decoding c(n) after the primary signal is estimated and removed.The derivation assumes perfect primary-signal removal for the intermediate received signal.
  • A. PSR Setup: The primary signal acts as a fast-varying channel response when the PR decodes the BD signal.Its squared envelope follows an exponential distribution, which is used in the BD-rate analysis.

B. CSR Setup

In the CSR setup, one BD symbol spans N primary-symbol periods, allowing the primary signal to assist BD detection through combining while reducing BD symbol rate. The primary rate is averaged over the BD symbol.

  • B. CSR Setup: CSR uses Tc = NTs, so one BD symbol is transmitted across N successive primary-symbol periods.N is an integer and the BD symbol period covers N primary symbol periods.
  • B. CSR Setup: Compared with PSR, CSR provides a much lower BD transmission rate.
  • B. CSR Setup: The primary transmission can gain an additional scattered path, and the gain increases with backscatter-link SNR for a fixed direct-link SNR.
  • B. CSR Setup: The PR requires a training symbol to estimate the equivalent channel, and for sufficiently large N the training overhead is ignored.
  • B. CSR Setup: The average primary rate is obtained by averaging the direct-link rate over the random BD signal c.
  • B. CSR Setup: The CSR analysis models the relevant signal power using a noncentral chi-square distribution and interprets its parameters through direct- and backscatter-link SNRs.
  • B. CSR Setup: The primary signal can be viewed as a length-N spread-spectrum code for BD symbols.
  • B. CSR Setup: CSR increases BD decoding SNR by N times at the cost of reducing symbol rate by 1/N.

IV. TRANSMIT BEAMFORMING PROBLEM FORMULATION

The paper formulates beamforming problems to characterize rate tradeoffs between primary and BD transmissions, including weighted sum-rate maximization for PSR and CSR. These problems are generally non-convex.

  • IV. TRANSMIT BEAMFORMING PROBLEM FORMULATION: The section formulates weighted sum-rate maximization and transmit power minimization problems for MISO symbiotic radio.
  • A. Weighted Sum-Rate Maximization: The WSRM objective maximizes a weighted sum of the primary rate and BD rate by optimizing the transmit beamforming vector w.
  • A. Weighted Sum-Rate Maximization: The weight factor ρ ranges from 0 to 1, while the index i identifies the PSR and CSR setups.
  • A. Weighted Sum-Rate Maximization: The beamforming normalization constraint is included in the general WSRM formulation.
  • A. Weighted Sum-Rate Maximization: The achievable rate region contains all primary-BD rate pairs attainable under the considered beamforming scheme.
  • A. Weighted Sum-Rate Maximization: Varying ρ produces a sequence of WSRM solutions that traces the Pareto boundary of the SR rate region.
  • A. Weighted Sum-Rate Maximization: The PSR and CSR cases use their respective achievable-rate expressions to define separate WSRM problems.
  • A. Weighted Sum-Rate Maximization: Both WSRM problems are non-convex, making their optimal solutions difficult to obtain generally.

B. Transmit Power Minimization

The transmit power minimization formulation minimizes PT power subject to primary and BD rate requirements, but the resulting problems are non-convex. The paper therefore develops generally suboptimal algorithms using semidefinite relaxation.

  • B. Transmit Power Minimization: The TPM objective minimizes PT transmit power while meeting prescribed primary and BD rate requirements.
  • B. Transmit Power Minimization: The design jointly optimizes the transmit beamforming vector w and transmit power p.
  • B. Transmit Power Minimization: For PSR, rate requirements can be converted into SINR and SNR constraints before rewriting the TPM problem.
  • B. Transmit Power Minimization: The CSR BD-rate requirement cannot readily be converted into an SNR constraint.
  • B. Transmit Power Minimization: The TPM problems are feasible when no channel vector is zero and h1 and h2 are not parallel.
  • B. Transmit Power Minimization: Both TPM formulations are non-convex and difficult to solve optimally.
  • B. Transmit Power Minimization: The proposed solution procedures use semidefinite relaxation with a PSD matrix variable W and relax the rank-one constraint.
  • B. Transmit Power Minimization: The algorithms solve the relaxed problems with CVX, search over an auxiliary variable when needed, and use rank-one recovery or randomization.

2) CSR Setup:

The CSR transmit-power and beamforming problems are transformed and solved using semidefinite relaxation, with randomized rank-one recovery for approximate solutions.

  • The SDR of the reformulated optimization problems is a convex problem that can be solved efficiently.
  • A randomized procedure recovers a transmit beamforming vector and transmit power from the SDR solution.
  • The same SDR-based recovery approach is applied to equivalent optimization problems, with implementation details omitted for brevity.
  • For rank(W⋆) ≠ 1, phase randomization finds a feasible beamforming vector under the transmit-power constraint.

W Tr(W) (31a)

The paper introduces a low-complexity beamforming scheme by restricting the optimal beamformer to the space spanned by two normalized channel vectors.

  • Large antenna arrays make direct optimization over an M-by-M matrix computationally expensive, motivating a low-complexity scheme.
  • The optimal beamforming vector has the structure w⋆ = α1h̃1 + α2h̃2, with normalized channel vectors spanning its solution space.
  • The reduced WSRM and TPM formulations are solved using the SDR technique after variable transformation.
  • The reduced formulation replaces the M-by-M variable W with a 2-by-2 variable A.
  • This reduction leads to significantly lower computational complexity, especially when M is practically large.

VII. SIMULATION RESULTS

Simulations evaluate rate and power behavior for PSR and CSR under beamforming and rate constraints. CSR can exploit the backscattered signal as a multipath component, while the low-complexity method closely matches the conventional method.

  • The simulations assume i.i.d. Rayleigh fading for direct and forward links, a static backward link, and averaging over 10^4 channel realizations.
  • Weighted Sum-Rate Maximization: The achievable PSR rate region enlarges as SNR increases, improving both primary and BD transmission rates.
  • Weighted Sum-Rate Maximization: For CSR with N = 128, the primary rate exceeds PSR with N = 1 because CSR decoding treats the BD signal as a multipath component rather than interference.
  • Beamforming comparison: The low-complexity beamforming method has almost the same WSRM and TPM performance as the conventional method.
  • Weighted Sum-Rate Maximization: CSR achieves a higher sum rate than the primary-only system and supports concurrent backscatter communication without spectral-efficiency loss.
  • Transmit Power Minimization: Minimum transmit power generally increases with BD rate requirement, more dramatically for CSR because longer symbols require compensation for rate loss.
  • Transmit Power Minimization: In PSR, increasing the BD rate requirement can slowly increase primary rate through additional transmit power, although a tight primary constraint initially keeps the curve flat.
  • Transmit Power Minimization: At high primary-rate requirements in PSR, transmit power diverges, while CSR's BD constraint becomes slack without becoming tight again.

VIII. CONCLUSIONS

The paper proposes symbiotic radio for passive IoT by integrating a backscatter device with a primary communication system and jointly optimizing both transmissions. It derives achievable rates, formulates beamforming optimizations, and reports that properly designed BD transmission can improve the primary system rate.

  • Symbiotic radio integrates a backscatter device with a primary communication system for passive IoT.
  • The primary transmitter and receiver are designed to optimize and decode both the primary and BD transmissions.
  • The paper formulates weighted sum-rate maximization and transmit power minimization problems by optimizing the primary transmitter's beamforming vector.
  • The non-convex beamforming problems are recast with a positive semidefinite matrix variable and solved approximately using semi-definite relaxation.
  • A novel transmit beamforming structure reduces the computational complexity of the semi-definite-relaxation solutions.
  • Simulation results show that BD transmission is enabled and the primary system achievable rate improves by exploiting the BD's scattering in the CSR setup.

APPENDIX A PROOF OF PROPOSITION 1

The appendix proves a proposition by characterizing a beamforming-related random variable through Gaussian and noncentral chi-square distributions, then deriving beamforming structure conditions. It also uses normalized coefficient constraints and transfers the resulting expressions to transmit-power-minimization problems.

  • Distributional characterization: The proof models T as a linear transformation of a complex Gaussian variable and derives its distribution and real-imaginary component statistics.
  • Distributional characterization: The squared magnitude s is identified as a noncentral chi-square random variable, whose noncentrality parameter and probability density function are then used in the proof.
  • Beamforming structure: The beamforming vector is decomposed into channel-aligned and null-space components to establish the structure that improves the relevant SNR or SINR objectives.
  • Beamforming structure: After fixing the beamforming structure, the proof reduces optimization to the associated coefficients, with normalized weights satisfying |α1|^2 + |α2|^2 = 1.
  • Implication for optimization: Because the transmit-power-minimization problems have the same SNR or SINR expressions, the proof states that the same results apply to them.
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