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Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization with QoS Constraint Approach

Rahman Saadat Yeganeh, Mohammad Javad Omidi, Mohammad Ghavami

TL;DR

The paper tackles energy consumption optimization in a CSR-based multi-SBD 6G IoT network by formulating a non-convex resource allocation problem that minimizes total BS transmit energy while guaranteeing SBD throughput. It introduces Timing-SR (T-SR) scheduling to partition energy harvesting and information transmission, and develops three convexification techniques—SDR, Sequential Quadratic (SQ), and Conic Quadratic Representation (CQR)—to solve the problem efficiently. Empirical results show that CQR generally outperforms SQ in energy efficiency and convergence speed, and that T-SR substantially improves energy efficiency over TDMA and outperforms several conventional IoT protocols in EE under dense deployments. The findings highlight the practicality of CSR with optimized scheduling and advanced convexification methods for scalable, energy-efficient 6G IoT networks.

Abstract

The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green communication networks. This system enables us to establish secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC.

Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization with QoS Constraint Approach

TL;DR

The paper tackles energy consumption optimization in a CSR-based multi-SBD 6G IoT network by formulating a non-convex resource allocation problem that minimizes total BS transmit energy while guaranteeing SBD throughput. It introduces Timing-SR (T-SR) scheduling to partition energy harvesting and information transmission, and develops three convexification techniques—SDR, Sequential Quadratic (SQ), and Conic Quadratic Representation (CQR)—to solve the problem efficiently. Empirical results show that CQR generally outperforms SQ in energy efficiency and convergence speed, and that T-SR substantially improves energy efficiency over TDMA and outperforms several conventional IoT protocols in EE under dense deployments. The findings highlight the practicality of CSR with optimized scheduling and advanced convexification methods for scalable, energy-efficient 6G IoT networks.

Abstract

The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green communication networks. This system enables us to establish secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC.
Paper Structure (17 sections, 54 equations, 12 figures, 3 tables, 2 algorithms)

This paper contains 17 sections, 54 equations, 12 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. SYSTEM MODEL
  4. Problem Formulation
  5. ENERGY AND RATE OPTIMIZATION
  6. Overcoming Non-Convex Objective Function
  7. Semidefinite Relaxation of Constraint (10d)
  8. Sequential Quadratic (SQ)
  9. Conic Quadratic Representation (CQR)
  10. COMPUTATIONAL COMPLEXITY
  11. SIMULATION RESULTS
  12. Comparison of Proposed Methods
  13. Convergence and Computational Complexity
  14. Comparison of T-SR and TDMA Modes
  15. Comparison Proposed Method With Other IoT Protocols
  16. ...and 2 more sections

Figures (12)

  • Figure 1: Symbiotic radio system model with multiple SBDs
  • Figure 2: The TDD frame for EHS and MTI modes in T-SR instantaneous transmission model
  • Figure 3: EC versus SBDs throughput requirement in CQR method for $M = 1,2,...,6$, $I = 6$
  • Figure 4: EC versus SBDs throughput requirement where $N=1$ and $I = 2,3,4$
  • Figure 5: EC versus SBDs throughput requirement where $I=4$ and ${N} = 1,4,9$
  • ...and 7 more figures