[Paper Review] Optimal Resource Allocation in Full-Duplex Ambient Backscatter Communication Networks for Green IoT

Gang Yang, Dongdong Yuan+1 more|arXiv (Cornell University)|May 3, 2018

Energy Harvesting in Wireless Networks14 references3 citations

TL;DR

This paper proposes a joint optimization framework for full-duplex ambient backscatter communication networks to maximize the minimum throughput among multiple backscatter devices (BDs) by jointly tuning backscatter time portions, power reflection coefficients, and subcarrier power allocation. Using block coordinate descent and successive convex optimization, the algorithm converges efficiently and achieves significant throughput gains over equal-resource allocation benchmarks, revealing key tradeoffs between BD throughput, energy harvesting, and legacy user performance.

ABSTRACT

Ambient backscatter communication (AmBC) enables wireless-powered backscatter devices (BDs) to transmit information over ambient radio-frequency (RF) carriers without using an RF transmitter, and thus has emerged as a promising technology for green Internet-of-Things. This paper considers an AmBC network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing (OFDM) signals to its legacy user (LU) and receives uplink signals backscattered from multiple BDs in a time-division-multiple-access manner. To enhance the system performance from multiple design dimensions and ensure fairness, we maximize the minimum throughput among all BDs by jointly optimizing the BDs' backscatter time portions, the BDs' power reflection coefficients, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. As such, we propose an efficient iterative algorithm for solving the formulated non-convex problem by leveraging the block coordinated decent and successive convex optimization techniques. We further show the convergence of the proposed algorithm, and analyze its complexity. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gains as compared to the benchmark scheme with equal resource allocation.

Motivation & Objective

• To address the challenge of throughput fairness and system efficiency in full-duplex ambient backscatter communication (AmBC) networks for green IoT.
• To jointly optimize three key design variables: BD backscatter time portions, power reflection coefficients, and FAP subcarrier power allocation.
• To maximize the minimum throughput among all BDs while satisfying LU throughput constraints and energy harvesting requirements.
• To develop a practical, convergent algorithm for solving the non-convex optimization problem arising from joint resource allocation.
• To reveal fundamental tradeoffs between BD throughput, energy harvesting, and legacy user performance in full-duplex AmBC systems.

Proposed method

• Formulates a non-convex optimization problem to maximize the minimum BD throughput under LU throughput, energy harvesting, and hardware constraints.
• Applies block coordinate descent (BCD) to alternately optimize three variable blocks: backscatter time, reflection coefficients, and subcarrier power.
• Uses successive convex optimization (SCO) to approximate non-convex subproblems into convex ones for tractable solution in each iteration.
• Employs a penalty method to handle the non-convexity of the reflection coefficient constraint, enabling iterative refinement.
• Guarantees convergence of the algorithm by showing monotonic improvement of the objective function in each iteration.
• Analyzes the polynomial-time complexity of the algorithm, confirming practical feasibility for moderate-sized networks.

Experimental results

Research questions

• RQ1 How can joint optimization of backscatter time, reflection coefficient, and subcarrier power improve fairness and throughput in full-duplex AmBC networks?
• RQ2 What is the tradeoff between BD throughput and the legacy user’s required data rate in such a system?
• RQ3 How does the energy harvesting constraint affect the achievable throughput of backscatter devices?
• RQ4 Can the proposed iterative algorithm converge to a stable solution for the non-convex optimization problem?
• RQ5 What performance gains are achievable through joint design compared to equal resource allocation?

Key findings

• The proposed joint design achieves significant max-min throughput gains over the benchmark scheme with equal resource allocation, especially under high SNR conditions.
• As the legacy user’s throughput requirement increases, the max-min throughput among BDs decreases, confirming a clear throughput tradeoff between BDs and the LU.
• Lower energy harvesting requirements lead to higher max-min throughput, demonstrating a tradeoff between BD throughput and energy constraints.
• Higher subcarrier peak power increases the achievable max-min throughput, indicating that power allocation is a critical design knob.
• The proposed algorithm converges to a stable solution, with polynomial-time complexity, making it suitable for practical deployment in full-duplex AmBC networks.
• Simulation results based on 100 channel realizations confirm robust performance gains across varying SNR, energy, and power constraints.