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Maximizing Uplink and Downlink Transmissions in Wirelessly Powered IoT Networks

Xiaoyu Song, Kwan-Wu Chin

TL;DR

The paper tackles maximizing the total number of decoded uplink and downlink packets $R$ in a RSMA-based IoT network powered by wireless energy, using a mode-based time structure. It proposes an MILP formulation that optimizes the mode of each slot, per-packet transmit powers, device power-splitting ratios, and uplink decoding order under non-causal channel state information, and a learning-based approach that operates with causal CSI. Key contributions include the first MILP for this system, a reduced-order decoding set to trim complexity, and a Q-learning solution that achieves about $90\%$ of the MILP performance while outperforming round-robin and random baselines. Results highlight the critical roles of HAP transmit power, SIC threshold, device count, and channel distance on the achievable throughput, demonstrating the practical viability of mode-based RSMA with RF charging for IoT networks.

Abstract

This paper considers the problem of scheduling uplinks and downlinks transmissions in an Internet of Things (IoT) network that uses a mode-based time structure and Rate Splitting Multiple Access (RSMA). Further, devices employ power splitting to harvest energy and receive data simultaneously from a Hybrid Access Point (HAP). To this end, this paper outlines a Mixed Integer Linear Program (MILP) that can be employed by a HAP to optimize the following quantities over a given time horizon: (i) mode (downlink or uplink) of time slots, (ii) transmit power of each packet, (iii) power splitting ratio of devices, and (iv) decoding order in uplink slots. The MILP yields the optimal number of packet transmissions over a given planning horizon given non-causal channel state information. We also present a learning based approach to determine the mode of each time slot using causal channel state information. The results show that the learning based approach achieves 90% of the optimal number of packet transmissions, and the HAP receives 25% more packets as compared to competing approaches.

Maximizing Uplink and Downlink Transmissions in Wirelessly Powered IoT Networks

TL;DR

The paper tackles maximizing the total number of decoded uplink and downlink packets in a RSMA-based IoT network powered by wireless energy, using a mode-based time structure. It proposes an MILP formulation that optimizes the mode of each slot, per-packet transmit powers, device power-splitting ratios, and uplink decoding order under non-causal channel state information, and a learning-based approach that operates with causal CSI. Key contributions include the first MILP for this system, a reduced-order decoding set to trim complexity, and a Q-learning solution that achieves about of the MILP performance while outperforming round-robin and random baselines. Results highlight the critical roles of HAP transmit power, SIC threshold, device count, and channel distance on the achievable throughput, demonstrating the practical viability of mode-based RSMA with RF charging for IoT networks.

Abstract

This paper considers the problem of scheduling uplinks and downlinks transmissions in an Internet of Things (IoT) network that uses a mode-based time structure and Rate Splitting Multiple Access (RSMA). Further, devices employ power splitting to harvest energy and receive data simultaneously from a Hybrid Access Point (HAP). To this end, this paper outlines a Mixed Integer Linear Program (MILP) that can be employed by a HAP to optimize the following quantities over a given time horizon: (i) mode (downlink or uplink) of time slots, (ii) transmit power of each packet, (iii) power splitting ratio of devices, and (iv) decoding order in uplink slots. The MILP yields the optimal number of packet transmissions over a given planning horizon given non-causal channel state information. We also present a learning based approach to determine the mode of each time slot using causal channel state information. The results show that the learning based approach achieves 90% of the optimal number of packet transmissions, and the HAP receives 25% more packets as compared to competing approaches.
Paper Structure (21 sections, 1 theorem, 55 equations, 7 figures, 3 tables, 1 algorithm)

This paper contains 21 sections, 1 theorem, 55 equations, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related works
  3. Joint Uplink and Downlink
  4. Rate Splitting Multiple Access
  5. Research Gaps
  6. system model
  7. Optimization Model
  8. Linearization
  9. Reduced Order Set
  10. A reinforcement learning-based approach
  11. MDP
  12. Q-Learning
  13. Downlink Linear Program (DLP)
  14. Uplink Linear Program (ULP)
  15. Evaluation
  16. ...and 6 more sections

Key Result

Proposition 1

Assume there are $N$ devices and $T$ slots. Then the solution space has size $\sum_{i=0}^T \binom{T}{i} \left(\sum_{j=1}^{N} \binom{2N}{j} (2N)!\right)^i$.

Figures (7)

  • Figure 1: A HAP and $N$ RF-energy harvesting sensor devices. (a) In downlink mode, the HAP transmits data to devices as per RSMA. Devices simultaneously decode information and harvest energy using power splitting. (b) In uplink mode, devices transmit data to the HAP as per RSMA simultaneously.
  • Figure 2: Two examples of charging and data collection schedule: (a) mode selection structure, and (b) conventional TDD frame structure.
  • Figure 3: Impact of different decay factors.
  • Figure 4: Weighted sum-throughput for varying HAP and device distance.
  • Figure 5: Impact of HAP transmit power on weighted sum-throughput.
  • ...and 2 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof