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Structured Connectivity for 6G Reflex Arc: Task-Oriented Virtual User and New Uplink-Downlink Tradeoff

Xinran Fang, Chengleyang Lei, Wei Feng, Yunfei Chen, Ning Ge, Shi Jin

TL;DR

A task-oriented UL&DL optimization scheme jointly optimizes UL&DL transmit power, time, bandwidth, and CPU frequency to minimize the control linear quadratic regulator (LQR) cost.

Abstract

To accommodate the evolving demands of unmanned operations, the future sixth-generation (6G) network will support not only communication links but also sensing-communication-computing-control ($\mathbf{SC}^3$) loops. In each $\mathbf{SC}^3$ cycle, the sensor uploads sensing data to the computing center, and the computing center calculates the control command and sends it to the actuator to take action. To maintain the task-level connections between the sensor-computing center link and the computing center-actuator link, we propose to treat the sensor and actuator as a virtual user. In this way, the two communication links of the $\mathbf{SC}^3$ loop become the uplink and downlink (UL&DL) of the virtual user. Based on the virtual user, we propose a task-oriented UL&DL optimization scheme. This scheme jointly optimizes UL&DL transmit power, time, bandwidth, and CPU frequency to minimize the control linear quadratic regulator (LQR) cost. We decouple the complex problem into a convex UL&DL bandwidth allocation problem with the closed-form solution for the optimal time allocation. Simulation results demonstrate that the proposed scheme achieves a task-level balance between the UL&DL, surpassing conventional communication schemes that optimize each link separately.

Structured Connectivity for 6G Reflex Arc: Task-Oriented Virtual User and New Uplink-Downlink Tradeoff

TL;DR

A task-oriented UL&DL optimization scheme jointly optimizes UL&DL transmit power, time, bandwidth, and CPU frequency to minimize the control linear quadratic regulator (LQR) cost.

Abstract

To accommodate the evolving demands of unmanned operations, the future sixth-generation (6G) network will support not only communication links but also sensing-communication-computing-control () loops. In each cycle, the sensor uploads sensing data to the computing center, and the computing center calculates the control command and sends it to the actuator to take action. To maintain the task-level connections between the sensor-computing center link and the computing center-actuator link, we propose to treat the sensor and actuator as a virtual user. In this way, the two communication links of the loop become the uplink and downlink (UL&DL) of the virtual user. Based on the virtual user, we propose a task-oriented UL&DL optimization scheme. This scheme jointly optimizes UL&DL transmit power, time, bandwidth, and CPU frequency to minimize the control linear quadratic regulator (LQR) cost. We decouple the complex problem into a convex UL&DL bandwidth allocation problem with the closed-form solution for the optimal time allocation. Simulation results demonstrate that the proposed scheme achieves a task-level balance between the UL&DL, surpassing conventional communication schemes that optimize each link separately.

Paper Structure

This paper contains 5 sections, 2 theorems, 22 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Virtual-User-Based $\mathbf{SC}^3$ Loop Model
  3. Task-Oriented UL&DL Optimization Scheme
  4. Simulation Results and Discussion
  5. conclusion

Key Result

Lemma 1

The optimal solution to (P2) is to achieve a task-level balance between UL&DL:

Figures (4)

  • Figure 1: Illustration of the reflex arc model, conventional $\mathbf{SC}^3$ loop model, and new $\mathbf{SC}^3$ loop model using the virtual user.
  • Figure 2: The LQR cost varies with the available bandwidth resources under three UL&DL configuration schemes.
  • Figure 3: The UL&DL task-related information and the LQR cost under the proposed scheme, the scheme proposed in ref1, and the static configuration 3gpp_r17.
  • Figure 4: The LQR cost contour map varying with the bandwidth and computing CPU frequency.

Theorems & Definitions (2)

  • Lemma 1
  • Theorem 1