BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection

TL;DR

This paper addresses a mixed service scenario wherein high-priority ultra-reliable low latency communication (URLLC) and low-priority distributed learning services run concurrently over a network via BSAC-CoEx, a framework based on the branching soft actor-critic (BSAC) algorithm.

Abstract

Recent advances in distributed intelligence have driven impressive progress across a diverse range of applications, from industrial automation to autonomous transportation. Nevertheless, deploying distributed learning services over wireless networks poses numerous challenges. These arise from inherent uncertainties in wireless environments (e.g., random channel fluctuations), limited resources (e.g., bandwidth and transmit power), and the presence of coexisting services on the network. In this paper, we investigate a mixed service scenario wherein high-priority ultra-reliable low latency communication (URLLC) and low-priority distributed learning services run concurrently over a network. Utilizing device selection, we aim to minimize the convergence time of distributed learning while simultaneously fulfilling the requirements of the URLLC service. We formulate this problem as a Markov decision process and address it via BSAC-CoEx, a framework based on the branching soft actor-critic (BSAC) algorithm that determines each device's participation decision through distinct branches in the actor's neural network. We evaluate our solution with a realistic simulator that is compliant with 3GPP standards for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delays of the distributed learning service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all wireless resources.

Figures (5)

• Figure 1: The illustration of training delay in distributed AI workflow.
• Figure 2: The simulation setup.
• Figure 3: Coexistence performance for the benchmark with semi-random devices. (a) Empirical CDF of URLLC devices' availability, $\hat{\alpha}_i^\Gamma$, where the shaded area around each plot indicates $99.99$% confidence bounds. (b) Training delay, $d_k^{\mathrm{AI}}$, where each box plot represents the minimum, $25$th percentile, median, $75$th percentile, and maximum of the training delay distribution.
• Figure 4: Device selection policy of BSAC-Coex for the benchmark with semi-random devices. (a) Empirical PMF of the number of selected devices, $m_k$. (b) Participation ratio of each device.
• Figure 5: Coexistence performance for the benchmark with random devices. (a) Empirical CDF of URLLC devices' availability, $\hat{\alpha}_i^\Gamma$, where the shaded area around each plot indicates $99.99$% confidence bounds. (b) Training delay, $d_k^{\mathrm{AI}}$, where each box plot represents the minimum, $25$th percentile, median, $75$th percentile, and maximum of the training delay distribution.