Vidit Saxena

Jakob Stigenberg, Vidit Saxena, Soma Tayamon et al.

Fifth-generation (5G) New Radio (NR) cellular networks support a wide range of new services, many of which require an application-specific quality of service (QoS), e.g. in terms of a guaranteed minimum bit-rate or a maximum tolerable delay. Therefore, scheduling multiple parallel data flows, each serving a unique application instance, is bound to become an even more challenging task compared to the previous generations. Leveraging recent advances in deep reinforcement learning, in this paper, we propose a QoS-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks. In contrast to state-of-the-art scheduling heuristics, the QADRA scheduler explicitly optimizes for the QoS satisfaction rate while simultaneously maximizing the network performance. Moreover, we train our algorithm end-to-end on these objectives. We evaluate QADRA in a full scale, near-product, system level NR simulator and demonstrate a significant boost in network performance. In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the QoS satisfaction rate of VoIP users served by the network, compared to state-of-the-art baselines.

Vidit Saxena, Hugo Tullberg, Joakim Jaldén

Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called link adaptation. Classical link adaptation relies on tuning parameters that are challenging to configure for optimal link performance. Recently, reinforcement learning has been proposed to automate link adaptation, where the transmission parameters are modeled as discrete arms of a multi-armed bandit. In this context, we propose a latent learning model for link adaptation that exploits the correlation between data transmission parameters. Further, motivated by the recent success of Thompson sampling for multi-armed bandit problems, we propose a latent Thompson sampling (LTS) algorithm that quickly learns the optimal parameters for a given channel state. We extend LTS to fading wireless channels through a tuning-free mechanism that automatically tracks the channel dynamics. In numerical evaluations with fading wireless channels, LTS improves the link throughout by up to 100% compared to the state-of-the-art link adaptation algorithms.

Vidit Saxena, Joseph E. Gonzalez, Joakim Jaldén

We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint. For a few real-world instances of this problem, constrained extensions of the well-known Thompson Sampling (TS) heuristic have recently been proposed. However, finite-time analysis of constrained TS is challenging; as a result, only O(\sqrt{T}) bounds on the cumulative reward loss (i.e., the regret) are available. In this paper, we describe LinConTS, a TS-based algorithm for bandits that place a linear constraint on the probability of earning a reward in every round. We show that for LinConTS, the regret as well as the cumulative constraint violations are upper bounded by O(\log T) for the suboptimal arms. We develop a proof technique that relies on careful analysis of the dual problem and combine it with recent theoretical work on unconstrained TS. Through numerical experiments on two real-world datasets, we demonstrate that LinConTS outperforms an asymptotically optimal upper confidence bound (UCB) scheme in terms of simultaneously minimizing the regret and the violation.

Vidit Saxena, Joseph E. Gonzalez, Ion Stoica et al.

Wireless communication systems operate in complex time-varying environments. Therefore, selecting the optimal configuration parameters in these systems is a challenging problem. For wireless links, \emph{rate selection} is used to select the optimal data transmission rate that maximizes the link throughput subject to an application-defined latency constraint. We model rate selection as a stochastic multi-armed bandit (MAB) problem, where a finite set of transmission rates are modeled as independent bandit arms. For this setup, we propose Con-TS, a novel constrained version of the Thompson sampling algorithm, where the latency requirement is modeled by a high-probability linear constraint. We show that for Con-TS, the expected number of constraint violations over T transmission intervals is upper bounded by O(\sqrt{KT}), where K is the number of available rates. Further, the expected loss in cumulative throughput compared to the optimal rate selection scheme (i.e., the egret is also upper bounded by O(\sqrt{KT \log K}). Through numerical simulations, we demonstrate that Con-TS significantly outperforms state-of-the-art bandit schemes for rate selection.