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, 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 linear constraint on arm selection probabilities. Since our algorithm learns a Bayesian model of the wireless link, it can be adapted to exploit prior knowledge often available in practical wireless networks. Through numerical results from simulated experiments, we demonstrate that Con-TS significantly outperforms state-of-the-art bandit algorithms proposed in the literature. Further, we compare Con-TS with the outer loop link adaptation (OLLA) scheme, which is the state-of-the-art in practical wireless networks and relies on carefully tuned offline link models. We show that Con-TS outperforms OLLA in simulations, further, it can elegantly incorporate information from the offline link models to substantially improve performance.
State-of-the-art wireless networks address rate selection though a combination of offline and online approaches. An offline model is carefully tuned for a broad class of wireless channels and rates @cite_14 @cite_11 . The transmitter uses this offline model to obtain initial estimates of the transmission success probabilities for each candidate rate for the current channel conditions. The transmitter then refines these estimates through (OLLA) step adjustments based on the observed transmission successes and failures. However, OLLA suffers from two major shortcomings: (i) OLLA converges to match the latency constraint @cite_10 , however, in Sec. , we show that such a rate selection strategy is not necessarily optimal. (ii) The OLLA step sizes are heuristically defined, and often lead to poor convergence and steady state performance steady-state performance of OLLA depends on the scaling factor of the step sizes @cite_19 . In Sec. , we describe the OLLA algorithm and in Sec. , we show that Con-TS significantly outperforms OLLA for simulated experiments.
@cite_19 @cite_14 @cite_10 @cite_11
1985853617 2145850537 2099059301
In this letter, a comprehensive analysis of throughput performance statistics in a live LTE network is presented. The analysis shows the relationship between several widely accepted throughput performance indicators, i.e., the user throughput, the cell throughput, and the radio link throughput, and how these indicators are related to signal quality statistics. The analysis is performed on a per-cell and per-connection basis. For this pur- pose, throughput and signal quality statistics are collected from network performance counters and call traces in cells of a live LTE system. Results show that all throughput measures are strongly affected by chatty applications dominating current LTE networks due to the last transmission time interval transmissions and the outer loop link adaptation mechanism. Index Terms—LTE, throughput, CQI, live cellular network. This paper gives an overview of some so-called link performance models used in system level simulations to determine the link packet error rate (PER) at reduced complexity. A subset of link performance models is evaluated in terms of PER prediction accuracy focusing on a single receive and transmit antenna OFDM link with different coding options and channel characteristics. The results demonstrate that a mutual-information based metric which accounts for the modulation alphabet is preferable in the considered cases and, furthermore, applicable to the large class of MIMO-OFDM transmission techniques with linear pre- and post-processing This paper studies the tradeoff between channel coding and ARQ (automatic repeat request) in Rayleigh blockfading channels. A heavily coded system corresponds to a low transmission rate with few ARQ retransmissions, whereas lighter coding corresponds to a higher transmitted rate but more retransmissions. The optimum error probability, where optimum refers to the operating point that maximizes the average successful throughput, is derived and is shown to be a decreasing function of the average signal-to-noise ratio and of the channel diversity order. A general conclusion of the work is that the optimum error probability is quite large (e.g., 10 or larger) for reasonable channel parameters, and that operating at a very small error probability can lead to a significantly reduced throughput.