Name | Last modified | Size | Description | |
---|---|---|---|---|

Parent Directory | - | Root of the website (in English or in French) | ||

plots/ | 2020-04-27 12:19 | - | ||

Makefile | 2019-03-28 20:04 | 1.5K | ||

preprocess_tex.sh | 2019-03-28 20:04 | 119 | ||

slides.md | 2019-04-06 11:23 | 11K | Common repository for remark.js slideshows, open-source on GitHub. Readables on naereen.github.io/slides | |

slides.pdf | 2019-04-06 11:39 | 1.6M | Common repository for remark.js slideshows, open-source on GitHub. Readables on naereen.github.io/slides | |

slides.pdfpc | 2019-03-28 20:04 | 32 | Common repository for remark.js slideshows, open-source on GitHub. Readables on naereen.github.io/slides | |

slides_169.pdf | 2019-04-06 11:39 | 1.7M | ||

slides_169.pdfpc | 2019-03-28 20:04 | 36 | ||

slides_pandoc.pdfpc | 2019-03-28 20:04 | 39 | ||

README.md | 2019-04-01 17:12 | 10K | Some details about this page |

--- title: Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions subtitle: MoTION Workshop @ IEEE WCNC 2019 author: Lilian Besson institute: SCEE Team, IETR, CentraleSupĂ©lec, Rennes date: Monday 14th of April, 2019 lang: english --- ### *1st MoTION Workshop - 2019*: "**Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions**" - *Date* :date: : $15$th of April $2019$ - *Who:* [Lilian Besson](https://GitHub.com/Naereen/slides/) :wave: , PhD Student in France, co-advised by | *Christophe Moy* <br> @ IETR, Rennes | *Emilie Kaufmann* <br> @ CNRS & Inria, Lille | |:---:|:---:| | ![8%](../common/LogoCS.png) ![14%](../common/LogoIETR.png) | ![12%](../common/LogoInria.jpg) ![16%](../common/LogoCNRS.jpg) | > See our paper at [`HAL.Inria.fr/hal-02049824`](https://hal.inria.fr/hal-02049824) --- # :timer_clock: Outline ## 1. Motivations ## 2. System model ## 3. Multi-armed bandit (MAB) model and algorithms ## 4. Proposed heuristics ## 5. Numerical simulations and results #### Please :pray: ask questions *at the end* if you want! > By R. Bonnefoi, L. Besson, J. Manco-Vasquez and C. Moy. --- # 1. Motivations - IoT networks are interesting and will be more and more present, - More and more IoT objects - $\Longrightarrow$ networks will be more and more occupied But... - Heterogeneous spectrum occupancy in most IoT networks standards - Maybe IoT objects can improve their communication by *learning* to access the network more efficiently (e.g., by using the less occupied spectrum channel) - Simple but efficient learning algorithm can give great improvements in terms of successful communication rates - $\Longrightarrow$ can fit more objects in the existing IoT networks :tada: ! --- # 2. System model ### Wireless network - In ISM band, centered at $433.5$ MHz (in Europe) - $K=4$ (or more) orthogonal channels ### One gateway, many IoT devices - One gateway, handling different objects - Communications with ALOHA protocol **with retransmission** - Objects send data for $1$s in one channel, wait for an *acknowledgement* for $1$s in same channel, use Ack as feedback: success / failure --- ### Transmission and retransmission model - Each object communicates from time to time (e.g., every $10$ s) $\Longleftrightarrow$ probability $p$ of transmission at every time (Bernoulli process) - Retransmit at most $M$ times if first transmission failed (until Ack is received) - Retransmissions can use a different channel that the one used for first transmission - Retransmissions happen after a random back-off time back-off time $\sim\mathcal{U}(0,\cdots,M-1)$ ### The goal of each object Is to *max*imize its successful communication rates $\Longleftrightarrow$ *max*imize its number of received Ack. --- # Do we need learning for transmission? Yes! #### First hypothesis The surrounding traffic is not uniformly occupying the $K$ channels. #### Consequence - Then it is always sub-optimal to use a (naive) uniformly random channel access - $\Longrightarrow$ we can use online machine learning to let each IoT device learn, on its own and in an automatic and decentralized way, which channel is the best one (= less occupied) in its current environment. - Learning is actually *needed* to achieve (close to) optimal performance. --- # Do we need learning for *re*transmission? #### Second hypothesis Imagine a set of IoT devices learned to transmit efficiently (in the most free channel), in one IoT network. #### Question - Then if two devices collide, do they have a higher probability of colliding again *if retransmissions happen in the same channel* ? --- # Mathematical intution and illustration Consider one IoT device and one channel, we consider two probabilities: - $p_c$ : suffering a collision at first transmission, - $p_{c1}$ : collision at the first retransmission (if it uses the same channel). In an example network with... - a small transmission probability $p=10^{-3}$, - from $N=50$ to $N=400$ IoT devices, - $\Longrightarrow$ we ran simulations showing that $p_{c1}$ can be more than twice of $p_c$ (from $5\%$ to $15\%$!) --- ![75%](plots/Approximation_m10.png) --- # Do we need learning for *re*transmission? Yes we do! #### Consequence - Then if two devices collide, they have a higher probability of colliding again *if retransmissions happen in the same channel* - $\Longrightarrow$ we can also use online machine learning to let each IoT device learn, on its own and in an automatic and decentralized way, which channel is the best one (= less occupied) to retransmit a packet which failed due to a collision. - Learning is again *needed* to achieve (close to) optimal performance. --- # 3. Multi-Armed Bandits (MAB ## 3.1. Model ## 3.2. Algorithms --- # 3.1. Multi-Armed Bandits Model - $K \geq 2$ resources (*e.g.*, channels), called **arms** - Each time slot $t=1,\ldots,T$, you must choose one arm, denoted $C(t)\in\{1,\ldots,K\}$ - You receive some reward $r(t) \sim \nu_k$ when playing $k = C(t)$ - **Goal:** maximize your sum reward $\sum\limits_{t=1}^{T} r(t)$, or expected $\sum\limits_{t=1}^{T} \mathbb{E}[r(t)]$ - Hypothesis: rewards are stochastic, of mean $\mu_k$. Example: Bernoulli distributions. ## Why is it famous? Simple but good model for **exploration/exploitation** dilemma. --- # 3.2. Multi-Armed Bandits Algorithms ### Often "*index* based" - Keep *index* $U_k(t) \in \mathbb{R}$ for each arm $k=1,\ldots,K$ - Always use channel $C(t) = \arg\max U_k(t)$ - $U_k(t)$ should represent our belief of the *quality* of arm $k$ at time $t$ ### Example: "Follow the Leader" - $X_k(t) := \sum\limits_{s < t} r(s) \bold{1}(A(s)=k)$ sum reward from arm $k$ - $N_k(t) := \sum\limits_{s < t} \bold{1}(A(s)=k)$ number of samples of arm $k$ - And use $U_k(t) = \hat{\mu}_k(t) := \frac{X_k(t)}{N_k(t)}$. --- # *Upper Confidence Bounds* algorithm (UCB) - Instead of using $U_k(t) = \frac{X_k(t)}{N_k(t)}$, add an *exploration term* $$ U_k(t) = \frac{X_k(t)}{N_k(t)} + \sqrt{\alpha \frac{\log(t)}{N_k(t)}} $$ ### Parameter $\alpha$: tradeoff exploration *vs* exploitation - Small $\alpha$: focus more on **exploitation**, - Large $\alpha$: focus more on **exploration**, - Typically $\alpha=1$ works fine empirically and theoretically. --- # *Upper Confidence Bounds* algorithm (UCB) ![90%](plots/Algorithm1_UCB.png) --- # 4. We Study Different Heuristics (5) - They all use one UCB algorithm to decide the channel to use for first transmissions of any message - They use different approaches for retransmissions: - "Only UCB": use same $\mathrm{UCB}$ for retransmissions, - "Random": uniformly random retransmissions, - "UCB": use another $\mathrm{UCB}^r$ for retransmissions (no matter the channel for first transmission), - "K-UCB": use $K$ different $\mathrm{UCB}^j$ for retransmission after a first transmission on channel $j\in\{1,\cdots,K\}$, - "Delayed UCB": use another $\mathrm{UCB}^d$ for retransmissions, but launched after a delay $\Delta$. --- # 4.0. Only UCB Use the same $\mathrm{UCB}$ to decide the channel to use for any transmissions, regardless if it's a first transmission or a retransmission of a message. ![80%](plots/Algorithm1_UCB.png) --- # 4.1. UCB + Random Retransmissions ![90%](plots/Algorithm2_UCB_RandomRetransmission.png) --- # 4.2. UCB + a single UCB for Retransmissions ![90%](plots/Algorithm3_UCB_UCBRetransmission.png) --- # 4.3. UCB + $K$ UCB for Retransmissions ![85%](plots/Algorithm4_UCB_KUCBRetransmission.png) --- # 4.4. UCB + Random Retransmission ![85%](plots/Algorithm5_UCB_DelayedUCBRetransmission.png) --- # 5. Numerical simulations and results ### What - We simulate a network, - With many IoT dynamic devices. ### Why - They implement the UCB learning algorithm to learn to optimize their *first* transmission of any uplink packets, - And the different heuristic to (try to) learn to optimize their *retransmissions* of the packets after any collision. --- # 5.1. First experiment We consider an example network with... - $K=4$ channels (e.g., like in LoRa), - $M=5$ maximum number of retransmission, - $m=5$ maximum back-off interval, - $p=10^{-3}$ transmission probability, - $5=20 \times 10^4$ time slots, - from $N=1000$ IoT devices. Non uniform occupancy of the $4$ channels: they are occupied $10$, $30$, $30$ and $30\%$ of times (by other IoT networks). --- ![80%](plots/ResultsUCB.png) --- # 5.2. Second experiment Non uniform occupancy of the $4$ channels: they are occupied $40$, $30$, $20$ and $30\%$ of times (by other IoT networks). --- ![80%](plots/ResultsUCB2.png) --- # 6. Summary (1/3) ## Settings 1. For **LPWA networks** based onan **ALOHA protocol** (slotted both in time and frequency), 2. We presented a **retransmission model** 3. Dynamic **IoT devices** can use **simple machine learning algorithms**, to improve their successful communication rate, 4. We focus on the packet retransmissions upon radio collision, by using low-cost **Multi-Armed Bandit** algorithms, like **UCB**. --- # 6. Summary (2/3) ## We presented Several **learning heuristics** - that try to learn how to transmit and retransmit in a smarter way - by using the classical UCB algorithm for **channel selection for first transmission**: it has a **low memory and computation cost**, easy to add on an embedded CPU of an IoT device - and different ideas based on UCB for the retransmissions upon collisions, that add no cost/memory overhead. --- # 6. Summary (3/3) ## We showed - Using machine learning for the *transmission* is **needed** to achieve optimal performance, and can lead to significant gain in terms of successful transmission rates (up-to 30% in the example network). - Using machine learning for the *retransmission* is also useful, and improves over previous approach unaware of retransmission. - The proposed heuristics outperform a naive random access scheme. - Surprisingly, the main take-away message is that a simple UCB learning approach, that retransmit in the same channel, turns out to perform as well as more complicated heuristics. --- # 6. Future works - Implement our proposed approach in a real-world demo For instance using USRP boards. - Study a real IoT LPWAN protocol (e.g., LoRa) - Explore in LoRa how to use machine learning (e.g., Multi-Armed Bandit algorithms) to let IoT devices learn on their own the best retransmission pattern to follow in a given scenario. --- # More ? ### â†’ See our paper: [`HAL.Inria.fr/hal-02049824`](https://hal.inria.fr/hal-02049824) ### :pray: Please ask questions ! <span class="fontify">Thanks for listening !</span>