Marceau Coupechoux

This article develops a simple model of the smartphone circular economy where the buy-back price offered to consumers is the main control variable. The model explicitly integrates refurbishment, recycling, and premature phone replacement encouraged by buy-back incentives. We distinguish three types of smartphones: premium, basic, and refurbished premium phones. Furthermore, consumers are segmented by buy-back sensitivity: sensitive users tend to return their phones and shorten their device lifespan, while non-sensitive users keep their phones longer. Additionally, refurbished consumers can buy new phones in case of insufficient supply. Our analysis reveals that with realistic parameters, there exists an optimal buy-back level that minimizes CO₂eq. emissions and waste, and this optimum coincides with the equilibrium between supply and demand on the refurbished market. However, depending on the system parameters, negative effects can be observed. For example, if most components are replaced during refurbishment, emissions increase with the buy-back. More generally, our results highlight the importance of eco-design. While the model is applied to smartphones, its structure is generic and can be adapted to other products with refurbishment and buy-back incentives.

Using the stochastic geometry framework, we study a millimeter wave (mmWave) Device-to-Device (D2D) network dedicated to Ultra-Reliable Low Latency Communications (URLLC), where users employ multiple antennas to perform beamforming. We leverage the notion of meta-distribution in order to capture the reliability requirement of URLLC. The packet transmission process is divided into two phases: a beam training phase, during which exhaustive beam sweeping is adopted, and a data transmission phase. The paper investigates the misalignment error distribution resulting from an imperfect training phase, due to the finite codebooks resolution and the fast variation of the channel. For the data transmission phase, closed-form expressions for all the moments of the conditional rate coverage probability are derived, and the meta-distribution is approximated using the beta approximation. The study evaluates the overall network performance through the effective rate meta-distribution, which accounts for the training overhead and beam misalignment errors. The results show the detrimental impact of misalignment errors when URLLC requirements are stringent and highlight the trade-off between the training overhead and the gain brought by multiple antennas. Insights are provided for optimally and jointly choosing the codebook size and tbe number of antennas.

This article addresses the problem of Ultra Reliable Low Latency Communications (URLLC) in wireless networks, a framework with particularly stringent constraints imposed by many Internet of Things (IoT) applications from diverse sectors. We propose a novel Deep Reinforcement Learning (DRL) scheduling algorithm, named NOMA-PPO, to solve the Non-Orthogonal Multiple Access (NOMA) uplink URLLC scheduling problem involving strict deadlines. The challenge of addressing uplink URLLC requirements in NOMA systems is related to the combinatorial complexity of the action space due to the possibility to schedule multiple devices, and to the partial observability constraint that we impose to our algorithm in order to meet the IoT communication constraints and be scalable. Our approach involves 1) formulating the NOMA-URLLC problem as a Partially Observable Markov Decision Process (POMDP) and the introduction of an agent state , serving as a sufficient statistic of past observations and actions, enabling a transformation of the POMDP into a Markov Decision Process (MDP); 2) adapting the Proximal Policy Optimization (PPO) algorithm to handle the combinatorial action space; 3) incorporating prior knowledge into the learning agent with the introduction of a Bayesian policy. Numerical results reveal that not only does our approach outperform traditional multiple access protocols and DRL benchmarks on 3GPP scenarios, but also proves to be robust under various channel and traffic configurations, efficiently exploiting inherent time correlations.

For decades, Simultaneous Ascending Auction (SAA) has been the most popular mechanism used for spectrum auctions. It has recently been employed by many countries for the allocation of 5G licences. Although SAA presents relatively simple rules, it induces a complex strategic game for which the optimal bidding strategy is unknown. Considering the fact that sometimes billions of euros are at stake in an SAA, establishing an efficient bidding strategy is crucial. In this work, we model the auction as a $n$ -player simultaneous move game with complete information and propose the first efficient bidding algorithm that tackles simultaneously its four major strategic issues: the exposure problem , the own price effect , budget constraints and the eligibility management problem . Our solution, called $SMS^\alpha$ , is based on Simultaneous Move Monte Carlo Tree Search (SM-MCTS) and relies on a new method for the prediction of closing prices. By introducing a new reward function in $SMS^\alpha$ , we give the possibility to bidders to define their own level of risk-aversion. Through extensive numerical experiments on instances of realistic size, we show that $SMS^\alpha$ largely outperforms state-of-the-art algorithms, notably by achieving higher expected utility while taking less risks.

Teleworking became a vastly popular practice lately owing to the Covid-19 outbreak. Our present study aims to evaluate the operating energy consumption in 5G versus Wifi6 networks for this remote working case study. We specifically study the detailed operations of transmission and reception at both radio access networks and quantify their power consumption in the downlink, analytically, using simulations and, for the case of Wifi6. We focus on three case studies in the framework of a teleworking tool: audio session, video session and shared screen session. Our results show that for the use case of video session in the downlink, when the number of users is rather small, Wifi6 consumes less power, however, as the number of users gets larger, 5G with Multi-User MIMO (MU-MIMO) outperforms the individual Wifi accesses. These results depend on the number of simultaneous MUMIMO streams as well as on the fixed component of the power consumption of the 5G, for which we present a sensitivity analysis as well.