# A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario
## 3 Evaluation Scenario and Reference Results  
In the technical report in [3], 3 GPP has defined evaluation scenarios to assess the coexistence between systems that operate on unlicensed spectrum. One of these scenarios is the indoor scenario, which presents the most challenging interference profile, and it has multiple users and multiple cells. This scenario, depicted in Figure 1, has two operators, each with four cells, in a room with dimensions 120 \(\times\) 50 m and no walls. The distance between access points of different Radio Access Technologies (RATs) is \(d\), and between the same RAT is \(BS\_space\). In addition to the cells, forty stations are randomly deployed within this rectangular region, with twenty stations from each operator [3]. Figure 1: Indoor coexistence scenario proposed by the 3 GPP [3]. To model and evaluate the coexistence in this scenario, we use the ns-3 simulator [36]. The ns-3 is a widely used and accepted open-source event discrete simulation tool, which is implemented in C++ and complies with standards defined by IEEE, 3 GPP, and Wi-Fi Alliance. To particularly address the coexistence in unlicensed bands, a modified version known as ns3-lbt [37] was developed by the Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), University of Washington, and funded by Wi-Fi Alliance. This modified version implements DC transmission of LTE-U using the approach presented in [38]. According to this approach, LTE-U transmission defines a time window called Almost Blank Subframe (ABS) of 40 ms. As each subframe of an LTE transmission is 1 ms, a bitmask of 40 bits (one bit for the subframe) is defined to control the ON-OFF pattern. This modified version of the simulator can be found on [[http://code.nsnam.org/laa/ns-3-lbt/file/9529](http://code.nsnam.org/laa/ns-3-lbt/file/9529) febb7 ebc]([http://code.nsnam.org/laa/ns-3-lbt/file/9529](http://code.nsnam.org/laa/ns-3-lbt/file/9529) febb7 ebc) (accessed on 1 January 2023). In our previous work [10], we presented preliminary results using the same simulator and scenario described above. Our main conclusions, as presented in [10], are summarized below:  
1. The DC value that results in maximum system throughput depends on the offered data rate and changes every time that the offered data rates change;
2. If the offered data rate for each operator is unbalanced, the best DC value, resulting in maximum system throughput, is the one that is proportional to the imbalance. For example, if we offer 4 Mbps for LTE-U users and 500 kbps for Wi-Fi users, the optimal DC value is approximately 0.8, because it gives 80% of the channel usage time for LTE-U and 20% for Wi-Fi, matching the demand;
3. A fixed DC value is not enough to achieve maximum throughput in a dynamic system where the data rates are constantly changing. In order to reach maximum throughput in such scenarios, a dynamic algorithm is needed to automatically and dynamically change the DC value based on the system's current state. 4. Choosing the best DC value for the current state guarantees that the system will always be as close to its maximum throughput as possible, since there is an optimal DC value for each combination of offered data rates;
5. Q-Learning is highly dependent on how the state-action pair is modeled, and this has a great impact on convergence. If the number of state-action pairs is high, the algorithm may not converge before it is time to begin calculating again. On the other hand, if the number of state-action pairs is low, the algorithm may converge to a locally optimal point, or it may not explore all possibilities provided by the environment. Therefore, fine-tuning is necessary when using this approach for optimal decision making. Based on these conclusions, we developed a framework using the Multi-Armed Bandit problem formulation, and the set of parameters used is adapted from [10], as presented in Table 1. We perform simulation rounds of 20 s for each fixed value of DC and collect the average throughput of each operator.