Recent literature demonstrated promising results of Long-Term Evolution (LTE) deployments over unlicensed bands when coexisting with Wi-Fi networks via the Duty-Cycle (DC) approach. However, it is known that performance in coexistence is strongly dependent on traffic patterns and on the duty-cycle ON-OFF rate of LTE. Most DC solutions rely on static coexistence parameters configuration, hence real-life performance in dynamically varying scenarios might be affected. Advanced reinforcement learning techniques may be used to adjust DC parameters towards efficient coexistence, and we propose a Q-learning Carrier-Sensing Adaptive Transmission mechanism which adapts LTE duty-cycle ON-OFF time ratio to the transmitted data rate, aiming at maximizing the Wi-Fi and LTE-Unlicensed (LTE-U) aggregated throughput. The problem is formulated as a Markov decision process, and the Q-learning solution for finding the best LTE-U ON-OFF time ratio is based on the Bellman's equation. We evaluate the performance of the proposed solution for different traffic load scenarios using the ns-3 simulator. Results demonstrate the benefits from the adaptability to changing circumstances of the proposed method in terms of Wi-Fi/LTE aggregated throughput, as well as achieving a fair coexistence.
Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.
3GPP has been working in establishing a new network infrastructure based on unlicensed spectrum known as LTE Secondary Cell (SCell) in a carrier aggregation scenario in which LTE user terminals would still be anchored to an LTE Primary Cell (PCell) over licensed spectrum. On this scenario, best-effort content would be delivered on the unlicensed band SCell, while QoS traffic and control signaling would still be a PCell responsibility. This work makes a performance analysis of 3GPP's solution, named LTE License Assisted Access (LTE-LAA), which allows the LTE system to operate on unlicensed spectrum, likewise comparing with other alternative coexistence solution called LTE duty cycle (LTE-DC) and making a Wi-Fi performance evaluation with those different coexistence solutions when operating in 5GHz band.
Joint design of control and communication in Wireless Networked Control Systems (WNCS) is a promising approach for future wireless industrial applications. In this context, Age of Information (AoI) has been increasingly utilized as a metric that is more representative than latency in the context of systems with a sense-compute-actuate cycle. Nevertheless, AoI is commonly defined for a single communication direction, Downlink or Uplink, which does not capture the closed-loop dynamics. In this paper, we extend the concept of AoI by defining a new metric, Age of Loop (AoL), relevant for WNCS closed-loop systems. The AoL is defined as the time elapsed since the piece of information causing the latest action or state (depending on the selected time origin) was generated. We then use the proposed metric to learn the WNCS latency and freshness bounds and we apply such learning methodology to minimize the long term WNCS cost with the least amount of bandwidth. We show that, using the AoL, we can learn the control system requirement and use this information to optimize network resources.
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