Vicente A. de Sousa

Bio: Vicente A. de Sousa is an academic researcher from Federal University of Rio Grande do Norte. The author has contributed to research in topics: Spectrum management & Computer science. The author has an hindex of 8, co-authored 33 publications receiving 353 citations. Previous affiliations of Vicente A. de Sousa include Federal University of Ceará & Nokia.

Enabling the coexistence of LTE and Wi-Fi in unlicensed bands

Abstract: The expansion of wireless broadband access network deployments is resulting in increased scarcity of available radio spectrum. It is very likely that in the near future, cellular technologies and wireless local area networks will need to coexist in the same unlicensed bands. However, the two most prominent technologies, LTE and Wi-Fi, were designed to work in different bands and not to coexist in a shared band. In this article, we discuss the issues that arise from the concurrent operation of LTE and Wi-Fi in the same unlicensed bands from the point of view of radio resource management. We show that Wi-Fi is severely impacted by LTE transmissions; hence, the coexistence of LTE and Wi-Fi needs to be carefully investigated. We discuss some possible coexistence mechanisms and future research directions that may lead to successful joint deployment of LTE and Wi-Fi in the same unlicensed band.

DM-CSAT: a LTE-U/Wi-Fi coexistence solution based on reinforcement learning

Abstract: 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.

Temperature Impact in LoRaWAN—A Case Study in Northern Sweden

Abstract: LoRaWAN has become popular as an IoT enabler. The low cost, ease of installation and the capacity of fine-tuning the parameters make this network a suitable candidate for the deployment of smart cities. In northern Sweden, in the smart region of Skelleftea, we have deployed a LoRaWAN to enable IoT applications to assist the lives of citizens. As Skelleftea has a subarctic climate, we investigate how the extreme changes in the weather happening during a year affect a real LoRaWAN deployment in terms of SNR, RSSI and the use of SF when ADR is enabled. Additionally, we evaluate two propagation models (Okumura-Hata and ITM) and verify if any of those models fit the measurements obtained from our real-life network. Our results regarding the weather impact show that cold weather improves the SNR while warm weather makes the sensors select lower SFs, to minimize the time-on-air. Regarding the tested propagation models, Okumura-Hata has the best fit to our data, while ITM tends to overestimate the RSSI values.

Propagation Model Evaluation for LoRaWAN: Planning Tool Versus Real Case Scenario

Abstract: LoRa has emerged as a prominent technology for the Internet of Things (IoT), with LoRa Wide Area Network (LoRaWAN) emerging as a suitable connection solution for smart things. The choice of the best location for the installation of gateways, as well as a robust network server configuration, are key to the deployment of a LoRaWAN. In this paper, we present an evaluation of Received Signal Strength Indication (RSSI) values collected from the real-life LoRaWAN deployed in Skelleftea, Sweden, when compared with the values calculated by a Radio Frequency (RF) planning tool for the Irregular Terrain Model (ITM), Irregular Terrain with Obstructions Model (ITWOM) and Okumura-Hata propagation models. Five sensors are configured and deployed along a wooden bridge, with different Spreading Factors (SFs), such as SF 7, 10 and 12. Our results show that the RSSI values calculated using the RF planning tool for ITWOM are closest to the values obtained from the real-life LoRaWAN. Moreover, we also show evidence that the choice of a propagation model in an RF planning tool has to be made with care, mainly due to the terrain conditions of the area where the network and the sensors are deployed.

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework.

Abstract: 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.

LTE-unlicensed: the future of spectrum aggregation for cellular networks

Abstract: The phenomenal growth of mobile data demand has brought about increasing scarcity in available radio spectrum. Meanwhile, mobile customers pay more attention to their own experience, especially in communication reliability and service continuity on the move. To address these issues, LTE-Unlicensed, or LTEU, is considered one of the latest groundbreaking innovations to provide high performance and seamless user experience under a unified radio technology by extending LTE to the readily available unlicensed spectrum. In this article, we offer a comprehensive overview of the LTEU technology from both operator and user perspectives, and examine its impact on the incumbent unlicensed systems. Specifically, we first introduce the implementation regulations, principles, and typical deployment scenarios of LTE-U. Potential benefits for both operators and users are then discussed. We further identify three key challenges in bringing LTE-U into reality together with related research directions. In particular, the most critical issue of LTE-U is coexistence with other unlicensed systems, such as widely deployed WiFi. The LTE/WiFi coexistence mechanisms are elaborated in time, frequency, and power aspects, respectively. Simulation results demonstrate that LTE-U can provide better user experience to LTE users while well protecting the incumbent WiFi users’ performance compared to two existing advanced technologies: cellular/WiFi interworking and licensed-only heterogeneous networks (Het-Nets).

Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks

Abstract: With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: 1) spectrum efficiency versus energy efficiency; 2) deployment efficiency versus energy efficiency; 3) delay versus power; and 4) bandwidth versus power. In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing and non-orthogonal aggregation, multiple input multiple output, and heterogeneous networks. We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks.

IEEE 820.11ax: High-Efficiency WLANs

Abstract: IEEE 802.11ax-2019 will replace both IEEE 802.11n-2009 and IEEE 802.11ac-2013 as the next high-throughput Wireless Local Area Network (WLAN) amendment. In this paper, we review the expected future WLAN scenarios and use-cases that justify the push for a new PHY/MAC IEEE 802.11 amendment. After that, we overview a set of new technical features that may be included in the IEEE 802.11ax-2019 amendment and describe both their advantages and drawbacks. Finally, we discuss some of the network-level functionalities that are required to fully improve the user experience in next-generation WLANs and note their relation with other on-going IEEE 802.11 amendments.

IEEE 802.11ax: High-efficiency WLANS

Abstract: IEEE 802.11ax-2019 will replace both IEEE 802.11n-2009 and IEEE 802.11ac-2013 as the next high-throughput WLAN amendment. In this article, we review the expected future WLAN scenarios and use cases that justify the push for a new PHY/MAC IEEE 802.11 amendment. After that, we overview a set of new technical features that may be included in the IEEE 802.11ax-2019 amendment, and describe both their advantages and drawbacks. Finally, we discuss some of the network-level functionalities that are required to fully improve the user experience in next-generation WLANs and note their relation with other ongoing IEEE 802.11 amendments.

Coexistence of LTE-LAA and Wi-Fi on 5 GHz With Corresponding Deployment Scenarios: A Survey

Abstract: Long term evolution (LTE) carrier aggregation with 5 GHz unlicensed national informational infrastructure band has been pointed out by the industry as a good solution to handle the rapidly increasing amounts of data traffic. To provide fair coexistence of LTE-licensed assisted access (LTE-LAA) and Wi-Fi on 5 GHz, several coexistence mechanisms have already been proposed. This paper provides a comprehensive survey of the coexistence of LTE-LAA and Wi-Fi on 5 GHz with corresponding deployment scenarios. We first analyze coexistence-related features of those two technologies, including motivation, LTE carrier aggregation with unlicensed band, LTE and Wi-Fi medium access control protocols comparison, coexistence challenges and enablers, performance difference between LTE-LAA and Wi-Fi, as well as co-channel interference. Second, we further extensively discuss current considerations about the coexistence of LTE-LAA and Wi-Fi. Third, influential factors for the classification of small cell scenarios, as well as four representative scenarios are investigated in detail. Then we explore a relatively smooth technical route for solving coexistence-related problems, which practically takes features of a specific scenario as the base for designing deployment mode of LTE-LAA and/or Wi-Fi. A scenario-oriented decision making procedure for the coexistence issue and the analysis on an example deployment scenario, including design and performance evaluation metrics focusing on the concept of the scenario-oriented coexistence are presented. We finally forecast further research trends on the basis of our conclusion.