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1. The Impact of Bundling Licensed and Unlicensed Wireless Service

   https://doi.org/10.1109/INFOCOM.2018.8485852  

   Wang, Xu; Berry, Randall A. (April 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications)

   Unlicensed spectrum has been viewed as a way to increase competition in wireless access and promote innovation in new technologies and business models. However, several recent papers have shown that the openness of such spectrum can also lead to it becoming over congested when used by competing wireless service providers (SPs). This in turn can result in the SPs making no profit and may deter them from entering the market. However, this prior work assumes that unlicensed access is a separate service from any service offered using licensed spectrum. Here, we instead consider the more common case were service providers bundle both licensed and unlicensed spectrum as a single service and offer this with a single price. We analyze a model for such a market and show that in this case SPs are able to gain higher profit than the case without bundling. It is also possible to get higher social welfare with bundling. Moreover, we explore the case where SPs are allowed to manage the customers' average percentage of time they receive service on unlicensed spectrum and characterize the social welfare gap between the profit maximizing and social welfare maximizing setting. 

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2. Machine Learning Enhanced Channel Selection for Unlicensed LTE

   https://doi.org/10.1109/DySPAN.2019.8935859  

   Tonnemacher, Matthew; Tarver, Chance; Cavallar, Joseph; Camp, Joseph (November 2019, 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   We propose a mechanism for unlicensed LTE channel selection that not only takes into account interference to and from Wi-Fi access points but also considers other LTE operators in the unlicensed band. By collecting channel utilization statistics and sharing this information periodically with other unlicensed LTE eNBs, each eNB can improve their channel selection given their limited knowledge of the full topology. While comparing our algorithm to existing solutions, we find that the similarity between sensed Wi-Fi occupation at neighboring eNBs greatly impacts the performance of channel selection algorithms. To achieve better performance across diverse scenarios, we expand on our statistical channel selection formulation to include reinforcement learning, thereby balancing the shared contextual information with historical performance. We simulate operation in the unlicensed band using our channel selection algorithm and show how Wi-Fi load and inter-cell interference estimation can jointly be used to select transmission channels for all small cells in the network. Our approaches lead to an increase in user-perceived throughput and spectral efficiency across the entire band when compared to the greedy channel selection. 

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3. A Spatial-Spectral Interference Model for Millimeter Wave 5G Applications

   https://doi.org/10.1109/VTCFall.2017.8288066  

   Niknam, Solmaz; Mehrpouyan, Hani; Natarajan, Balasubramaniam (September 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall))

   The potential of the millimeter wave (mmWave) band in meeting the ever growing demand for high data rate and capacity in emerging fifth-generation (5G) wireless networks is well-established. Since mmWave systems are expected to use highly directional antennas with very focused beams to overcome severe pathloss and shadowing in this band, the nature of signal propagation in mmWave wireless networks may differ from current networks. One factor that is influenced by such propagation characteristics is the interference behavior, which is also impacted by the simultaneous use of the unlicensed portion of the spectrum by multiple users. Therefore, considering the propagation characteristics in the mmWave band, we propose a spatial-spectral interference model for 5G mmWave applications, in the presence of Poisson field of blockages and interferers operating in licensed and unlicensed mmWave spectrum. Consequently, the average bit error rate of the network is calculated. Simulation is also carried out to verify the outcomes of the paper. 

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4. On Modeling and Optimizing LTE/Wi-Fi Coexistence with Prioritized Traffic Classes

   https://doi.org/10.1109/DySPAN.2018.8610465  

   Hirzallah, Mohammed; Xiao, Yong; Krunz, Marwan (October 2018, 2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   The dramatic growth in demand for mobile data service has prompted mobile network operators (MNOs) to explore new spectrum resources in unlicensed bands. MNOs have been recently allowed to extend LTE-based service called LTE-LAA over 5 GHz U-NII bands, currently occupied by Wi-Fi. To support applications with diverse QoS requirements, both LTE and Wi-Fi technologies introduce multiple priority classes with different channel contention parameters for accessing unlicensed bands. How these different priority classes affect the interplay between coexisting LTE and Wi-Fi technologies is still relatively under-explored. In this paper, we develop a simple and efficient framework that helps MNOs assess the fair coexistence between MNOs and Wi-Fi operators with prioritized channel access under the multi-channel setting. We derive an approximated closed-form solution for each MNO to pre-evaluate the probability of successful transmission (PST), average contention delay, and average throughput when adopting different priority classes to serve different traffics. MNOs and Wi-Fi operators can fit our model using measurements collected offline and/or online, and use it to further optimize their systems’ throughput and latency. Our results reveal that PSTs computed with our approximated closed-form model approach those collected from system-level simulations with around 95% accuracy under scenarios of dense network deployment density and high traffic intensity. 

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5. Cross-Layer Band Selection and Routing Design for Diverse Band-Aware DSA Networks

   https://doi.org/10.1109/GLOBECOM42002.2020.9322500  

   Upadhyaya, Pratheek S.; Shah, Vijay K.; Reed, Jeffrey H. (December 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020)

   As several new spectrum bands are opening up for shared use, a new paradigm of Diverse Band-aware Dynamic Spectrum Access (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in multiple bands, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios. 

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