With both small-cell LTE and 802.11 networks now available as alternatives for deployment in unlicensed bands at 5 GHz, investigation into their coexistence is a topic of great interest. ETSI Rel. 14 has standardized LTE licensed assisted access (LAA) that seeks to make LTE more coexistence friendly with Wi-Fi by incorporating listen before talk (LBT). However, the fairness of Wi-Fi and LTE-LAA sharing is a topic that has not been adequately explored. In this work, we first investigate the 3GPP definition of fair coexistence in 3GPPTR via new analytical models. By tuning the LTE-LAA parameters, we exemplify scenarios when the 3GPP notion of fairness is achieved and conversely, when not achieved. The formal notions of access and proportional fairness is then considered for these scenarios to compare and contrast with the 3GPP definition.

IEEE 802.11be (Wi-Fi 7) introduces a new concept called multilink operation (MLO), which allows multiple Wi-Fi interfaces in different bands (2.4, 5, and 6 GHz) to work together to increase network throughput, reduce latency, and improve spectrum reuse efficiency in dense overlapping networks. To make the most of MLO, this article proposes a new data-driven resource allocation algorithm for the 11be network with the aid of an access point (AP) controller. To maximize network throughput, a network topology optimization problem is formulated for 11be network, which is solved by exploiting the totally unimodular property of the bipartite graph formed by the connection between AP and station (STA) in Wi-Fi networks. Subsequently, a proportional fairness algorithm is applied for radio link allocation, network throughput optimization considering the channel condition, and the fairness of the multilink device (MLD) data rate. The performance of the proposed algorithm on two main MLO implementations—multilink-multiradio (MLMR) with simultaneous transmission and reception (STR), and the interplay between multiple nodes employing them are evaluated through cross-layer (PHY-MAC) data rate simulation with PHY abstraction.

The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum. In this work, we focus on the LTE-Unlicensed (LTEU) specification developed by the LTE-U Forum, which uses the duty-cycle approach for fair coexistence. The specification suggests reducing the duty cycle at LTE-U base-station (BS) when the number of co-channel Wi-Fi basic service sets (BSSs) increases from one to two or more. However, without decoding the Wi-Fi packets, detecting the number of Wi-Fi BSSs operating on the channel in real-time is a challenging problem. In this work, we demonstrate a novel ML-based approach which solves this problem by using energy values observed during the LTE-U OFF duration. It is relatively straightforward to observe only the energy values during the LTE-U BS OFF time compared to decoding the entire Wi-Fi packet, which would require a full Wi-Fi receiver at the LTE-U base-station. We implement and validate the proposed ML-based approach by real-time experiments and demonstrate that there exist distinct patterns between the energy distributions between one and many Wi-Fi AP transmissions. The proposed ML-based approach results in a higher accuracy (close to 99% in all cases) as compared to the existing auto-correlation (AC) and energy detection (ED) approaches.

Coexistence of small-cell LTE-U andWi-Fi networks in unlicensed bands at 5 GHz is a topic of active interest, primarily driven by industry groups affiliated with the two (cellular and Wi-Fi) segments. Our focus in this work is on real-time deployment aspects of such coexisting networks, a topic which has seen little traction in the existing literature. The aim is to explore the following questions: (1) How doWi-Fi clients associate with aWi-Fi access point (AP) when a LTE-U base station is active with its maximum duty cycle, (2) How is the latency of Wi-Fi connection affected (in terms of association and data transmission) during coexistence with LTE-U, and (3) How does the energy sensing threshold of Wi-Fi affect the latency and throughput performance of Wi-Fi and LTE-U?.

Future spectrum sharing rules very likely will be based on device environment: indoors or outdoors. For example, the 6 GHz rules created different power regimes for unlicensed devices to protect incumbents: “indoor” devices, subject to lower transmit powers but not required to access an Automatic Frequency Control database to obtain permission to use a channel, and “outdoor” devices, allowed to transmit at higher power but required to do so to determine channel availability. However, since there are no reliable means of determining if a wireless device is indoors or outdoors, other restrictions were mandated: reduced power for client devices and indoor access points that cannot be battery powered, have detachable antennas or be weatherized. These constraints lead to sub-optimal spectrum usage and potential for misuse. Hence, there is a need for robust identification of device environments to enable spectrum sharing. In this paper we study automatic indoor/outdoor classification based on the radio frequency (RF) environment experienced by a device. Using a custom Android app, we first create a labeled data set of a number of parameters of Wi-Fi and cellular signals in various indoor and outdoor environments, and then evaluate the classification performance of various machine learning (ML) models on this data set. We find that tree-based ensemble ML models can achieve greater than 99% test accuracy and F1-Score, thus allowing devices to self-identify their environment and adapt their transmit power accordingly.