Radio signals are well suited for user localization because they are ubiquitous, can operate in the dark and maintain privacy. Many prior works learn mappings between channel state information (CSI) and position fully-supervised. However, that approach relies on position labels which are very expensive to acquire. In this work, this requirement is relaxed by using pseudo-labels during deployment, which are calculated from an inertial measurement unit (IMU). We propose practical algorithms for IMU double integration and training of the localization system. We show decimeter-level accuracy on simulated and challenging real data of 5G measurements. Our IMU-supervised method performs similarly to fully-supervised, but requires much less effort to deploy.

Intelligent integrated sensing and communication is one of key aspects of future wireless networks in which sensing can be leveraged to enhance communications and vice-versa. In this paper, we propose a novel sensing solution that can be used to represent an RF-environment. The proposed solution accounts for practical challenges such as limited time resolution due to limited bandwidth with no angle measurements while providing robustness to wireless propagation phenomena such as diffraction. Our proposed method leverages offline data collection during RF-mapping, and finds the location of virtual anchors (VAs), i.e., mirror images of a physical anchor w.r.t reflectors, through an iterative process called successive tap removal (STR). Afterwards, machine learning (ML) models are trained to predict dominant multipath components of the received wireless channel at a given location. Found VAs and their associated ML models stand for intermediate entities that represent an RF-environment. As an application, we use the developed models in the context of multipath assisted positioning to improve positioning accuracy in challenging indoor environments with heavy non-line-of-sight (NLoS) conditions. Finally, we extend our ideas to systems with multi-antenna transmitters and show that VA detection accuracy can be improved, bringing higher accuracy to the downstream positioning applications.

In this paper, we present Sense-Bandits, an AI-based framework for distributed adaptation of the sensing thresholds (STs) over shared spectrum. This framework specifically targets the coexistence of heterogenous technologies, e.g., Wi-Fi, 4G Licensed-Assisted Access (LAA), and 5G New Radio Unlicensed (NR-U), over unlicensed channels. To access the channel, a device compares the measured power with a predefined ST value and accordingly decides if the channel is idle or not. Improper setting of the ST values creates asymmetric sensing floors, resulting in collisions due to hidden terminals and/or reduction in the spatial reuse due to exposed terminals. Optimal ST setting is challenging because it requires global knowledge of mobility, traffic loads, and channel access behavior of all contending devices. Sense-Bandits tackles this problem by employing a clustering-based multi-armed bandit (MAB) algorithm, which adapts its learning behavior based on network dynamics. Clustering allows the algorithm to track network changes in real-time, ensuring fast learning of the best ST values by classifying the state and dynamics of coexisting networks. We develop a C++-based network simulator that implements Sense-Bandits and we apply it to evaluate the coexistence of Wi-Fi and 5G NR-U systems over the unlicensed 5 GHz U-NII bands. Our simulation results indicate that ST-adaptive devices employing Sense-Bandits do not harm neighboring devices that adopt a fixed ST value.

In this paper, we consider the scenario in which mobile network operators (MNOs) share network infrastructure for operating 5G new radio (NR) services in unlicensed bands, whereby they reduce their deployment cost and extend their service coverage. Conserving privacy of MNOs' users, maintaining fairness with coexisting technologies such as Wi-Fi, and reducing communication overhead between MNOs are among top challenges limiting the feasibility and success of this sharing paradigm. To resolve above issues, we present MatchMaker, a novel framework for joint network infrastructure and unlicensed spectrum sharing among MNOs. MatchMaker extends the 3GPP's infrastructure sharing architecture, originally introduced for licensed bands, to have privacy-conserving protocols for managing the shared infrastructure. We also propose a novel privacy-conserving algorithm for channel assignment among MNOs. Although achieving an optimal channel assignment for MNOs over unlicensed bands dictates having global knowledge about MNOs' network conditions and their interference zones, our channel assignment algorithm does not require such global knowledge and maximizes the cross-technology fairness for the coexisting systems. We let the manager, controlling the shared infrastructure, estimate potential interference among MNOs and Wi-Fi systems by asking MNOs to propose their preferred channel assignment and monitoring their average contention delay overtime. The manager only accepts/rejects MNOs' proposals and builds contention graph between all colocated devices. Our results show that MatchMaker achieves fairness up to 90% of the optimal alpha-fairness-based channel assignment while still preserving MNOs' privacy.

We consider the application of asymmetric fullduplex (AFD) communications in wireless local area networks (WLANs), exemplified by a Wi-Fi system. A full-duplex (FD)enabled Wi-Fi access point communicates simultaneously uplink (UL) and downlink (DL) with a pair of half-duplex (HD) Wi-Fi stations (STAs). AFD enhances spectrum efficiency and reduces latency for STAs; however, it faces challenges related to node selection, switching between AFD and HD modes, and rate control. In this paper, we propose the asymmetric FD-mode and rate adaptation (AFRA) scheme. AFRA relies on partially observable Markov decision process (POMDP) to adapt the UL and DL transmission rates as well as the transmission mode (i.e., AFD or HD; UL-only or DL-only), considering the future expected channel and interference conditions. Our simulation results indicate that AFRA outperforms classical rate-adaptation schemes and achieves up to 95% of the optimal performance of AFD communications.

Network slicing has been considered as a key enabling technology for 5G due to its ability to customize and "slice" a common resource to support diverse services and verticals. This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can cooperate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For the licensed band slicing, we propose the inter-operator spectrum aggregation method which allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Since all MNOs enjoy equal rights to access unlicensed bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a modified back-of-the-envelop method for the MNOs to evaluate their value of rights when coexisting with other wireless technologies. We develop a network slicing game based on the overlapping coalition formation game to investigate the possible cooperation between MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To evaluate the practical performance of our proposed framework, we develop a C++-based discrete-event simulator and simulate a possible implementation of our proposed framework over 400 base station locations deployed by two primary cellular operators in the city of Dublin. Numerical results show that our proposed framework can almost double the capacity for all supported services for each operator under certain conditions.

Network slicing is one of the key enabling technologies for 5G due to its ability to customize and “slice” a common resource to support diverse services and verticals. This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can coordinate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For licensed band slicing, we propose an inter-operator spectrum aggregation method that allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Because all MNOs enjoy equal rights to access these bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a modified back-of-the-envelope method for MNOs to evaluate their Value-of-Rights when coexisting with other wireless technologies. A network slicing game based on the overlapping coalition formation game is formulated to investigate the possible cooperation among MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To implement our proposed framework without requiring MNOs to reveal private information to other MNOs, we develop a distributed algorithm called distributed alternating direction method of multipliers with partially variable splitting. Performance evaluation of our proposed framework is provided using a discrete-event simulator that is driven by real MNO deployment scenarios based on over 400 base station locations deployed by two primary cellular operators in the city of Dublin. Numerical results show that our proposed frameworks can almost double the capacity for all supported services for each MNO in an urban setting.

The rapid increase in wireless demand prompted the FCC to open up parts of the 5-GHz band for unlicensed access. This caught the interest of 4G/LTE providers, who wish to extend their LTE-A services to the unlicensed spectrum (LTE-U). In LTE-U, small-cell base stations aggregate unlicensed and licensed bands to increase the throughput. Wi-Fi/LTE-U coexistence is a challenging issue due to the different access mechanisms of these two systems, which may cause high collision rates and delays. By leveraging self-interference-suppression techniques, we propose joint mode/rate adaptation strategies for Wi-Fi/LTE-U coexistence. Specifically, a full-duplex enabled Wi-Fi station can transmit and receive data simultaneously to increase the throughput, or transmit and sense (TS mode) simultaneously to monitor the LTE-U activity. We model the LTE-U interference as a hidden Markov process, and solve the problem of jointly adapting Wi-Fi rates/modes using a framework of partially observable Markov decision process. A detection approach based on the sliding window correlator is analyzed for the TS mode, which can differentiate between Wi-Fi and LTE-U signals. Our results indicate that our scheme provides 1.5x (1.9x) average throughput gain for Wi-Fi system in the low (high) signal-to-interference-and-noise regime relative to a half-duplex-based scheme.

In this paper, we investigate the coexistence problem between Wi-Fi and a pre-standard form of LTE over unlicensed bands, namely, LTE-Unlicensed (LTE-U). We address two coexistence problems. First, the different access mechanisms for Wi-Fi and LTE-U can lead to an increase in the collision rate and higher latency for both systems. We propose a modified Wi-Fi operation mode, whereby Wi-Fi stations (STAs) carry out simultaneous spectrum sensing and transmission to reduce the time required for collision detection. Specifically, we propose and analyze a full-duplex (FD) based detection framework that can differentiate between Wi-Fi and LTE-U signals while taking into account residual self- interference. Second, the ability to differentiate between Wi-Fi and LTE-U signals motivates the idea of adapting the clear channel assessment (CCA) threshold according to the type of the detected signal. Inspired by upcoming Wi-Fi standards (e.g., IEEE 802.11ax), we propose a CCA threshold adaptation scheme and study via simulations its optimal setting so as to maximize the spatial reuse while maintaining fairness between LTE-U and Wi-Fi systems.