Today's radio access networks (RANs) are monolithic entities, which remain fixed to a given set of parameters for the entirety of their operations. Conversely, to implement realistic and effective spectrum policies, RANs will need to seamlessly and intelligently change their operational parameters. In stark contrast with existing paradigms, the new Open RAN (O-RAN) framework for 5G-and-beyond networks (NextG) separates the logic that operates the RAN from its hardware components. In this context, we propose Channel-Aware Reactive Mechanism (ChARM), a data-driven framework for O-RAN-compliant NextG networks that allows to (i) sense the spectrum to understand the current context; (ii) react in real time by switching the distributed unit (DU) and RU operational parameters according to a specified spectrum access policy. We demonstrate the performance of ChARM in the context of spectrum sharing among LTE and Wi-Fi in unlicensed bands, where an LTE BS senses the spectrum and switches cell frequency to avoid Wi-Fi. We leverage the Colosseum channel emulator to collect a large-scale waveform dataset to train our neural networks with, and develop a full-fledged standard-compliant prototype of ChARM using srsLTE. Experimental results show the ChARM accuracy in real-time communication classification and demonstrate its effectiveness as a framework for spectrum sharing.

Reconfigurable Intelligent Surfaces (RISs) are considered one of the key disruptive technologies towards future 6G networks. RISs revolutionize the traditional wireless communication paradigm by controlling the wave propagation properties of the impinging signals at will. A major roadblock for RIS is though the need for a fast and complex control channel to continuously adapt to the ever-changing wireless channel conditions. In this paper, we ask ourselves the question: Would it be feasible to remove the need for control channels for RISs? We analyze the feasibility of devising Self-Configuring Smart Surfaces that can be easily and seamlessly installed throughout the environment, following the new Internet-of-Surfaces (IoS) paradigm, without requiring modifications of the deployed mobile network. To this aim we design MARISA, a self-configuring metasurfaces absorption and reflection solution. Our results show that MARISA achieves outstanding performance, rivaling with state-of-the-art control channel-driven RISs solutions.

Optical camera communication (OCC) has attracted increasing attention recently thanks to the wide usage of LED and high-resolution cameras. The lens-image sensor structure enables the camera distinguish light from various source, which is ideal for spatial MIMO. Hence, OCC can be applied to several emerging application scenarios, such as vehicle and drone communications. However, distance is a major bottleneck for OCC system, because the increase in distance makes it difficult for the camera to distinguish adjacent LED, which we call LED spatial mixing.
In this paper, we propose a novel hierarchical coding scheme name as OnionCode to support dynamic range of channel capacity in one-to-many OCC scenario. OnionCode adopts a multi-priority receiving scheme, i.e., the receivers can dynamically discard the low-priority bit stream according to the measured channel capacity. OnionCode achieves this based on a key insight that, the luminance level of a mix-LED is distinguishable. We prototype an LED-based OCC system to evaluate the efficacy of OnionCode and the results show that OnionCode achieves a higher conding efficiency and overall throughput compared with the existing hierarchical coding.

The next generation of cellular networks will be characterized by softwarized, open, and disaggregated architectures exposing analytics and control knobs to enable network intelligence. How to realize this vision, however, is largely an open problem. In this paper, we take a decisive step forward by presenting and prototyping OrchestRAN, a novel orchestration framework that embraces and builds upon the Open RAN paradigm to provide a practical solution to these challenges. OrchestRAN has been designed to execute in the non-real-time RAN Intelligent Controller (RIC) and allows Telcos to specify high-level control/inference objectives (i.e., adapt scheduling, and forecast capacity in near-real-time for a set of base stations in Downtown New York). OrchestRAN automatically computes the optimal set of data-driven algorithms and their execution location to achieve intents specified by the Telcos while meeting the desired timing requirements. We show that the problem of orchestrating intelligence in Open RAN is NP-hard, and design low-complexity solutions to support real-world applications. We prototype OrchestRAN and test it at scale on Colosseum. Our experimental results on a network with 7 base stations and 42 users demonstrate that OrchestRAN is able to instantiate data-driven services on demand with minimal control overhead and latency.

Long Range Wide Area Network, using linear chirps for data modulation, is known for its low-power and long-distance communication that can connect massive Internet-of-Things devices at low cost. However, LoRaWAN throughput is far behind the demand for the dense and large-scale IoT deployments, due to the frequent collisions with the by-default random channel access (i.e., ALOHA). Recently, some works enable an effective LoRa carrier-sense for collision avoidance. However, the continuous back-off makes the network throughput easily saturated and degrades the energy efficiency at end LoRa nodes. In this paper, we propose CurveALOHA, a brand-new media access control scheme to enhance the throughput of random channel access by embracing non-linear chirps enabled quasi-orthogonal logical channels. First, we empirically show that non-linear chirps can achieve similar communication distance and energy level as the linear one does. Then, we observe that multiple non-linear chirps can create new logical channels which are quasi-orthogonal with the linear one and each other. Finally, given a set of non-linear chirps, we design two random chirp selection methods to guarantee an end node can access a channel with less collision probability. Extensive experiments with USRP show that the network throughput of CurveALOHA is 59.6% higher than the state-of-the-arts.

LoRa technology promises to connect billions of battery-powered devices over a long range for years. However, recent studies and industrial deployment find that LoRa suffers severe signal attenuation because of signal blockage in smart cities and long communication ranges in smart agriculture applications. As a result, weak LoRa packets cannot be correctly demodulated or even be detected in practice. To address this problem, this paper presents the design and implementation of MALoRa: a new LoRa reception scheme which aims to improve LoRa reception performance with antenna diversities. At a high level, MALoRa improves signal strength by reliably detecting and coherently combining weak signals received by multiple antennas of a gateway. MALoRa addresses a series of practical challenges, including reliable packet detection, symbol edge extraction, and phase-aligned constructive combining of weak signals. Experiment results show that MALoRa can effectively expand communication range, increase battery life of LoRa devices, and improve packet detection and demodulation performance especially in ultra-low SNR scenarios.

LoRa is widely deployed for various applications. Though the knowledge of the channel occupancy is the prerequisite of all aspects of network management, acquiring the channel occupancy for LoRa is challenging due to the large number of channels to be detected. In this paper, we propose LoRadar, a novel LoRa channel occupancy acquirer based on cross-channel scanning. Our in-depth study finds that Channel Activity Detection (CAD) in a narrow band can indicate the channel activities of wide bands because they have the same slope in the time-frequency domain. Based on our finding, we design the cross-channel scanning mechanism that infers the channel occupancy state of all the overlapping channels by the distribution of CAD results. We elaborately select and adjust the CAD settings to enhance the distribution features. We also design the pattern correction method to cope with distribution distortions. We implement LoRadar on commodity LoRa platforms and evaluate its performance on the indoor testbed and the outdoor deployed network. The experimental results show that LoRadar can achieve a detection accuracy of 0.99 and reduce the acquisition overhead by up to 0.90, compared to existing traversal-based methods.

LoRa is widely deploying in aquatic environments to support various Internet of Things applications. However, floating LoRa networks suffer from serious performance degradation due to the polarization loss caused by the swaying antenna. Existing methods that only control the transmission starting from the aligned attitude have limited improvement due to the ignorance of aligned period length. In this paper, we propose PolarScheduler, a dynamic transmission control method for floating LoRa networks. PolarScheduler actively controls transmission configurations to match polarization aligned periods. We propose a V-zone model to capture diverse aligned periods under different configurations. We also design a low-cost model establishment method and an efficient optimal configuration searching algorithm to make full use of aligned periods. We implement PolarScheduler on commercial LoRa platforms and evaluate its performance in a deployed network. Extensive experiments show that PolarScheduler can improve the packet delivery rate and throughput by up to 20.0% and 15.7%, compared to the state-of-the-art method.