Ambient environment information, including reflectors’ geometrical layout, dimension, and reflectivity, is a key input to versatile millimeter-wave networking and sensing applications. It has found versatile applications in optimizing network coverage and robustness, enhancing mobile link performance, and enabling high-accuracy indoor localization and navigation. Recent approaches of deriving mmWave environment information require heavy infrastructure support and rely on costly software-defined radios, which prevent their usage in practice. In this work, we design and implement e-mmRanger, which can efficiently sense the environment without infrastructure support. e-mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot, which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves. It then re-engineers the time-domain signal series to derive the spatial-domain environment structure through novel reflection path extraction and clustering algorithms. Moreover, e-mmRanger accelerates the mapping process by incorporating a novel Space-knit algorithm, which strategically plans an optimal movement route consisting of minimum sampling locations for the robot. Our experiments verify that e-mmRanger can accurately and efficiently sense the surrounding environment, and the learned information can bring multi-fold performance gain over empirical approaches in mmWave networks.

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we proposeMDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction,MDSRtakes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph,MDSRimproves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluateMDSRon the testbed using commodity mmWave radios. The evaluation results demonstrate thatMDSRimproves network throughput by multi-folds compared with the state-of-the-art one.

Multi-Gbps 60 GHz millimeter wave (mmWave) networks, are considered as the enabling technology for emerging applications such as untethered VR and 4K/8K Miracast. However, user motion, and even orientation change, can cause mis-alignment between mmWave transceivers’ directional beams and thus severe link outage. Within the practical 3D spaces, the combination of location and orientation dynamics leads to the exponential growth of beam searching complexity, which substantially exacerbates the outage. In this paper, we first measure the impact of 3D motion on 60 GHz link performance in the context of VR and Miracast applications. We find that 3D motion exhibits inherent non-predictability, so conventional beam steering solutions are no longer effective. Therefore, we propose a model-driven 3D beam-steering mechanism called Parallel Scanner (PSCAN), which can maintain high performance for mobile 60 GHz links. To enable PSCAN, we first discover and prove a hidden interaction between 3D beams and the spatial channel profile of 60 GHz radios. Leveraging on which, PSCAN strategically scans the 3D space to reduce the search latency by more than one order of magnitude. Experiment results based on a custom-built 60 GHz platform demonstrate PSCAN’s remarkable throughput gain, up to$5\times $, compared with the state-of-the-art.

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate concurrently without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns generated by commodity mmWave radios and strong reflections. In this paper, we conduct an extensive measurement on the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction-based interference-resolving approaches are insufficient. Motivated by the findings, we propose mmMuxing, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, mmMuxing takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfectness and reflections. Using the conflict graph, mmMuxing designs a joint user-beam selection algorithm, which can determine the optimal user-beam combination and lead to the minimum interference in each schedule. We prototype and evaluate mmMuxing over testbed using commodity mmWave radios. The evaluation results demonstrate that mmMuxing improves network throughput by multi-folds compared with the state-of-the-art.

Ambient environment information, including reflectors' location, dimension and reflectivity, is a key input to many millimeter-wave (mmWave) networking and sensing applications. It has found versatile applications in optimizing network coverage and robustness, enhancing mobile link performance, and enabling high-accuracy indoor localization and navigation. Recent approaches of deriving mmWave environment information require heavy infrastructure support or non-trivial human labor, and rely on costly software defined radios, which prevent their usage in practice. In this work, we design and implement mmRanger, a system can automatically sense environment without any infrastructure support. mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot, which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves and rotates. mmRanger then re-engineers the time-domain signal series to derive the spatial-domain environment structure, through novel reflection path extraction and reflector mapping algorithms. Our experiments verify that mmRanger can accurately sense a given environment with minimal overhead, and the learned information can bring 1.6× and 2.1× performance gain, in terms of network coverage and mobile link throughput, respectively, over empirical approaches in mmWave networks.