Data Mining Practical Machine Learning Tools and Techniques

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.

Spatially Sparse Precoding in Millimeter Wave MIMO Systems

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

In-band full-duplex (IBFD) operation has emerged as an attractive solution for increasing the throughput of wireless communication systems and networks. With IBFD, a wireless terminal is allowed to transmit and receive simultaneously in the same frequency band. This tutorial paper reviews the main concepts of IBFD wireless. One of the biggest practical impediments to IBFD operation is the presence of self-interference, i.e., the interference that the modem's transmitter causes to its own receiver. This tutorial surveys a wide range of IBFD self-interference mitigation techniques. Also discussed are numerous other research challenges and opportunities in the design and analysis of IBFD wireless systems.

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In-Band Full-Duplex Wireless: Challenges and Opportunities

In-band full-duplex (IBFD) operation has emerged as an attractive solution for increasing the throughput of wireless communication systems and networks. With IBFD, a wireless terminal is allowed to transmit and receive simultaneously in the same frequency band. This tutorial paper reviews the main concepts of IBFD wireless. Because one the biggest practical impediments to IBFD operation is the presence of self-interference, i.e., the interference caused by an IBFD node's own transmissions to its desired receptions, this tutorial surveys a wide range of IBFD self-interference mitigation techniques. Also discussed are numerous other research challenges and opportunities in the design and analysis of IBFD wireless systems.

mmWave 5G networks promise to enable a new generation of networked applications requiring a combination of high throughput and ultra-low latency. However, in practice, mmWave performance scales poorly for large numbers of users due to the significant overhead required to manage the highly-directional beams. We find that we can substantially reduce or eliminate this overhead by using out-of-band infrared measurements of the surrounding environment generated by a LiDAR sensor. To accomplish this, we develop a ray-tracing system that is robust to noise and other artifacts from the infrared sensor, create a method to estimate the reflection strength from sensor data, and finally apply this information to the multiuser beam selection process. We demonstrate that this approach reduces beam-selection overhead by over 95% in indoor multi-user scenarios, reducing network latency by over 80% and increasing throughput by over 2× in mobile scenarios.

SpaceBeam: LiDAR-driven one-shot mmWave beam management

Immersive face-to-virtual-face telecommunication is one unique use case for virtual reality (VR) technologies. Existing camera-based telephony systems cannot be used for such immersive VR video chat, due to the physical occlusions of head-mounted displays (HMDs) and/or unwieldy positioning of cameras. To address these, we present ExGSense, a new VR input modality that can sense and reconstruct both upper and lower facial gestures, by only using lightweight biopotential sensors embedded within the HMDs. We optimize the sensor arrangement based on facial anatomy and employ a multiview classification pipeline to exploit the multiple dimensions of signal features. We thus enable ExGSense to detect whole facial gestures by using a sparse set of biopotential transducers. We prototyped ExGSense and evaluated its performance with 42 facial gestures and across different users. We showed a 93% accuracy for user-specific evaluation, and 77% accuracy for user-independent evaluation with low calibration overhead. We believe ExGSense constitutes a promising input modality for immersive VR interactions.

/pdf/exgsense-toward-facial-gesture-sensing-with-a-sparse-near-1157y258t4.pdf

ExGSense: Toward Facial Gesture Sensing with a Sparse Near-Eye Sensor Array

Next generation wireless networks embrace mmWave technology for its high capacity. Yet, mmWave radios bear a fundamental coverage limitation due to the high directionality and propagation artifacts. In this paper, we explore an economical paradigm based on 3D printing technology for mmWave coverage expansion. We propose MilliMirror, a fully passive metasurface, which can reshape and resteer mmWave beams to anomalous directions to illuminate the coverage blind spots. We develop a closed-form model to efficiently synthesize the MilliMirror design with thousands of unit elements and across a wide frequency band. We further develop an economical process based on 3D printing and metal deposition to fabricate MilliMirror. Our field test results show that MilliMirror can effectively fill the coverage holes and operate transparently to the standard mmWave beam management protocols. 

MilliMirror

To enable accurate indoor localization at low cost, recent research in visible light positioning (VLP) proposed to employ existing ceiling lights as location landmarks, and use smartphone cameras or light sensors to identify the different lights using statistical visual/optical features. Despite the potential, we find such solutions are unreliable: the features are easily corrupted with a slight rotation of the smartphone, and are not discriminative enough for many practical light models with different size/shape/intensity. In this work, we propose Auto-Litell to resolve these critical challenges and make VLP truly robust. Auto-Litell builds a customized deep-learning neural network model to automatically distill the "invisible" visual features from the lights, which are resilient to smartphone orientation and light models. Moreover, Auto-Litell introduces a Light-CycleGAN to generate "fake" light images to augment the training data, so as to relieve human labors in data collection and labeling. We have implemented Auto-Litell as a real-time localization and navigation system on Android. Our experiments demonstrate Auto-Litell's high accuracy in discriminating the lights in the same building, and high reliability across a variety of practical usage scenarios.

Learning to Recognize Unmodified Lights with Invisible Features

3D orientation tracking is an essential ingredient for many Internet-of-Things applications Yet existing orientation tracking systems commonly require motion sensors that are only available on battery-powered devices In this paper, we propose Tagyro, which attaches an array of passive RFID tags as orientation sensors on everyday objects Tagyro uses a closed-form model to transform the run-time phase offsets between tags into orientation angle To enable orientation tracking in 3D space, we found the key challenge lies in the imperfect radiation pattern of practical tags, caused by the antenna polarity, non-isotropic emission and electromagnetic coupling, which substantially distort phase measurement We address these challenges by designing a set of phase sampling and recovery algorithms, which together enable reliable orientation sensing with 3 degrees of freedom We have implemented a real-time version of Tagyro on a commodity RFID system Our experiments show that Tagyro can track the 3D orientation of passive objects with a small error of 4°, at a processing rate of 377 samples per second

Xinyu Zhang

Papers

SpaceBeam: LiDAR-driven one-shot mmWave beam management

ExGSense: Toward Facial Gesture Sensing with a Sparse Near-Eye Sensor Array

MilliMirror

Learning to Recognize Unmodified Lights with Invisible Features

Gyro in the Air: Tracking 3D Orientation of Batteryless Internet of Things