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.

/pdf/in-band-full-duplex-wireless-challenges-and-opportunities-djlyhtrciu.pdf

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.

IMU based inertial tracking plays an indispensable role in many mobility centric tasks, such as robotic control, indoor navigation and virtual reality gaming. Despite its mature application in rigid machine mobility (e.g., robot and aircraft), tracking human users via mobile devices remains a fundamental challenge due to the intractable gait/posture patterns. Recent data-driven models have tackled sensor drifting, one key issue that plagues inertial tracking. However, these systems still assume the devices are held or attached to the user body with a relatively fixed posture. In practice, natural body activities may rotate/translate the device which may be mistaken as whole body movement. Such motion artifacts remain as the dominating factor that fails existing inertial tracing systems in practical uncontrolled settings. Inspired by the observation that human heads induces far less intensive movement relative to the body during walking, compared to other parts, we propose a novel multi-stage sensor fusion pipeline called DeepIT, which realizes inertial tracking by synthesizing the IMU measurements from a smartphone and an associated earbud. DeepIT introduces a data-driven reliability aware attention model, which assesses the reliability of each IMU and opportunistically synthesizes their data to mitigate the impacts of motion noise. Furthermore, DeepIT uses a reliability aware magnetometer compensation scheme to combat the angular drifting problem caused by unrestricted motion artifacts. We validate DeepIT on the first large-scale inertial navigation dataset involving both smartphone and earbud IMUs. The evaluation results show that DeepIT achieves multiple folds of accuracy improvement on the challenging uncontrolled natural walking scenarios, compared with state-of-the-art closed-form and data-driven models.

https://dl.acm.org/doi/pdf/10.1145/3463517

Robust Inertial Motion Tracking through Deep Sensor Fusion across Smart Earbuds and Smartphone

WiFi Display, also called Miracast, is an emerging technology that allows a mobile device (source) to duplicate its screen content to an external display (sink) via a peer-to-peer WiFi link. Despite its diverse application scenarios and growing popularity, Miracast consumes substantial power due to a combination of video encoding/decoding and transmission. In this paper, we first conduct a measurement study to quantify and model key parameters that scale Miracast's power consumption. We then propose a set of optimization mechanisms to bypass redundant codec operations, reduce video tail traffic, and relocate the Miracast channel dynamically to maximize transmission efficiency. We have implemented this energy-efficient Miracast framework on an Android smartphone. Experimental results show that the legacy Miracast system costs 1.3 to 2.4 Watts. Our framework reduces the power consumption by 29% to 61%, depending on the Miracast application's video traffic patterns. Our optimization mechanisms do not affect the video quality, and can even reduce the latency of certain Miracast applications.

/pdf/energy-efficient-wifi-display-32ikdwe7vq.pdf

Energy Efficient WiFi Display

Systems and methods for system for channel access adaptation are disclosed. One system includes a plurality of remote antennas and a plurality of access points. The remote antennas transmit data to receivers and obtain channel state information. Additionally, each access point controls a different cluster of the remote antennas and receives the respective channel state information from the remote antennas of the cluster. Further, each access point is configured to, independently from other access points, adapt channel allocations to the remote antennas of the respective cluster based on a tracking of sums of collision loss probabilities. Each given sum is determined by the access point for a different given set of a plurality of sets of cooperating remote antennas in the respective cluster, where each constituent collision loss probability in the given sum is determined by the access point from a different interference clique to which the given set belongs.

Scalable network mimo for wireless networks

We explore the use of TV White Space (TVWS) wireless networks for providing robust and long range connectivity to vehicles. A key distinctive requirement of TVWS networks is the power asymmetry — the static APs are allowed to transmit at up to 4 W, while the mobile clients in vehicles are limited to only 100 mW. Our measurements reveal that the power asymmetry not only causes severe uplink blackouts but also poses significant coexistence problems, as high-power fixed nodes can easily starve the low-power mobile ones due to carrier sensing loss. To tackle these unique challenges, we propose a cross-layer design of a Direct-Sequence Spread Spectrum (DSSS) based system. We employ an adaptive DSSS mechanism that strategically configures the spreading code, so as to boost uplink coverage while maximizing throughput. We further design a traffic-aware code assignment algorithm for uplink packets to balance the requirement of throughput-intensive and latency-sensitive flows. We have implemented the design on a TVWS software-radio platform on a moving vehicle in an urban environment, and demonstrated that link asymmetry can be completely removed to support realistic application traffic, while the carrier sense loss rate at fixed nodes can be reduced by around 85%.

Bridging link power asymmetry in mobile whitespace networks

To handle increased capacity demands, sophisticated MIMO-based transmission strategies, based on transmitter cooperation, have emerged. However, different types of users' channels (e.g., static vs mobile, stable vs dynamic channels) that make up today's enterprises, require different MIMO transmission strategies. With the wrong strategy, a user could even see a degradation in performance. Our overarching goal is to design and implement a framework, TRINITY, that can simultaneously cater to a heterogeneous mix of users, by intelligently combining a plurality of MIMO transmission strategies wherein the transmitters at different nodes can cooperate to deliver significant performance gains. Three key challenges that we address in building TRINITY are: (i) how to categorize users into channel profiles such that a single transmission strategy caters to the users of a profile, (ii) how to combine strategies to communicate with users of different profiles simultaneously, and (iii) what is the granularity of transmitter cooperation needed to balance efficiency with complexity. We implement and evaluate TRINITY on our WARP radio testbed. Our extensive experiments show that TRINITY's intelligent combining of transmission strategies improves the total network rate by 50%-150%, satisfies the QoS requirements of thrice as many users, and improves PSNR for video traffic by 10 dB compared to individual transmission strategies.

Xinyu Zhang

Papers

Robust Inertial Motion Tracking through Deep Sensor Fusion across Smart Earbuds and Smartphone

Energy Efficient WiFi Display

Scalable network mimo for wireless networks

Bridging link power asymmetry in mobile whitespace networks

TRINITY: A Practical Transmitter Cooperation Framework to Handle Heterogeneous User Profiles in Wireless Networks