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No description available.

No description available.

<p>High PAPR is one of the major challenges in using OFDM for communication, resulting in using inefficient linear amplifiers at the transmitter, or distortion if nonlinear amplifiers are used. Means of reducing PAPR in OFDM waveforms have been extensively researched, but those methods still have some need to avoid distortion. Using a pair of complementary sequences, an OFDM signal can be created that is constant in envelope, reducing the PAPR to unity and allowing the use of nonlinear amplifiers. Sequence pairs restricted to a particular alphabet are information-inefficient so instead a means of encoding onto a pair of complementary sequences is developed such that the length of the sequence increases linearly with the number of information symbols. The structure of this constant-modulus OFDM signal can also be used at the receiver to deal with channel fading and improve performance in noise. All of these concepts are extended to two-dimensional complementary sequence pairs for use in MIMO or broadcast applications using a uniform linear array while maintaining constant modulus on all antennas. This extension also includes exploration of additional structure imposed by the method of construction in multiple dimensions.</p>

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<p>Recent advancements in deep learning (DL) and high-communication bandwidth access networks such as 5G enable applications that require intelligence and faster computational power at the edge with low power consumption. In this thesis, we study how to improve the Quality-of-Experience (QoE) of these emerging 5G applications, e.g., virtual reality (VR) and video analytics on edge devices. These 5G applications either require high-quality visual effects with a stringent latency requirement (for VR) or high analytics accuracy (for video analytics) while maintaining frame rate requirements under dynamic conditions. </p> <p>In part 1, we study how to support high-quality untethered immersive multiplayer VR on commodity mobile devices. Simply replicating the prior-art for a single-user VR will result in a linear increase in network bandwidth requirement that exceeds the bandwidth of WiFi (802.11ac). We propose a novel technique, <em>Coterie, </em>that splits the rendering of background environment (BE) frames between the mobile device and the edge server that drastically enhances the similarity of the BE frames and reduces the network load via frame caching and reuse. Our proposed VR framework, Coterie, reduces per-player network requirement by over 10x and easily supports 4 players on Pixel 2 over 802.11ac while maintaining the QoE constraints of 4K VR.</p> <p>In part 2, we study how to achieve high accuracy of analytics in video analytics pipelines (VAP). We observe that the frames captured by the surveillance video cameras powering a variety of 24X7 analytics applications are not always pristine -- they can be distorted due to environmental condition changes, lighting issues, sensor noise, compression, etc. Such distortions not only deteriorate the accuracy of deep learning applications but also negatively impact the utilization of the edge server resources used to run these computationally expensive DL models. First, we study how to dynamically filter out low-quality frames captured. We propose a lightweight DL-based quality estimator, <em>AQuA</em>, that can be used to filter out low-quality frames that can lead to high-confidence errors (false-positives) if fed into the analytic units (AU) in the VAP. AQuA-filter reduces false positives by 17% and the compute and network usage by up to 27% when used in a face-recognition VAP. Second, we study how to reduce such poor-quality frame captures by the camera. We propose <em>CamTuner, </em>a system that automatically and dynamically adapts the complex camera settings to changing environmental conditions based on analytical quality estimation to enhance the accuracy of video analytics. In a real customer deployment, <em>CamTuner</em> enhances VAP accuracy by detecting 15.9% additional persons and 2.6%–4.2% additional cars (without any false positives) than the default camera setting. While <em>CamTuner</em> focuses on improving the accuracy of single-AU running on a camera stream, next we present <em>Elixir</em>, a system that enhances the video stream quality for multiple analytics on a video stream by jointly optimizing different AUs’ objectives. In a real-world deployment, <em>Elixir</em> correctly detects 7.1% (22,068) and 5.0% (15,731) more cars, 94% (551) and 72% (478) more faces, and 670.4% (4975) and 158.6% (3507) more persons than the default-camera-setting and time-sharing approaches, respectively.</p>

Over the past three decades, agricultural machinery has made the transition from purely mechanical systems to hybrid machines, reliant on both mechanical and electronic systems. A this transformation continues, the most modern agricultural machinery uses networked systems that require a network connection to function to their full potential. In rural areas, providing this network connection has proven difficult. Obstacles, distance from access points, and incomplete coverage of cellular connection are all challenges to be overcome. “Off the shelf” commercial-grade Wi-Fi equipment, including many products from Ubiquiti like the Bullet M2 transceiver and the PowerBeam point-to-point linking system, as well as antennas by Terrawave, Crane, and Hawking, were installed in a purpose-built system which could be implemented on a production farm. This system consisted of a tower-mounted access point which used an antenna with a 65^o beamwidth, and the test included distances up to 1150 meters in an agricultural setting with corn and soybeans. Some sensors were stationary and the other platform was a tractor following a path around the farm with both 8dBi and 15dBi gain antennas. Through all tests, throughput never dropped below 5 Mb/s, and the latency of successful connections never exceeded 20ms. Packets were rarely dropped and never accounted for a significant portion of all packet transmission attempts. Environmental effects like immediate precipitation, crop heights, recent rainfall, and ambient temperature had little or no effect on wireless network characteristics. As a result, it was proven that as long as line-of-sight was maintained, reliable wireless connectivity could be achieved despite varying conditions using microwave radiation. Network throughput was marginally affected by the change in free space path loss due to increased distance between the access point and the client, as well as travel by the mobile client outside the beamwidth of the access point. By enabling this coverage, it is hoped that the implementation of new agricultural technology utilizing a live network connection will progress more rapidly.

<p>Future wireless applications will require networks to provide high rates, reduced power consumption, reliable communications, and low latencies in a wide range of deployment scenarios. To support the never-ending growth in wireless data traffic, a solution is to operate wireless networks on the wide bandwidth available at higher frequencies, e.g., millimeter wave (mmWave) and sub-terahertz (sub-THz) bands. However, new challenges arise as networks operating at higher frequencies experience harsher propagation characteristics. To compensate for such severe signal attenuation, the directional beamforming via massive multipleinput multiple-output (MIMO) is adopted to provide array gains, but it necessitates accurate MIMO channel state information incurring unacceptably large training overhead. Wireless system engineers will require to develop fast and efficient channel training algorithms for massive MIMO systems. Another new challenge arises in scenarios without a direct link between the source and destination due to serious pathloss, which requires cooperative relay beamforming to enhance the communication coverage. The beamforming weights of the distributed relays and the receive combiner can be jointly optimized to enhance Quality-of-Service in multi-user relay beamforming networks. Our contributions cover three specific topics as follows: First, we develop a learning-based beam alignment approach, which enables the position-aided beam recommendation to support users at new positions, to reduce the training overhead in MIMO systems. Second, we propose a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels with dual-wideband effect. Lastly, we propose a joint relay beamforming and receive combiner design, considering an optimization problem formulation that maximizes the minimum of the receiving signal-to-interference-plus-noise ratios among multiple users. In each specific topic, we provide the algorithms and show the numerical results to demonstrate the improved performance over the state-of-the-art techniques.</p>

<div>The allocated frequency bands for the incoming fifth generation (5G) wireless communication technologies spread broadly from sub 6 GHz to K and potentially W bands. The evolution of the future generations toward higher frequency bands will continue and presents significant challenges in terms of excessive system complexity, production and maintenance costs. Reconfigurable radio architecture with frequency-tunable components is one of the most feasible and cost-effective solutions to meet such challenges. Among these technologies, evanescent-mode (EVA) cavity tunable resonator have demonstrated many of the needed features such as wide tunability, low loss and high linearity. Such a technology typically employs a movable membrane that controls the resonant frequency of a post-loaded cavity. </div><div><br></div><div>The first part of this work focuses on advancing such technology into the mm-wave frequency bands and beyond. Manufacturing tolerance and tuner performance are the two main limiting factors addressed here. This work develops a cost-effective micro-fabrication and package assembly flow which addresses the manufacturing related limitations. On the other hand, introducing micro-corrugated diaphragms and gold-vanadium co-sputtered thin film deposition technology, significantly reduces (4 times) the tuning voltage and enhances tuning stability (7 times). We demonstrate a tunable two-pole band-pass filter (BPF) prototype as the first EVA cavity tunable filter operating in the K-Ka band. </div><div><br></div><div>The second part of this work extensively discusses an optimal RF design flow based on the developed manufacturing technology. It considers all technology constrains and allows the actualization of a high Q transfer function with minimum bandwidth variation within an octave tuning range. Moreover, a new fully passive input/output feeding mechanism that facilitates impedance matching over the entire tuning range is presented. The devised RF methodology is validated through the design and testing of a two-resonator BPF. Measurements demonstrate a tuning range between 20-40 GHz, relative bandwidth of 1.9%-4.7%, and impedance matching over the entire tuning range which is upto 2 times better than previously reported state-of-the-art MEMS tunable filters of this type.</div><div><br></div><div>The third part of this work further advances the technology by proposing the first MEMS-based low-power bi-directional EVA tuning approach that employs both the main bias circuitry as well as a new corrective biasing technique that counteracts viscoelastic memory effects. The two key enabling technologies are extensively discussed: a) a new metal-oxide-metal (MOM) sealed cavity that maintains high quality without requiring complicated metal bonding; and b) a new electrostatic bi-directional MEMS tuner that implements the needed frequency tuning without lowering the resonator quality factor. </div><div><br></div><div>Furthermore, we explore important design and fabrication trade-offs regarding sensitivity to non-ideal effects (residual stress, fabrication imperfections). Measurement of the new prototype bi-directional design, prove that this technology readily corrects residual post-bias displacement of 0.1 um that shifts the frequency by over 1 GHz with less than 2.5 V. It takes over 100 seconds to recover this error in the uni-directional case. This correction does not adversely affect the filter performance.</div>

<p>    </p> <p>In order to advance next-generation communication systems, it is critical to enhance the state-of-the-art communication architectures, such as device-to-device (D2D), multiple- input multiple-output (MIMO), and intelligent reflecting surface (IRS), in terms of achieving high data rate, low latency, and high energy efficiency. In the first part of this dissertation, we address joint learning and optimization methodologies on cutting-edge network archi- tectures. First, we consider D2D networks equipped with MIMO systems. In particular, we address the problem of minimizing the network overhead in D2D networks, defined as the sum of time and energy required for processing tasks at devices, through the design for MIMO beamforming and communication/computation resource allocation. Second, we address IRS-assisted communication systems. Specifically, we study an adaptive IRS control scheme considering realistic IRS reflection behavior and channel environments, and propose a novel adaptive codebook-based limited feedback protocol and learning-based solutions for codebook updates. </p> <p><br></p> <p>Furthermore, in order for revolutionary innovations to emerge for future generations of communications, it is crucial to explore and address fundamental, long-standing open problems for communications, such as the design of practical codes for a variety of important channel models. In the later part of this dissertation, we study the design of practical codes for feedback-enabled communication channels, i.e., feedback codes. The existing feedback codes, which have been developed over the past six decades, have been demonstrated to be vulnerable to high forward/feedback noises, due to the non-triviality of the design of feedback codes. We propose a novel recurrent neural network (RNN) autoencoder-based architecture to mitigate the susceptibility to high channel noises by incorporating domain knowledge into the design of the deep learning architecture. Using this architecture, we suggest a new class of non-linear feedback codes that increase robustness to forward/feedback noise in additive White Gaussian noise (AWGN) channels with feedback. </p>