In the present work, a methodology has been proposed to design analog/mixed-signal circuits using digital-in-concept circuits in digital technology. This paper presents a digital standard cell-based, scalable, highly synthesizable analog voltage comparator designed in 180 nm CMOS technology at the supply voltage of 1.8 V. The digital design requires less design effort and is highly immune to process, voltage and temperature variations. The proposed comparator has been designed and simulated in the Cadence virtuoso analog design environment. It is observed from the simulation results that the total power dissipation and propagation delay is 143.2 μW and 1.07 ns, respectively. The offset voltage of the proposed comparator is 4.98 mV. Also, as a proof-of-concept, the feasibility of the proposed analog voltage comparator is carried out on Artix-7 field-programmable gate array (FPGA) using Xilinx Basys-3 FPGA kit and other off-the-shelf components. The proposed digital-in-concept comparator is suitable for low power and high-speed SoC application with reduced design effort and lesser time-to-market.

We present BurstTracker, the first tool that developers can use to detect if the LTE downlink is the bottleneck for their applications. BurstTracker is driven by our discovery that the proprietary LTE downlink schedulers running on LTE base stations allocate resources to users in a way that reveals if a user's downlink queue runs empty during a download. We demonstrate that BurstTracker works across Tier-1 cellular providers and across a variety of network conditions. We also present a case study that shows how application developers can use this tool in practice. Surprisingly, with BurstTracker, we find that the LTE downlink may not be the bottleneck for video streaming on several Tier-1 providers, even during peak hours at busy locations. Rather, transparent TCP middleboxes deployed by these providers lead to downlink underutilization, because they force Slow-Start Restart. With a simple workaround, we improve video streaming bitrate on busy LTE links by 35%.

Modern mobile networks are facing unprecedented growth in demand due to a new class of traffic from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional real-time applications such as voice-calling and video. This problem is extremely challenging because conventional traffic is highly dynamic across space and time, so its performance is significantly impacted if all IoT traffic is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic. Using 4 weeks of real network data from downtown Melbourne, Australia spanning diverse traffic patterns, we demonstrate that our RL scheduler can enable mobile networks to carry 14.7% more data with minimal impact on existing traffic, and outpeforms heuristic schedulers by more than 2x. Our work is a valuable step towards designing autonomous, "self-driving" networks that learn to manage themselves from past data.

The rapid growth in mobile connectivity is enabling phones to support a wide range of societally-important applications. In this work, we show that broad classes of popular mobile applications have distinct radio resource allocation signatures. Using this insight, we design a mobile application classifier, and demonstrate that (1) an application can infer its own type solely from its resource allocation patterns, and (2) anyone can accurately infer the type of application being served by each session on a particular cell tower. We present our findings by showing the breakdown of applications being served by an LTE base station belonging to a Tier 1 US provider in downtown Palo Alto. Our work encourages an open discussion about LTE standards, and whether they might need to be enhanced to mask features that can be exploited to infer application type from signals broadcast over the air.

In this paper, we propose an optimal strategy for the transmission of scalable video over packet-based multiple-input multiple-output (MIMO) systems. The scalable extension of H.264/AVC that provides a combined temporal, quality and spatial scalability is used. For given channel conditions, we develop a method for the estimation of the distortion of the received video and propose different error concealment schemes. We show the accuracy of our distortion estimation algorithm in comparison with simulated wireless video transmission with packet errors. In the proposed MIMO system, we employ orthogonal space-time block codes (O-STBC) that guarantee independent transmission of different symbols within the block code. In the proposed constrained bandwidth allocation framework, we use the estimated end-to-end decoder distortion to optimally select the application layer parameters, i.e., quantization parameter (QP) and group of pictures (GOP) size, and physical layer parameters, i.e., rate-compatible turbo (RCPT) code rate and symbol constellation. Results show the substantial performance gain by using different symbol constellations across the scalable layers as compared to a fixed constellation.

We propose a low-delay low-complexity end-to-end video transmission system that integrates the latest scalable H.264 codec and the full-rate full-diversity quasi-orthogonal space-time block codes. At the encoder of the scalable H.264 codec, we have developed a low-delay low-complexity method for the estimation of the video distortion at the receiver for given channel conditions. The distortion estimation algorithm is validated using experimental results. In this work, we show clearly the advantage of using the quasi-orthogonal space-time block codes (STBC), which provide a higher rate compared to the orthogonal space-time block codes in a wireless video transmission system. This is in spite of the use of a non-optimal signal constellation structure. The performance results are obtained after addressing the bandwidth allocation problem that employs the scalable decoder distortion estimation algorithm for the optimal selection of the application layer and physical layer parameters.

We propose an efficient strategy for the transmission of scalable video over multiple-input multiple-output (MIMO) wireless systems. In this paper, we use the latest scalable H.264 codec (SVC), which provides combined temporal, quality and spatial scalability. At the transmitter, we estimate the decoded video distortion for given channel conditions taking into account the effects of quantization, packet loss and error concealment. The proposed scalable decoder distortion algorithm offers low delay and low complexity. The performance of this method is validated using experimental results. In our proposed system, we use a MIMO system with orthogonal space-time block codes (O-STBC) that provides spatial diversity and guarantees independent transmission of different symbols within the block code. The bandwidth constrained allocation problem considered here is simplified and solved for one O-STBC symbol at a time. Furthermore, we take the advantage of the hierarchical structure of SVC to attain the optimal solution for each group of pictures (GOP) of the video sequence. We incorporate the estimated decoder distortion to optimally select the application layer parameter, i.e., quantization parameter (QP), and physical layer parameters, i.e., channel coding rate and modulation type for wireless video transmission.

We propose an optimal strategy for the transmission of scalable video over multiple-input multiple-output (MIMO) wireless systems. In this paper, we use the latest scalable H.264 codec which provides combined temporal, quality and spatial scalability. At the transmitter, we have developed a method for the estimation of the video distortion at the receiver for given channel conditions. The accuracy of this method is validated using experimental results. In our proposed system, we use a MIMO system with orthogonal space-time block codes (O-STBC) that provides spatial diversity and guarantees independent transmission of different symbols within the block code. Rate-compatible punctured convolutional (RCPC) codes are used for unequal error protection of the scalable layers. In the constrained bandwidth allocation framework considered here, we use the estimated decoder distortion to optimally select the application layer parameter, i.e. quantization parameter (QP), and physical layer parameters, i.e. RCPC and modulation.

In this correspondence, we propose a wavelet-based hierarchical approach using mutual information (MI) to solve the correspondence problem in stereo vision. The correspondence problem involves identifying corresponding pixels between images of a given stereo pair. This results in a disparity map, which is required to extract depth information of the relevant scene. Until recently, mostly correlation-based methods have been used to solve the correspondence problem. However, the performance of correlation-based methods degrades significantly when there is a change in illumination between the two images of the stereo pair. Recent studies indicate MI to be a more robust stereo matching metric for images affected by such radiometric distortions. In this short correspondence paper, we compare the performances of MI and correlation-based metrics for different types of illumination changes between stereo images. MI, as a statistical metric, is computationally more expensive. We propose a wavelet-based hierarchical technique to counter the increase in computational cost and show its effectiveness in stereo matching.