An underwater wireless optical communication link can provide high-speed data transfer for robotics applications in deep waters. However, optical links are limited in terms of coverage range because of the high attenuation of light in water caused by absorption and scattering effects. In this work, a new optical transceiver architecture is proposed to solve this coverage problem via a novel Orthogonal Frequency Division Multiplexing (OFDM)-based Polarized Pulse Position Modulation (in short, P-OFDM-PPM) with time-frequency spreading. The optical polarization diversity and multiplexing are utilized at the optical transmitter to improve the system’s robustness and the transmission data rate. This new scheme is able to boost the range coverage by several folds as verified via simulations using realistic models of optical channel propagation. The proposed architecture can be integrated into existing underwater robots to enable next-generation range-extended and high-speed optical links for oceanic explorations.

The signal recovery performance of the rectangular-type Analog Joint Source-Channel Coding (AJSCC) is analyzed in this work for high and medium/low Signal-to-Noise Ratio (SNR) scenarios in a wireless sensing system with the Doppler channel. The analytical formulations of the Mean Square Error (MSE) performance are derived based on the geometry of the rectangular-type AJSCC on analog sensing and system with Doppler effects. The comprehensive listing of all cases in the three-dimensional geometric signal mapping curve is provided to obtain the theoretical formulations for the medium/low SNR scenario. Evaluation results indicate that there are optimal parameters in the rectangular-type AJSCC to minimize the signal recovery MSE for analog sensing and system with Doppler effects in both high and medium/low SNR scenarios. The analysis of the work provides insights on the optimization of the rectangular-type AJSCC in practical wireless sensing systems.

Safely writing high-performance concurrent programs is notoriously difficult. To aid developers, we introduce Armada, a language and tool designed to formally verify such programs with relatively little effort. Via a C-like language and a small-step, state-machine-based semantics, Armadagives developers the flexibility to choose arbitrary memory layout and synchronization primitives so that they are never constrained in their pursuit of performance. To reduce developer effort, Armadaleverages SMT-powered automation and a library of powerful reasoning techniques, including rely-guarantee, TSO elimination, reduction, and pointer analysis. All of these techniques are proven sound, and Armadacan be soundly extended with additional strategies over time. Using Armada, we verify five concurrent case studies and show that we can achieve performance equivalent to that of unverified code.

Safely writing high-performance concurrent programs is notoriously difficult. To aid developers, we introduce Armada, a language and tool designed to formally verify such programs with relatively little effort. Via a C-like language and a small-step, state-machine-based semantics, Armada gives developers the flexibility to choose arbitrary memory layout and synchronization primitives so they are never constrained in their pursuit of performance. To reduce developer effort, Armada leverages SMT-powered automation and a library of powerful reasoning techniques, including rely-guarantee, TSO elimination, reduction, and alias analysis. All these techniques are proven sound, and Armada can be soundly extended with additional strategies over time. Using Armada, we verify four concurrent case studies and show that we can achieve performance equivalent to that of unverified code.

Blockchains, and specifically smart contracts, have promised to create fair and transparent trading ecosystems.Unfortunately, we show that this promise has not been met. We document and quantify the widespread and rising deployment of arbitrage bots in blockchain systems, specifically in decentralized exchanges (or "DEXes"). Like high-frequency traders on Wall Street, these bots exploit inefficiencies in DEXes, paying high transaction fees and optimizing network latency to frontrun, i.e., anticipate and exploit, ordinary users' DEX trades.We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots. We also study bots' profit-making strategies, with a focus on blockchain-specific elements. We observe bots engage in what we call priority gas auctions (PGAs), competitively bidding up transaction fees in order to obtain priority ordering, i.e., early block position and execution, for their transactions. PGAs present an interesting and complex new continuous-time, partial-information, game-theoretic model that we formalize and study. We release an interactive web portal, frontrun.me, to provide the community with real-time data on PGAs. We additionally show that high fees paid for priority transaction ordering poses a systemic risk to consensus-layer security. We explain that such fees are just one form of a general phenomenon in DEXes and beyond-what we call miner extractable value (MEV)-that poses concrete, measurable, consensus-layer security risks. We show empirically that MEV poses a realistic threat to Ethereum today. Our work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.

The research challenge of current wireless sensor networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single power charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities is proposed. Sensing is exclusively the responsibility of analog substrate-consisting of low-power, low-cost all-analog sensors-that sits beneath the traditional WSN comprising of digital nodes, which does all the processing of the sensor data received from analog sensors. A low-power and low-cost solution for substrate sensors has been proposed using analog joint source-channel coding (AJSCC) realized via the characteristics of metal-oxide-semiconductor field-effect transistor (MOSFET). Digital nodes (receiver) also estimate the source distribution at the analog sensors (transmitter) using machine learning techniques so as to find the optimal parameters of AJSCC that are communicated back to the analog sensors to adapt their sensing resolution as per the application needs. The proposed techniques have been validated via simulations from MATLAB and LTSpice to show promising performance and indeed prove that our framework can support large-scale high density and persistent WSN deployment.

Inter-cell interference is a major issue for next-generation wireless cellular networks, due to the increased user mobility, high user density, and backhaul bandwidth constraint. In this work, a unified approach is proposed to address these challenges. The proposal firstly performs a per-cell signal spreading by Zadoff-Chu (ZC) sequence, then the spread signal is precoded by a modified coordinated beamforming scheme. The proposal is robust to Doppler caused by user mobility, supports a high co-channel user capacity for ultra-dense networks, and requires only limited backhaul signaling exchange between basestations thus being suitable for massive MIMO deployment. The advantages of the proposal are firstly analyzed in theory, then evaluation results are presented to validate the proposal for MIMO and massive MIMO setups. It is found that the proposed scheme consistently outperforms the traditional scheme under various scenarios. The practical issues related to synchronization in coordinated beamforming are discussed.

A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power consumption of the sensor is achieved by an all-analog circuit implementing Analog Joint Source-Channel Coding (AJSCC) compression. The sensor is applicable to a wide range of biomedical applications that require real-time concurrent molecular biomarker and physiological signal monitoring.

Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and/or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages-it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.

Multi-cell interference coordination via Block Diagonalization (BD), which is widely used in LTE-Advanced interference management, has three main drawbacks: (1) limitation on the number of supported co-channel users; (2) performance degradation in Doppler channels with feedback delay and quantization; and (3) high signaling overhead to exchange Channel State Information (CSI) and to generate precoding matrix. In this paper, a new approach is proposed to overcome these drawbacks and to improve the multi-cell interference coordination. The core idea is to create a null space only to the users within one cell; to achieve this goal, Zadoff-Chu Sequence (ZCS) spreading is used to eliminate the multi-cell interference by leveraging the zero-correlation property of cyclic shifts of the ZCS. This way, the capacity of the cellular network is improved in Doppler channels with delayed and quantized feedback; moreover, signaling overhead and computational complexity are both reduced. The auxiliary sampling capability of the hardware is utilized to realize the higher sampling rate required by the proposed approach. The new interference-management solution is compared against the existing one via computer simulations and is shown to lead to significant capacity gains even in high-mobility scenarios.

A low-complexity and low-power all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC). The proposed idea is to adopt Voltage Controlled Voltage Source (VCVS) to realize the rectangular-type mapping in AJSCC. The proposal is verified by Spice simulations as well as via breadboard and Printed Circuit Board (PCB) implementations. Field testing results indicate that the design is feasible for low-complexity and low-power systems such as wireless sensor networks for environmental monitoring.

The performance degradation in convolutionally coded V-BLAST system is analyzed in this paper. Compared with coded linear filtering approach, it is shown that the coded V-BLAST suffers from serious degradation due to the effect of error propagation. Considering the constraint of the system, a performance improvement method is proposed. It adjusts the weighting of soft bits, where the weighting for one stream is found based on the equivalent error rate of that stream. Additional computation needed by the proposed method is neglectable. Both theoretical analysis and simulation verify the effectiveness of the proposed method.

Mode selection problem in MIMO-OFDM system is addressed. A general formulation is introduced to decompose the problem into two decoupled sub-problems: modulation and coding scheme (MCS) selection, and space-time processing (STP) optimization, each of which can be tackled more systematically. Mode selection algorithms can then be developed using a channel quality indicator (CQI) metric. The methodology is illustrated using the platform of IEEE 802.11n. The qualities of multiple CQI metrics are compared in terms of "coherency" and their performances are evaluated in terms of system goodput. This has been shown that the metric we proposed outperforms the other metrics.

This paper describes a channel estimation scheme which utilizes interlaced transmit pilots to estimate multiple input multiple output (MIMO) channel responses in orthogonal frequency division multiplexing (OFDM) systems. This least square (LS) based channel estimation scheme adopts discrete Fourier transform (DFT) to improve the estimation accuracy. The constraint condition of the estimation scheme is obtained using frequency-domain sampling theorem. Simulation shows that the proposed scheme shows good stability when the constraint condition fails, which is much better than other two commonly-used MIMO-OFDM channel estimation schemes.