Robust Wireless Communication for Multi-Antenna, Multi-Rate, Multi-Carrier Systems

Abstract Today's trend of migrating radio devices from hardware to software provides potential to create flexible applications for both commercial and military use. However, this raises security concerns, as malicious attackers can also be generated easily to break legitimate communications. In this research work, our goal is to design a robust anti-jamming radio framework. We particularly investigate three different aspects of jamming threats: high-power jammers, link attacks on rate adaptation, and jamming in multicarrier systems. The threats of high-power jamming to wireless communications today are realistic due to the ease of access to powerful jamming sources such as the availability of commercial GPS/WiFi/cellular devices on the market, or RF guns built from microwave ovens' magnetron. To counter high-power jamming attacks, we develop SAIM which is a hybrid system capable of resisting jammers of up to 100,000 times higher power than legitimate communication nodes. The system robustness relies on our own antenna structure specially designed for anti-jamming purpose. We develop an efficient algorithm for auto-configuring the antenna adaptively to dynamic environments. We also devise a software-based jamming cancellation technique for appropriately extracting original signals, which is more robust than traditional MIMO approaches, as pilot signals are not required in SAIM. In spite of the robustness of SAIM, our design is more appropriate for malicious environments with powerful jammers, where mechanical steering is feasible, e.g., military applications. Residential and commercial wireless communication systems are still vulnerable to even limited-power jamming, as in today's standard wireless protocols, rate information is exposed to adversaries. Rate-based attacks have been demonstrated to severely degrade the networks at very low cost. To mitigate rate-based attacks, we develop CBM, a system capable of hiding rate and -at the same time -increasing resiliency against jammers up to seven times higher than regular systems, where rate is exposed. We achieve the resiliency boost by generalizing Trellis Coded Modulation to allow non-uniform codeword mapping. We develop an efficient algorithm for finding good non-uniform codes for all modulations in {BPSK, QPSK, 8-PSK, 16-QAM, 64-QAM}. To conceal rate information, we devise an efficient method for generating cryptographic interleaving functions. In recently deployed communication networks such as WiFi and LTE systems, MIMO and OFDM are the two main techniques for increasing bandwidth efficiency. While MIMO increases the channel capacity by spatial processing on multiple received signals, OFDM mitigates impacts of dynamic variations in wide-band channels and allows frequency reuse with overlapping carriers. Synchronization is a key for high-throughput performance in MIMO and OFDM systems. In this work, we study impacts of jamming attacks specifically targeting to control channels in WiFi and LTE networks. Our study focuses on efficient techniques for both jamming and anti-jamming in multicarrier systems

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening