The minimalist and ultra-low-power natures of ambient backscatter communications (AmBC) make it a compelling wireless paradigm for Internet of Things (IoT), which in turn puts AmBC at great risk of being monitored. Recently, covert backscatter systems have been explored to secure transmission without detection, but they mainly introduce artificial noise and beamforming, consuming additional energy. In this paper, we propose a spatial modulation (SM)-enabled multi-antenna covert AmBC system that guarantees covertness and raises the covert capacity without requiring additional energy consumption. Specifically, the multiple-antenna backscatter tag transmits covert information by adopting SM, while its remaining antennas superimpose overt messages to shelter the covert transmission. We derive the closed-form expression of the minimum detection error probability (DEP) to capture the covertness, that represents the worst-case for the tag. Furthermore, we formulate a covert capacity maximization problem by optimizing the power reflection coefficient under the constraints of the covertness requirement. By exploiting the objective function's monotonic properties and the constraints, we derive the optimal power reflection coefficient and the corresponding covert capacity. Due to the target expression is very complex and lacks a closed-form solution, we introduce a Taylor expansion to simplify it. Finally, numerical results demonstrate the performance and effectiveness of the proposed multi-antenna covert backscatter communication system. As a result, it shown that the covert capacity can be significantly improved compared with the benchmark scheme.

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Multiple-antenna backscatter is emerging as a promising approach to combat the cascaded backscatter channel fading to offer a high data rate for ambient backscatter communication (AmBC). However, related designs in AmBC fail to provide both diversity and multiplexing gains to backscatter tags. To address this issue, in this paper, we present a unified generalized space-time shift keying (GSTSK)-Backscatter architecture for AmBC to strike a flexible tradeoff between the attainable diversity and multiplexing gains in an efficient but low-complexity manner. Moreover, taking the non-identically distributed characteristics of the backscatter channel into account, we first derive asymptotic BER bounds for both the source and backscatter signals, which are verified by simulation results. Furthermore, we propose a residual- and threshold-based joint error correction mechanism (JECM) and design the near-optimal and low-complexity detector for GSTSK-Backscatter systems. Simulation results validate the analysis and show that our proposed JECM-based detector approaches the optimal detector and balances the BER and complexity performance.

In this paper, we propose a new framework for covert communication in hybrid active/passive networks, which explores the inherent uncertainty of the backscatter transmissions to achieve active and passive communication reciprocity in security. Under this framework, we first model the aggregate interference from the sporadic backscatter transmissions and derive the covert outage probability and the transmission success probability to capture the covertness and reliability. Then, we formulate a transmit power optimization problem to maximize the covert throughput subject to certain covertness and reliability requirements and derive a closed-form approximation of the maximum covert throughput. Particularly, we investigate the worst-case scenario of covert communication in which warden always uses the optimal detection thresholds. Finally, numerical results demonstrate that covert communication with the help of uninformed backscatters that are not coordinated with Alice in the hybrid network is feasible. Results also provide design guidelines for the optimal choice of interference parameters, which is capable of striking a balance between covertness and reliability.

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. However, existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the restrictive relationship between the energy harvesting rate and achievable rate with estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max-min optimization problem by maximizing the minimum achievable rate for all backscatter tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the achievable rate for maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Our numerical results indicate that our scheme significantly outperforms state-of-the-art energy beamforming schemes. Additionally, the achievable rate of our scheme approaches more than 90% of the rate limit achieved via beamforming with perfect CSI for both receivers.

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. Existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the trade-off between the energy harvesting rate and achievable rate using estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max- min optimization problem by maximizing the minimum achievable rate for all tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the ergodic achievable rate. Our numerical results indicate that our scheme can significantly outperform state-of-the-art energy beamforming schemes. Additionally, the proposed scheme achieves performance comparable to that obtained via beamforming with perfect CSI.