INFOCOM 2025 Papers

Due to the continuous traffic of ubiquitous cellular networks and the ultra low-power low-cost characteristics of backscatter communication, cellular backscatter communication is a crucial technology for passive Internet of Things. However, existing cellular backscatter systems suffer from the lack of downlink control ability, low-efficiency modulation, and unreliable demodulation for megabit-rate backscatter transmission. This paper proposes CellScatter to overcome such drawbacks. First, we design a synchronization-and-control module for a CellScatter tag, which exploits cellular reference signals to achieve accurate synchronization and reliable multi-tag control without extra time overhead. Then, we design a single-sideband high-order modulation module to focus the backscatter signal power to the desired band and improve the spectrum utilization efficiency. Furthermore, we establish an explicit model to represent the spectrum-expanded backscatter signal without requiring a high-speed analog-to-digital converter, and derive a closed-form solution for the user equipment to recover the data of the tag at low complexity. We prototype the CellScatter system and evaluate its performance via ambient 4G LTE and 5G NR signals. Experimental results show that CellScatter can achieve 2.24 Mbps backscatter communication within a range of 30 meters, 6.7× lower BER and 33% higher rate than state of the art solutions.

Wireless neural receivers provide a promising alternative to conventional receivers. To perform well in different channel environments, online adaption is required. However, during this process, performance remains low. Thus, an approach called federated collaborative learning with pruned-data aggregation (FedPDA) is developed to reduce online-adaptation frequency. The basic idea is that, upon online adaptation, mobile terminals further update their neural receivers collaboratively via federated learning. To reduce memory consumption, neural receivers follow a main-side network architecture where only the side network needs retraining during collaborative learning. To avoid catastrophic forgetting during continual learning, local data on terminals are pruned, with only a small percent sent to the base station. With such data, the base station also trains a neural receiver before conducting model aggregation. FedPDA is distinct with several features: 1) small memory footprint and no storage burden on terminals; 2) no catastrophic forgetting issue; 3) low communication cost. Performance results show that FedPDA reduces online adaptation by more than 90% and memory footprint by 70%. It achieves comparable performance as centralized schemes, but reducing communication cost by 78%. Compared to vanilla federated learning, FedPDA resolves the catastrophic forgetting issue without storage burden, and also reduces the communication cost by 50%.

With the aging population, there is an increasing demand for rehabilitation services for people with chronic diseases. However, limitations such as medical resources, geographic barriers, and cost make home rehabilitation an option for more patients. Existing wearable devices and vision methods are effective but face problems with portability, cost, and privacy concerns. As for existing wireless sensing methods, they can only extract coarse features for activity recognition. Therefore, we present EchoHealth, which utilizes a smart speaker for rehabilitation exercise detection and assessment. We upgrade the smart speaker into an active sonar system without hardware modification to generate acoustic micro-distance images with motion information. Then, time-domain motion detection and distance-domain feature extraction are utilized to filter out the effects of non-motion time and distance to extract patient motion features for motion recognition. We further assess the patient's rehabilitation exercises from five aspects, based on which EchoHealth provides rehabilitation guidance. Extensive experiments with 15 participants performing 12 rehabilitation motions confirmed that EchoHealth can achieve 97.4% average accuracy in recognition of rehabilitation motion and provide accurate rehabilitation indicators in various environments.

The complexity of routing requirements leads to increasingly intricate routing metrics. Existing routing algebra theories have demonstrated that convergent and optimal routing algorithms can be designed only when path metrics satisfy certain properties such as monotonicity and isotonicity. Furthermore, some non-isotonic metrics can be converted into isotonic forms on partial orders through reduction. However, practical scenarios often involve non-commutative algebraic properties, which are overlooked by existing theories. For these problems, there lacks a unified framework to study their solvability, a systematic method for their reduction, and an efficient algorithm to compute optimal routes. In this work, we extend routing algebra to accommodate non-commutative routing problems, propose general reduction methods for them, and explore their solvability. In addition, we design a link state algorithm that converge fast on a reduced partial order. All these discussions are supported by concrete examples, theoretical proofs, and simulations on various network topologies.