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1. NetScatter: Enabling Large-Scale Backscatter Networks

   Hessar, Mehrdad; Najafi, Ali; Gollakota, Shyamnath (February 2019, NSDI)

   We present the first wireless protocol that scales to hundreds of concurrent transmissions from backscatter devices. Our key innovation is a distributed coding mechanism that works below the noise floor, operates on backscatter devices and can decode all the concurrent transmissions at the receiver using a single FFT operation.Our design addresses practical issues such as timing and frequency synchronization as well as the near-far problem. We deploy our design using a testbed of backscatter hardware and show that our protocol scales to concurrent transmissions from 256 devices using a bandwidth of only 500 kHz.Our results show throughput and latency improvements of14–62x and 15–67x over existing approaches and 1–2 orders of magnitude higher transmission concurrency. 

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2. NetScatter: Enabling Large-Scale Backscatter Networks

   Hessar, M; Najafi, A: Gollakota (January 2019, Proceedings of the 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’19))

   We present the first wireless protocol that scales to hundreds of concurrent transmissions from backscatter devices. Our key innovation is a distributed cod- ing mechanism that works below the noise floor, operates on backscatter devices and can decode all the concurrent transmissions at the receiver using a single FFT operation. Our design addresses practical issues such as timing and fre- quency synchronization as well as the near-far problem. We deploy our design using a testbed of backscatter hardware and show that our protocol scales to concurrent transmis- sions from 256 devices using a bandwidth of only 500 kHz. Our results show throughput and latency improvements of 14–62x and 15–67x over existing approaches and 1–2 orders of magnitude higher transmission concurrency. 

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3. LeakyScatter: A Frequency-Agile Directional Backscatter Network Above 100 GHz

   Atsutse Kludze and Yasaman Ghasempour (January 2023, NSDI 2023: USENIX Symposium on Networked Systems Design and Implementation)

   Wireless backscattering has been deemed suitable for various emerging energy-constrained applications given its low-power architectures. Although existing backscatter nodes often operate at sub-6 GHz frequency bands, moving to the sub-THz bands offers significant advantages in scaling low-power connectivity to dense user populations; as concurrent transmissions can be separated in both spectral and spatial domains given the large swath of available bandwidth and laser-shaped beam directionality in this frequency regime. However, the power consumption and complexity of wireless devices increase significantly with frequency. In this paper, we present LeakyScatter, the first backscatter system that enables directional, low-power, and frequency-agile wireless links above 100 GHz. LeakyScatter departs from conventional backscatter designs and introduces a novel architecture that relies on aperture reciprocity in leaky-wave devices. We have fabricated LeakyScatter and evaluated its performance through extensive simulations and over-the-air experiments. Our results demonstrate a scalable wireless link above 100 GHz that is retrodirective and operates at a large bandwidth (tens of GHz) and ultra-low-power (zero power consumed for directional steering and ≤ 1 mW for data modulation). 

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4. Mobile Underwater Backscatter Networking

   https://doi.org/10.1145/3718958.3750507  

   Wang, Purui; Afzal, Sayed Saad; Adib, Fadel (August 2025, ACM)

   Underwater backscatter is a promising technology for ultra-lowpower underwater networking, but existing systems break down in mobile scenarios. This paper presents EchoRider, the first system to enable reliable underwater backscatter networking under mobility. EchoRider introduces three key components. First, it incorporates a robust and energy-efficient downlink architecture that uses chirp-modulated transmissions at the reader and a sub-Nyquist chirp decoder on backscatter nodes—bringing the resilience of LoRa-style signaling to underwater backscatter while remaining ultra-lowpower. Second, it introduces a NACK-based full-duplex retransmission protocol, enabling efficient, reliable packet delivery. Third, it implements a Doppler-resilient uplink decoding pipeline that includes adaptive equalization, polar coding, and dynamic retraining to combat channel variation. We built a full EchoRider prototype and evaluated it across over 1,200 real-world mobile experiments. EchoRider improves bit error rate by over 125× compared to a state-of-the-art baseline and maintains underwater goodput of 0.8 kbps at speeds up to 2.91 knots. In contrast, the baseline fails at speeds as low as 0.17 knots. Finally, we demonstrate EchoRider in end-to-end deployments involving mobile drones and sensor nodes, showing its effectiveness in practical underwater networked applications. 

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5. CASTLE over the Air -- Distributed Scheduling for Cellular Data Transmissions (demo)

   https://doi.org/10.1145/3307334.3328576  

   Dhawaskar Sathyanarayana, Sandesh; Lee, Jihoon; Lee, Jinsung; Im, Youngbin; Rahimzadeh, Parisa; Zhang, Xiaoxi; Hollingsworth, Max; Joe-Wong, Carlee; Grunwald, Dirk; Ha, Sangtae (June 2019, MobiSys '19: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services)

   We present the demonstration of a fully distributed scheduling framework called CASTLE (Client-side Adaptive Scheduler That minimizes Load and Energy) that jointly optimizes the spectral efficiency of cellular networks and battery consumption of smart devices. To do so, we focus on scenarios when many smart devices compete for cellular resources in the same base station: spreading out transmissions over time so that only a few devices transmit at once and improves both spectral efficiency and battery consumption. To this end, we devise two novel features in CASTLE. First, we explicitly consider inter-cell interference for accurate cellular load estimation in our machine learning algorithm. Second, we propose a fully distributed scheduling algorithm that coordinates transmissions between clients based on the locally estimated load level at each client. Our formulation for minimizing battery consumption at each device leads to an optimized back off-based algorithm that fits practical environments. Our comprehensive experimental results show that CASTLE's load estimation is up to 91 % accurate, and that CASTLE achieves higher spectral efficiency with less battery consumption, compared to existing centralized scheduling algorithms as well as a distributed CSMA-like protocol. Furthermore,we develop a light-weight SDK that can expedite the deployment of CASTLE into smart devices and evaluate it in a commercial LTE network. 

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