# I/Q Published articles for I/Q. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Communication-Efficient Model-Distributed Inference for CBRS Radar Detection at the Edge DevFeed: [Communication-Efficient Model-Distributed Inference for CBRS Radar Detection at the Edge]() Original publisher: [Read original article]() Author: Teng Li, Hulya Seferoglu Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Distributed Computing]() Topics: [AI Infrastructure](), [Local AI](), [Data transmission](), [Jetson Orin](), [communications]() Tags: [5g](), [cbrs](), [communication](), [compression](), [cs-dc](), [data](), [detection](), [device](), [distributed](), [distributed-inference](), [edge](), [eess-sp](), [i-q](), [inference](), [jetson](), [jetson-orin](), [jetson-orin-nano](), [pipeline](), [quality](), [radar](), [radio](), [raw]() ### AI overview CEDAR reduces communication costs in edge-distributed CBRS radar detection by redesigning both the detector and model partitioning. CEDAR-YOLO removes YOLO's multi-scale neck and two detection scales, using a single-scale path with independently configured frequency and time resolutions. CEDAR-MDI adds lightweight activation compressors and chooses each compressor's partition to minimize the pipeline bottleneck. On an NVIDIA Jetson Orin Nano with real-world over-the-air data, CEDAR-YOLO reduced parameters by 35-57% and inference time by 16-33%; CEDAR-MDI achieved up to 3.27x the throughput of single-device inference. The abstract reports preserved detection quality for both, but provides no further evaluation details. ### Source excerpt arXiv:2611.00625v1 Announce Type: new Abstract: The Citizens Broadband Radio Service (CBRS) shares the 3550-3700 MHz band between incumbent radar systems and commercial LTE/5G users, so radar activity must be detected accurately and promptly. Spectrogram-based YOLO detectors can localize coexisting radar and cellular signals, but running them on resource-constrained edge devices is challenging. Model-distributed inference (MDI) can increase throughput by partitioning a model across edge devices. Using an end-to-end pipeline from raw I/Q samples to on-device detection, we show that partitioning recent YOLO generations incurs high activation-transfer costs due to their multi-scale structures. To reduce these costs, we propose CEDAR, a communication-efficient, edge-distributed architecture for radar detection. Its RF-aware detector, CEDAR-YOLO, removes YOLO's multi-scale neck and two of its three detection scales, leaving a single-scale path with independently configured frequency and time resolutions, which lowers the communication cost of partitioning. Its compression-aware MDI framework, CEDAR-MDI, integrates lightweight activation compressors and selects, for each compressor, the partition that minimizes the pipeline bottleneck. On an NVIDIA Jetson Orin Nano testbed with real-world over-the-air data, CEDAR-YOLO reduces the parameter count by 35-57% and model inference time by 16-33%, and CEDAR-MDI achieves up to $3.27\times$ the throughput of single-device inference, with detection quality preserved in both cases. ## ACHEM: A Real-Time Digital Twin Framework with Channel and Radio Emulation DevFeed: [ACHEM: A Real-Time Digital Twin Framework with Channel and Radio Emulation]() Original publisher: [Read original article]() Author: Anil Gurses, Mihail L. Sichitiu Published: 2026-10-22T04:00:00Z Content type: article Language: en Sources: [arXiv: Networking]() Topics: [5G]() Tags: [4g](), [5g](), [antenna](), [channel](), [communication](), [cs-ni](), [deployment](), [digital-twin](), [eess-sp](), [emulation](), [fpgas](), [framework](), [i-q](), [mimo](), [mobility](), [models](), [multiple](), [node](), [open-source](), [optimizing](), [radio](), [real-time]() ### AI overview ACHEM is a software-based, end-to-end channel emulation framework for USRP-based wireless systems. It emulates systems at the I/Q level in a digital environment, avoiding specialized hardware such as USRPs, FPGAs, GPUs, or vehicles. It supports multiple transmitters and receivers, MIMO, multiple frequencies, differing sampling rates, real-time mobility through vehicle emulation, antenna radiation patterns, and multiple channel models. The authors report validation with GNU Radio, srsRAN 4G/5G, and OpenAirInterface. The abstract positions ACHEM as an alternative to costly, cumbersome testbeds and simulations that may simplify channel/radio behavior or omit full-stack production code. ### Source excerpt arXiv:2604.04742v2 Announce Type: replace-cross Abstract: Digital twins are becoming an important tool for designing, developing, testing, and optimizing next-generation wireless communication systems. Over the past decade, system softwarization has become a reality, and wireless communication systems are no exception. Software-Defined Radios (SDRs), in general, and Universal Software Radio Peripherals (USRPs), in particular, are often used for prototyping and testing advanced wireless systems. Unfortunately, there is currently no end-to-end, software-based, general-purpose testing environment for SDR-based systems: developers often rely on benchtop setups or even small testbeds, but those are costly and cumbersome to build. At the other end of the spectrum, simulations often rely on simplified channel/radio models and typically do not execute full-stack production code, which can increase development effort and reduce fidelity. In this paper, we propose ACHEM (A Channel Emulator), the first software-based, end-to-end wireless channel emulation environment and toolset for communication systems based on SDRs, specifically USRPs. With the proposed emulator and toolkit, any USRP-based system can be fully emulated at the I/Q level in a pure digital environment without requiring specialized hardware (e.g., vehicles, USRPs, FPGAs, or GPUs). The proposed emulator supports multiple transmitters and receivers, MIMO communications, multiple frequencies, heterogeneous sampling rates, real-time node mobility through vehicle emulation, antenna radiation patterns, and various channel models. ACHEM facilitates wireless digital twin development and deployment. ACHEM is validated with several popular open-source USRP-based wireless communication applications, including GNU Radio, srsRAN 4G/5G, and OpenAirInterface. ## TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shift DevFeed: [TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shift]() Original publisher: [Read original article]() Author: Chuan Liu, Tianya Zhao, Xuyu Wang Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Agentic AI](), [Large Language Model](), [trust](), [reliability-engineering]() Tags: [acquisition](), [agentic](), [confidence](), [consistency](), [costs](), [cs-ai](), [cs-lg](), [datasets](), [defer](), [deployment](), [device](), [diagnostics](), [domain](), [environment](), [estimator](), [framework](), [i-q](), [language-model](), [large-language-model](), [planning](), [rf](), [shift]() ### AI overview TrustAgent-RFF addresses unreliable radio frequency fingerprinting under domain shift, where errors may retain high softmax confidence and confidence thresholding alone is insufficient. It keeps the I/Q classifier frozen and uses no target-domain labels. For each prediction, it gathers counterfactual stability, prototype consistency, acquisition-domain similarity, and signal-quality evidence. A source-calibrated estimator combines these with classifier confidence, prediction margin, and evidence-availability indicators; an LLM planner chooses further diagnostics based on trust state, action costs, and remaining budget. A deterministic source-calibrated layer then marks predictions Accept, Reject, or Defer. Across five RFF datasets and classifier backbones, the authors report a 38.7% average source-to-target accuracy drop and many highly confident incorrect target predictions. ### Source excerpt arXiv:2610.24930v1 Announce Type: new Abstract: Radio frequency fingerprinting (RFF) models can achieve accuracy under source domain conditions but become unreliable when the receiver, distance, collection day, or environment changes. Incorrect predictions in unseen domains may receive high softmax confidence, making confidence thresholding insufficient for trustworthy deployment. We propose TrustAgent-RFF, a prediction level reliability framework for RFF that operates with a frozen in-phase and quadrature (I/Q) classifier without target domain labels. For each device prediction, the framework acquires radio frequency evidence: counterfactual stability, prototype consistency, acquisition domain similarity, and signal quality. A source calibrated trust estimator combines these diagnostics with classifier confidence, prediction margin, and evidence availability indicators. Source derived lower tail penalties and cross module interactions reduce trust when diagnostic signals indicate weak support. A large language model (LLM) based planner adaptively selects the next diagnostic action based on evidence, trust state, action costs, and remaining budget. A deterministic source calibrated decision layer updates the prediction state as Accept, Reject, or Defer. Experiments across five RFF datasets and classifier backbones reveal an average source to target accuracy drop of 38.7% and show that many incorrect target domain predictions remain highly confident. ## Semtech LR2021 LoRa chip gets the SDR treatment, supports 100 MHz to 2.74 GHz DevFeed: [Semtech LR2021 LoRa chip gets the SDR treatment, supports 100 MHz to 2.74 GHz]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-10-20T07:00:17Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Software Dev](), [Embedded Systems](), [communications](), [ESP32-S3](), [lora](), [meshtastic]() Tags: [ble](), [chip](), [embedded](), [embedded-systems](), [esp32](), [firmware](), [hack](), [hardware](), [i-q](), [lilygo](), [lora](), [lr2021](), [messages](), [open-source](), [radio](), [receiver](), [reverse-engineering](), [sdr](), [semtech](), [software-defined-radio](), [transceiver](), [voice](), [wireless]() ### AI overview The open-source LR2021-SDR project uses an undocumented ROM command to expose raw I/Q samples from Semtech's LR2021 transceiver, enabling software decoding of P25 voice, POCSAG pager messages, and analog FM without an external SDR dongle. Tests on one board demonstrated reception from 100 MHz to 2.74 GHz, though the results do not establish calibrated sensitivity or coverage on other hardware. I/Q capture rates above 250,000 complex samples per second overflowed the FIFO, and transmission remains limited to short bursts. ### Source excerpt Earlier this month, we saw how the humble ESP32-S3 could be turned into an SDR transmitter and receiver; then we saw developer h0m3us3r release the w1700k-sdr project, which turns a Gemtek W1700K Wi-Fi 7 router into a wideband SDR receiver. The LR2021-SDR open-source project by Samuel Reynolds (SAMS0N1TE) follows the sample principles and turns the Semtech LR2021 LoRa transceiver into a software-defined radio (SDR) capable of receiving signals from 100 MHz to 2.74 GHz. Using an undocumented command in the chip's ROM, this project exposes raw 16-bit I/Q samples, allowing software to decode P25 Phase 1 digital voice, POCSAG pager messages, and analog FM signals without an external SDR dongle. LR2021-SDR firmware features: Supported hardware - Semtech LR2021 running ROM 1.24; not compatible with the SX1262 variant for raw I/Q access. Receive frequency range - 100 MHz to 2.74 GHz (experimentally tested), extending beyond the datasheet's specified 150-960 MHz and 1.5-2.5 [...] The post Semtech LR2021 LoRa chip gets the SDR treatment, supports 100 MHz to 2.74 GHz appeared first on CNX Software - Embedded Systems News. ## Turning a Wi-Fi Adapter into an SDR DevFeed: [Turning a Wi-Fi Adapter into an SDR]() Original publisher: [Read original article]() Author: Aaron Beckendorf Published: 2026-10-18T08:00:22Z Content type: article Language: en Sources: [Hackaday]() Topics: [Wi-Fi](), [Undocumented feature](), [input](), [ESP32]() Tags: [bandwidth](), [esp32](), [firmware](), [i-q](), [interface](), [mediatek](), [monitor-mode](), [radio-hacks](), [raw](), [sdr](), [wi-fi]() ### AI overview Two independent projects have adapted the MediaTek MT7921AU Wi-Fi adapter for software-defined radio use by accessing its raw I/Q sampling features. BirriaSDR streams samples over libusb without permanent firmware changes, while Mtk79ICAP uses a modified driver and firmware changes, offering wider bandwidth and 5 GHz tuning. The adapter can also operate in monitor mode while streaming SDR data when automatic gain control is enabled. ### Source excerpt Since the discovery of an undocumented I/Q sampling feature in ESP32 chips about two weeks ago, there's been a flood of similar features being found in other wireless chips, including ...read more