Beamforming Innovations

Happy to share our latest research, where we introduce DRIP waveforms—a novel space-time ISAC waveform family for dynamic control of beams, coded data, interference-aware, and peak-to-average power ratio (PAPR) with beamforming capabilities and radar similarity features. 📝 Authors: Dexin Wang, Ahmad Bazzi, Marwa Chafii 🌐 What makes DRIP unique? The thing is that these waveforms can be used directly on OFDM subcarriers to achieve joint sensing and communications capabilities while passing DRIP waveforms through high linear power amplifiers. This means that passing an OFDM waveform via the DRIP methodology can serve multi-user communications and allow to sense multiple targets at desired bearing directions, with good enough radar similarity constraints, so that the backscattered returns are optimized for radar processing, e.g. delay-doppler, while satisfying practical PAPR constraints for high power amplifiers, which will be part of any future ISAC system. DRIP waveforms are also interference and clutter-aware, an important nuisance that "eats" part of the dynamic range. 📈 How to generate DRIP waveforms ? DRIP waveforms are generated though solving a non-convex optimization challenge in DRIP waveform generation, where we developed a block-cyclic coordinate descent algorithm to iteratively converge towards an optimal ISAC waveform solution. 💡 Key Results: Our simulations show that DRIP waveforms deliver high performance, versatility, and fruitful ISAC trade-offs, making them very favorable for advanced sensing and communication systems. 🔗 Link: https://lnkd.in/dkD6mJgj 📝 Abstract: The following paper introduces Dual beam-similarity awaRe Integrated sensing and communications (ISAC) with controlled Peak-to-average power ratio (DRIP) waveforms. DRIP is a novel family of space-time ISAC waveforms designed for dynamic peak-to-average power ratio (PAPR) adjustment. The proposed DRIP waveforms are designed to conform to specified PAPR levels while exhibiting beampattern properties, effectively targeting multiple desired directions and suppressing interference for multi-target sensing applications, while closely resembling radar chirps. For communication purposes, the proposed DRIP waveforms aim to minimize multi-user interference across various constellations. Addressing the non-convexity of the optimization framework required for generating DRIP waveforms, we introduce a block cyclic coordinate descent algorithm. This iterative approach ensures convergence to an optimal ISAC waveform solution. Simulation results validate the DRIP waveforms' superior performance, versatility, and favorable ISAC trade-offs, highlighting their potential in advanced multi-target sensing and communication systems. 🧳Affiliations: New York University Abu Dhabi, NYU Tandon School of Engineering, NYU WIRELESS.

📣📣📣 To correctly perform multi-user MIMO transmissions, beamformers need to frequently acquire a steering matrix from each connected beamformee. The key issue is that the size of the matrix grows with the number of antennas and subcarriers, resulting in an increasing amount of airtime overhead and computational load at the beamformee. In our recent IEEE ICDCS 2023 paper (https://lnkd.in/g4rWcBPh), we have proposed SplitBeam, a new approach where a split deep neural network is trained to directly output the steering matrix given the channel state information matrix as input. The head model generates a latent representation of the input, which is then used by the beamformer to produce the steering matrix using the tail model. This way, the computation requirement at the beamformee and the feedback size can be significantly decreased. We have performed extensive experimental data collection with off-the-shelf Wi-Fi devices in two distinct environments and compared the performance of SplitBeam with the standardized IEEE 802.11 algorithm and the state of the art data-driven approach based on autoencoders. Our results show that our data-driven approach reduces the beamforming feedback size and computational complexity by up to 84% while also being able to decrease the bit error rate with respect to existing approaches. To allow full reproducibility, we have released our code and datasets to the community, which is available for download at https://lnkd.in/gY6UfsTZ Yoshitomo Matsubara Niloofar Bahadori Marco Levorato Institute for the Wireless Internet of Things (WIoT) #ai #ml #mimo #wireless #wifi #ofdm #neuralnetworks

💡 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗣𝗵𝗮𝘀𝗲𝗱 𝗔𝗿𝗿𝗮𝘆𝘀? 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗕𝗲𝗮𝗺𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗠𝗮𝘁𝘁𝗲𝗿𝘀. Phased array antennas are transforming communications in 𝗱𝗲𝗳𝗲𝗻𝘀𝗲, 𝟱𝗚, 𝘁𝗲𝗹𝗲𝗰𝗼𝗺, 𝗮𝗻𝗱 𝘀𝗽𝗮𝗰𝗲, thanks to their beam-steering agility and flat-panel form factor. But great hardware isn’t enough — the 𝗸𝗲𝘆 𝘁𝗼 𝗵𝗶𝗴𝗵-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗿𝗿𝗮𝘆𝘀 𝗶𝘀 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗯𝗲𝗮𝗺𝗳𝗼𝗿𝗺𝗶𝗻𝗴 that meets stringent pattern masks and regulatory requirements. To achieve that, designers need 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗲𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗲𝗹𝗲𝗺𝗲𝗻𝘁 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 that capture 𝗲𝗱𝗴𝗲 𝗲𝗳𝗳𝗲𝗰𝘁𝘀 and 𝗺𝘂𝘁𝘂𝗮𝗹 𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴 — not just best guesses. Many engineers resort to clever workarounds: ➤ Use an infinite array approximation ➤ Model a small subset to estimate coupling or edge effects But these shortcuts often miss the mark, leading to poor beamforming and degraded system performance. 🚀 At 𝗧𝗜𝗖𝗥𝗔, we’re changing that — with a 𝗻𝗲𝘄, 𝗱𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗮𝗿𝗿𝗮𝘆 𝗥𝗙 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹, launching in early 2026. What makes it a game-changer? ✅ 𝗙𝘂𝗹𝗹-𝘄𝗮𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 of large finite arrays, to account for edge effects and mutual coupling ✅ Powerful built-in 𝗮𝗺𝗽𝗹𝗶𝘁𝘂𝗱𝗲 & 𝗽𝗵𝗮𝘀𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻 to meet stringent pattern requirements ✅ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 of the full scattering matrix  ✅ No need for oversized design margins or performance compromises 📸 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: A 12×12 Ka-band array with dual-polarised stacked patches was analysed and optimised (amplitude & phase) to produce a 𝗳𝗹𝗮𝘁-𝘁𝗼𝗽 𝗯𝗲𝗮𝗺 with co- and cross-polarisation masks. The full model— including coupling and edge effects — ran in minutes on a standard laptop. The software turns 𝗺𝘂𝘁𝘂𝗮𝗹 𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴 from an unwanted effect into a 𝗸𝗲𝘆 𝗲𝗻𝗮𝗯𝗹𝗲𝗿 of high-performance array design. 🔧𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗽𝗵𝗮𝘀𝗲𝗱 𝗮𝗿𝗿𝗮𝘆𝘀, 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝘁𝗼𝗼𝗹 𝘆𝗼𝘂’𝘃𝗲 𝗯𝗲𝗲𝗻 𝘄𝗮𝗶𝘁𝗶𝗻𝗴 𝗳𝗼𝗿. #PhasedArrays #AntennaDesign #Beamforming #RFSimulation #5G #SatCom #DefenseTech #SpaceComms #TICRA #Electromagnetics #MutualCoupling #AntennaTechnology

🚀 AI-RAN Meets the I/Q Frontier: Unlocking RAN Intelligence Through Raw Signal Data 📶 In the era of AI-native RAN, we often talk about automation, intent-based orchestration, and LLM-driven RICs — but what if the real goldmine for intelligence is buried deeper? 💡 Enter I/Q data: the In-phase and Quadrature components of modulated RF signals. It’s the rawest, richest representation of the wireless environment — capturing everything from signal distortions to propagation nuances. And thanks to AI/ML, we now have the tools to decode its potential in real time. 🔍 AI/ML-based I/Q signal analysis is emerging as a game-changer in AI-RAN architectures by enabling data-driven optimization at the physical layer: 1️⃣ Channel Estimation & Prediction LSTM-based models can learn from historical I/Q sequences to predict Channel State Information (CSI), enabling smarter beamforming and scheduling before issues arise. 2️⃣ Modulation Recognition CNNs and SVMs can identify modulation types in real time, helping to mitigate interference and dynamically adapt to changing network conditions. 3️⃣ Anomaly Detection & Fault Diagnosis Autoencoders can flag deviations in I/Q patterns — from hardware degradation to malicious signal injection — for proactive maintenance and increased network security. 4️⃣ Beam Management Optimization AI agents trained on I/Q patterns can adjust beam direction and power dynamically, improving coverage, energy efficiency, and user experience in dense deployments. 5️⃣ Interference Identification & Cancellation Reinforcement learning enables real-time interference mitigation by learning adaptive transmission policies directly from I/Q observations. 6️⃣ Device Localization & Mobility Prediction Using spatial-temporal I/Q data, ML models can localize users and anticipate mobility even under NLOS conditions — critical for applications like autonomous transport and emergency services. 7️⃣ Intelligent Resource Allocation Predictive scheduling based on real-time I/Q data allows the RAN to allocate spectrum and processing resources where they’re needed most — maximizing throughput and minimizing latency. 📡 Bottom line: AI-RAN isn’t just about orchestration at the top. It’s about intelligence at the wavefront. By combining real-time I/Q analytics with AI-native RAN principles, we unlock a new layer of observability and control — paving the way for self-optimizing, resilient, and intent-aware 5G and 6G networks. 🧠 It’s time to bring machine learning closer to the signal. Let the RAN see and learn from what it transmits and receives. AI-RAN Alliance https://ai-ran.org #AI #RAN #AIRAN #5G #AIforRAN #IQData #RANIntelligence #5GOptimization #Beamforming #InterferenceMitigation #AIinTelecom #MLforRAN #TelecomAI #EdgeAI #SignalProcessing #AIEngineering #6GReady

Near-field communication with large antenna arrays offers significant beamforming and multiplexing gains but it is highly-sensitive to user movements. In this new work, my current and former PhD students Hao Luo and Yu Zhang propose Sphere Precoding —propose 𝐒𝐩𝐡𝐞𝐫𝐞 𝐏𝐫𝐞𝐜𝐨𝐝𝐢𝐧𝐠 — a robust and low-complexity precoding approach for near-field communications. They introduce the “one-sphere channel model” that extends the one-ring model to better capture spatial correlation in near-field and use it to develop the low-complexity precoding technique. Sphere precoding maintains the signal power and mitigates interference within protected spheres around the users that adapt to their mobility, achieving an efficient balance between high data rates and robustness to mobility in near-field communication systems. Paper: https://lnkd.in/gnG6BypE #MIMO #NearFieldCommunication #Beamforming #6G 

Happy to share our recent article published in Nature Communications! 🚀 In this work, we propose a fundamentally new approach for mitigating one of the long-standing challenges in mmWave and THz wireless networks: blockage. Rather than relying on reflections or alternative transmitters, we can form a beam that curves around the obstruction!! While infinitely many curved beams can be generated, not all improve signal quality. In this paper, we develop the first framework to identify the optimal curved-beam trajectory that maximizes power delivery under blockage. This work is led by my fantastic PhD students: Haoze Chen and Atsutse Kludze! Grateful for the support from the National Science Foundation (NSF), Air Force Office of Scientific Research (AFOSR), and the Qualcomm Innovation Fellowship. https://lnkd.in/dYcHtmTS

Our joint work carried out with my colleagues Yasemin Karacora, Aydin Sezgin, and Walid Saad on event-based beam tracking for (sub)-THz is now available on Early Access and has been published in IEEE Transactions in Communications. In this study, we address the specific challenges associated with THz channels, including micro-mobility, blockages, and molecular absorption. Our approach offers a novel solution to these challenges by implementing an event-based beam tracking methodology that considers the tradeoff between received signal strength and coverage. This is particularly important in the #THz realm, where the use of pencil narrow beams is essential and the channel exhibits high variability. To learn more about this: https://lnkd.in/gdnivTgj IEEE Communications Society #THz #6G #Beamtracking #beamforming #subTHz