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

𝗠𝗮𝘀𝘀𝗶𝘃𝗲 𝗠𝗜𝗠𝗢: 𝗛𝗼𝘄 𝗕𝗲𝗮𝗺𝘀 𝗖𝗵𝗮𝗻𝗴𝗲 𝘁𝗵𝗲 𝗥𝘂𝗹𝗲𝘀 𝗼𝗳 𝗥𝗙 𝗗𝗲𝘀𝗶𝗴𝗻 Massive MIMO is one of the defining innovations of 5G, yet it is also one of the most misunderstood. Many still think of it as “just more antennas” or “stronger coverage.” In reality, Massive MIMO fundamentally changes how RF behaves, how cells interact, and how optimization must be approached. Traditional RF design relied on static cell patterns, fixed antenna sectors, and predictable radiation footprints. With Massive MIMO, those assumptions no longer hold. 🔹 𝟏. 𝐁𝐞𝐚𝐦𝐬 𝐑𝐞𝐩𝐥𝐚𝐜𝐞 𝐭𝐡𝐞 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐞𝐥𝐥 𝐅𝐨𝐨𝐭𝐩𝐫𝐢𝐧𝐭 A Massive MIMO site doesn’t radiate a single wide coverage pattern. Instead, it forms multiple dynamic beams—each targeting specific users or directions. This means: • Coverage becomes user-specific, not sector-specific. • Beam performance depends on mobility, environment, and traffic load. • Small beam misalignments can cause large variations in SINR. 🔹 𝟐. 𝐈𝐧𝐭𝐞𝐫𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐁𝐞𝐜𝐨𝐦𝐞𝐬 𝐌𝐨𝐫𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱—𝐚𝐧𝐝 𝐌𝐨𝐫𝐞 𝐒𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧𝐚𝐥 Cells don’t interfere as static sectors anymore. Beams can cause interference only when pointed at certain angles or when multiple users align in similar directions across cells. This introduces interference scenarios that are: • dynamic, • user-dependent, • and harder to predict with static models. 🔹 𝟑. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐈𝐬 𝐍𝐨 𝐋𝐨𝐧𝐠𝐞𝐫 𝐀𝐛𝐨𝐮𝐭 𝐓𝐢𝐥𝐭 𝐚𝐧𝐝 𝐏𝐨𝐰𝐞𝐫 𝐀𝐥𝐨𝐧𝐞 Beamforming parameters—such as downtilt offsets, beam shapes, layer configurations, and codebook selection—play a bigger role than physical tilt ever did. Traditional RF tuning is still important, but insufficient. 🔹 𝟒. 𝐔𝐬𝐞𝐫 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐌𝐨𝐫𝐞 𝐓𝐡𝐚𝐧 𝐄𝐯𝐞𝐫 A simple shift in where users congregate (stadiums, events, traffic corridors) can reshape the effective coverage of a site. Massive MIMO cells “follow the user”—and the optimization must follow them too. 🔹 𝟓. 𝐁𝐞𝐚𝐦 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐃𝐫𝐢𝐯𝐞𝐬 𝐭𝐡𝐞 𝟓𝐆 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 Beam sweeping, beam measurement, beam reporting, and beam failure recovery are the heart of mobility in 5G NR. A solid design must ensure: • Stable SSB beam coverage • Clean neighbor relationships • Smooth beam transitions under mobility Massive MIMO is not just an upgrade—it’s a new RF paradigm. Once beams become the primary unit of coverage and interference, the rules of design and optimization must evolve accordingly. #5G #MassiveMIMO #Beamforming #RFOptimization #RANEngineering #TelecomInnovation #NetworkPerformance #5GNR #WirelessEngineering #ORAN #SMO #BeamManagement

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 

𝑯𝒚𝒃𝒓𝒊𝒅 𝑩𝒆𝒂𝒎𝒇𝒐𝒓𝒎𝒊𝒏𝒈 𝒗𝒔. 𝑭𝒖𝒍𝒍𝒚 𝑫𝒊𝒈𝒊𝒕𝒂𝒍 𝒊𝒏 𝒎𝒎𝑾𝒂𝒗𝒆 𝒂𝒏𝒅 𝑻𝑯𝒛: As wireless systems push into mmWave and THz frequencies, beamforming architectures face a critical trade-off between performance, power consumption, and hardware complexity. Two dominant strategies, Fully Digital Beamforming and Hybrid Beamforming define the landscape of advanced antenna array design for 5G, 6G, and beyond. 1. Fully Digital Beamforming: - Each antenna element is connected to its own RF chain, enabling per-element control. - Enables multiple beams and spatial multiplexing. - Digital signal processing (DSP) allows fine-grained control over amplitude and phase. - High power consumption and cost make it impractical for very large arrays. - Beamforming Vector: → w = [w₁, w₂, ..., w_N], where each wᵢ is optimized independently. 2. Analog Beamforming: - Uses a single RF chain with analog phase shifters to steer one beam. - Low complexity and power, but lacks flexibility. - Cannot support multiple beams or spatial multiplexing. - Beam pattern is frequency-dependent, leading to beam squint at mmWave. 3. Hybrid Beamforming: Best of Both Worlds: - Combines digital processing with analog phase shifters. - RF chains are reduced by grouping antenna elements into subarrays. - Supports multiple beams with fewer RF chains ideal for large mmWave MIMO systems. - Example Configuration: → For an N-element array with K RF chains: → Hybrid weights = W = F_RF × F_BB → F_RF = analog beamforming matrix (phase shifters) → F_BB = baseband digital precoding matrix - Hybrid systems reduce power and cost by ~60% compared to full-digital solutions. 4. Mathematical Modeling and Trade-Offs: - Spectral Efficiency comparison: → R_digital ≈ log₂|I + (SNR/N₀) · H · W · Wᴴ · Hᴴ| → R_hybrid ≈ log₂|I + (SNR/N₀) · H · F_RF · F_BB · (F_RF · F_BB)ᴴ · Hᴴ| - Energy Efficiency becomes critical at THz frequencies. - Hybrid systems balance spectral efficiency and energy efficiency for 6G deployment. 5. Industrial Use Cases and Deployment Examples: - 5G NR Base Stations: Hybrid architectures enable scalable beam management. - THz Backhaul Links: Enable long-range, high-capacity links with reduced circuit complexity. - LEO Satellites and HAPS: Reduce payload weight by minimizing high-power RF chains. - AR/VR Holographic Communications: Enable dynamic focus with power-aware beam switching. The image below shows the architectural comparison of digital, analog, and hybrid beamforming. Hybrid architectures achieve near-digital performance using fewer RF chains, offering a practical trade-off between efficiency and cost for real-world mmWave deployments. #Beamforming #mmWave #THz #HybridBeamforming #DigitalBeamforming #AntennaArrays #5G #6G #RFDesign #SignalProcessing #PhDResearch

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

New paper from RAND lab! We just published our latest work on multi-directional spectrum sensing using low-SWaP algorithms! This paper discusses the use of hybrid approximate-DFT with precision-FFT to measure the RF spectrum across 32 simultaneous beams, with spectral resolution of 100 kHz across a 100 MHz baseband centered around 5.7 GHz. We can compute the RF beams at O(N) complexity! Thats really good news for real-time multi-beam digital beamformers. This work is a culmination of multiple projects sponsored by ONR, DARPA, NSF, NTIA and NASA over the last several years. We are currently expanding the work to 360 Field of View using circular aperture multi-beam arrays. The co-authors include Renato Cintra, Thushara Gunaratne Chamira Edussooriya Keththura Lawrence Sivakumar Sivasankar Umesha Kumarasiri Read all about it here: https://lnkd.in/gHz7T3_A

I am pleased to see that the tri-hybrid MIMO architecture is being further developed by the research community. This architecture extends hybrid MIMO by adding reconfigurable antennas as an electromagnetic precoding layer. A tri-hybrid MIMO system includes three layers of precoding: digital, analog, and electromagnetic, at the transmitter and the receiver. I want to highlight some recent work that I have seen posted on arXiv in the past few months. Mengzhen Liu, Ming Li, Rang Liu, and Qian Liu, in “Tri-Timescale Beamforming Design for Tri-Hybrid Architectures with Reconfigurable Antennas,” propose to optimize the three layers of beamforming on different time scales, with the antenna layer operating on the slowest scale. Together with Lee Swindlehurst, they have also written “Reconfigurable Antenna Arrays: Bridging Electromagnetics and Signal Processing.” In that paper, the authors describe a dynamic connected tri-hybrid architecture, which was not included in my magazine paper, and summarize several open challenges including cross-domain design. Pinjun Zheng, Yuchen Zhang, Tareq Al-Naffouri, Md. Jahangir Hossain, and Anas Chaaban, in “Tri-Hybrid Multi-User Precoding Using Pattern-Reconfigurable Antennas: Fundamental Models and Practical Algorithms,” study the effect of discrete and continuous pattern reconfigurability on multiuser MIMO communication. They also examine how reconfigurable antennas can reduce the number of RF chains. Yinchen Li, Chenhao Qi, Shiwen Mao, and Octavia A. Dobre, in “Tri-Hybrid Beamforming for Radiation-Center Reconfigurable Antenna Array: Spectral Efficiency and Energy Efficiency,” analyze precoding with a radiation-center reconfigurable array implemented using reconfigurable pixel antennas. They compare selection among fixed-position antennas and fully digital systems and show improvements in energy efficiency. Jiangong Chen, Xia Lei, Yuchen Zhang, Kaitao Meng, and Christos Masouros, in “Integrated Sensing and Communication with Tri-Hybrid Beamforming Across Electromagnetically Reconfigurable Antennas,” explore the benefits of the tri-hybrid architecture for integrated sensing and communication. They formulate and solve optimization problems that configure the three layers of precoding to balance communication and sensing objectives, showing clear benefits from antenna reconfiguration. Zhenqiao Cheng, Chongjun Ouyang, and Nicola Marchetti, in “On the Performance of Tri-Hybrid Beamforming Using Pinching Antennas,” connect the tri-hybrid architecture to the emerging area of pinching antennas, which are a form of reconfigurable antenna. They formulate an optimization that configures the pinching mechanism to serve multiple users over a large area and demonstrate improvements compared with hybrid-only systems. Much more work can be viewed through the framework of the tri-hybrid MIMO architecture. These papers are only a few that mention it explicitly. Links to the papers are in the comments.