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

𝑯𝒐𝒘 𝑹𝑭 𝑴𝑬𝑴𝑺 𝑩𝒆𝒂𝒎𝒇𝒐𝒓𝒎𝒊𝒏𝒈 𝑰𝒔 𝑪𝒉𝒂𝒏𝒈𝒊𝒏𝒈 𝑬𝒍𝒆𝒄𝒕𝒓𝒐𝒏𝒊𝒄 𝑾𝒂𝒓𝒇𝒂𝒓𝒆? 1. What Makes RF MEMS Beamforming Different? Traditional beamforming relies on semiconductor phase shifters, which introduce loss, power consumption and limited linearity at high frequencies. RF MEMS beamforming replaces these with micro-electromechanical switches that physically reconfigure RF paths. These devices offer extremely low insertion loss, high isolation and near-ideal linearity. This allows precise phase control across antenna arrays with minimal signal degradation, making them highly suitable for wideband and high-frequency EW applications. 2. How System Behavior Improves with MEMS-Based Beamforming? In electronic warfare, speed and precision of beam control are critical. RF MEMS enables rapid beam steering with reduced distortion, allowing systems to track, jam or avoid signals more effectively. The low loss of MEMS phase shifters improves overall system efficiency, enabling higher effective radiated power without increasing transmitter output. Additionally, MEMS-based arrays can support reconfigurable beam patterns, adaptive nulling and multi-beam operation, making them highly flexible in dynamic RF environments. 3. Why This Matters for the Future of Electronic Warfare? RF MEMS beamforming shifts the advantage toward systems that can precisely control energy in space and frequency. As EW environments become more congested and adaptive, the ability to dynamically steer beams, suppress interference and maintain signal integrity becomes decisive. MEMS technology enables lighter, more power-efficient and highly reconfigurable antenna systems, which are essential for next-generation platforms including UAVs, satellites and mobile EW units. 4. Critical Formulas: a) Beamforming phase relation → φ = (2πd sinθ) / λ φ = phase shift || d = element spacing || θ = steering angle || λ = wavelength b) Array factor → AF = Σ e^{j(nφ)} AF = array factor || n = element index || φ = phase shift c) Effective radiated power → EIRP = Pₜ G Pₜ = transmitted power || G = antenna gain d) Wavelength relation → λ = c / f λ = wavelength || c = speed of light || f = frequency 5. Real World Examples: - MEMS-based phased arrays are being developed for compact EW systems with low power consumption and high efficiency. - UAV-mounted EW platforms benefit from lightweight MEMS beamforming for adaptive signal control. - Satellite communication and defense systems use MEMS arrays for precise beam steering and interference mitigation. - Modern radar systems integrate MEMS phase shifters to improve beam agility and reduce signal loss. The simulation below shows dynamic beam steering using RF MEMS switching where instead of broadcasting everywhere, the system focuses and shifts RF energy directionally enabling precise tracking or jamming with minimal power loss. #ElectronicWarfare #RFEngineering #Beamforming #AntennaDesign #DefenseTech

💡 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

What if your antenna could think, and decide exactly where to send its energy? Beamforming today is powerful but it’s fundamentally engineered. Phase shifters, time delays, calibration loops, and DSP pipelines do the heavy lifting to shape and steer radiation. But imagine an antenna that doesn’t rely on electronics to steer its beam… An antenna that adapts its radiation pattern the way a living organism adapts to its environment. If antennas could self-direct their energy, the entire architecture of beamforming would shift. Antenna-level shifts • Geometry becomes computation Instead of tuning delays or weights, the antenna’s structure itself would generate the right phase gradients automatically. • Arrays that self-organize No RF chain per element. No calibration drift. The array would reconfigure its internal field distribution in response to incoming signals, obstacles, or user movement. • Beams that follow you naturally Like a sunflower tracking the sun, the beam would pivot continuously, without switches or control loops. • Feeding becomes simpler and smarter A single excitation could cascade into a spatially distributed, adaptive field pattern that “decides” the optimal direction. Electromagnetics-level shifts 1. Beamforming becomes a material phenomenon Metasurfaces, gradient-index media, and nonlinear materials could embed intelligence into their response, shifting from circuit-driven to physics-driven steering. 2. RF and optics converge further Beams would steer the way light refracts smoothly, passively, and with minimal overhead. 3. New emergent modes Hybrid waves that reshape themselves based on boundary interactions or environmental cues. 4. Simulations evolve Solvers would need to couple EM fields with adaptive, state-dependent material behavior a new class of modeling. Mental model Today: Beamforming = control → phase → pattern Tomorrow (in this thought experiment): Beamforming = physics → adaptation → pattern Instead of commanding the beam, we’d design conditions under which the beam forms itself. More physics. Fewer components. New possibilities. If antennas could think tomorrow, what would you redesign first arrays, materials, waveguides, or the entire RF front end? #Antennas #Beamforming #Electromagnetics #RFEngineering #6G #Metamaterials #AntennaDesign #EngineeringThoughtExperiment

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

𝗖𝗼𝘂𝗹𝗱 𝗱𝗶𝗿𝗲𝗰𝘁 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗥𝗙 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗯𝗲 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝘀𝘁𝗲𝗽 𝗶𝗻 𝗰𝗼𝗺𝗽𝗮𝗰𝘁 𝗦𝗔𝗧𝗖𝗢𝗠 𝗽𝗮𝘆𝗹𝗼𝗮𝗱𝘀? If high-efficiency RF transmitters could be created directly from digital logic—without DACs or mixers—the way we design space communications hardware could significantly change. To demonstrate this concept in practice, I implemented a 𝗯𝗮𝗻𝗱-𝗽𝗮𝘀𝘀 𝘀𝗶𝗴𝗺𝗮-𝗱𝗲𝗹𝘁𝗮 (𝗕𝗣-ΣΔ) modulator on a Lattice Semiconductor CertusPro-NX FPGA to generate a 𝟭-𝗯𝗶𝘁 𝗥𝗙 𝘀𝗶𝗴𝗻𝗮𝗹 with amplitude modulation at 10.7 MHz. An internal FPGA PLL derives fs = 42.8 MHz from a 125 MHz reference, so the tone is placed at fs/4 = 10.7 MHz, perfectly aligned with an IF ceramic filter. The 𝗯𝗮𝗻𝗱-𝗽𝗮𝘀𝘀 ΣΔ 𝗺𝗼𝗱𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗽𝘂𝘀𝗵𝗲𝘀 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗻𝗼𝗶𝘀𝗲 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝘂𝘀𝗲𝗳𝘂𝗹 𝗯𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵, enabling a surprisingly clean AM signal even though the output is binary. The attached image shows the 1-bit signal before filtering, the resulting analog AM waveform after the ceramic filter, and the hardware setup used. Although this first implementation uses general-purpose I/O, the technique can be scaled to 𝗚𝗛𝘇-𝗿𝗮𝗻𝗴𝗲 𝗥𝗙 using the 𝗙𝗣𝗚𝗔’𝘀 𝗠𝘂𝗹𝘁𝗶-𝗚𝗶𝗴𝗮𝗯𝗶𝘁 𝗧𝗿𝗮𝗻𝘀𝗰𝗲𝗶𝘃𝗲𝗿𝘀 (𝗠𝗚𝗧). These use dedicated low-jitter clocking resources, unlike the internal clock tree, which is more susceptible to supply noise. This allows 1-bit BP-ΣΔ DACs to achieve compact solutions, fully digital RF transmitters suitable for beamforming or payload processing in SATCOM. This experiment shows how a 𝟭-𝗯𝗶𝘁 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗼𝘂𝘁𝗽𝘂𝘁 𝗰𝗮𝗻 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗥𝗙 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿, using only: – band-pass ΣΔ modulation – high-speed clocking – a narrowband analog filter The band-pass ΣΔ modulator can be efficiently implemented using MathWorks HDL Coder (see comments for more details on the internal architecture). For those exploring 𝗳𝘂𝗹𝗹𝘆 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗥𝗙 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘀𝗽𝗮𝗰𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, these works illustrate potential applications in digital beamforming with 256 1-bit BP-ΣΔ modulators in Q-V-band: 𝗤/𝗩-𝗕𝗮𝗻𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗕𝗙 𝗔𝗻𝘁𝗲𝗻𝗻𝗮 𝘄𝗶𝘁𝗵 𝗗𝗶𝗿𝗲𝗰𝘁 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗰𝗲𝗶𝘃𝗲𝗿 𝗳𝗼𝗿 𝗟𝗘𝗢 𝗖𝗼𝗻𝘀𝘁𝗲𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲𝘀 https://lnkd.in/dEvbD7ye 𝗗𝗲𝘀𝗶𝗴𝗻 𝗮𝗻𝗱 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮 𝗤-𝗕𝗮𝗻𝗱 𝟰-𝗘𝗹𝗲𝗺𝗲𝗻𝘁 𝗗𝗕𝗙 𝗔𝗻𝘁𝗲𝗻𝗻𝗮 𝗮𝘀 𝗣𝗮𝗿𝘁 𝗼𝗳 𝗗𝗶𝗿𝗲𝗰𝘁 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗥𝗙 𝗧𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿 𝗔𝗻𝘁𝗲𝗻𝗻𝗮 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝗳𝗼𝗿 𝗢𝗻-𝗕𝗼𝗮𝗿𝗱 𝗦𝗔𝗧𝗖𝗢𝗠 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 https://lnkd.in/dUwq8XqA 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗺𝗺𝗪𝗮𝘃𝗲 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗕𝗲𝗮𝗺𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗧𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿 𝘂𝘀𝗶𝗻𝗴 𝗦𝗶𝗴𝗺𝗮-𝗗𝗲𝗹𝘁𝗮 𝗥𝗮𝗱𝗶𝗼-𝗢𝘃𝗲𝗿-𝗙𝗶𝗯𝗲𝗿 𝗟𝗶𝗻𝗸 https://lnkd.in/dPv97B7y #FPGA #DSP #RF #Wireless #SATCOM

Is it time for Electromagnetic Waves to enter the AI field? Our latest paper explores this question looking at the intersection of Stacked Intelligent Metasurfaces (SIM) and Neural Networks. We explore how SIMs enable wave-domain beamforming, channel modeling, and the development of a hybrid optical-electronic neural network (HOENN) for applications like disaster monitoring and direction-of-arrival estimation. We also discuss key challenges and opportunities for future research in this dynamic field.

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 

Cohere Technologies is a pioneer in spectrum management but most folks are unaware of its use of prediction and #ai in its software. As the telco industry vets out solid use cases for applying AI, Cohere is implementing it today. The integration of AI with USM marks a major leap forward in wireless channel modeling. By harnessing the vast data generated by USM—including uplink and downlink channel measurements, multipath components, delay spreads, and interference patterns—AI-powered models can more accurately capture the complexities of real-world wireless environments. Unlike traditional statistical methods, these models dynamically incorporate temporal and spatial dependencies, environmental factors, and real-time network conditions. Cohere is redefining channel estimation by shifting from traditional statistical methods to an innovative approach that models channels rather than frequencies. This approach integrates temporal and spatial dependencies, environmental factors, and real-time network conditions, enabling more precise tuning of RAN parameters such as modulation schemes, coding rates, and power allocation. Cohere’s method calculates radio channel requirements based on user device range and velocity, as well as signal propagation from the cell site to the device. Instead of relying on time and frequency, it leverages distance (measured in signal delay) and speed (measured in Doppler shift) to generate a channel map that remains valid for up to 50 milliseconds. This significantly reduces processing loads on base stations, which would otherwise need to frequently re-estimate channel conditions. As a result, channels remain usable for longer, effectively mitigating the effects of channel aging. By employing the delay-Doppler model, Cohere maintains a real-time, comprehensive view of the wireless channel, optimizing network performance and enhancing user experience. This approach maps all energy, interference, and reflectors, creating a detailed representation of both the physical and wireless environments. With a more precise understanding of signal propagation in a given setting, beamforming can be optimized for individual user equipment (UEs), and spectrum utilization can be maximized. Unlike conventional methods that require separate time or frequency slots for each user, Cohere’s approach enables multiple users to share the same time and frequency slots, improving spectral efficiency and overall network capacity. Check out Appledore Research report on Cohere and Robert Curran analysis of the benefits of the technology. https://lnkd.in/esAiHn8C #5G #spectrum #network optimization #telco Ronny Haraldsvik Raymond Dolan Art King