Beamforming Technology

Beam Management in 5G NR In 5G New Radio (NR), beamforming plays a key role in achieving high data rates and reliable communication, especially in higher frequency bands like mmWave. Unlike earlier technologies that used wide-area broadcast signals, 5G NR often uses narrow, directional beams between the base station (gNB) and user equipment (UE). To maintain a good connection, these beams must be continuously monitored and updated. This is where beam management comes in. It includes four basic operations: beam sweeping, measurement, determination, and reporting. Here, we focus on three critical aspects of beam management: tracking, failure, and recovery. Beam Tracking Beam tracking is the process of keeping the communication beam aligned as the user or environment changes. Both the gNB and UE must adjust their beams regularly. The UE uses reference signals such as CSI-RS (Channel State Information Reference Signals) or SSB (Synchronization Signal Blocks) to measure the quality of received beams. It evaluates metrics like RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), or SINR (Signal to Interference plus Noise Ratio). The UE can send beam quality reports back to the gNB either periodically or when certain conditions are met. Based on these reports, the gNB can update its transmit beam, and the UE can switch to a better receive beam. This process helps maintain strong signal quality during mobility or when channel conditions change. Beam Failure Beam failure occurs when the quality of the current beam falls below a configured threshold and remains low for a specified configured duration. This might happen due to obstacles like buildings, a user turning away from the gNB, or even hand blockage in the case of smartphones. Beam failure is usually detected by monitoring the quality of SSB or CSI-RS signals. Once a beam failure is detected, the UE needs to act quickly to prevent complete radio link failure (RLF). The UE checks whether any backup beam from a preconfigured set (configured through RRC signaling) is available. If a good candidate beam is already known, the UE can prepare to switch over. If no good beam is available, the connection is at risk, and beam recovery must be triggered. Beam Recovery Beam recovery is the process of re-establishing communication after a beam failure is detected. There are two main approaches to recovery: Beam Failure Recovery (BFR) and Random Access based recovery. In BFR, the UE identifies a new candidate beam and sends a Beam Failure Recovery Request message to the gNB. This message includes the identity of the failed beam and the index of the new candidate beam. The request can be sent using physical layer signaling (using dedicated preambles on the PRACH) or MAC Control Elements. If BFR does not succeed or is not configured, the UE may fall back to the Random Access Procedure. In this case, the UE starts a new random access attempt on a better beam it has found.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗯𝗲𝗮𝗺𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲 𝗼𝗯𝘀𝗲𝗿𝘃𝗲𝗱 𝗹𝗶𝘃𝗲 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗼𝗻 𝗮𝗻 𝗙𝗣𝗚𝗔? In RF systems, beamforming is often designed and validated in simulation. Array factors, steering angles, sidelobes… everything looks perfect on MATLAB or Python plots. But the real question is: 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘁𝗵𝗼𝘀𝗲 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗿𝘂𝗻 𝗼𝗻 𝗮𝗰𝘁𝘂𝗮𝗹 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲? Hardware-in-the-loop (HIL) provides a powerful bridge between theory and reality. By closing the loop between digital algorithms and physical hardware, it becomes possible to validate beamforming behavior under realistic constraints such as quantization, timing, update rates, and real-time control. In this setup, a digital beamforming algorithm runs on a Lattice Semiconductor 𝗖𝗲𝗿𝘁𝘂𝘀𝗣𝗿𝗼-𝗡𝗫 𝗙𝗣𝗚𝗔. Beamforming weights are updated dynamically via UART, and the resulting 𝗮𝗿𝗿𝗮𝘆 𝗳𝗮𝗰𝘁𝗼𝗿 𝗰𝗮𝗻 𝗯𝗲 𝗼𝗯𝘀𝗲𝗿𝘃𝗲𝗱 𝗹𝗶𝘃𝗲 using Digilent R-2R DACs and an oscilloscope, either in polar form (XY mode) or in Cartesian coordinates. This enables real-time visualization of beam steering and beam sweep effects, long before integrating an RF front-end or an antenna array. In this demo, the FPGA implements a 𝘄𝗮𝘃𝗲𝗳𝗿𝗼𝗻𝘁 𝗽𝗵𝗮𝘀𝗲 𝗲𝗺𝘂𝗹𝗮𝘁𝗼𝗿, a 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗯𝗲𝗮𝗺𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 (𝗗𝗕𝗙𝗡), and 𝗹𝗼𝗴𝗮𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗰𝗼𝗺𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 to visualize the array factor using low-resolution DACs (8-bit). A Chebyshev amplitude taper is applied, resulting in sidelobe levels of −20 dB. This kind of hardware-in-the-loop approach is already widely used in control, automotive, and radar systems, and it is becoming increasingly relevant for 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗙 𝗽𝗵𝗮𝘀𝗲𝗱 𝗮𝗿𝗿𝗮𝘆𝘀, 𝘄𝗶𝗿𝗲𝗹𝗲𝘀𝘀 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮���𝗶𝗼𝗻𝘀, 𝗮𝗻𝗱 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗽𝗮𝘆𝗹𝗼𝗮𝗱𝘀. For those exploring HIL, MathWorks provides a detailed introduction, Rohde & Schwarz explains how to generate realistic radar signals in an HIL environment, and the IEEE paper below presents a practical example of FPGA-based digital beamforming using HIL with MATLAB-driven weight updates. 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 (𝗛𝗜𝗟)? 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀, 𝘄𝗵𝘆 𝗶𝘁 𝗶𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁, 𝗮𝗻𝗱 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 https://lnkd.in/eeCxsbE8 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗥𝗮𝗱𝗮𝗿 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 𝗶𝗻 𝗮 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗟𝗼𝗼𝗽 (𝗛𝗜𝗟) 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 https://lnkd.in/eHKAdFFz 𝗥𝗙 𝗮𝗿𝗿𝗮𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝗾𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘁𝗿𝘂𝗲 𝘁𝗶𝗺𝗲 𝗱𝗲𝗹𝗮𝘆 𝘄𝗶𝘁𝗵 𝗙𝗣𝗚𝗔 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 https://lnkd.in/e9rpXNtJ #FPGA #DSP #RF #Wireless #Antenna

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.

Can simple switch-based analog beamforming architectures achieve antenna gains comparable to all-digital beamformers? Yes, but… …you must model them correctly! In our recent Asilomar paper, “Joint Beamforming and Matching for Ultra-Dense Massive Antenna Arrays,” we show that low-cost switch-based beamforming architectures can approach the antenna gains of all-digital solutions when evaluated with a physically consistent electromagnetic model that accounts for antenna coupling, matching losses, radiation patterns, etc. We build on our general model for reconfigurable electromagnetic structures (REMSs), which adheres to Maxwell’s equations and enables us to evaluate and optimize novel architectures and beamforming performance under realistic conditions. Our results demonstrate that simple switch-based beamforming architectures are sufficient to approach the antenna gains of all-digital solutions at significantly lower cost and complexity. This is joint work with Carolina Nolasco Ferencikova, Georg Schwan, Dr. Raphael Rolny, and Alexander Stutz-Tirri. The Asilomar paper preprint is available on arXiv: https://lnkd.in/endKrSjr And the general REMSs model used in our work can be found here: https://lnkd.in/eGbak46f

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

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.

📣📣📣 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

Beamforming: A Key Enabler of 5G Performance — 𝗕𝗶𝘁𝗲-𝘀𝗶𝘇𝗲𝗱 Beamforming is revolutionizing wireless communication by enabling base stations to direct their signals precisely toward individual users, rather than broadcasting energy in all directions. Why does it matter? In legacy LTE systems, limited antenna counts (e.g., 4 antennas) made it difficult to control the shape and direction of transmitted signals. This led to: 🔻Wasted energy in non-target directions. 🔻High interference between users. 🔻Limited SINR (Signal-to-Interference-plus-Noise Ratio) Enter Beamforming with 5G: With large antenna arrays, 5G gNodeBs can dynamically adjust the phase and amplitude of signals at each antenna element, allowing: ⬆️ Sharper beams directed at specific users. ⬆️ Reduced interference from neighboring cells. ⬆️ Improved SINR, boosting throughput and reliability. Multi-User MIMO (MU-MIMO) Beamforming also enables simultaneous communication with multiple users using the same time and frequency resources, as long as the beams don’t interfere with each other. This dramatically improves: ● Spectral efficiency. ● Cell capacity. ● User experience, especially in dense deployments. Beamforming it’s a foundational technology that makes high-capacity, low-latency 5G networks possible. 📎 Related content: Article: 5G Beamforming & Massive MIMO. https://lnkd.in/eGKMj9-4 Post: Beam management in 5G. https://lnkd.in/eynDcPMG #5G #MassiveMIMO #Beamforming

Team climbed a 5G cell tower last week. Counted 64 antenna elements on ONE panel. 4G tower? 8 antennas. Here's why 5G antennas are completely different: --- THE TRANSFORMATION: 4G antenna: → 2-8 antenna elements → Passive (fixed beam) → One beam covers entire sector → Simple, cheap 5G antenna: → 64-256 antenna elements → Active (electronically steered) → Multiple beams tracking individual users → Complex, revolutionary This is Massive MIMO. --- WHY SO MANY ANTENNAS? The 5G challenge: → Need 10x more capacity than 4G → Same or less spectrum → Without adding towers Solution: Massive MIMO → Multiple antennas transmit simultaneously → Each user gets dedicated beam → Same frequency, different spatial paths Result: 5-10x spectral efficiency --- THE 3 TYPES: Type 1: Sub-6 GHz Massive MIMO → 64 or 128 elements → 3.5 GHz most common → Panel: 1.2m × 0.5m, 40-50 kg → Coverage: 500m-2km radius → Cost: $8K-15K per panel Example - China Mobile: → 64 transmit/receive antennas → Result: 10x capacity vs 4G --- Type 2: mmWave Arrays → 256-1024 tiny elements → 26 GHz, 28 GHz frequencies → Pencil-thin beams → Coverage: 200-400m only → Blocked by everything → Cost: $15K-30K per site Example - Verizon: → 28 GHz mmWave → Peak speeds: 4 Gbps → But line-of-sight only --- Type 3: Dual-Band Active → 4G + 5G in one panel → Shares 64 elements → Dynamic spectrum sharing → Saves tower space This is becoming standard. HOW BEAMFORMING WORKS: Traditional 4G: → One beam covers 120° sector → All users share same beam 5G Massive MIMO: → 12-16 simultaneous user beams → Each beam follows its user → Updates every 5ms → Same frequency, different directions Example - Stadium: → 50,000 people → One panel serves 16 simultaneously → Dynamically switching between users → Each thinks they have dedicated tower This is why 5G capacity is 10x better. THE CHALLENGES: 1. Power Consumption → 4G: 100-200W → 5G: 600-1,200W → 5-6x more electricity → Solution: AI energy management, sleep modes 2. Weight & Tower Upgrades → 5G panel: 40-50 kg (vs 15-20 kg for 4G) → Tower reinforcement needed → Cost: $5K-10K per tower 3. Complexity → 64 antennas + 32 transceivers + 64 power amps → More components = higher failure rate → Solution: Predictive maintenance 4. Cost → 5G site: $25K vs $8K for 4G → 3x more expensive → But 10x more capacity → Cost per Mbps: 50-70% lower THE ECONOMICS: Per 5G site: → Massive MIMO panel: $12K → Installation: $3K → Tower mods: $7K → Power/commissioning: $3K Total: ~$25K For 50,000-site network: → Total investment: $1.25B But: → 10x capacity increase → Better long-term ROI → Enables new use cases Join my Free 5G/6G Learning Free whatsapp Channel : https://lnkd.in/gerTY-kr ♻️ Repost this to help your network get started ➕ Follow Nitin Gupta for more

📡 Beamforming vs Traditional Antennas — What’s the Difference? Many junior engineers hear the word beamforming and think: “Isn’t that just a stronger antenna?” Not exactly. The difference between traditional antennas and beamforming is not power — it’s how energy is used. ⸻ 1️⃣ Traditional antennas spread energy Traditional base-station antennas: • Transmit energy over a fixed wide area • Use static antenna patterns • Serve all users in the sector at the same time This works well for coverage, but: • Energy is wasted in directions with no users • Interference increases • Capacity is limited ⸻ 2️⃣ Beamforming focuses energy With beamforming, the base station: • Uses multiple antennas together • Shapes narrow beams dynamically • Directs energy toward active users Instead of broadcasting everywhere, the network aims where it matters. ⸻ 3️⃣ The key difference: spatial control Traditional antennas control: • Power • Tilt • Coverage area Beamforming adds spatial control: • Different beams for different users • Same time, same frequency • Less interference between users This is why beamforming improves SINR, not just signal strength. ⸻ 4️⃣ Why beamforming boosts capacity By separating users in space: • Multiple users can be served simultaneously • Spectrum is reused more efficiently • Cell capacity increases without new bandwidth This is a major reason why beamforming is essential for 5G. ⸻ 📌 In short • Traditional antennas broadcast • Beamforming targets Beamforming doesn’t just make signals stronger — it makes them smarter. #Beamforming #MassiveMIMO #5G #WirelessEngineering #TelecomBasics #JuniorEngineer