5G Network Implementation

I love Markus J. Buehler's work, and his latest paper "Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks" does not disappoint, revealing powerful structures to accelerate scientific discovery. Central insights (link to paper in comments): 🤖 Self-organizing knowledge networks help open-ended discovery. Unlike conventional knowledge graph models, the proposed framework iteratively expands and refines its knowledge representation with autonomous reasoning. By integrating a reasoning language model with a dynamically updated graph, the system develops a scale-free network where hubs and bridges naturally emerge, leading to continuous knowledge expansion. 🌍 Bridging nodes drives interdisciplinary knowledge integration. Over hundreds of iterations, the model reveals an increasing number of bridge nodes—concepts that connect distinct domains—mirroring the way human scientific discovery links disparate ideas. These nodes enable cross-disciplinary insights, showing that autonomous reasoning systems can generate novel, high-impact connections. 🔁 Recursive graph expansion mirrors scientific breakthroughs. The study reveals alternating phases of stability and conceptual restructuring, similarly to the advance of human knowledge. Some concepts gradually accumulate influence, while others experience sudden bursts of connectivity, showing breakthrough moments in knowledge formation. This suggests that AI-driven knowledge synthesis can replicate real-world scientific discovery dynamics. 📈 Scale-free and small-world properties enhance knowledge navigation. The resulting knowledge graphs exhibit hallmark properties of efficient information networks: they are scale-free (few highly connected hubs, many weakly connected nodes) and small-world (short paths between most nodes). These make the system navigable, coherent, and easily searchable. 🔄 Agentic graph-based reasoning strengthens scientific hypothesis generation. By using an iterative reasoning process, the system autonomously identifies and refines scientific hypotheses. The study demonstrates how AI can assist in knowledge synthesis for fields like materials science and sustainability, accelerating discovery processes by revealing hidden relationships between research areas. 🛠 Future AI systems could simulate scientific thought processes. The findings suggest that AI models capable of recursively structuring knowledge—rather than merely extracting or predicting information—could revolutionize scientific research. By allowing concepts to evolve over time, these systems may eventually approach human-like intelligence in scientific reasoning, with applications spanning biomedicine, engineering, and more. These are the kind of structures that will help accelerate scientific progress.

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

On this #EarthDay let’s not forget that as we build faster networks with more features and capabilities to meet the demands of today’s (and tomorrow’s) ever-connected world, we must also keep sustainability front and center in everything we do. After all, more than three-quarters of customers we recently surveyed said energy efficiency is key for selecting IT vendors, and nearly every single RFP we receive includes environmental questions. So as genAI use cases consume unprecedented energy and stretch network infrastructure to new power-hungry heights, we as an industry need to innovate with #sustainability at the heart.   It’s why we’ve engineered silicon to be up to 73% more power efficient than its previous generation. Why we’re designing network equipment that can automatically shut down during off-peak hours to conserve energy. Why we’re exploring liquid cooling technology for our data center equipment that’s more efficient than traditional air cooling. Why we’ve eliminated plastic packaging for new products under 70 pounds. And why we keep developing #AI technology that can automatically fix network issues to save truck rolls.   Those are just a few examples of the work we’re doing, and we know we have more to do. But Juniper Networks plays a huge part in keeping the world connected, and I’m proud of the intentional progress we’ve made toward hitting net-zero emissions by 2040 and minimizing our footprint on the planet.   It’s not just good for business. It’s the right thing to do.

Why 5G Demands a New Transport Network 5G isn’t just faster - it’s smarter, more diverse, and ultra-responsive. To meet these demands: •Peak data rates: up to 20× higher than 4G •Latency: 10× lower for ultra-reliable low-latency communication (URLLC) To deliver this, the transport network has evolved into three key segments, collectively called xHaul: Fronthaul, Midhaul, and Backhaul. 🔹 Fronthaul: From CPRI to eCPRI •Throughput: up to 50 Gbps •Latency: <0.1 ms •Packet-based, more efficient, carrying only selected functions thanks to DU/RU splitting •Enables flexible deployment of Distributed Units at cell sites or edge nodes 🔹 Midhaul: Connecting DU to CU •Data rates: up to 10 Gbps •Latency: <2 ms •Distance: up to 200 km depending on deployment •Provides flexibility in placing DU/CU based on service or traffic needs 🔹 Backhaul: Linking CU/Edge to Core •Data rates: up to 100 Gbps •Latency: <10 ms •Aggregates traffic from multiple sites •Ensures service continuity, handovers, and core-level policy enforcement 💡 Bottom line: 5G’s xHaul architecture transforms the transport network from a single path in 4G into a segmented, performance-tiered network. eCPRI powers real-time fronthaul Midhaul enables flexible DU-CU placement Robust backhaul ensures the core keeps pace with distributed intelligence at the edge The result? A transport network ready to handle 5G’s speed, scale, and diversity. To learn about 5G evolution, refer to the course - https://lnkd.in/eKKRjTUY #5G #Telecom #xHaul #TransportNetwork #eCPRI #DU #CU #EdgeComputing #NetworkInnovation

💸 Tokenisation for cross-border payments – new update 💸 According to today’s update from the Bank for International Settlements (BIS): ✅ 40+ private sector financial firms ✅ + the Bank for International Settlements  ✅ + leading central banks will join Project Agorá to explore how tokenisation can enhance wholesale cross-border payments. ❓ What is Project Agorá? Project Agorá (Greek for "marketplace") is a public-private collaboration. 7️⃣ central banks:  ✔ Bank of France (representing the Eurosystem) ✔ Bank of Japan ✔ Bank of Korea ✔ Bank of Mexico ✔ Swiss National Bank ✔ Bank of England  ✔ Federal Reserve Bank of New York will work in partnership with the selected financial firms, and the Institute of International Finance (IIF) will act as the private sector convener. ❓What challenges does it seek to solve? Challenges for cross-border payments include different legal, regulatory and technical requirements as well as various operating hours and time zones. There is also increased complexity of carrying out financial integrity controls (eg customer verification and anti-money laundering), often repeated several times for the same transaction. ❓And how? The project builds on the unified ledger concept proposed by the BIS. ✳ Will investigate how tokenised commercial bank deposits can be integrated with tokenised wholesale central bank money in a public-private programmable core financial platform. ✳ This could enhance the functioning of the monetary system and provide new solutions using smart contracts and programmability, while maintaining its two-tier structure. ❓Who can participate? Participating firms must:  ✅ be regulated in a participating jurisdiction as a commercial bank, payment services provider, or financial market infrastructure company  ✅ be significantly involved in cross-border payments  ✅ have innovation expertise Firms represent a diversity of private sector partners in terms of business models, institution size, expertise and geography. The BIS and the IIF selected a diverse set of firms from applicants that met the eligibility requirements and other criteria. Project Agorá will now begin the design phase of the project. ......... 🤔 What are your thoughts? #tokenisation #tokenization #BIS #crossborderpayments #payments #BoE

Building #CrossBorder Alliances for driving and accelerating innovation in Cross Border Payments (Part 2 of 2) The solution lies in fostering collaborative #ecosystems that bring together fintechs, traditional financial institutions, regulators, and governments. Here's how we can build these alliances and drive innovation (its a long list and its not exhaustive): Embracing Cutting-Edge Technologies ✔ Using #Blockchain and DLT can provide a shared, immutable ledger for recording transactions, reducing intermediaries and increasing transparency ✔ Use of #AI can enhance fraud detection, automate compliance processes, and optimize currency exchange rates Forging Partnerships between Fintechs and Traditional Players ✔ Traditional banks & remittance co. can partner with fintechs to modernize their offerings ✔ Collaboration between Intl #Innovation Hubs where fintechs and traditional institutions can collaborate, share ideas, and test new solutions in a controlled environment. Pursuing Regulatory Harmonization ✔ Implement cross-border regulatory sandboxes to allow fintechs to test innovative solutions in multiple jurisdictions simultaneously. ✔ Work towards common regulatory standards for KYC, AML, and data protection across regions to reduce compliance complexity. ✔ Invest in RegTech solutions to automate and streamline compliance processes across borders Fostering Intergovernmental Cooperation ✔ Collaborate on the development of interoperable CBDCs to facilitate seamless cross-border transactions (Project mBridge, Agora to name two such initiatives) ✔ Support initiatives such as Project Nexus. ✔ Establish frameworks for secure, privacy-compliant data sharing across borders to enhance the efficiency of cross-border payments. Standardization and Interoperability ✔ Accelerate the adoption of ISO 20022 as a global standard for payment messaging to enhance data richness and interoperability. ✔ Develop and adopt common API standards for payment initiation, account information, and transaction status across different systems and countries. Focus on Financial Inclusion ✔ Develop cross-border payment solutions that are accessible via mobile devices to reach underbanked populations. ✔ Utilize alternative data sources and AI to assess creditworthiness, enabling cross-border microlending and remittances for underserved communities. Enhancing User Experience ✔ Implement end-to-end tracking of cross-border payments, providing transparency and certainty to users. ✔ Create unified digital identity solutions that streamline customer onboarding across multiple jurisdictions. The key to success is a multi-faceted approach as we build these global and domestic fintech alliances, we're not just improving a payment system – we're creating a more interconnected, inclusive, and efficient global economy. The journey has begun, but there's still much work to be done. #Fintech #CrossBorderPayments #FinancialInnovation #GlobalAlliances

🚀 The Evolution of Carrier Aggregation Across 3GPP Releases 🚀 📡 Carrier Aggregation (CA) has been a game-changer in mobile networks, enabling higher throughput, better spectral efficiency, and improved user experience. 📅 3GPP Release-Wise Evolution of Carrier Aggregation 📍 Release 10 (2011) – LTE-Advanced Debut ✅ Carrier Aggregation (CA) introduced with support for up to 5 component carriers (CCs). ✅ Maximum aggregated bandwidth: 100 MHz (5 × 20 MHz LTE carriers). ✅ Intra-band and inter-band CA for FDD DL; initial support for UL CA. ✅ Backward compatibility: Rel-8/9 UEs see a single carrier, while CA-capable UEs can aggregate multiple carriers. 📍 Release 11 (2013) – Enhanced CA Flexibility ✅ Multiple Timing Advance (TA) Groups introduced for uplink CA across different bands. ✅ Inter-band CA for TDD enabled, allowing better use of TDD spectrum. ✅ Signaling improvements for better CA scheduling across CCs. 📍 Release 12 (2014) – Dual Connectivity & Inter-RAT CA ✅ FDD-TDD Carrier Aggregation introduced, allowing cross-duplex aggregation. ✅ LTE-LTE Dual Connectivity (DC) laid the foundation for multi-node aggregation. ✅ Expanded inter-band CA combinations, improving LTE throughput. 📍 Release 13 (2015) – LTE-Advanced Pro & LAA ✅ Support for up to 32 CCs (theoretical aggregated bandwidth 640 MHz). ✅ Licensed-Assisted Access (LAA) introduced to aggregate LTE with 5 GHz unlicensed spectrum. ✅ Cross-carrier scheduling enhancements to handle high-CC aggregation. 📍 Release 14 (2017) – eLAA & Gigabit LTE ✅ Enhanced LAA (eLAA): Uplink CA now supported on unlicensed spectrum. ✅ Introduction of 256-QAM for UL, boosting LTE capacity. ✅ 4x4 MIMO + CA combinations brought Gigabit LTE. 📍 Release 15 (2018) – 5G NR & Multi-RAT Aggregation ✅ 5G NR introduces Carrier Aggregation, supporting up to 16 CCs. ✅ Wider channels: 100 MHz per CC in FR1, 400 MHz per CC in FR2 (mmWave). ✅ Inter-band CA between sub-6 GHz and mmWave, leveraging both coverage and capacity. ✅ LTE-NR Dual Connectivity (EN-DC): LTE and 5G NR aggregated to boost early 5G speeds. 📍 Release 16 (2020) – 5G CA Enhancements ✅ FR2 (mmWave) CA expanded: up to 6 CCs in one mmWave band. ✅ Higher-order modulation (256-QAM in mmWave) boosts peak data rates. ✅ Supplementary Uplink (SUL): Improves UL coverage by adding lower-band uplink. ✅ Lower latency CA setup, reducing activation delays. ✅ 5G in unlicensed spectrum (NR-U): 5G NR CA now includes 5 GHz & 6 GHz unlicensed spectrum. 📍 Release 17 (2022) – 5G-Advanced Foundations ✅ New Frequency Range (FR2-2): Expanding 52.6–71 GHz mmWave bands. ✅ More NR-CA combinations including multi-band NR-DC & CA for FR2-2 bands. ✅ Uplink 256-QAM introduced, improving UL CA capacity. ✅ Energy efficiency improvements: Smarter bandwidth part switching, reducing battery drain

Addressing the latency bottleneck in long-context LLMs has been a critical challenge. A new paper (and code) from Microsoft called MInference slashes inference latency by up to 10× for 1M-token prompts. This novel technique tackles one of the biggest bottlenecks in long-context LLMs: the pre-filling stage—the phase where the model processes an input before generating its first token, often resulting in long delays for large prompts. Unlike older methods that slow down with complex calculations, MInference speeds things up by using a clever approach called dynamic sparse attention—a way to focus only on the most important parts of the input. How it works: (1) Pattern identification – Breaks down attention into three efficient patterns: A-shape, Vertical-Slash, and Block-Sparse. (2) Dynamic optimization – Builds sparse indices on the fly to process only the relevant data. (3) Optimized GPU kernels – Ensures faster, smoother calculations. These steps result in a 10x speedup on a single A100 GPU while keeping (or even improving) accuracy on tasks like QA, retrieval, and summarization. This could accelerate adoption of LLM for real-world applications with long-context dependencies—think legal document analysis, repository-level code understanding, and more. MInference already supports Llama 3.1, Phi-3, and Qwen2, with additional model support currently in development. Paper https://lnkd.in/gwfxPHJz Code https://lnkd.in/gZs7-D7v Note: the TTFT initials in the attached video stand for Time To First Token — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai

Mile High Video Spotlight: Adeia’s Low-Latency Streaming Innovations At Mile High Video 2025, VP of Advanced R&D Chris Phillips detailed Adeia's approach to low-latency streaming, showcasing three key technologies: • Low Latency Streaming: Adeia minimizes delay by optimizing video segment prediction and buffering. This ensures consistent playback quality even under fluctuating network conditions, delivering a seamless viewing experience. • Encoding Optimization: Adeia uses machine learning to dynamically adjust encoding parameters based on real-time network feedback. This balances video quality and bandwidth efficiency, reducing buffering without compromising visual fidelity. • Selective L4S Markings: Adeia leverages Low Latency, Low Loss, Scalable Throughput (L4S) technology by selectively marking packets to prioritize latency-sensitive video data. This reduces delay and packet loss, enhancing reliability over congested networks. Adeia also presented a paper, “On Ultra-Low Latency Multimedia Delivery: An Approach for Selective L4S Enablement,” exploring how selective L4S marking can enhance low-latency streaming, paving the way for next-generation video delivery solutions. Chris shared his bullish outlook on VVC (Versatile Video Coding), emphasizing its potential for improved compression efficiency and enhanced video quality. For a deeper dive into Adeia’s low-latency streaming technologies, read the full interview or watch the video, both at the link below.