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The combined company will bring together two cybersecurity SIEM and UEBA innovation leaders with renowned and demonstrated track records in serving customers with effective threat detection, investigation, and response (TDIR)

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The MXO 5C series of low-profile oscilloscopes from R&S provides a bandwidth of up to 2 GHz and either four or eight channels. Although they lack displays, the rack-mount scopes deliver the same performance as the MXO 5 series, while occupying only a quarter of the vertical height (3.5 inches or 8.9 cm).

Built with two in-house ASICs for fast response, the MXO 5C delivers an acquisition capture rate of up to 4.5 million waveforms per second. It also features a 12-bit ADC with a high-definition mode that increases vertical resolution to 18 bits. A small front-panel E-ink display shows key information, such as IP address, firmware version, and connectivity status.

Four-channel models offer bandwidths of 350 MHz, 500 MHz, 1 GHz, and 2 GHz. Eight-channel models provide the same bandwidths, with the addition of 100 MHz and 200 MHz options. Standard acquisition memory of 500 Mpoints per channel can be optionally upgraded to 1 Gpoint per channel.

Although tailored for rack-mount applications, the MXO 5C oscilloscopes can also be used on a bench by connecting an external display via their HDMI or DisplayPort interfaces. Other connectivity interfaces include two USB 3.0 and one 1-Gbit LAN.

The MXO 5C series oscilloscopes are now available from R&S and select distribution channel partners.

MXO 5C series product page

Rohde & Schwarz 

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A single-chip inverse cable equalizer, the QPC7330 from Qorvo allows CATV operators to upgrade their hybrid fiber coax (HFC) networks to DOCSIS 4.0. The QPC7330 streamlines field installation by eliminating the need for plug-ins or complicated circuitry to implement the input cable simulation function. Programmed through an I2C interface, the device seamlessly integrates into the automated setup routine.

The function of the QPC7330 75-Ω inverse cable equalizer is to flatten out an input signal with too much uptilt in a line extender or system amplifier. It features 25 states to simulate the loss of different lengths of coaxial cable, offering tilt adjustments from 1 dB to 24 dB (measured from 108 MHz to 1794 MHz). The device integrates all equalizer functions, including a low-loss bypass mode, into a 10×14-mm laminate over-mold module.

The QPC7330 inverse cable equalizer is sampling now, with production quantities available in August 2024.

QPC7330 product page

Qorvo

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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ST’s A6983 step-down synchronous DC/DC converters provide space savings in light-load, low-noise, and isolated automotive applications. The series offers flexible design choices, including six non-isolated step-down converters in low-power and low-noise configurations, plus one isolated buck converter. With compensation circuitry on-chip, these devices help minimize both size and design complexity.

Non-isolated A6983 converters supply a load current up to 3 A and achieve 88% typical efficiency at full load. Low-power variants minimize drain on the vehicle battery in applications that remain active when parked. Low-noise types operate with constant switching frequency and reduce output ripple across the load range. These devices offer a choice of 3.3-V, 5.0-V, and adjustable output voltage.

The A6983I is a 10-W isolated buck converter with primary-side regulation that eliminates the need for an optocoupler. It allows accurate adjustment of the primary output voltage, while the transformer turns ratio determines the secondary voltage.

All of the AEC-Q100 qualified converters have a quiescent operating current of 25 µA and a power-saving mode that draws less than 2 µA. Input voltage ranges from 3.5 V to 38 V, with load-dump tolerance up to 40 V.

The converters come in 3×3-mm QFN16 packages. Prices start at $1.75 and $1.81 for the A6983 and A6983I, respectively, in lots of 1000 units. Free samples are available from the ST eStore.

A6983 product page

A6983I product page

STMicroelectronics

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The Melexis MLX90427 magnetic position sensor is intended for applications requiring high automotive functional safety levels, such as steer-by-wire systems. It provides stray field immunity and EMC robustness, as well as SPI output. Additionally, the device transitions seamlessly between four operating modes, including rotary, joystick, rotary with stray field immunity, and raw data.

At the heart of the MLX90427 is a Triaxis Hall magnetic sensing element that is sensitive to three components of flux density (BX, BY, and BZ) applied to the IC. This allows the sensor to detect movement of any magnet in its vicinity. The part also integrates an ADC, DSP, and output stage driver for SPI signal output.

In addition to AEC-Q100 Grade 0 qualification, the MLX90427 is SEooC ASIL C ready in accordance with ISO 26262 and can be integrated into automotive safety-related systems up to ASIL D. To simplify system integration, the sensor is compatible with 3.3-V and 5-V designs and operates over a temperature range of -40°C to +160°C. Self-diagnostics are built in to ensure swift fault reporting.

The MLX90427 position sensor comes in an 8-pin SOIC package. A fully redundant dual-die variant in a 16-pin TSSOP is due to launch in Q4 2024.

MLX90427 product page

Melexis

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Murata’s L Cancel Transformer (LCT) neutralizes the equivalent series inductance (ESL) of a capacitor to optimize its noise-reducing capabilities. Leveraging nonmagnetic ceramic multilayer technology, the LCT improves power supply noise suppression, while cutting component count.

The LCT component suppresses harmonic noise in power lines within a frequency range of a few MHz to 1 GHz. It achieves this by using negative mutual inductance to lower a capacitor’s ESL, thereby increasing the capacitor’s noise-reduction effectiveness. Murata states that the LCT also significantly reduces the number capacitors required in a power supply noise-reduction circuit design.

Operating at temperatures up to 125°C, the LCT ensures stable negative inductance and low DC resistance of 55 mΩ maximum. Rated current is 3 A maximum. The part is suitable for a wide range of consumer, industrial, and healthcare products. Dimensions of the surface-mount device are just 2.0×1.25×0.95 mm.

The L Cancel Transformer, part number LXLC21HN0N9C0L, is entering production. Samples are available now.

LCT product page

Murata

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Open radio access network (O-RAN) technology is driving the mobile communication industry toward an open, virtualized, and disaggregated architecture. O-RAN breaks traditional hardware-centric RANs into building blocks—radios, hardware, and virtualized functions—enabling mobile network operators to create their RANs using a multivendor, interoperable, and autonomous supply ecosystem. Using open and standardized interfaces, O-RAN enables network vendors to focus on specific building blocks rather than creating an entire RAN. Similarly, operators can mix and match components from multiple vendors. While O-RAN delivers many improvements, energy efficiency is a top priority.

Global efforts toward a carbon-neutral future and consumer demand for greener products have increased the urgency to focus on energy efficiency. Sustainability is a critical priority for the information and communications technology (ICT) industry, which is committed to making 6G and 5G a green reality.

Three key performance indicators define objectives and characterize improvements for a RAN energy optimization effort:

• Energy consumption (EC) represents the energy used to power the infrastructure. The European Telecommunications Standards Institute (ETSI) ES 202 706-1 defines EC as the integral of power consumption.
• Energy savings (ES) represents the reduction of energy consumed with minimal impact on the quality of service (QoS).
• Energy efficiency (EE) refers, in a general sense, to a measure of how an appliance or system uses energy. EE is the ratio between useful output or service over the required energy input.

These three factors are essential to consider. EE improvement strategies aim to apply several mechanisms when there is no need for all the available performance, thereby minimizing the impact on QoS and the user experience. Engineers need a balanced approach depending on QoS goals. They must make insightful measurements to understand power consumption rates for different load conditions and metrics.

The ETSI ES 203 228 test specification considers the gNodeB as a whole. However, the O-RAN Alliance® recognizes the urgency of addressing EE and EC in a disaggregated RAN. For example, the initial version of the O-RAN fronthaul interface specification included signaling mechanisms to notify the radio unit about periods of non-usage of radio symbols. These signaling mechanisms enabled the radio to halt transmission and conserve power. The fronthaul interface now provides the capability to inform the network about energy-saving capabilities in each radio, such as carrier deactivation, enabling automated activation and deactivation of energy-saving mechanisms. The O-RAN Alliance is also developing energy-saving test cases to ensure conformance and enhance vendor interoperability.

As shown in Figure 1, energy consumption spans the entire network, including the grid, RAN, core, and transport, depending on many parameters: from RF channels to topology. Therefore, energy consumption requires a comprehensive approach involving multiple parts of an operator’s organization to capture all components. Test engineers must consider and tweak numerous parameters and variables to identify optimal configurations. The main question remains: How can test engineers reduce energy consumption and costs without impacting QoS?

Figure 1 O-RAN architecture with user equipment (UE) and core network. Source: Keysight

There are numerous techniques to reduce EC at a network level, each requiring varying levels of effort for implementation. Migrating technology from legacy platforms onto the most recent and energy-efficient platforms can immediately reduce network energy consumption. Such a migration requires an upfront investment in new equipment and resources to perform the upgrade, but it is a relatively low engineering effort. If investing in equipment upgrades is not possible, analyzing existing deployments, eliminating redundancies, and identifying overprovisioned devices helps improve energy consumption.

While it requires a mix of engineering and equipment investments, network topology optimization can also help reduce EC by determining the ideal minimum subset of equipment necessary to cover different topologies without sacrificing the QoS.

To maximize energy efficiency, engineers must continuously adapt user demand to the supply of network resources. They need to dynamically allocate the correct number of computing services and radio resources to match demand and aggregate user demand to ensure the entire use of each resource. For example, engineers can avoid using two servers at 50% load each. By applying the methodology at various levels, from the system to the device/chipset levels, engineers can optimize EE.

System-level intelligence is another way to maximize EE by performing dynamic resource allocation decisions at the system level. Engineers would activate or turn off nodes on wireless networks and perform load balancing to redirect users to active nodes. Similarly, they can allocate resources to the device hardware and chipset level.

Semiconductors and chipsets are the first elements of the energy chain. Hence, they are the main contributors to energy consumption and efficiency. New chipset generations provide advanced resource optimization capabilities, such as turning on or off discrete digital resources on the chip. Engineers can accomplish this mechanism by changing analog parameters (clock speed and bandwidth) to adjust the desired performance level and reduce power consumption since the energy is a function of electrical transitions in each gate.

At the radio level, engineers can perform additional optimization with innovative scheduling capabilities in the O-RAN distributed unit when traffic is low. They can regroup physical resource blocks (PRBs) from multiple symbols into a reduced number and augment the transmission blanking time.

Optimizing EE requires using chipset-level power-saving capabilities to the fullest extent possible. Only then can engineers determine the hosting of the power decision entity to prevent conflicts.

The emergence of standardized O-RAN drives the need to define standards that enable intelligent control and energy optimization of multivendor-based networks. Ultimately, the Alliance’s work will result in new O-RAN specifications and technical reports sections. The Alliance’s ongoing work includes defining procedures, methodology, use cases, and test case definitions for cell/carrier switch on/off, RF channel selection, advanced sleep modes, and cloud resource management.

Intelligent control loops significantly contribute to energy optimization at the system level. But these loops are also appropriate at the chipset level as they contribute to local power optimization within a device.

Chipset sleep mode mechanisms consist of deactivating or slowing down function within a specified period. Different sleep levels enable multiple levels of energy saving.

However, each sleep mode comes at a cost: the deeper the sleep and energy-saving, the more time the chipset remains in the sleep mode and the wake-up transitions. These transitions are not energy-efficient and may offset gains from the sleep phase. Therefore, defining sleep strategies optimizes the trade-off between transitions and sleep phases.

Figure 2 shows an O-RAN architecture which consists of the following components:

• O-RAN radio unit (O-RU) for processing the lower part of the physical layer
• O-RAN distributed unit (O-DU) for baseband processing, scheduling, radio link control, medium access control, and the upper part of the physical layer
• O-RAN central unit (O-CU) for the packet data convergence protocol layer
• O-RAN intelligent controller to gather information from the network and perform the necessary optimization tasks

Figure 2 An overview of O-RAN architecture with the O-RU for processing the lower part of the PHY layer, O-DU for processing the upper part of the PHY layer, O-CU for the packet data convergence protocol layer, and an O-RAN intelligent controller to perform optimization. Source: Keysight

Energy plane testing requires a cross-domain measurement system and cross-correlation of the data to gain meaningful insights into the energy performance of the RAN components. The testing combines power measurement with protocol and RF domains. As O-RAN and the 3rd Generation Partnership Project (3GPP) are fast-evolving standards, equipment manufacturers must ensure product compliance with the latest versions. Automation of the test cases and report generation are the keys to ensuring compatibility with the latest standards of regression testing.

RAN energy consumption and efficiency improvement requires minimizing power usage while maximizing performance. For RU testing, the ETSI ES 202 706 standard, which describes the test methodology to measure power consumption in a gNodeB, can be adapted to make similar measurements in an O-RU under different load conditions, representing a typical day in the life of a RU—the load changes during the test in low, medium, high, and complete steps (Figure 3). So, by measuring the O-RU at different loads, we can calculate the total energy consumed.

Figure 3 Decoded constellation, signal spectrum, allocated PRBs, EVM per modulation type and decoded bits. Source: Keysight

To measure the energy efficiency of an O-RU, test engineers need an O-DU emulator, a DC power supply, and an RF power sensor (Figure 4). The O-DU emulator generates different static traffic levels from low, medium, busy, to full load traffic as defined by the ETSI ES 202 706-1 standard. A DC power supply provides power to the O-RU and measures the accumulated power consumption over time. The RF power sensor measures the output power at the antenna connector port. The ratio of output RF power to input DC power represents the energy efficiency measurement.

Figure 4 O-RU test set-up with an O-DU emulator, power sensor, as well as a power supply and analyzer. Source: Keysight

Ensuring accurate and standardized EE of O-DU and O-CU in an O-RAN involves assessing various factors related to power consumption, resource utilization, and overall network performance. As EE is the ratio of delivered bits and consumed energy, test engineers need access to the user equipment (UE) throughput data to ensure that lower EC is not at the cost of lower quality of service. The fronthaul and backhaul interface require emulation to measure the EE of an O-DU and O-CU. In addition, test engineers must be able to simulate the traffic profiles of different pieces of UE.

The fronthaul requires an O-RU emulator to provide the interface to the O-DU. A UE emulator simulates the traffic flow to the O-RU emulator the UEs request. The backhaul requires a core emulator or a live core network. An AC or DC power supply capable of recording the output power measures the combined energy consumption of O-DU / O-CU. The ETSI specification does not refer to the disaggregated base station architecture, so the points of power measurement can vary depending on the implementation. The test software generates an energy efficiency report by simulating different UE traffic profiles with varying path loss, file size, and throughput.

To test gNodeB, test engineers can use a set of automated test cases and analytics tools based on ETSI standards. The test setup should include a UE emulator, a core network emulator, and a power analyzer. The UE emulator emulates stateful UE traffic and measurements, while the core network emulator terminates the calls from the UE emulator for stateful O-DU/O-CU testing. Both emulators require dimensioning to load testing scenarios, and a power analyzer measures the server’s power consumption.

While the wireless communication industry increasingly prioritizes sustainability and net zero strategies, achieving energy efficiency has become as important as performance, reliability, and security. As wireless networks evolve into multivendor disaggregated systems, collaboration among chipsets, equipment, and test vendors is necessary to optimize power consumption without compromising performance.

Moving forward, test and measurement companies should focus on delivering cutting-edge technology and tools that accelerate the transition to green and sustainable wireless communications, realizing the network performance and capital expenditure (CapEx) advantages of O-RAN. To achieve that, understanding the energy performance of RAN components is key. As highlighted in this article, there are methodologies providing standardized and accurate assessments of energy efficiency in RAN components, essential for optimizing network performance while minimizing energy consumption in the increasingly dynamic and complex telecommunications landscape.

Chaimaa Aarab is a use case marketer focused on the wireless industry (5G, 6G, Wi-Fi 7, O-RAN) at Keysight Technologies. Her background is in electronics engineering with previous experience as a technical support engineer and market industry manager. 

 Related Content

• Creating more energy-efficient mobile networks with O-RAN
• O-RAN is transforming 5G network design and component interoperability
• O-RAN challenges from the fronthaul
• Making waves: Engineering a spectrum revolution for 6G
• 5G NR base stations bring new conformance testing challenges

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Spectrum Instrumentation presents versatile Python programming for all its 200+ products

Bangalore, India. – 16. May 2024. Spectrum Instrumentation presents a new open-source Python package (“spcm”) that is now available for the current line of all Spectrum Instrumentation test and measurement products. The new package makes the programming of all 200+ instruments, offering sampling rates from 5 MS/s to 10 GS/s, faster and easier. Python, popular for its simplicity, versatility and flexibility, boasts an extensive collection of libraries and frameworks (such as NumPy) that significantly accelerates programming development cycles. The new spcm package allows users to take full advantage of the Python language by providing a high-level Object-Oriented Programming (OOP) interface that is specifically designed for the Spectrum Instrumentation Digitizer, AWG and Digital I/O products. It includes the full source code as well as a number of detailed examples. Available on GitHub, spcm is free of charge under the MIT license.

Spectrum’s Python package safely handles the automatic opening and closing of cards, groups of cards and Ethernet instruments, as well as the allocation of memory for transferring data to and from these devices. All the device specific functionality is capsulated in easy-to-use classes. This includes clock and trigger settings, hardware channel settings, card synchronization, direct memory access (DMA) and product features such as Block Averaging, DDS and Pulse Generator.

The package supports the use of real-world physical quantities and units (e.g. “10 MHz”) enabling the user to directly program driver settings in their preferred unit system. This removes the need for tedious manual conversions to cryptic API settings. Moreover, this package also includes support for calculations with NumPy and Matplotlib, allowing the user to handle data coming from, or going to, the products with the vast toolbox provided by those packages. Detailed examples can be found in the GitHub repository.

Installing the package is easy, thanks to its availability in the pip repository. Simply install Python and then the package with a single command: $ pip install spcm

Users can include the Spectrum Instrumentation Python package in their own programs, or fork to the repository to add more functionality. The package is directly maintained by Spectrum engineers and updates are released regularly offering bug-fixes and new features.

The example in the photo shows the opening of the first analog-output card (AWG) and programming of a simple 10 MHz sine-wave output using the DDS option.

The Spectrum Python repository is found under: https://github.com/SpectrumInstrumentation/spcm

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By 2026, 80% of Large Software Engineering Organizations Will Establish Platform Engineering Teams, up from 45% in 2022

Gartner, Inc. announced the top five strategic technology trends in software engineering for 2024 and beyond. Analysts presented these findings during the Gartner Application Innovation & Business Solutions Summit, which is taking place here through today.

Meeting business objectives is one of their top three performance objectives for 65% of software engineering leaders, according to a Gartner survey of 300 software engineering and application development team managers in the U.S. and UK in the fourth quarter of 2023. By investing in disruptive technologies, software engineering leaders can empower their teams to meet business objectives for productivity, sustainability and growth.

“The technology trends Gartner has identified are already helping early adopters to achieve business objectives, “said Joachim Herschmann, VP Analyst at Gartner. “These disruptive tools and practices enable software engineering teams to deliver high-quality, scalable AI-powered applications, while reducing toil and friction in the software development life cycle (SDLC), improving developer experience and productivity.”

The top five strategic technology trends for software engineering for 2024 are-

• Software Engineering Intelligence

Software engineering intelligence platforms provide a unified, transparent view of engineering processes that helps leaders to understand and measure not only velocity and flow but also quality, organizational effectiveness and business value.

Gartner predicts by 2027, 50% of software engineering organizations will use software engineering intelligence platforms to measure and increase developer productivity, compared to 5% in 2024.

• AI-Augmented Development

Software engineering leaders need a cost-effective way to help their teams build software faster. According to the Gartner survey, 58% of respondents said their organization is using or planning to use generative AI over the next 12 months to control or reduce costs.

AI-augmented development is the use of AI technologies, such as generative AI and machine learning, to aid software engineers in designing, coding and testing applications. AI-augmented development tools integrate with a software engineer’s development environment to produce application code, enable design-to-code transformation and enhance application testing capabilities.

“Investing in AI-augmented development will support software engineering leaders in boosting developer productivity and controlling costs and can also improve their teams’ ability to deliver more value,” said Herschmann.

• Green Software Engineering

Green software engineering is the discipline of building software that is carbon-efficient and carbon-aware. Building green software involves making energy-efficient choices for architecture and design patterns, algorithms, data structures, programming languages, language runtimes and infrastructure.

Gartner predicts by 2027, 30% of large global enterprises will include software sustainability in their non-functional requirements, up from less than 10% in 2024.

The use of compute-heavy workloads increases an organization’s carbon footprint, and generative AI-enabled applications are especially energy-intensive, so implementing green software engineering will help organizations prioritize their sustainability objectives.

• Platform Engineering

Platform engineering reduces cognitive load for developers by offering underlying capabilities via internal developer portals and platforms that multiple product teams can use. These platforms provide a compelling “paved road” to software development, which saves time for developers and improves their job satisfaction.

Gartner predicts that by 2026, 80% of large software engineering organizations will establish platform engineering teams, up from 45% in 2022.

• Cloud Development Environments
  Cloud development environments provide remote, ready-to-use access to a cloud-hosted development environment with minimal effort for setup and configuration. This decoupling of the development workspace from the physical workstation enables a low-friction, consistent developer experience and faster developer onboarding.

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Save space and ease integration in car body electronics, audio systems, and inverter gate drivers

STMicroelectronics has introduced new automotive-qualified step-down synchronous DC/DC converters that save space and ease integration in applications including body electronics, audio systems, and inverter gate drivers.

The A6983 converters offer flexible design choices, comprising six non-isolated step-down converters in low-consumption and low-noise configurations and the A6983I isolated buck converter. With compensation circuitry on-chip, these highly integrated monolithic devices need only minimal external components including filtering, feedback, and a transformer with the A6983I.

The non-isolated A6983 converters can supply up to 3A load current and achieve 88% typical efficiency at full load. The low-consumption variants (A6983C) are optimized for light-load operation, with high efficiency and low output ripple, to minimize drain on the vehicle battery in applications that remain active when parked. The low-noise A6983N variants operate with constant switching frequency and minimize output ripple across the load range for optimum performance in applications such as audio-system power supplies. Both types offer a choice of 3.3V, 5.0V, and adjustable output voltage from 0.85V to VIN.

The A6983I is a 10W iso-buck converter with primary-side regulation that eliminates the need for an optocoupler. Ideal for use as an isolated gate driver for IGBTs or silicon-carbide (SiC) MOSFETs in traction inverters and on-board chargers (OBCs), this converter allows accurate adjustment of the primary output voltage. The transformer turns ratio determines the secondary voltage.

All isolated and non-isolated variants have a low quiescent operating current of 25µA and a power-saving shutdown mode that draws less than 2µA. The input-voltage range from 3.5V to 38V, and load-dump tolerance up to 40V, prevent disruption due to transients on the main supply bus. There is also output overvoltage protection, thermal protection, and internal soft start. In addition, optional spread-spectrum operation helps lower electromagnetic interference (EMI) for noise-sensitive applications, and a power-good pin that enables power sequencing. The A6983I and A6983 allow synchronization to an external clock.

The converters are offered in a 3mm x 3mm QFN16 package. Pricing starts at $1.75 for the A6983 and $1.81 for the A6983I, for orders of 1000 pieces, and free samples of the A6983 and A6983I are available from the ST eStore. The STEVAL-A6983CV1 and STEVAL-A6983NV1 A6983 evaluation boards and STEVAL-L6983IV for the A6983I are available to kickstart development and accelerate project completion.

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