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Bipolar stepper motors provide precise position control while operating in an open loop. Industrial automation applications—such as robots and processing and packaging machinery—and consumer products—such as 3D printers and office equipment—effectively take advantage of the stepper’s inherent position retention. This eliminates the need for convoluted sensor technology, processing power requirements, or complex control algorithms.

However, driving a stepper motor in an open-loop methodology requires the motion profile to be errorless. Any glitch in which the stepper’s load abruptly changes results in step loss, which desynchronizes the stepper position from the application’s perceived position. In most cases, this position tracking loss is problematic. For example, in a label printer, step loss could cause the print to be skewed with the label, resulting in skewed label prints.

This article will describe a simple implementation that gives stepper motor the ability to sense its position and actively correct any error that might accrue during actuation.

 

Design assumptions

For this article, we will assume that a bipolar stepper motor with 200 steps per revolution is employed to drive a mechanism that is responsible for opening and closing some sort of flap or valve while servicing a production line. To make motion smooth, we will utilize a bipolar stepper driver with 8 degrees of microstepping, resulting in 1,600 step commands per full rotor revolution.

In order to fully open or close said mechanism, we will need multiple rotor turns; for simplicity, assume we need 10 full turns. In this case, the controller would need to send 16,000 step commands on each direction to successfully actuate the mechanism.

When the current is high enough to overcome any torque variation, the stepper moves accordingly and can fully open and close the control surface. In this scenario, the position is preserved. If steps are lost, however, the controller loses synchronization with the motor, and the actuation becomes compromised.

Newer technologies attempt to provide checks, such as stall detection, by measuring the motor winding’s back electromotive force (BEMF) when the applied revolving magnetic field crosses the zero-current magnitude. Stall detection only tells the application whether the motor is moving; it fails to report how many steps have been effectively lost. In cases like this, it’s worthwhile to explore closing the loop on the rotor position using sensing technology.

Sensor selection

In some cases, using simple limit switches—like magnetic, optical, or mechanical—might suffice to drive the stepper motor until the limits are met. However, there are plenty of cases where the available space does not allow the use of such switches. If a switch cannot be used, it might make sense to populate an optical shaft encoder (relative or absolute) at the motor’s back side shaft, but there is a high cost associated with these solutions.

An affordable solution for this dilemma is a contactless angular position sensor. This type of sensor involves the use of readily available magnetics with precise and accurate semiconductors that employ Hall sensors, which extract the rotor’s position with as much as 15 bits worth of resolution. That means each rotor revolution can be encoded to as much as 215 = 32,768 units, or 0.01 degrees (360/32,768).

For this example, an 11.5-bit resolution was selected, as that will be sufficient to encode the 1,600 microsteps. By using 11.5 bits of resolution, we can obtain 2,896.31 effective angle segments. A Hall-effect based contactless sensor such as the MA732 provides absolute position encoding with 11.5 bits of resolution.

When coupled to a diametrically magnetized round magnet, the sensor is periodically sampled through its serial peripheral interface (SPI) port at 1-ms intervals (Figure 1). When a read command is issued, the sensor responds with a 16-bit word. The application uses the 16 bits worth of information, although the system’s accuracy is driven by the effective 11.5-bit resolution.

Figure 1 The Hall-effect sensor is connected to the MCU through the SPI ports. Source: Monolithic Power Systems

Power stage selection

Driving bipolar steppers require two full H-bridges. The two main implementations to drive bipolar stepper motors are using a dual H-bridge power stage with a microcontroller unit (MCU) to generate sine/cosine wave pairs or using a fully integrated step indexer engine with microstepping support. Using an MCU and dual H-bridge combination provides more flexibility in terms of how to regulate the sine wave currents, but it also increases complexity.

For this article, a fully integrated step indexer with as much as 16 degrees of microstepping was selected (Figure 2). The integrated step indexer in this article is MP6602, which provides up to 4 A of current drive and is capable of driving NEMA 17 and NEMA 23 bipolar stepper motors. Meanwhile, the MCU drives all control signals, communicates with the indexer through the SPI port, and samples the fault information.

Figure 2 The step indexer is connected to an MCU to drive the bipolar stepper motor. Source: Monolithic Power Systems

Final implementation

For a closed-loop stepper implementation, the sensor and power stage should be controlled by an off-the-shelf ARM Cortex M4F MCU. The MCU communicates with both devices through a single SPI port with two chip selects. An internal timer generates the steps. The board measures 1.35”x1.35” and is small enough to fit behind a NEMA17 stepper motor (Figure 3). This allows the reference design to be used in a larger motor frame size such as the NEMA 23.

Figure 3 The PCB’s bottom side has the MA732 angle sensor. Source: Monolithic Power Systems

Figure 4 shows the motor assembly, in which Figure 4a (above) shows the motor assembly with a diametrically magnetized round magnet facing MA732 sensor, and Figure 4b (below) shows the final solution.

Figure 4 Assemble the motor such that the housing is invisible. Source: Monolithic Power Systems

Absolute position and sensor overflow

Although the contactless magnetic based sensor is an absolute position encoder, this is only true on a per-revolution basis. That is, throughout the rotor’s angular travel through each revolution, the sensor provides a 16-bit number that the MCU reads, which essentially allows the firmware to learn the rotor’s absolute position at any given time.

As the motor revolves, however, each new revolution is indistinguishable from the previous revolution. We can add angular position readings into a much larger number, which can be expressed as a variable that takes all the angle readings to obtain the entire position as an absolute value (called Rotor_Angle_Absolute). This variable is a 32-bit signed integer.

If the motor moves forward, increment the variable, and vice versa. Assuming 16-bit readings, 1,600 microsteps per revolution, and a 1,000-rpm step rate, it would take 22.37 hours for the variable to overflow. The MCU must ensure that the sensor readings are added correctly, even as the rotor goes through its overflow region. This absolute position correction must be executed whether the motor is rotating clockwise or counterclockwise; in other words, the sensor position is incrementing or decrementing.

Figure 5 shows how the angle position changes over time.

Figure 5 The angle position changes over time as the motor revolves. Source: Monolithic Power Systems

Figure 5 shows that the angular displacement (MA732_Angle_Delta, denoted as AD in figure) is computed at periodic intervals (1ms). During each sample, the previous read is stored within MA732_Angle_Prev (denoted as Prev Angle in figure), the new sample is stored at MA732_Angle_New (denoted as New Angle in figure). MA732_Angle_Delta can be calculated with Equation 1:

The result of Equation 1 is added to MA732_Angle_Absolute. If the rotor moved clockwise (forward), the displacement is positive; if the motor moves counterclockwise (reverse), the displacement is negative.

A special consideration must be made during angle sensor overflows. If the sensor moves forward past the maximum of 0xFFFF (denoted as OvF+AD in Figure 5), or if the sensor decrements its position past 0x0000 (denoted as OvF-AD in Figure 5), the previous equation can no longer be used. In both scenarios, the FW logic chooses one of the following equations, depending on which case we are servicing.

If the angle displacement overflows when counting up and exceeds the maximum (OvF+AD), then MA732_Angle_Delta can be calculated with Equation 2:

If the angle displacement overflows when counting down and falls below the minimum (OvF-AD), then MA732_Angle_Delta can be calculated with Equation 3:

Stepper motor: New frontiers

Using an off-the-shelf MCU, we can interface the stepper motor driver and Hall-sensor based sensor via an SPI port. The firmware can then continuously interrogate the position sensor and extrapolate the motor rotor position at all times. By comparing this position to a commanded position, the motor can be commutated to reach the commanded position in a timely fashion.

If an external force causes the motor to lose steps, the sensor information tracks how many steps were lost, which then allows the MCU to close the loop on position and successfully bring the stepper motor to the commanded position.

Although stepper motors are mostly used in open-loop applications, there are plenty of advantages in closing the loop on position. By employing cost-effective, Hall-sensing technologies, and an easy-to-use index-based stepper drivers, the application can now add servo-like properties to their stepper-based applications.

Jose Quinones is senior application engineer at Monolithic Power Systems (MPS).

Related Content

• Stepper Motor Controller
• Stepper Motors: Care & Feeding
• Stepper Motor Controller Eliminates Need for Tuning
• Standard Step-Motor Driver Interface Limits Performance
• Why microstepping in stepper motors isn’t as good as you think

The post No more missed steps: Unlocking precision with closed-loop stepper control appeared first on EDN.

Melexis Ukraine для Спорткомплексу КПІ kpi пт, 10/17/2025 - 20:12

KPI ID: система єдиної аутентифікації і використання Дія.Підпис для користування університетськими сервісами kpi пт, 10/17/2025 - 18:40

WDT in safety standards

With the prevalence of microcontrollers (MCUs) as processing units in safety-related systems (SRS) comes the need for diagnostic measures that will ensure safe operation. IEC 61508-2 specifies self-test supported by hardware (one channel) as one of the recommended diagnostic techniques for processing units. This measure uses special hardware that increases speed and extends the scope of the failure detection, for instance, a watchdog timer (WDT) IC that cyclically monitors the output of a certain bit pattern from the MCU.

The basic functional safety (FS) standard IEC 61508-2 Annex A Table A.10 recommends several diagnostic techniques and measures to control hardware failures in the program sequences of digital devices. Such techniques include a watchdog with a separate time base with or without a time window, as well as a combination of temporal and logical monitoring of program sequences. While each of these has corresponding maximum claimable diagnostic coverage, all these techniques employ WDTs.

This article will show how to implement these diagnostic functions using WDTs. Furthermore, the article will provide insights into the differences of program sequence monitoring diagnostic measures in terms of operation and diagnostic coverage when implemented with ADI’s high-performance supervisory circuits with watchdog function.

Low diagnostic coverage

Part 2 of IEC 61508 describes simple watchdogs as external timing elements with a separate time base. Such devices allow the detection of program sequence failures in a computer device, such as MCUs, within a specified interval. This is done by having a mechanism that allows either:

1. The MCU is to issue a signal to reset the watchdog before it reaches the timeout
2. The watchdog timeout period to be reached so that the watchdog can issue a reset signal to the MCU

Step #1 occurs when the program sequence is running smoothly, while step #2 happens when it is not.

Figure 1a shows an example of the watchdog implementation with a separate time base but without a time window through the MAX6814. Notably, MCUs usually have an internal WDT, but it cannot be solely relied on to detect a fault if it is part of the defective MCU, which will be an issue considering common cause failures (CCF).

To address such CCF concerns, a separate WDT is used to ensure the MCU is placed in reset [1, 2]. Through a flowchart, Figure 1b illustrates the behavior of the WDT as embedded in the MCU’s program execution. Before the flow starts, it’s important to set the watchdog timeout period or the WDT’s maximum reset interval. When such a period or interval is defined, the WDT will run upon execution of the program. The MCU must be able to send a signal to the MAX6814’s WDI pin before it reaches timeout, as the device will issue a reset signal to the MCU if the timeout period is reached. When the MCU resets, the system will be placed into a safe state.

Figure 1 Simple watchdog operation showing (a) an example of the watchdog implementation with a separate time base but without a time window and (b) the behavior of the WDT as embedded in the MCU’s program execution. Source: Analog Devices

Such a WDT’s timeout period will capture program sequence issues; for example, a program sequence gets stuck in a loop, or an interrupt service routine does not return in time. For instance, only 5 of the 10 subroutines meant to be run on every loop of the software are executed.

However, the WDT’s timeout period will not cover other issues concerning program sequence issues—whether execution of the program took longer or shorter than expected, or if the sequence of the program sections is correctly executed. This can be solved by the next type of WDTs.

Medium diagnostic coverage

Since the existence of a separate time window allows for the detection of both excessive delays and premature execution, windowed WDTs prohibit the MCU from responding longer or shorter than the WDT’s open window. This is also referred to as a valid window specification. As compared to simple watchdogs, it guarantees that all subroutines are executed by the program in a timely manner; otherwise, it will assert the MCU into reset [3].

Figure 2 shows an example implementation of program sequence monitoring using the MAX6753. It comes with a windowed watchdog with external-capacitor-configurable watchdog periods.

Figure 2 Sample implementation of a windowed watchdog operation with external-capacitor-configurable watchdog periods.

Figure 3, on the other hand, shows another implementation using the MAX42500, whose watchdog time settings can be configured through I2C—effectively reducing the number of external components. This allows for the capability to increase fault coverage through a packet error checking (PEC) byte as shown in Figure 4. The PEC byte increases diagnostic coverage against I2C communication-related failures such as bus errors, stuck-bus conditions, timing problems, and improper configuration.

Figure 3 Another implementation: windowed watchdog through I2C, reducing the number of external components compared to Figure 2. Source: Analog Devices

Figure 4 PEC byte coverage to I2C interface failures, such as bus errors, stuck-bus conditions, timing problems, and improper configuration. Source: Analog Devices

 While watchdogs with a separate time base and time window offer higher diagnostic coverage compared to simple WDTs, they still cannot capture issues concerning whether the software’s subroutines have been executed in the correct sequence. This is what the next type of diagnostic technique addresses.

High diagnostic coverage

Diagnostic techniques involving the combination of temporal and logical monitoring provide high diagnostic coverage to program sequences according to IEC 61508-2. One implementation of this technique involves a windowed watchdog and a capability to check whether the program sequence has been executed in the correct order.

An example can be visualized when the circuit in Figure 2 is combined with the sequence in Figure 5, where the MCU has each of its program routines employing a unique combination of characters and digits. Such unique combinations are then placed in an array each time a routine is executed. After the last routine, the MCU will only kick, or send a reset signal to, the watchdog if all words are correctly set in the array.

Figure 5 Checking the correct logic of the sequence through markers. Source: Analog Devices

Highest diagnostic coverage

In some systems, more diagnostic coverage may be required to capture failures of the MCU, which may mean simply that sending back a pulse in a windowed time is not enough. With this, it may be beneficial to require the MCU to perform a complex task, such as calculating, to ensure that it’s fully operational. This is where the MAX42500’s challenge/response watchdog can come into play.

In this watchdog mode, there’s a key-value register in the IC that must be read as the starting point of the challenge message. The MCU must use this message to calculate the appropriate response to send back to the watchdog IC, ensuring the watchdog is kicked within the valid window. This type of challenge/response watchdog operates similarly to a simple windowed one, except that the key register is updated rather than the watchdog being refreshed with a rising edge. This is shown in Figure 6. Notably, for the MAX42500’s WDT, the watchdog input is implemented using the I2C, while the watchdog output is the output reset pin.

Figure 6 A challenge/response windowed watchdog example where the MCU reads the challenge message in the IC and calculates an appropriate response to be sent back to the watchdog IC to allow it to be kicked within the valid window. Source: Analog Devices

The MAX42500 contains a linear-feedback shift key (LFSK) register with a polynomial of x8 + x6 + x5 + x4 + 1 that will shift all bits upward towards the most significant bit (MSB) and insert the calculated bit as the new least significant bit (LSB). With this, the MCU must compute the response in this manner and return it to the register of the MAX42500 through I2C. Notably, such a polynomial is identified as primitive and at the same time, a maximal length feedback polynomial for 8 bits. This ensures that all bit value combinations (1 to 255) are generated by the polynomial, and the order of the numbers is indeed pseudo-random [4][5].

Such a challenge/response can offer more coverage than the combination of temporal and logical program sequence monitoring, as it shows that the MCU can still do actual calculations. This is as opposed to an MCU just implementing decision-making routines, such as only checking whether the array of words is correct before issuing a signal to reset the watchdog.

Diagnostic coverage claims

The basic functional safety standard has maximum claimable diagnostic coverage for each diagnostic measure recommended per block in an SRS. Table 1 corresponds to the program sequence according to IEC 61508, which utilizes WDTs.  

Diagnostic Technique/Measure	Maximum DC Considered Achievable
Watchdog with a separate time base without a time window	Low
Watchdog with a separate time base and time window	Medium
Combination of temporal and logical monitoring of program sequences	High

Table 1 Watchdog program sequence according to IEC 61508-2 Annex A Table A.10.

Furthermore, with the existence of different implementations that may not be covered in the standard, a claimed diagnostic coverage can only be validated through fault insertion testing.

Diagnostic measures using WDTs

This article enumerates three types of diagnostic measures that use WDTs as recommended by IEC 61508-2 to address failures in program sequence. The first type of watchdog, which has a separate time base but without a time window, can be implemented using a simple watchdog. This diagnostic measure can only claim low diagnostic coverage.

On the other hand, the second type of watchdog, which has both a separate time base and a separate time window, can be implemented by a windowed watchdog. This measure can claim a medium diagnostic coverage.

To improve diagnostic coverage to high, one can employ logical monitoring aside from the usual temporal monitoring using watchdogs. A challenge/response windowed watchdog architecture can further increase diagnostic coverage against program sequence failures with its capability to check an MCU’s computational ability.

Bryan Angelo Borres is a TÜV-certified functional safety engineer who focuses on industrial functional safety. As a senior power applications engineer, he helps component designers and system integrators design functionally safe power products that comply to industrial functional safety standards such as the IEC 61508. Bryan is a member of the IEC National Committee of the Philippines to IEC TC65/SC65A and IEEE Functional Safety Standards Committee. He also has a postgraduate diplomat in power electronics and more than seven years of extensive experience in designing efficient and robust power electronics systems.

Christopher Macatangay is a senior product applications engineer supporting the industrial power product line. Since joining Analog Devices in 2015, he has played a key role in enabling customer success through technical support, system validation, and application development for analog and mixed-signal products. Christopher spent six years prior to ADI as a test development engineer at a power supply company, where he focused on the design and implementation of automated test solutions for high-reliability products.

References

1. “IEC 61508 All Parts, Functional Safety of Electrical/Electronic/Programmable Electronic Safety-Related ” International Electrotechnical Commission, 2010.
2. “Top Misunderstandings About Functional Safety.” TÜV SÜD,
3. “Basics of Windowed Watchdog Operation.” Analog Devices, Inc. December
4. “Pseudo Random Number Generation Using Linear Feedback Shift Registers.” Maxim, June 2010.
5. Mohammed Abdul Samad AL-khatib and Auqib Hamid Lone “Acoustic Lightweight Pseudo Random Number Generator based on Cryptographically Secure LFSR.” International Journal of Computer Network and Information Security, Vol. 2, February

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The post Program sequence monitoring using watchdog timers appeared first on EDN.

Developed with SiFive, the dual-core SoC uses patented error correction to achieve a record 16.8 µW/MHz/DMIPS, targeting next-gen wearables, drones, and IoT sensors.

“AI is definitely one technology that is developing very fast. And power management is the second big area”, says Gagan Bansal, President-Sales & Marketing, TDK, in an exclusive conversation with the ELE Times.  As the Indian electronic manufacturing industry aims to reach a worth of $300 billion by the end of 2026, the industry not only has to boost manufacturing but also innovative manufacturing to cater to the growing demands.

He navigated from providing to a wide spectrum in the electronic manufacturing market to the research and development for more innovative technology to meet the demands of tomorrow. Also offering a peek into TDK’s strategy to face global uncertainties ,along with promising technology that TDK is working on.

Moving from Traditional to Innovative Technology

Tracing TDK’s journey from creating the famous magnetic cassette tapes to batteries and now advanced technology with ADAS and XEV applications in radars and sensors, Gagan Bansal provides an introductory brief into TDK’s capabilities.

He further introduces TDK’s innovative product line, not only for the industrial sector but also for automotive and daily requirements, wherein he underlines TDK’s major mainstay in business as the market moves from ICE vehicles to EVs with nearly 60-70% electronics involved in it.

Apart from the automotive industry, he also mentions a product developed by TDK, which is the noise-cancellation in spatial atmosphere, simply said: noise cancellation without headphones. At this point, he specifically touches the combination of MEMS-based microphone, piezo-listen speaker, and digital signal processing involved in the product.

Incorporation of AI into TDK

“A lot of our sensor products are now related to AI software,” Gagan Bansal responded to the question on the

integration of AI. Recognizing the growing role of AI, he revealed that TDK has taken multiple steps for its inclusion by providing mission-critical components in terms of inductive components and electrolytic capacitors for various AI server applications.

He also recalled that many of their innovative technologies are incorporated with AI, namely the AR-VR technology and smart sensors for spatial noise cancellation and detection of head movement. He further added that their global company, TDK Sensor EI (Edge Intelligence), overlays software for artificial intelligence at the edge of the device, above the existing sensing devices; a growing demand in upcoming technology.

TDK in India

“Electronics manufacturing in India, on a penetration level, is relatively low as compared to the developed part of the world,” as he underlined the low penetration into the Indian market as compared to the developed part of the world, he recognized India as a place full of scale and scope.

To give an idea of TDK’s extensive presence in India, he says, “So we have a localization drive and we are very proud that in our existing units, more than 50% of the inputs are sourced locally over a period of time.” With six operational plants in India, TDK has also invested in four early-stage deep tech ventures into the fields of industrial IoT, agritech, EV charging, and EV bikes, making India the cornerstone in its global strategy.

TDK’s vision for the next half of the decade

Talking about the future of technology and innovation, he underlines the pace at which AI is developing while also sharing concerns around its power consumption and the indispensable need to develop power-efficient alternatives. Stepping into its feet, a bit more in this area, TDK has already begun working on developing an analogue memory product, called spin Memristor, inspired by the functioning of the human brain.

Comparing the human brain to digital memory, he highlighted how, despite being bigger in size, the human brain consumes comparatively less energy in contrast to a digital memory system. Their new product, Spin Memristor, based on the spintronics technology of an electron, aims to store data in an analogue format, which can be used in AI servers to consume less energy.

Concluding his conversation, Gagan Bansal says,” India is a formidable force to reckon with. It is a part of our global strategy as part of TDK, and it will remain to be so.” He also pitches TDK as a technology-ready company for the business to be with in furthering their ambitions and products on a global stage.

The post Exclusive Insights: “With Spin Memristor, we’re bringing the brain’s analog intelligence to modern memory technology,” says TDK’s Gagan Bansal. appeared first on ELE Times.

Skyworks Solutions Inc of Irvine, CA, USA (which manufactures analog and mixed-signal semiconductors) has introduced its expanded Wi-Fi 7 portfolio, with new front-end modules (FEMs) and bulk acoustic wave (BAW) filters designed to enhance range and coverage across 2.4GHz, 5GHz, and 6GHz bands...

Efficient Power Conversion Corp (EPC) of El Segundo, CA, USA — which makes enhancement-mode gallium nitride on silicon (eGaN) power field-effect transistors (FETs) and integrated circuits for power management applications — is developing power converters to accelerate the adoption of 800VDC distribution systems for the next generation of AI data centers...

NUBURU Inc of Centennial, CO, USA — which was founded in 2015 and developed and previously manufactured high-power industrial blue lasers — has reaffirmed that it remains in full compliance with NYSE American regulatory disclosure requirements. This follows the resolution of a technical communication timing issue, related to the coordination of press release dissemination...

Raj Gawera has been appointed as chief operating officer (COO) of the UK Semiconductor Centre, leading its early mobilization and engagement as it builds plans to champion the UK semiconductor sector on the global stage...

The development of communication networks has entered a revolutionary phase. As 5G continues to mature and 6G research gains momentum, the world stands at the cusp of a hyper-connected era driven by real-time intelligence, automation, and pervasive connectivity.

From autonomous mobility and telemedicine to smart manufacturing and immersive AR/VR, the success of these innovation rests on one invisible foundation trustworthy mobile network performance.

Behind this dependability lies mobile network testing the unseen but critical layer ensuring that every connection performs seamlessly.

This diagram shows how data flows through a telecom network—from user devices to access technologies like Small Cells and Massive MIMO, through transport layers like fiber and edge data centers, into a cloud-based core network, and finally through testing layers using mmWave, cybersecurity, AI, and digital twins to ensure performance and reliability.

The Technology Behind Network Testing

• With mmWave, massive MIMO, network slicing, and Open RAN shaping 5G architecture, testing has become a complex science demanding unprecedented accuracy, flexibility, and speed.
• Today’s testing solutions are evolving with AI-powered analytics, cloud-based digital twins, and cybersecurity validation frameworks, enabling operators, equipment manufacturers, and researchers to ensure reliability across both physical and virtualized networks.
• As the world transitions toward 6G featuring terahertz (THz) frequencies, AI-native architectures, and intelligent automation network testing will remain the quiet enabler that keeps our connected future secure and scalable.

Innovations Powering Modern Network Testing

1. Predictive Analytics Powered by AI

Machine learning is reshaping network validation by predicting faults before they affect service. AI models analyze vast datasets from live networks to predict congestion, optimize routing, and reduce downtime, turning testing from reactive to proactive.

2. Cloud-Based Simulation & Digital Twins

Digital twins now simulate entire networks in the cloud from traffic behavior to mobility and interference patterns. This reduces field testing costs while improving accuracy, enabling virtual prototyping of real-world networks.

3. Open RAN & Massive MIMO Validation

Open RAN drives multi-vendor interoperability but demands strict conformance testing for timing, synchronization, and RF performance between distributed and radio units. Testing tools ensure each vendor’s hardware performs harmoniously in shared environments.

4. Cybersecurity & Resilience Testing

With 5G serving critical industries, penetration testing, vulnerability scanning, and cyberattack emulation are essential to preserve network integrity. The shift toward zero-trust architectures is also reshaping validation methodologies.

Industry Insights:

Some of the most significant advancements in mobile network testing are being led by industry pioneers like Anritsu and Keysight Technologies. Their innovative tools and forward-thinking approaches are not only addressing current 5G challenges but also laying the groundwork for the 6G era.

Anritsu Insight:

“5G came with big promises and bigger testing challenges,” says Madhukar Tripathi, Associate Director – Marketing & Business Development, Anritsu India.

“Technologies like mmWave, URLLC, and network slicing push testing boundaries. At Anritsu, our platforms like the MT8000A Radio Communication Test Set, Shockline VNA, and Network Master Pro MT1000A are enabling operators and manufacturers to validate performance at every stage — from R&D to deployment.”

Synchronization and interoperability are key challenges in Open RAN, where timing precision determines network reliability. “Our MT1000A and MS2850A test solutions perform PTP/SyncE and RF conformance tests to ensure accurate timing across multi-vendor O-RAN environments,” he adds.

AI-powered analytics further help in predictive fault detection. “By integrating data-driven insights into our instruments, we make spectrum analysis more intelligent — transforming network testing from a reactive process to a predictive one.”

 “Synchronization is key for Open RAN. Test platforms emulate and measure time errors with atomic clock precision, ensuring reliable multi-vendor timing.”

Also advancing 6G research, collaborating globally on FR3 (7–24 GHz) and sub-THz frequencies. Tools like the VectorStar broadband VNA and Scenario Edit Environment Kit (SEEK) automate multi-domain testing, preparing networks for future intelligent connectivity.

— Madhukar Tripathi, Anritsu India

Keysight Insights:

“The transition from simulation to digital twinning is redefining how networks are designed and optimized,” says Mombasawala Mohmedsaeed, CTO, Keysight Technologies India.

With RaySim and EXata, Keysight provides an end-to-end digital twin ecosystem to model base stations, channels, and user equipment under realistic mobility and interference conditions. “Tools allow operators to virtually replicate entire cities or rural regions and optimize networks for energy efficiency and coverage before physical deployment.”

For Non-Terrestrial Networks (NTNs), tools like Propsim and UE Sim emulate satellite-based communications to ensure seamless coverage.

On the security front, Keysight’s CyPerf, BreakPoint, and Threat Simulator emulate real-world cyberattacks to test network resilience. The Software Bill of Materials (SBOM) and Riscure solutions add another layer by tracing vulnerabilities within semiconductor and IoT ecosystems.

“Performance validation alone isn’t enough anymore — continuous cybersecurity testing is critical to protect mission-critical networks,” Mohmedsaeed emphasizes.

“Digital twins and cybersecurity intelligence are twin pillars of modern network assurance.”

— Mombasawala Mohmedsaeed, Keysight Technologies India

The Road Ahead: Testing Beyond 5G

The future of network testing will revolve around network slicing, private 5G, and 6G prototyping. Ultra-low latency, massive device connectivity, and AI-native architectures will demand test automation, real-time data visualization, and cross-domain validation.

Furthermore, the convergence of terrestrial and satellite networks will require innovative methods to ensure reliability and performance even in remote and harsh environments.

As the digital horizon expands toward 6G, the invisible precision of network testing will be what keeps the hyper-connected world running flawlessly. Every autonomous car that drives safely, every remote surgery that succeeds, and every virtual experience that feels real will owe its reliability to the unseen rigor of testing.

In the race to the future, innovation may set the pace but precision testing ensures the world never loses connection.

The post Building Reliable 5G and 6G Networks Through Mobile Network Testing appeared first on ELE Times.

Author: STMicroelectronics

Smartphones have become some of the most ubiquitous devices in modern history. For most of us, the smartphone is an indispensable tool not only to communicate, but to manage our lives – work, personal relationships, travel, shopping, entertainment, photography, and video creation. In short, smartphones have become a hub for life. 

 The touchscreen was transformational in the smartphone’s adoption and use. But in the future, the smartphone is set to become a platform for immersive experiences. And when aligned to innovations that will extend battery life and even see smartphones harvesting their own energy, along with new ways to stay constantly connected, their usefulness will only increase.  

 A powerful processor in your pocket

Smartphones have become incredibly powerful processing devices. Indeed, in comparison to the most powerful supercomputers of the 1980s, today’s smartphones can process information more than 5,000 times faster. 

In some ways, however, the way that we interact with our smartphones has progressed least since their arrival. For many people, the touchscreen remains the primary – if not only – way that they access and view the interactive services and rich experiences provided by their smartphone. The coming years will see that transformed and, with it, the idea of what a smartphone is. 

A reduced reliance on the smartphone display as the principal way to interact with the device and receive information fundamentally changes the role of the smartphone. As a powerful computing device in its own right, but also connected to cloud-based computing resources, the smartphone potentially becomes a platform for delivering immersive experiences and valuable services to the user in numerous new ways.  

New models for smartphone interaction

Voice assistants have become one of the first steps into a new world of accessing services via our smartphones. Whether issuing voice commands and queries directly into the device or having these relayed via connected headphones and earbuds, consumers are realising the convenience of voice and audio interaction. An additional benefit, of course, is that the smartphone itself can remain in a pocket or bag, out of harm’s way. 

Eyeglasses featuring augmented reality (AR) display technology are an ideal solution. These can visually display directions in the user’s eyeline, while also overlaying other useful or interesting information. With more information and experiences layered over the real world, discovering a new city will be more rewarding than ever before, with less potential for a misstep along the way. 

Artificial intelligence (AI) will also enable proactive and predictive services that help us manage our daily lives. For example, by understanding the current traffic conditions, AI might bring an alert for your next meeting across town 30 minutes earlier. With the alert appearing on your smartwatch, more efficient travel could be proposed, with directions to the closest public transport appearing in your eyeglasses’ AR display. 

Gesture recognition and haptic feedback

Gesture recognition is emerging as another way to interact with services provided by smartphones. Less obvious that either using a touchscreen or voice, subtle gestures to make or answer calls or respond to messages will be quick and convenient methods of interaction. Who knows, you might well respond to the latest message received with an actual thumbs up, rather than having to find and type the emoji itself.  

We might be on the cusp of a whole new vocabulary of gestures as commands. Google is one company looking at how devices can be controlled by natural human gestures, many of which we use subconsciously. Other advances in hardware, such as the latest generation of Time-of-Flight (ToF) sensors, will support more accurate detection of gestures in and around smartphones. 

Haptic feedback is the use of vibrations or sensations to enrich the experience of using a device. At a basic level, most of us already experience haptic feedback in our smartphone use. Vibrations rather than a ringtone to signify an incoming call is a simple example, but the nature and application of haptic feedback is rapidly evolving. 

Imagine shopping online and being able to ‘feel’ different types of fabric through haptic feedback via your smartphone’s screen. Subtle vibrations from different parts of smart eyeglasses could be used to enrich visual experiences or help with directions. Research is even looking at ultrasound and “mid-air” haptics, where the sensation of physical touch is created in the air. Such haptic feedback could augment gesture control or enhance touchless interfaces. 

The potential for neural interfaces

Though still in its early stages, the idea of interacting with devices merely by thinking is becoming more real. Various non-invasive neural interfaces are in development.  

Electroencephalography (EEG) sensors placed on the head via headsets, or potentially even embedded in hats and headbands, are a direct way to tap into the brain’s activity. Neural wristbands detect signals from nerves connecting the brain to an individual’s hands, whereby just thinking about a gesture or action could act as a command.  

So-called “silent speech” interfaces detect subtle changes in expression or movements in the vocal chords, where simply mouthing words would be detected as accurately as voice. Data from wearables such as smartwatches, rings, and earbuds could identify cognitive load and emotional state, triggering proactive alerts, suggestions, or experiences to help alleviate issues.  

Projecting further into the future, neural interfaces and advanced haptic feedback could be combined to create a new world of deeply immersive experiences, all powered by the not-so-humble smartphone. 

Always connected

Of course, this vision of the smartphone as a platform for new services and experiences relies on an almost constant connection to cloud-based computing resources. Fortunately, alongside the innovations in smartphone interface technologies, we’re seeing continued development of technologies that ensure we remain connected, wherever we are. 

As we recently highlighted, the need to connect the world of increasingly intelligent “things” – not only smartphones, but billions of sensors, machines, and consumer products – is being supported by innovation in communications technology. This includes further evolution of established infrastructure, with 6G telecommunications networks arriving in the coming years, but also the significant expansion of satellite-based communications networks.  

When the smartphone arrived, it was exactly that: a phone with additional capabilities. We can all appreciate how far it has moved beyond that simple description, and over a relatively short period of time. While we might need a new name for the device, we certainly need to change our understanding of what this powerful pocket processing device represents.

New ways to interact with our smartphones, innovation in the delivery of seamless immersive experiences, universal connection, and improved battery life and self-charging will see them become the primary digital platform for every aspect of our lives.

• Discover our Time-of-Flight sensors

The post Beyond the Screen: envisioning a giant leap forward for smartphones from physical objects to immersive experiences appeared first on ELE Times.

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