Algorithm Design and Development Services

Expert algorithm design and development for a wide range of applications.

From decomposing business requirements into precise technical specifications to delivering robust algorithms and implementing them in hardware, firmware, and software, ORTENGA provides end-to-end support across the full development lifecycle.

Our engineering team brings deep experience from industries including Autonomous Automotive, SATCOM, Radar, Smart City, WiFi, and Mobile Terrestrial Radio Communications Systems.

ORTENGA helps businesses identify the technical capabilities needed to achieve their product and market goals.

 

 

What is an Algorithm?

An algorithm is a set of rules, step by step, and/or process for completing a very specific task.

In High tech industry, within the invention of any machine, automate piece of equipment, always if not many, at least one algorithm is used for expediting the execution of sequence of an event.

In particular, Wireless Communication and Radar Systems industries use many algorithms to accomplish and maintain functionality as well as performance of your wireless device, phone, laptop, microwave oven, etc.

ORTENGA subject matter experts design and develop algorithms, such as but not limited to Automatic Gain Control, AGC, of transmitter and/or receiver chains, aka transceiver, synchronization between transmitter and receiver, calibration of RFIC, RF impairment estimations and corrections, controlling  beamforming of a Software Defined Antenna.

Whether you use ASIC devices from vendors or you design and develop ASIC which needs to interface with multiple devices within the HW systems environment in any Radio Applications and Standard, ORTENGA can develop appropriate algorithms to meet the requirements.

SLL Management Algorithm

Side Lobe Level, SLL, management is requirement for any beamforming and beam tracking radio communications or radar systems.  gNB , LEO SATCOM, 802.11ad/ay, and radar systems rely on beamforming and beam tracking technologies as well as SLL management algorithm to comply with FCC unwanted emission requirements which are really needed for multiple access or users’ radio coexistence.

SLL management algorithm works hand in hand with beamforming and beam tracking algorithms.  As the beam formed and tracks the targets, the radiation pattern is dynamically changing every fraction of second, e.g. every couple of hundred microseconds in LEO SATCOM, therefore the SLL must be managed automatically to meet FCC requirements at all times to avoid interfering with other users.  This is only possible if SLL management algorithm is in place within the radio system to ensure not exceeding an upper level SLL, hence safe level for other users or misidentify the target in radar applications.

Partner with ORTENGA to design and develop SLL management algorithm for your product.

 

Beam Tracking Algorithm

5G and SATCOM not only rely on Beamforming but also on Beam Tracking algorithm.

Beam tracking is needed when transmitter, receiver, or both are mobile, and required in addition to Beam forming algorithm.

Beam tracking algorithm can be categorized into 3 different use cases, namely; gNB, GEO and LEO SATCOM applications.

  1. Beam tracks mobile targets, while the transmitter is stationary. For instance, gNB tracks mobile UE.
  2. Beam tracks stationary target, while the transmitter is mobile. For instance, mobile SATCOM UT is tracking GEO satellite.
  3. Beam tracks mobile targets, while the transmitter is mobile. For instance, mobile SATCOM UT is tracking LEO satellites.

All of the above algorithms can be realized for new technologies.

ORTENGA can design and develop the above algorithms depending on the applications and use cases.

 

 

What is Beam Splitting?

Beamforming, BF, is an electronic technique to form Electromagnetic waves in the desired direction either for Transmit or Reception of radio waves.   BF can be designed with either Phased Array Antennas or Holographic Metamaterial Antennas.

In gNB or LEO applications, there are use cases which either multiple users or multiple satellites have to be illuminated.  In those scenarios, the Phased Array Antennas or HGBF antennas can split the beam in two or even multiple directions with the expense of losing antenna array gain or increased HPBW.

Since the beam splitting occurs electronically, therefore depending on the electronics of the radio front end, this can occur in fraction of seconds and appears seamless connectivity.

In fact, gNB infrastructure currently is using this technique for 28 and 39 GHz mmW 5G technology.

The Beam Splitting technique can also be utilized in LEO SATCOM application.

Partner with ORTENGA to design and develop your BF product.

 

 

The Art of Triangulation Location Finding

Location finding of mobile UE in radio network has become ubiquitous feature with multiple use cases.

The art is to triangulate the UE as accurate as possible.  The triangulation means determining the timing measurements from at least 3 reference points and calculating the distance from these points to locate the UE.

The following diagram illustrates triangulation of UE by three cellular sites.

In practice, it boils down to detecting signals from the Cellular Sites, estimating the relative timing based on the signal strength and estimation of appropriate part of the sub-frame of cellular packet format/protocol as well as digital signal processing algorithms which are based on statistical analysis.

The calculated location of the UE would be to within a “Triangle”, or uncertainty.  The smaller triangle the better the signal detection and/or algorithms.

Partner with ORTENGA for location finding HW/Algorithm design and development.

 

 

Why DSP Algorithms are implemented via Fixed Point in FPGA?

Currently, typical HW simulators such as Matlab use 64-Bit representations for numbers.

1 Bit can be defined as distinguishing an in-distinguishable.

Typical DSP algorithms do not need such wide, 64-Bit, word for its variable to arrive at acceptable computation tolerances.  In fact, implementing 64-Bit Algorithm could be costly in terms of power consumption, required memory, and computation time, critical algorithm metrics.

On the other hand, lack of adequate Word Length could cause convergence issue, inaccurate results, and erroneous decision makings.

Proper Algorithms are optimized for all of the above metrics.

Algorithm designer can calculate the required number of Bits, Word Length, for tolerable error in computation.  This calculation is called Floating Point to Fixed Point Conversion.

There are many techniques to make the conversion and even Matlab can do that for you.

However, there is more efficient technique which will be faster to make the conversion and ORTENGA utilize that.

Partner with ORTENGA for DSP Algorithms and FPGA implementations.

 

Antenna Tuners

Historically antenna tuners meant any passive interface impedance matching device between the antenna and the RF front end.  This terminology has carried over to UE and/or mobile devices.  In addition, as the need for multiple bands antenna increased, the need for antennas that can operate at multiple bands became prime interest of ODMs.  Nowadays, antenna tuning could either imply antenna impedance tuning or antenna aperture tuning.   The aperture tuning mechanism is part of antenna structure and changes antenna resonance frequency, hence operating at multiple bands.

Partner with ORTENGA to design and develop Antenna Tuning Algorithms for your new product.

 

 

SLL Management Algorithm Flow

Side Lobe Level, SLL, are undesired radiation characteristic of any antenna or antenna arrays.

Many companies spent significant amount of time and resources during the prototype development phase to mitigate SLL and qualify/certify the radiation pattern by FCC.  The issue which remains is two folds even after they succeed to meet FCC and regulatory requirements.

First, the SLL mitigation technique used are not optimum design performances and typically sacrifice antenna gain and power efficiency of Power Amplifier to meet regulations.  This in turn translates to lower C/N and/or Eb/N0, therefore lower throughput.  In other words, the transmitter suffers power efficiency as well as optimum throughput, two critical metrics for any transmitter in exchange for mitigating SLL, which should have been part of original design to begin with, hence lack of optimum design.

Spending resources to design efficient power amplifier, yet backing off from efficiency sweet spot of Power Amplifier due to undesired and uncontrolled SLL defeats the purpose of the overall system metrics; that power efficiency and throughput, both of which impact bottom lines of the network operator.

Second, the additional time which takes for trial and error is reducing the chances of being in the market during the window of opportunity.

ORTENGA SLL Management Algorithm is proactive design and embedded during the development phase to expedite TTM while producing optimum design trade-off Gain vs. SLL, hence power efficiency and throughput, below algorithm flow.

 

Partner with ORTENGA for design and development of radio algorithms and implementations in your new product.

 

 

Beam Tracking Algorithm Flow

Beam Tracking Algorithm is utilized when there is beamforming and mobility either at transmitter or receiver.

The following diagram illustrates top level algorithms flow interactions, namely; Beamforming, SLL management, and Beam tracking.

It is worth mentioning that although the high level flow diagram is similar yet actual flow diagram may change depending on the applications and use cases.  For instance, in some use cases or applications, there is need for Target Acquisition Algorithm, before beam tracking algorithm of the target is invoked.

Partner with ORTENGA in design and development of any of the above algorithms for your new product.

 

Machine Learning Algorithms

There are many design problems which do not have known theoretical or analytical solutions, yet they have practical applications, therefore demand in the technical market space.

M2M communications require some level autonomous in learning and decisions making to ensure, no human interactions.

If you have any of these problems, ORTENGA has subject matter expertise to help you define the scope and then provide appropriate ML algorithm to address that.

 

Clutter Rejection Algorithm

Radar clutter reduces SNR therefore it reduces the detection, consequently the radar capability in identify the object of interests.

Regarding Autonomous Automotive, reducing the clutter enhances the radar in identifying and distinguishing various objects’ scatterer on and near the road.

Augment ORTENGA in your Radar design and development.

 

Direction Finding Algorithms

Bat has direction finding sensors and organic intelligence or algorithm which helps them to navigate, detect and track prey.  Bat uses audio signals, sonar anywhere between 15 to 200 KHz to echolocate prey and navigate its surroundings.

The speed of sounds in the air is 343 m/s at 20°C air.  That makes the wavelength anywhere between 15 to 30 cm.  Yet, bat has spatial resolution as small as 0.3 mm, which is 2 orders of magnitude smaller than operating wavelength.  This implies that bat direction finding algorithm is phase-based interferometry.  In fact, bat temporal resolution is anywhere between 10 to 400 ns which corroborates phase interferometry direction finding algorithm.

To echolocate, there are at least two sensors/ears are required.  Any waveform has amplitude, frequency, and phase attributes. At least one of the waveform attributes can be compared by the two sensors to echolocate the source of that signal.

The same techniques can be used for an emitter of electromagnetic signals.

Orca has direction finding sensors and organic intelligence or algorithm which helps them to detect, identify, and track prey.  Orca has a meticulous eating menu and does not eat outside of their menu.  They are highly intelligent and works as team/family for developing hunting skills for that particular prey.

The speed of sound in ocean water is 1500 m/s.  Orca also use sonar with frequency between 0.5 to 25 KHz with the peak energy between 1 to 6 KHz.  That makes the operating wavelength anywhere between 0.25 to 1.5 m.  With this wavelength range and the size of orca and menu targets, it can be deduced that orca can either use amplitude or phase-based interferometry.

Phase based interferometry is more accurate technique for direction finding of the emitter.

Electromagnetic signals can be located with direction finding algorithms either amplitude or frequency, depending on the required spatial resolution, available HW, and the baseline of the multiple sensors.

Human uses the ears to echolocate the source of the received audio using amplitude comparison direction finding.

ORTENGA helps businesses to identify required technical features to realize their business goals.

 

MUSIC Algorithm

Multiple Signals Classification, MUSIC is an advanced direction-finding algorithm.

MUSIC algorithm uses Jacobi algorithm to find Eigenvalues and Eigenvectors of incoming multiple signals in order to reduce the computations and determine real time direction of EM emitters.

Jacobi algorithm is an iterative technique to determine the solution to a system of linear equations.

MUSIC is an effective real time algorithm for accurate spatial resolution of multiple EM emitters and/or multiple sensors in the environment.

ORTENGA helps businesses to identify required technical features to realize their business goals.

 

Envelope Tracking Algorithm 

Envelope Tracking, ET, Algorithm have mitigated Power Added Efficiency, PAE, of Power Amplifiers, PA, in UE as well as eNB infrastructure during that past decade.

It is well known that maximum PAE is reached when PA is near compression point.  However, operating PA at or near compression point produces signal distortions due to non-linearity behavior of PA which effectively reduces EVM of transmitting signal, system KPI.

In order to mitigate the signal distortion, the PA nonlinearity can be simulated or characterized.  Then the desired signal can be distorted intentionally prior feeding the PA in such a way that after the PA amplification, the desired signal becomes linear again.  This algorithm is called Digital Pre Distortion, DPD, and widely used in UE, eNB, and gNB infrastructures.

ORTENGA has seasoned engineering from ASIC, SATCOM, radar, and mobile terrestrial radio communications industries in HW, FW, and SW engineering disciplines.

 

How to calculate Exact Number of Bits for DSP Algorithms?

Digital Signal Processing, DSP, algorithms are typically coded in MATLAB with floating point numbers.  Whereas the implementation of the same algorithm in HW/FPGA is bounded by limited number of bits, therefore, there is question of how many bits are actually required such that the given algorithm has adequate accuracy.

The short answer is that the exact number of bits can be analytically calculated similar to ADC or DAC’s ENOB and it can be validated via fixed point in MATLAB to ensure the exact number of bits produce the expected accuracy.

ORTENGA has seasoned engineering from ASIC, SATCOM, radar, and mobile terrestrial radio communications industries in HW, FW, and SW engineering disciplines.

 

Singular Value Decomposition, SVD Algorithm

SVD matrices are utilized in many engineering disciplines as well as finance industry.

In radio communications system, Multi Input Multi Output, MIMO architecture requires calculating inverse matrix of the channel.

The inverse matrix calculations are computationally very expensive, O (n3), and time and power consuming.

SVD matrix is utilized to diagonalize the channel matrix, therefore significantly reducing the computations for real time in mobile handset.

Finance industry use SVD to compress financial data, representing data using “less information” to save memory space.  SVD can be thought as data compression technique.

ORTENGA helps businesses to identify required technical features to realize their business goals.

ORTENGA is comprised of seasoned and skillful engineers who collaborate on innovative design in entrepreneurial environment to accomplish clients’ project.

Partner ORTENGA in your next product concept, design, and development to realize that business goal.

ORTENGA has seasoned engineering from Autonomous Automotive, SATCOM, radar, Smart City, WiFi, and Mobile Terrestrial Radio Communications industries in Antenna, ASIC, HW, FW, and SW engineering disciplines.

 

State Machine, AI, and Human

State machine is a model which describes a system behavior or its transitions by defining its state and input required for each transition.

Perhaps the simplest system is electrical light bulb where state machine is electrical switch.  There are two states, On or Off.  The electrical switch can force the state change from On to Off or vice versa.

Another yet more complicated system is 3 floors’ elevator.  There are 3 states or floors.  Depending on the state or floor level and which button is pressed, the elevator moves in certain up or down direction.

Human is the most sophisticated known system.  Our emotions are the states, e.g., angry, happy, rational, sad etc.  Our response is different depending which state of being we are in and how is that input is delivered.

Artificial Intelligence, AI is another system.  Your question (input) to AI, puts AI in a certain state depending on the programmer knowledge and definition of boundary conditions.  Therefore, the response would depend on that state and your question (input).

Given AI system and finite number of states, the response to your question is finite in numbers and may differ somewhat from time to time depending how you ask your question and initial conditions of AI.

Human state of mind or emotion can change.  Some of us are triggered by external events or words.  Therefore, our response would be different depending our state of being or emotion.

The easiest way to control our emotion can be similar to electrical switch.  Breath in and breath out could be human emotional switch.  Our state of mind would determine how we respond to others’ request.

Human with appropriate state of mind, curiosity, willingness, and calculated risk-taking initiative can discover new things or new ways of doing things, hence innovate and progress.

Partner with ORTENGA to design and develop state machine for your system.

 

Beamforming Algorithm

5G and Autonomous Automotive LIDAR and Radar rely on Beamforming Technology.

Beamforming, BF, is based on Antenna/Sensor Array, where collectively work together to form the beam in transmit or receive sensing.

The inputs to the algorithm are frequency, number of elements in the array, inter-element spacing in the array, polarization, pointing direction of the beam, required SLL.

The outputs of the algorithm are inter-element phase shift, antenna element voltage/current excitation for open loop implementation.

For closed loop implementation, the algorithm also utilizes the sensors in the array and/or feedback from receiver to make additional corrections due to impairments or tolerance of electronics in the system.

Partner with ORTENGA to design, develop, and implement BF algorithm in FPGA for your project.

 

 

AI and Radio Communication Systems Modeling Similarities

Artificial Intelligence, AI is a modeling platform that can use existing data and model to predict future events for a particular application.

AI relies on Machine Learning, ML to predict future outcome based on available historical or current data.

Data analytic is finding pattern in data and assigning predictable statistical behavior or distribution.

There are many probability distributions which predict outcomes based on available input data and the nature of incoming data.

The statistical data may have different pattern for different applications yet the modeling tools and analysis are the same regardless that application.  That is where AI and Radio Communication System modeling cross path.

Partner with ORTENGA to design and develop AI or ML model or algorithm for your new product.

 

ADC Calibrations

Analog to Digital Converter, ADC performance depends on its amplitude and timing precision.

The timing performance of ADC impacts the bandwidth of desired signal to be collected.

Whereas the amplitude performance of ADC impacts the minimum signal level to be collected.

Both of which impact the dynamic range of the ADC.

Partner with ORTENGA for your ADC background and foreground calibration algorithms.

Background ADC calibration occurs while ADC is operating on the incoming signal.

While Foreground ADC calibration requires taking ADC offline for more precise calibration.

ORTENGA can help you design and develop required calibration algorithms.

 

SVX Matrix Utilization by Tensor AI

Sparse Voxel Octree, SVX matrix is typically utilized by Tensor Artificial Intelligence, AI to save memory space.

Tensor is multi-dimensional matrix and AI relies large Tensors to interrelate features and corresponding data points for training AI.

The matrices have many zero elements.

To save memory space, SVX stores the non-zero elements and their indices.

SVX can be thought of as a data compression technique or algorithm.

Partner with ORTENGA for your new AI product and development.

 

Data Curation

Data Curation is the process of preparing data before training AI models.

The input data is not necessarily in the appropriate format for AI model and has to formatted in a such way that can be fed into the AI model.

AI models are not unique therefore each model has certain inputs and outputs.

Consequently, data curation is not unique and has to be tailored for particular AI model.

As a result, data curation algorithm would be input data and AI model dependent.

Partner with ORTENGA for Data Curation AI algorithm design and development.

 

Designing Algorithms for ARM Processor

ARM processors are designed for fast processing.

Artificial Intelligence, AI processor even requires faster processing which implies parallel instructions execution.

Consequently, AI or ARM processors Algorithms should be either designed or decomposed to parallel instructions’ steps which can independently be executed.

Partner with ORTENGA in your ASIC or Algorithms design and development.

 

Linearity and Superposition in AI Processing

Linear systems behavioral model is relatively simple.

When an input to the linear system is sophisticated function, the input can be decomposed to simpler inputs and ideally with known responses or model.

Then, the system behavior is superposition or summation of the all the simpler inputs response by the linear system, which is known as superposition principal.

Now, if the system is not linear, then one can decompose the overall region of interest to several quasilinear regions, where the linearity is good approximation for each region.

Consequently, the superposition principal can be utilized.

By quasilinear region, it is meant an area where the actual system response is closely related to its linearized model.

This linearization technique is applicable to many systems; such as AI model.

If the linearization is implemented properly, then it saves time and computational complexity which translate to power saving for operating the system.

Partner with ORTENGA in your ASIC or Algorithms design and development.

 

Electromagnetic Angle of Arrival or Direction-Finding Algorithms Dependency on Antenna

Electromagnetic, EM Angle of Arrival, AoA or Direction Finding, DF algorithms’ performance depends on the EM sensors, i.e. antennas utilized.

Typically, there is a distinction between AoA and DF algorithms, even though their names appear similar, yet in the radar industry AoA and DF have different implications.

AoA refers to angle in which EM wave source is located in azimuth plane or plane of reference.

Whereas, DF refers to AoA plus range to the EM source, i.e. geo location.

Therefor DF algorithm requires additional inputs relative to AoA algorithm.

AoA or DF requires at least two sensors for differential measurement of either amplitude, time, phase, or envelope of the EM waves.

Antennas utilized for EM sensing in AoA or DF are major contributors to the error in the measurements, therefore these antennas have to be specified, designed and developed with the AoA or DF performance objectives in mind.

Partner with ORTENGA for AoA or DF antenna, algorithm, and HW design and development.

 

Nature Solved Direction Finding Before We Did

What bats, orcas, and RF systems teach us about phase-based interferometry

Direction Finding Beyond Wavelength

How bats, orcas, and RF systems achieve sub-wavelength accuracy

Direction finding is often treated as a hardware problem—add more sensors, increase bandwidth, tighten tolerances. Nature shows otherwise. Long before engineered RF systems existed, bats and orcas solved direction finding under far stricter constraints, achieving spatial resolution far smaller than their operating wavelengths.

They did not violate physics. They exploited it.

The lesson is universal: when resolution requirements exceed wavelength limits, direction finding becomes a phase and system architecture problem—not a component problem.

Bats: Sub-Wavelength Direction Finding Through Phase

Bats use echolocation to navigate, detect, and track prey using ultrasonic signals typically ranging from 15 kHz to 200 kHz. In air, where the speed of sound is approximately 343 m/s, these frequencies correspond to wavelengths on the order of centimeters.

Yet bats routinely resolve spatial features as small as ~0.3 mm, more than two orders of magnitude smaller than the wavelength of the signals they emit. This capability cannot be explained by amplitude-based sensing alone.

Instead, bats rely on phase-based interferometry. By measuring extremely small timing and phase differences between signals received at both ears, bats infer angle of arrival with remarkable precision. Their auditory systems exhibit temporal resolution on the order of tens to hundreds of nanoseconds, fully consistent with phase-sensitive direction finding.

Key insight:
Δφ → θ
Sub-wavelength resolution emerges from phase differences, not signal strength.

Orcas: Scalable Interferometry in a Dense Medium

Orcas operate in seawater, where the speed of sound is approximately 1500 m/s, using sonar frequencies spanning roughly 0.5 kHz to 25 kHz, with peak energy between 1 kHz and 6 kHz. These frequencies correspond to wavelengths from ~0.25 m to 1.5 m.

At these wavelengths—and given the size of both the animal and its prey—orcas can leverage both amplitude and phase cues. However, for precise bearing estimation, particularly in cluttered environments or cooperative hunting scenarios, phase and timing differences dominate.

Orcas demonstrate that direction-finding architectures scale with medium, wavelength, and target size, while still relying on the same underlying interferometric principles.

Key insight:
Δt + Δφ → Bearing
Timing and phase together enable robust localization in complex environments.

RF Systems: Engineering Nature into Architecture

Engineers have long drawn inspiration from biological systems—such as bat and orca echolocation—to develop direction-finding techniques that extend into much higher frequencies. Nature demonstrated early that direction finding is fundamentally a differential measurement problem, not a brute-force sensing problem.

In biological systems, low operating frequencies produce large wavelengths, allowing differential phase and timing to be measured reliably across spatially separated sensors. Engineers have expanded these principles into electromagnetic domains where operating frequencies are orders of magnitude higher and system constraints are significantly tighter.

Modern direction-finding algorithms estimate target location by exploiting differential measurements across multiple sensors, including:

  • Amplitude differences
  • Frequency differences
  • Phase (or time) differences
  • Signal envelope characteristics

As frequency increases and wavelengths shrink, resolving differential phase becomes increasingly sensitive to clock accuracy, calibration, noise, and hardware mismatch. In these regimes, systems may trade ultimate angular resolution for robustness by relying more heavily on amplitude or envelope-based techniques. Conversely, when sub-wavelength accuracy is required, phase coherence across sensors becomes the dominant architectural requirement.

🔶 Executive System Framing — Read This First

Direction finding performance does not come from a single block.
It emerges from the interaction of sensor geometry, timing fidelity, signal processing algorithms, and system-level assumptions.

When accuracy targets push beyond wavelength limits, direction finding must be treated as a system architecture decision, not a component-level optimization.

Key insight:
Phase coherence → Angular accuracy

ORTENGA Perspective: From Physics to Outcomes

Direction-finding failures in RF systems rarely originate from a single component. They emerge from misalignment between sensors, silicon, algorithms, and system assumptions.

ORTENGA approaches direction finding as a system-level architecture problem, not a block-level exercise. Whether the application is RF sensing, radar, communications, or signal intelligence, the same questions apply:

  • Is the sensor baseline appropriate for the required angular resolution?
  • Is phase coherence preserved across hardware and processing domains?
  • Are algorithms matched to real, not idealized, signal conditions?

Key insight:
Sensors + ASIC + Algorithms
Performance emerges only when the system is architected holistically.

Call to Action

Audit Your Direction-Finding Architecture with ORTENGA

ORTENGA helps engineering teams identify phase, baseline, and system-level risks before they become performance shortfalls, schedule slips, or costly re-spins.

If your direction-finding accuracy depends on assumptions rather than verified architecture, it’s time for a system-level audit.

 

 

AI and Radio Communication Systems: Modeling Through Audit, Design, and Validation

Artificial Intelligence (AI) is fundamentally a modeling discipline. It uses existing data and mathematical representations to predict future behavior for a specific application. This is not new. Radio communication systems have relied on the same principles for decades—modeling uncertainty, extracting information from noise, and predicting system performance under imperfect conditions.

Understanding this shared foundation requires discipline, not hype. At ORTENGA, this discipline is enforced through Audit → Design → Validation.

Audit: Understanding the Data and Assumptions

AI relies on Machine Learning (ML) to predict outcomes based on historical or real-time data. Data analytics identifies patterns and assigns statistical behavior through probability distributions. However, predictions are only as good as the assumptions behind the data.

This is where many AI products fail.

Before designing a model or selecting an algorithm, the data and its assumptions must be audited:

  • What does the data actually represent?
  • Under what conditions was it collected?
  • What noise, bias, or uncertainty is embedded in it?

Radio communication systems have always treated this step as mandatory—modeling noise, interference, fading, and uncertainty before attempting detection or estimation. AI systems require the same rigor.

Design: Applying the Right Modeling Tools

There are many probability distributions capable of predicting outcomes from input data. While the data patterns may differ across applications, the underlying modeling tools—statistics, estimation, optimization—remain the same.

This is where AI and radio communication system modeling cross paths.

Design is the process of selecting and structuring these models correctly:

  • Choosing appropriate statistical representations
  • Defining system-level performance metrics
  • Making deliberate tradeoffs between complexity, performance, and robustness

Without disciplined design, AI models and radio systems alike may perform well in isolation but fail at the system level.

Validation: Proving the Model Works in Reality

Modeling does not end with design.

Validation confirms that the model behaves as expected under real operating conditions—not just in training data or simulations. In radio systems, this means verifying performance in the presence of real noise and interference. In AI systems, it means validating predictions against unseen data, distribution shifts, and edge cases.

Skipping or compressing validation leads to models that appear accurate but fail in deployment.

A Shared Modeling Discipline

AI and radio communication systems differ in application domain, but not in modeling discipline. Both depend on:

  • Statistical rigor
  • Explicit assumptions
  • System-level thinking
  • Validation against reality

The difference between success and failure is not the algorithm—it is the process.

Partner with ORTENGA

Partner with ORTENGA to audit, design, and validate AI or ML models and algorithms grounded in real system behavior—not assumptions. Whether applied to AI, wireless communication, or complex signal-processing systems, disciplined modeling reduces risk and increases the likelihood of real-world success.

 

Auditing Complexity for Edge AI

Why SVD Enables Efficient Intelligence at the Edge

Edge AI doesn’t fail because models are inaccurate—it fails because complexity goes unaudited. Algorithms that perform well in the cloud often collapse at the edge under tight constraints on power, memory, latency, and cost. Singular Value Decomposition (SVD) sits at the foundation of efficient Edge AI, exposing redundant dimensions and transforming uncontrolled computational growth into an explicit system decision before deployment makes it irreversible.

For decades, radio and wireless communication systems have faced the same constraints that now define Edge AI: limited power, strict latency budgets, and hard real-time requirements. In mobile devices, SVD has long been used in MIMO receivers to diagonalize complex channel matrices, replacing expensive matrix inversions with efficient scalar operations. This architectural shift made real-time wireless communication feasible on battery-powered devices. The same principle applies to Edge AI, where SVD reduces effective model dimensionality and enables compact representations that fit within edge-level compute, memory, and energy limits. While large AI models are typically trained in the cloud where compute is abundant, inference at the edge must operate under real-time, power, and memory constraints, making SVD-based techniques a practical enabler for deployment.

This is why the first step in Edge AI system design is a disciplined audit of the model or algorithm against computational complexity and required accuracy tolerances.

Audit: Model vs. Complexity vs. Accuracy Tolerances

The purpose of the Audit phase is to determine whether an Edge AI model or algorithm can meet required accuracy tolerances within realistic constraints on inference cost, MAC count, memory footprint, latency, and power. A model that achieves excellent accuracy in the cloud may still be unsuitable at the edge if its computational demands exceed what the device can support.

At this stage, accuracy alone is not the goal—feasibility is. The audit makes tradeoffs explicit early, before architecture, hardware, or software decisions lock in unnecessary complexity and cost.

Accuracy without feasibility is not a solution.

Design: SVD as an Edge Inference Primitive

Once feasibility boundaries are clear, the Design phase determines how to meet accuracy targets efficiently. This is where SVD becomes a system-level architectural tool.

By applying SVD to reduce effective rank, compress internal representations, and eliminate redundant dimensions, designers can significantly lower inference MAC counts and memory usage. These reductions enable tighter latency control, lower power consumption, and predictable scaling—without redefining the product’s intent.

In Edge AI systems, SVD is not a mathematical convenience; it is a deliberate design choice that converts abstract complexity into manageable engineering decisions.

Validate: Proving Edge Readiness Before Deployment

The Validate phase confirms that the system performs as intended under real operating conditions. This includes verifying that inference latency meets real-time targets, power and thermal limits are respected, memory fits on-device, and accuracy remains within defined tolerances after SVD-based reductions.

Edge readiness must be proven before deployment—not discovered through field failures, degraded performance, or costly redesigns.

Example: Edge Vision Inference

Consider an edge vision model trained in the cloud for object detection. While the full-rank model achieves high accuracy, its inference MAC count and memory footprint exceed the limits of the target device. Applying SVD to selected layers reduces effective rank, cutting computation and memory requirements while preserving accuracy within tolerance. The result is a deployable edge model that meets latency and power constraints without changing the application or retraining from scratch.

The same pattern applies to RF sensing, sensor fusion, and small on-device language models.

Why This Matters

Across Edge AI deployments, failures rarely stem from poor algorithms. They stem from skipping the step where complexity, accuracy, and system constraints are reconciled.

At the edge, accuracy is a requirement.
Feasibility is the constraint.

Auditing complexity early turns Edge AI from a risky experiment into an engineered system.

ORTENGA helps companies audit, design, and validate Edge AI systems before failure modes are baked in. By aligning algorithms with system constraints early, ORTENGA reduces execution risk, protects ROI, and enables efficient intelligence at the edge.

If you are deploying AI into power-, latency-, or memory-constrained environments, audit complexity before you design.

Partner with ORTENGA to make Edge AI deployable—not just accurate.

 

 

System-Aware ADC Calibration Algorithms

Recovering bandwidth, sensitivity, and dynamic range at the system level

Most ADCs don’t underperform due to architectural limits. They underperform because timing and amplitude errors compound across the signal chain, quietly eroding usable bandwidth, sensitivity, and dynamic range long before anyone notices.

Why ADC Calibration Is a System Problem

ADC performance is often discussed in isolation—ENOB, SNR, SFDR—but in real products, those metrics are shaped by the surrounding system.

Clock quality, front-end gain staging, temperature variation, power noise, layout parasitics, and signal statistics all influence how an ADC behaves in operation. Even a well-designed converter can underperform once these effects interact.

Calibration is the mechanism that bridges ideal ADC behavior to deployed system reality.

Timing Errors Define Bandwidth

Sampling jitter, clock phase noise, and aperture uncertainty directly limit the highest usable input frequency. As bandwidth increases, timing errors translate into non-recoverable SNR loss.

Without calibration:

  • High-frequency performance degrades first
  • Margins disappear unevenly across operating conditions
  • Bandwidth advertised on the datasheet becomes unusable in practice

System-aware calibration treats timing as a dynamic parameter, not a fixed assumption.

Amplitude Errors Define Sensitivity

Offset, gain error, nonlinearity, and mismatch raise the effective noise floor and reduce the minimum detectable signal level.

These errors:

  • Vary across process, voltage, and temperature
  • Drift over product lifetime
  • Interact with front-end analog design choices

Calibration restores sensitivity by continuously aligning amplitude behavior with system requirements, not just factory trim values.

Dynamic Range Is the Outcome

Bandwidth and sensitivity cannot be optimized independently. Improving one while ignoring the other often produces elegant lab results—and disappointing field performance.

Dynamic range is the system-level outcome of both timing and amplitude accuracy.

Calibration is what keeps that balance intact over real operating conditions.

Background vs. Foreground Calibration

Foreground Calibration
Foreground calibration takes the ADC offline to achieve the highest possible precision. It is typically used:

  • At manufacturing test
  • During controlled maintenance modes
  • When absolute accuracy is the priority

Background Calibration
Background calibration operates while the ADC processes live signals. It:

  • Tracks drift due to temperature and aging
  • Preserves performance without interrupting operation
  • Requires careful algorithm stability and convergence design

Most successful products use both, applied deliberately based on system constraints.

What Makes ORTENGA’s Approach Different

ORTENGA designs ADC calibration algorithms with awareness of:

  • Signal statistics, not idealized test tones
  • Power, latency, and silicon area constraints
  • Verification and validation realities

The goal is not theoretical perfection, but stable, deployable performance recovery.

Calibration algorithms are developed as part of the system architecture, not bolted on after silicon is done.

Where This Fits in ORTENGA’s Core Model

ADC calibration sits at the intersection of ORTENGA’s core strengths:

  • Antenna & RF Systems → Signal integrity and noise environment
  • ASIC Architecture & Design → Clocking, power, and calibration hooks
  • Algorithm Development → Estimation, adaptation, and convergence

This is where system-level thinking directly translates into measurable product performance.

If ADC performance matters beyond the datasheet,
ORTENGA helps teams design system-aware calibration algorithms that recover real bandwidth, sensitivity, and dynamic range.