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Enhancing Digital Communication Systems through Error Vector Magnitude (EVM) Measurements

In the realm of modern communication systems, ensuring the reliable and efficient transmission of signals is paramount. As communication technologies continue to evolve, the need for accurate performance metrics becomes increasingly vital. One such metric that plays a crucial role in evaluating and improving system-level performance is Error Vector Magnitude (EVM). EVM provides valuable insights into the quality of transmitted signals by quantifying the discrepancy between the ideal and received signals. In this article, we will delve into the significance of EVM measurement, its applications across various communication systems, and how it contributes to optimizing performance, ensuring interoperability, and enhancing spectral efficiency.

Error Vector Magnitude (EVM)

Error Vector Magnitude (EVM) is a metric used to quantify the accuracy and quality of a digital communication system. It measures the difference between the ideal transmitted signal and the received signal, taking into account impairments such as noise, distortion, and interference.

Importance of EVM as a Performance Metric

EVM provides insights into the overall system performance and signal quality, helping engineers evaluate the effectiveness of various modulation and demodulation techniques, coding schemes, and transmission environments.

By quantifying the difference between the received signal and the ideal reference signal, EVM enables engineers to assess the system’s ability to faithfully transmit information and maintain signal integrity. A lower EVM indicates better accuracy and fidelity of the received signal, while a higher EVM suggests increased distortion and errors.

Relationship between EVM and Signal Quality

A lower EVM corresponds to higher signal quality, indicating a smaller gap between the received and reference signals. Conversely, a higher EVM value indicates poorer signal quality and increased distortion.

Signal quality can be affected by factors such as noise, interference, multipath fading, non-linearities in amplifiers, and phase noise. By measuring EVM, engineers can identify and quantify these impairments, enabling them to optimize system design, diagnose problems, and enhance the performance of communication systems.

EVM in Different Modulation Schemes

  • Quadrature Amplitude Modulation (QAM): In QAM, EVM measures the accuracy of both the amplitude and phase of the received signal compared to the reference signal. It helps assess the impact of noise and interference on the constellation points of the received signal.
  • Orthogonal Frequency Division Multiplexing (OFDM): EVM is particularly important in OFDM systems due to their susceptibility to frequency-selective fading and inter-symbol interference. EVM quantifies the distortion introduced by the channel and helps evaluate the effectiveness of equalization and error correction techniques.
  • Single Carrier Modulation (SCM): EVM analysis in SCM systems is useful for evaluating the impact of nonlinear amplifiers, phase noise, and inter-symbol interference on the received signal quality.
  • Other modulation schemes: EVM can also be applied to other modulation schemes such as Phase Shift Keying (PSK), Frequency Shift Keying (FSK), and Continuous Phase Modulation (CPM), providing insights into their performance and identifying areas for improvement.


Constellation Diagram Obtain QAM 256


EVM Measurement Techniques

EVM Measurement in Digital Communication Systems

Time-Domain EVM Measurement: This method involves measuring the Error Vector Magnitude (EVM) in the time domain of a digital communication system. It evaluates the difference between the received signal and the ideal reference signal, representing the transmitted signal. Time-domain EVM measurement assesses the accuracy of the transmitted symbols by quantifying the distortion and impairments introduced during transmission.

Frequency-Domain EVM Measurement: In this approach, the EVM measurement is performed in the frequency domain of a digital communication system. It involves analyzing the spectrum of the received signal and comparing it to the expected ideal spectrum. Frequency-domain EVM measurement enables the assessment of the signal quality and the detection of impairments that may affect different frequency components differently

Symbol-Based EVM Measurement: Symbol-based EVM measurement evaluates the distortion and impairments in individual symbols of a digital communication system. It compares the received symbols to the expected ideal symbols and quantifies the deviation. This method provides a detailed analysis of symbol-level distortions, enabling a granular assessment of the system’s performance.


EVM Measurement Challenges and Solutions

Noise and Interference Mitigation: One of the challenges in EVM measurement is the presence of noise and interference that can degrade the quality of the received signal. To mitigate this challenge, various techniques such as error correction coding, equalization, and adaptive filtering can be employed to reduce the impact of noise and interference on the measured EVM.

Nonlinearities and Distortions: Nonlinearities in the transmitter or receiver components can introduce distortions that affect the EVM measurement. These nonlinearities can arise from amplifiers, mixers, or other components in the communication system. To address this challenge, linearization techniques, pre-distortion, and digital signal processing algorithms can be utilized to mitigate the effects of nonlinearities and distortions on the measured EVM.

Frequency and Phase Errors: Frequency and phase errors in the communication system can cause deviations from the expected signal, leading to increased EVM. These errors can result from imperfect frequency synthesis, clock synchronization issues, or Doppler effects in wireless channels. Calibration, synchronization algorithms, and carrier recovery techniques are employed to compensate for frequency and phase errors and improve the accuracy of EVM measurements.


Applications of EVM Measurement

  • Wireless Communication Systems
  • Cellular Networks (e.g., LTE, 5G)
  • Wi-Fi and Bluetooth Systems
  • Satellite Communication Systems
  • Broadcasting Systems (e.g., DVB, ATSC)
  • Radar and Sonar Systems
  • Optical Communication Systems
  • Internet of Things (IoT) Networks


Improving System-Level Performance Using EVM Measurement

Signal Quality Assessment and Optimization

Identifying and Addressing Sources of Error: This involves identifying and resolving various factors that can introduce errors in the system’s signal quality. These errors can be caused by factors such as noise, interference, distortion, or hardware limitations. By identifying these sources of error, appropriate measures can be taken to address and minimize their impact on the system’s performance.

Adaptive Equalization and Pre-Distortion Techniques: Adaptive equalization techniques are used to compensate for signal distortion caused by various factors like channel impairments or transmission medium characteristics. These techniques dynamically adjust the equalization parameters to mitigate distortion and improve signal quality. Pre-distortion techniques, on the other hand, involve modifying the input signal before transmission to compensate for known distortions in the system, thus improving the overall signal quality.

Frequency and Timing Synchronization: Frequency and timing synchronization ensures that transmitted signals are received and processed correctly at the receiver end. Achieving accurate synchronization helps in minimizing interference and optimizing the system’s performance. It involves techniques such as phase-locked loops, synchronization signals, or pilot signals to establish precise timing and frequency references for proper signal reception.


Efficiency Measurement of PAPR Reduction

Crest Factor (CF) is defined by the ratio of the Peak signal value and the average signal value. Crest Factor Reduction (CFR) algorithm reduces the CF value for the incoming baseband signal. Peak Cancellation CFR algorithm (PCFR) reduces PAPR of the incoming baseband signal to a desired PAPR value and balances between signal quality and out-of-band emission to keep the spectral properties of the incoming signal very close to the original value. Signal Quality is characterized by EVM and out-of-band emission is characterized by ACPR metric. Peaks are identified above a threshold and the threshold is determined by the target PAPR value.


PAPR Obtain for the DPD


PCFR module implemented by Faststream Technologies possesses novel architecture that can be targeted for RTL based implementation as well as processor based implementation. Pipeline concepts are implemented with the help of queue structure. The duration of the peaks are very small in 5G in comparison with 4G standard. Hence, a new aggregated peak concept is invented and implemented by Faststream Technologies to address the CFR implementation issues for 5G TDD and FDD incoming baseband samples.

This concept ensures that only one frequency is added for a set of aggregated peaks thus ensuring spectral properties. The number of maximum peaks considered simultaneously is a programmable parameter. Faststream Technologies PCFR and Digital Pre-Distortion (DPD) IP provides ACPR value -50 dBc or better for 37 dBm channel power while maintaining PAPR value less than 8 dB and EVM value less than 3.5 % for 256 QAM configurations.

Output Spectrum Obtain from CFR-DPD-PA system


Spectral Efficiency Enhancement

Bit Rate and Error Correction Improvement: Increasing the bit rate refers to transmitting more information in a given time. This can be achieved by employing more advanced modulation techniques or using higher-order modulation schemes. Error correction improvement involves implementing more sophisticated error correction codes that can detect and correct a higher number of errors, thereby improving the overall data reliability and spectral efficiency.

Modulation Scheme Selection: Modulation schemes determine how data is encoded onto a carrier signal for transmission. By selecting the appropriate modulation scheme, and considering factors like channel conditions and data rate requirements, spectral efficiency can be enhanced. Advanced modulation schemes, such as Quadrature Amplitude Modulation (QAM), can transmit more data symbols per unit of bandwidth, thus improving spectral efficiency.

Resource Allocation Optimization: Resource allocation optimization involves efficiently allocating system resources, such as bandwidth, power, and time slots, to different users or services. By intelligently managing and allocating these resources, the system can maximize its capacity and spectral efficiency. Techniques like dynamic resource allocation or adaptive modulation and coding can be employed to optimize the allocation based on real-time network conditions and user demands.


Interoperability and Compatibility Assurance

Conformance Testing and Certification: Conformance testing involves verifying whether a system or device complies with the relevant standards and specifications. It ensures that the system operates according to the defined protocols and interfaces, thus ensuring interoperability and compatibility with other devices or systems. The certification confirms that the system has successfully passed the conformance tests and meets the required standards

Ensuring Standard Compliance: To ensure interoperability and compatibility, it is essential for the system to adhere to established standards and protocols. Compliance with these standards enables seamless communication and interaction between different systems or devices from various manufacturers. By following the specified standards, potential issues related to compatibility and interoperability can be minimized or eliminated.

System Integration and Interference Mitigation: System integration involves the successful incorporation of different subsystems or components into a cohesive system. It includes addressing challenges related to compatibility, interfaces, and functionality to ensure smooth operation and optimal performance. Interference mitigation focuses on identifying and minimizing sources of interference that can degrade system performance or disrupt interoperability. Techniques like frequency planning, shielding, or signal filtering may be employed to mitigate interference effects.



Error Vector Magnitude (EVM) measurement has emerged as a vital metric for evaluating and improving system-level performance in communication systems. By quantifying the deviation between ideal and received signals, EVM enables engineers to assess signal quality, optimize system performance, enhance spectral efficiency, and ensure interoperability. EVM measurement techniques, such as time-domain, frequency-domain, and symbol-based approaches, provide valuable insights into the performance of various communication systems, including wireless networks, broadcasting systems, radar and sonar systems, optical communication systems, and IoT networks. By leveraging EVM measurement, engineers can identify and address sources of error, employ adaptive equalization and pre-distortion techniques, optimize resource allocation, and enhance compatibility and interoperability. Ultimately, the comprehensive understanding and effective utilization of EVM measurement contribute to the advancement of communication systems, leading to improved reliability, higher data rates, and enhanced user experiences in the modern digital age.