Adaptive Modulation And Coding Research Articles

Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.

Coverage hetnet based picocell and femtocell for uplink condition around building environment with single knife edge method

The development of HetNet (Heterogeneous Network) radio base stations has experienced many developments. This is indicated by the existence of microcells, macrocells, picocells, femtocells, and so on. In this research, the research is aimed at the propagation of user equipment communication systems in uplink conditions with HetNet picocells and femtocells. UE propagation is on a straight path between the building environment. The communication frequency used is 10 GHz. The communication between Tx and Rx is modeled as a diffraction mechanism, AWGN channel, and atmospheric attenuation. The Single Knife Edge (SKE) method is used to model the mechanism. The propagation channel is faced with AWGN (Additive White Gaussian Noise). The analysis of this research includes the SNR value, Adaptive Modulation and Coding (AMC) level, and percentage of communication coverage area. The AMC is based on the use of MCS (Modulation and Code Scheme). Some of the MCS modulations used QPSK, 16 QAM, and 64 QAM. As research results show that the percentage of communication coverage area obtained was gNB1 80.59 percent, gNB2 65.67%, and selection combining HetNet 95.52%.

Microwave Antenna Optimization for Low Latency and High Throughput Communication Systems

This paper presents a comprehensive exploration of microwave antenna optimization strategies aimed at achieving low latency and high throughput in modern communication systems. With the escalating demand for real-time applications and data-intensive services, the optimization of microwave antennas has become imperative to meet the stringent requirements of responsiveness and efficiency. The abstract delves into various facets of antenna optimization, including design parameters, signal processing techniques, and deployment considerations, all geared towards minimizing latency and maximizing throughput. Through a thorough review of existing literature and practical insights from real-world deployments, this research aims to provide valuable guidance for engineers and researchers in designing and implementing optimized microwave antennas for next-generation communication networks. By examining the intricate interplay between antenna design, signal processing algorithms, and deployment strategies, this paper elucidates the challenges and opportunities in achieving low latency and high throughput in microwave communication systems. Furthermore, it highlights the significance of factors such as gain, beamwidth, polarization, frequency band selection, multiple-input multiple-output (MIMO) systems, beamforming, adaptive coding and modulation (ACM), antenna placement, tower height, and line-of-sight considerations in optimizing antenna performance. The findings presented in this paper contribute to advancing the understanding of microwave antenna optimization and offer practical insights into enhancing the responsiveness, efficiency, and reliability of modern communication networks.

Machine learning assisted adaptive LDPC coded system design and analysis

AbstractThis paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

Joint Sub-Band and Transmission Rate Selection for Anti-Jamming Non-Contiguous Orthogonal Frequency Division Multiplexing System: An Upper Confidence Bound Based Reinforcement Learning Approach

Reinforcement Learning (RL) has been employed to assign transmission parameters to all sub-carriers in a set frequency band for anti-jamming Orthogonal Frequency Division Multiplexing (OFDM) systems. However, prior works often overlooked the influence of wireless environment fading and convergence issues stemming from overly large parameter sets. To address these challenges, an anti-jamming scheme was proposed based on the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) communication system integrated with reinforcement learning. First, all sub-carriers were divided into sub-bands, and a Finite State Markov Sub-bands (FSMS) model was established to describe the time-varying fading characteristics of each sub-band by combining Adaptive Modulation and Coding (AMC) technology. To mitigate instability due to the fading channel, a joint sub-band and modulation anti-jamming decision scheme was adopted, enabling the transmitter to select the optimal sub-band and transmission rate. Ultimately, this decision-making process was modeled as a Markov Decision Process (MDP), and an Upper Confidence Bound based Q-learning (UCB-Q) anti-jamming algorithm was proposed for obtaining the joint sub-band and transmission rate selection strategies. Simulation results indicate that the proposed algorithm demonstrates enhanced speed and superior average throughput. Additionally, the algorithm showcases the same commendable anti-jamming performance in scenarios with time-varying dynamic jamming.

Cognitive Radio Strategy Combined with MODCOD Technique to Mitigate Interference on Low-Orbit Satellite Downlinks.

The concept of cognitive radio (CR) as a tool to optimize the obstacle of spectral coexistence has promoted the development of shared satellite-terrestrial wireless networks. Nevertheless, in some applications like Earth Exploration Satellite Services, which demand high spectral efficiency (bps/Hz) for downlink transmissions, spectral coexistence amidst interferences from cellular Base Stations is still challenging. Our research aims to mitigate these interferences on low-orbit satellite downlinks carrying imaging data received from a ground station. In order to fulfill this, we present cognitive radio approaches to enhance spectrum exploitation and introduce the adaptive modulation and coding (MODCOD) technique to increase RF power and spectral efficiencies. Therefore, we propose a combined methodology using CR and adaptive MODCOD (ACM) techniques. Afterwards, we applied the solution by monitoring the signal to interference plus noise ratio and the MODCOD strategy. Finally, we provide a real in situ case study at the Cuiabá ground station located in Brazil's central area, which receives images from an Earth observation satellite (EOS). In addition to demonstrating the strategy effectiveness in this scenario, we conducted a bench test emulating the interfering wireless communication system. In this sense, we demonstrated the proposed approach, successfully mitigating the harmful effects on the received EOS images.

Link Adaptation Strategy for Underwater Acoustic Sensor Networks: A Machine Learning Approach

Abstract Due to growing concerns regarding their use in fields including oceanography, commercial marine operations, and military surveillance, demand in the exploration of underwater sensor networks for marine studies has developed. Network channels for underwater sensor network (USN) rapidly change (spatially and temporally) depending on the surroundings. To increase system efficiency by adjusting transmission parameters to channel fluctuations, it is alluring to utilize adaptive modulation and coding (AMC) for USNs. In order to determine the best link adaptation method based on the channel quality, this article focuses on evaluating a measured sea trial dataset utilizing a rule-based approach (i.e., three-dimensional evaluation, modulation-wise analysis, and a fixed-SNR strategy). To determine the optimum AMC combinations in terms of channel adaptively, we draw a situation of the measured USN data rate versus Bit Error Rate (BER) and Signal to Noise Ratio (SNR). The work further extends to apply machine learning (ML) methods to identify the MCS levels by looking into the channel characteristics due to the non-reversibility limitation of the rule-based strategy. One of the ML methods we used for the investigation, gradient boosted regression tree (GBRT), exhibits impressive accuracy of 99.988% in classifying MCS levels. The MCS levels are related to channel statistics and signal characteristics, particularly those that are susceptible to SNR and BER limitations, using an ensemble of trees that learns from the buoy and base station’s uplink data.

Robust Transmission using Efficient Adaptive Modulation and Coding in Modern OFDM Systems

Abstract: OFDM (Orthogonal Frequency Division Multiplexing) as a special multi-carrier transmission technology has good resistance to narrow-band interference and frequency selective fading ability. Using adaptive modulation and coding in an OFDM systems in comparison with traditional fixed modulation techniques, it becomes possible to take full advantage of spectrum resources enhancing bandwidth efficiency and system capacity making it suitable for the high-speed and reliable mobile communication systems in the future. In AMC (Adaptive Modulation and Coding) technique, modulation method, coding rate, and/or other signal transmission parameters are dynamically adapted to the channel conditions to increase the system performance in terms of Bit Error Rate (BER) and throughput (bps) in various conditions such as channel mismatch, Doppler spreads, fading, etc. The purpose of this paper is to investigate the effect of adaptive modulation and coding in an OFDM system in terms of bit error rate and SNR over fading communication channels. In this paper, we have considered fixed and adaptive modulation technique in an OFDM system over AWGN and RICEAN channel with convolutional and BCH coding. An OFDM system model is simulated in MATLAB programming environment to obtain the simulation result. From the comparative simulation result analysis in terms of BER v/s SNR, it can be concluded that Adaptive modulation with Convolutional coding over RICEAN channel significantly reduces bit error rate giving better trade-off between SNR and BER than all other options considered here.

Adaptive Modulation and Coding: a Brief Review of the Literature

This work provides a general review of Adaptive Modulation and Coding (AMC) techniques that optimize the utilization of available resources and parameters in wireless communication systems. AMC or link adaptation empowers communication systems to employ appropriate modulation and coding schemes in order to enhance link spectrum utilization over time-varying channels. In addition, the proper selection of Modulation and Coding Schemes (MCS) diminishes the Bit Error Rate (BER) of systems and reduces the required transmit power through which the interference to other users is minimized. This paper conducts a state-of-the-art review of the existing AMC technologies. The reviews provided in this paper shed light on the trends of research in AMC and bring focus to related techniques. First, the existing constraints and the related phenomena in advanced wireless communication, including channel capacity, path loss, fading, and interference, are thoroughly investigated to understand further and improve the AMC systems. Moreover, several scenarios of utilizing the AMC methods are provided in order to demonstrate the extent of the effectiveness and importance of accurate AMC. Lastly, the challenges, the likely problems, the research gaps, and the potential avenues for future work are declared.

Modulation and coding methods in 5th generation networks

The fifth generation (5G) of wireless communication networks represents a groundbreaking shift in the realm of connectivity, enabling a wide array of transformative applications and services. This article delves into the pivotal role played by modulation and coding methods in the context of 5G networks. It explores their significance, delves into recent research and publications within the field, and outlines the direction for future research endeavors. Modulation and coding (M&C) are fundamental for 5G networks, enhancing data rates, reliability, and reducing interference. This article delves into their role in 5G networks, addressing challenges, including fading channels, interference, and diverse user needs. Recent research explores advanced modulation schemes like 256-QAM and 1024-QAM, requiring error correction coding techniques like Turbo codes and LDPC codes. Massive MIMO, using multiple antennas at both ends, is supported by adaptive coding and modulation. Adaptive Modulation and Coding (AMC) dynamically adjusts schemes based on real-time channel conditions, balancing data rate and reliability. Non-Orthogonal Multiple Access (NOMA) accommodates massive device connectivity, allowing multiple users to share resources with different power levels and modulation schemes. The study aims to comprehensively analyze 5G's M&C methods, assess modulation schemes for higher data rates, evaluate error correction coding, investigate Massive MIMO and adaptive modulation for spectral efficiency, and scrutinize NOMA's potential for massive device connectivity. Results highlight the importance of advanced modulation schemes, efficient error correction coding, and adaptive techniques in realizing 5G objectives, enabling higher data rates, lower latency, and improved reliability. NOMA is promising for accommodating many connected devices in 5G. M&C are essential for efficient and reliable data transmission in 5G, with ongoing research refining techniques for 6G and beyond. Integrating machine learning and AI into M&C algorithms holds promise for enhancing network efficiency and reliability.

Multiple-Objective Packet Routing Optimization for Aeronautical Ad-Hoc Networks

Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad-hoc</i> networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this article, we develop a discrete <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> multi-objective genetic algorithm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link's achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.

Adaptive Modulation and Coding for Underwater Acoustic Communications Based on Data-Driven Learning Algorithm

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.

РАЗРАБОТКА АДАПТИВНОЙ СИСТЕМЫ СВЯЗИ НА ОСНОВЕ OFDM ДЛЯ ТРОПОСФЕРНОГО И РАДИОРЕЛЕЙНОГО КАНАЛА

It is known that inter-symbol interference may occur during data transmission in radio relayand tropospheric communication systems. The presence of multipath propagation, frequencyselectivefading and extreme instability in the tropospheric and radio relay channel significantlyreduces the energy efficiency of the communication system. The aim of the work was to increasethe efficiency of using the channel for radio relay and tropospheric communication by usingOFDM (orthogonal frequency-division multiplexing) signals in the system using adaptive codingand modulation. In the course of execution, the modulator and demodulator models of the OFDMsignal are implemented. When using various signal code structures in various reception/transmission conditions, it is possible to achieve optimal use of the frequency and energyresources, to create systems that adapt to the conditions of signal propagation. To implement thismechanism, a service field was introduced into the transmitted service data, which contains informationabout the code rate used, the modulation type, and the interleaving depth. This approachallows optimizing the use of energy and frequency resources. Together with the use of channelquality estimation algorithms, it becomes possible to dynamically change the signal-code structurewhen the reception conditions change. By adjusting the interleaving depth, it is possible to optimizethe S/N threshold or the amount of information delay in the channel, depending on the systemrequirements. The use of adaptive choice of code rate and modulation will allow more efficient useof the channel resource with a constant change in its state. The obtained results will significantlyincrease the energy efficiency of the OFDM system, lead to stable communication in nonstationarychannels and increase the throughput.

Machine Learning for Underwater Acoustic Communications

Energy-efficient and link-reliable underwater acoustic communication (UAC) systems are of vital importance to both marine scientific research and oceanic resource exploration. However, owing to the unique characteristics of marine environments, underwater acoustic (UWA) propagation experiences arguably the harshest wireless channels in nature. As a result, traditional model-based approaches to communication system design and implementation may no longer be effective or reliable for UAC systems. In this article, we resort to machine learning (ML) techniques to empower UAC with intelligence capabilities, which capitalize on the potential of ML in progressively improving system performance through task-oriented learning from data. We first briefly overview the literature of both UAC and ML. Then, we illustrate promising ML-based solutions for UAC by highlighting one specific niche application of adaptive modulation and coding (AMC). Lastly, we discuss other key open issues and research opportunities layer-by-layer, with focus on providing a concise taxonomy of ML algorithms relevant to UAC networks.

Receiver diversity with selection combining for drone communication around buildings at frequency 10 GHz

The communication network for cellular network keep development. This research analyzed about cellular network was used drone network. The mobile drone used frequency at 10 GHz for communication. The mobile drone moved around buildings. Buildings were used high variation. Base Station placed around building. This research was using macro diversity Base Station, variation building, variation modulation, and variation height of drone trajectory. Macro diversity mechanism used for that two Base Station. Selection Combining (SC) method was used for that macro diversity mechanism. The modulation communication based from Adaptive Modulation and Coding (AMC). Adaptive Modulation and Coding (AMC) was used Modulation and coding scheme (MCS). Modulation was used QPSK, 16 QAM, and 64 QAM. As the result described signal to noise ratio (SNR) at every node communication, probability MCS, and percentage coverage of drone trajectory. MCS probability for 64 QAM become increased with selection combining method. The percentages coverage of drone trajectory was obtained 77.2% of the first BS, 66.8% of the second BS, and 87.2% with SC method.

Slow and fast adaptive modulation and coding for uplink massive MIMO systems with packet retransmission

Here, the authors consider the uplink of a cellular massive multiple-input multiple-output (MIMO) system with maximum ratio combiner (MRC) at base stations (BSs). To achieve a better QoS provisioning, users in the cells employ link adaptation using adaptive modulation and coding (AMC) in conjunction with packet retransmission. The authors present a cross-layer approach for the AMC design exploiting the time variation feature of MIMO wireless channels. At first, a fast AMC (FAMC) scheme is proposed to adapt the fast small-scale fading of the wireless channel based on the instantaneous channel state information. Since the fast fading is asymptotically averaged out due to the channel hardening property of massive MIMO system, we then suggest a slow AMC (SAMC) scheme which adjusts the adaptation parameters based on only the large-scale channel fading statistics including shadow fading and pass loss of the channel. The SAMC substantially reduces the feedback rate and implementation cost because of using a larger coherence time compared to that of the FAMC. For the proposed schemes, the probability density function of signal-to-interference-plus-noise ratio (SINR) for MRC receivers at BSs is analytically derived and verified by simulation. Also, closed-form expressions are presented for the throughput and packet error outage of the system. Index Terms—Massive MIMO, MRC, AMC, ARQ, Performance analysis.

Weather-Conscious Adaptive Modulation and Coding Scheme for Satellite-Related Ubiquitous Networking and Computing

As a crucial part of ubiquitous networking and computing (UNC) technologies, low earth orbit (LEO) satellite communications aim at providing internet connectivity services everywhere. To improve the spectrum efficiency of satellite-to-ground communications, adaptive modulation and coding (AMC) are widely used, which can adjust the modulation and coding types according to the varying channel condition. However, satellite-to-ground communication channels have the characterizations such as fast dynamic change, fast switching, and significant fading. These characterizations make it challenging to predict the channel state information accurately and, thus, to perform accurate AMC. For example, rain loss is one of the crucial factors in satellite-to-ground channel fading. In general, it is difficult to build an integrated global model for rain loss because it varies in different regions around the world. Moreover, for the emerging applications of multiple antennas on satellites, the conventional look-up table method cannot cope with the high-dimensional inputs of the multiple antennas. To tackle the above challenges, we propose an AMC method based on deep learning (DL) and deep reinforcement learning (DRL) for ubiquitous satellite-to-ground networks. The proposed method directly processes real-time global weather and location information in the environment and intelligently selects encoding schemes to maximize system throughput. Simulation results show that the proposed method can increase the total throughput. The total number of correctly transmitted bits per unit time is improved, and the efficiency of the satellite-to-ground communication is enhanced.

Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices

Efficient utilisation of adaptive modulation and coding ensures the quality transmission of information bits through the significant reduction in bit error rate (BER). Channel prediction using parametric estimation is not efficient for massive machine-type communication (mMTC) devices under the 5G New Radio (NR). In this paper, we have proposed a channel prediction scheme based on a deep learning (DL) algorithm possessed by parametric analysis. In deep learning, the pipeline methodology is used along with the image processing technique to predict the channel condition for optimal selection of the adaptive modulation and coding (AMC) profile. The deep learning-based pipelining approach utilises image restoration (IR) and image super-resolution (SR). The super-resolution method is used to de-noise the low-pixel 2-D image that is obtained from the parametric value of the beacon to predict the channel condition. The estimation results are compared with the conventional minimum mean square error (MMSE) and an approximation to the linear MMSE (ALMMSE) method, which is obtained through channel state information (CSI). The comparison results show that the parametric-enabled deep learning approach is superior, especially in poorer channel conditions. The performance of BER through parametric estimation along with the DL approach is ~66% more efficient as compared to the conventional MMSE method for BPSK mapping.

Joint Demodulation and Error Correcting Codes Recognition Using Convolutional Neural Network

Demodulation of the communication signals and blind identification of error-correcting codes (ECC) are two essential purposes in adaptive modulation and coding (AMC) techniques and non-cooperative communication fields. The existing approaches treat them as two separate problems, namely the demodulation of signals with known a <i>priori</i> knowledge (such as channel state information (CSI) or channel noise) and the ECC type identification that depends on the demodulation results. In this paper, a novel one-stage ECC identification approach based on a multi-task deep convolutional neural network (MT-DCNN) is presented. With this architecture, the proposed method automatically recognizes the ECC types of the baseband in-phase and quadrature-phase (IQ) data without relying on any conventional demodulators. Precisely, the proposed MT-DCNN consists of three modules: the feature extraction module, the demodulation module, and the ECC types recognition module. Experiment results show that the proposed architecture can accurately identify the ECC types of the baseband IQ signals and is superior to those of the existing two-stage recognition approaches.

Experimental Evaluation of Transmitted Signal Distortion Caused by Power Allocation in Inter-Cell Interference Coordination Techniques for LTE/LTE-A and 5G Systems

Error vector magnitude (EVM) and out-of-band emissions are key metrics for evaluating in-band and out-band distortions introduced by all potential non-idealities in the transmitters of wireless systems. As EVM is a measure of the quality of the modulated signal/symbols, LTE/LTE-A and 5G systems specify mandatory EVM requirements in transmission for each modulation scheme. This paper analyzes the influence of the mandatory satisfaction of EVM requirements on the design of radio resource management strategies (RRM) (link adaptation, inter-cell interference coordination), specifically in the downlink (DL). EVM depends on the non-idealities of the transmitter implementations, on the allocated power variations between the subcarriers and on the selected modulations. In the DL of LTE, link adaptation is usually executed by adaptive modulation and coding (AMC) instead of power control, but some flexibility in power allocation remains being used. LTE specifies some limits in the power dynamic ranges depending on the allocated modulation, which ensures the satisfaction of EVM requirements. However, the required recommendations concerning the allowed power dynamic range when inter-cell interference coordination (ICIC) and enhanced ICIC (eICIC) mechanisms (through power coordination) are out of specification, even though the EVM performance should be known to obtain the maximum benefit of these strategies. We perform an experimental characterization of the EVM in the DL under real and widely known ICIC implementation schemes. These studies demonstrate that an accurate analysis of EVM is required. It allows a better adjustment of the design parameters of these strategies, and also allows the redefinition of the main criteria to be considered in the implementation of the scheduler/link adaptation concerning the allocable modulation coding scheme (MCS) in each resource block.