Adaptive Modulation Algorithm Research Articles

Enhancing QoS Performance for Cell Edge Users Through Adaptive Modulation and Coding in IEEE 802.11ac WLANs

Wireless communication networks, such as IEEE 802.11ac Wireless Local Area Networks (WLANs), often encounter challenges in providing consistent Quality of Service (QoS) to users situated at the cell edge. The inherent variations in channel conditions, particularly lower signal-to-noise ratios (SNRs) in these regions, lead to compromised data rates and reliability, resulting in significant degradation of throughput. This study presents an innovative solution in the form of an Adaptive Modulation and Coding Scheme (AMCS) algorithm tailored to enhance QoS performance for cell edge users. The primary objective of the AMCS algorithm is to optimize QoS by dynamically adjusting the transmission data rate based on the observed channel conditions, quantified using SNR as a channel state indicator. Conventional approaches might unilaterally select the lowest data rate in challenging conditions, prioritizing reliability at the expense of throughput. However, the proposed AMCS algorithm takes a distinct approach by intelligently determining the Modulation and Coding Scheme (MCS) that offers an optimal balance between throughput and reliability for the given SNR level. To achieve this, the algorithm utilizes real-time SNR measurements to select an MCS that ensures a stable connection while also maintaining an acceptable data rate. By adapting the MCS based on the current SNR, the algorithm aims to mitigate the adverse effects of poor channel conditions experienced by cell edge users. The innovation of the AMCS algorithm lies in its ability to make dynamic adjustments, allowing users to experience improved data rates without compromising connection stability. Through extensive simulations and evaluations, the proposed AMCS algorithm showcases its efficacy in enhancing QoS performance at the cell edge. The algorithm's adaptive approach successfully achieves higher data rates and improved reliability by selecting appropriate MCS configurations tailored to the observed SNR levels. This innovative technique provides a promising solution to the challenge of striking the right balance between throughput and reliability in wireless communication networks, ultimately leading to an improved user experience for those at the network's periphery.

Adaptive Modulation Based on Nondata-Aided Error Vector Magnitude for Smart Systems in Smart Cities

A smart city involves big data transmission (BDT) between smart systems, which increases queue delays and leads to difficulty in enhancing the spectral efficiency. Adaptive modulation is an effective technique for enhancing data transmission rates in smart systems. However, traditional adaptive modulation approaches are not suitable for BDT in smart systems because the delays caused by the large amount of transmitted data lead to difficulty in evaluating the channel quality. In this paper, we propose a nondata-aided error vector (NDA-EVM) that can be employed in adaptive modulation over wireless channels. The proposed NDA-EVM can be used to evaluate the channel quality and symbol error rate (SER), which reflect the quality of service (QoS) of the system. We formulated the relationship between the NDA-EVM and SER, which provides a basis for designing adaptive modulation techniques for smart systems. To address the low average spectrum efficiency (ASE) caused by BDT queue delays, an adaptive modulation strategy based on the finite-state Markov chain (FSMC) of the NDA-EVM (i.e., NDA-EVM-AM) was designed. This method simplifies the adaptive modulation algorithm for smart systems to search for the optimal transfer probability in the FSMC matrix based on two typical states: the resident state and transient state. Moreover, we proposed an analytical procedure to describe queuing behavior to analyze the performance of the NDA-EVM-AM algorithm for smart systems in smart cities. The performance is compared with that of a conventional adaptive modulation algorithm through simulations. The results show that compared with traditional adaptive modulation, NDA-EVM-AM obtains a lower packet loss rate and higher spectral efficiency for smart systems.

Based on adaptive modulation laser communication multi-microgrids scheduling system

In order to improve the data sharing and comprehensive information processing capabilities between multi-microgrids in the power system, the multi-microgrids scheduling system based on laser communication has been proposed. In order to reduce the error rate of laser communication and reduce the impact of atmospheric turbulence on signal acquisition, an adaptive modulation algorithm has been designed. A mathematical model for laser communication modulation and demodulation based on adaptive modulation algorithm has been constructed. In simulation analysis, the target signal was extracted from the original signal superimposed with atmospheric turbulence noise through filtering and demodulation. The energy fluctuation of the extracted signal decreased from 47.3 to 5 mV. The energy attenuation trend of communication lasers within the range of 0–6 km was experimentally tested. Within 2.0 km, the energy demodulation results of both algorithms are similar, both below 10%. After exceeding 2.0 km, the calculation error of the adaptive modulation algorithm remains basically unchanged, while the error of traditional algorithms increases by about twice. For the APD response value, the adaptive modulation algorithm demodulation has a higher response range concentration ratio and the higher envelope recognition. Under different nominal atmospheric turbulence values, the maximum error rates of the adaptive modulation algorithm are 5.8 × 10−8, 8.9 × 10−8, and 1.2 × 10−7, respectively, while the maximum error rates of the amplitude coherent algorithm are 2.9 × 10−5, 6.3 × 10−5, and 1.05 × 10−4, respectively. It can effectively suppress the impact of atmospheric turbulence on the error rate of laser communication by adaptive modulation algorithm.

Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation

The efficiency of link adaptation in wireless communications relies greatly on the accuracy of channel knowledge and transmission mode selection. In this paper, a novel deep learning based link adaptation framework is proposed for the orthogonal frequency-division multiplexing (OFDM) systems with compressed-sensing-assisted index modulation, termed as OFDM-CSIM, communicating over millimeter-wave (mmWave) channels. To achieve link adaptation, a novel multi-layer sparse Bayesian learning (SBL) algorithm is proposed for accurately and instantaneously providing the required channel state information. Meanwhile, a deep neural networks (DNN)-assisted adaptive modulation algorithm is proposed to choose the best possible transmission mode to maximize the achievable throughput. Simulation results show that the proposed multi-layer SBL algorithm enables more accurate channel estimation than the conventional techniques. The DNN-based adaptive modulator is capable of achieving a higher throughput than the learning-assisted solution based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -NN) algorithm, and also the classic average signal-to-noise ratio (SNR)-based solutions. Moreover, analysis shows that both the multi-layer SBL algorithm and the DNN-assisted adaptive modulator achieve better performance than their respective conventional counterparts while at a significantly lower computational complexity cost.

5G NOMA Defense Application Environment and Stacked LSTM Network Architectures

In 5G and beyond 5G wireless communication networks, the NOMA scheme is widely considered a major non-orthogonal access technique for improving system capacity and data rates. The main challenges in current NOMA systems are limited channel feedback and the difficulty of integrating it with advanced adaptive coding and modulation algorithms. This study analyses S-LSTM-based DL NOMA receivers in i.i.d. Nakagami-m fading channel circumstances as opposed to previously presented solutions. The LSTM has the advantage of responding dynamically to changing channel conditions. When compared to a typical NOMA system, a typical NOMA system has a 12% lower outage probability, a 39% increase in net throughput, and a maximum SER reduction of 48%. Complex modulated M-ary PSK and M-ary QAM data symbols are employed in D/L NOMA transmission. Classic receivers such as LS and MMSE are outperformed by the S-LSTM-based DL-NOMA receiver. The CP and non-linear clipping noise simulation curves compare the performance of the MMSE and LS receivers with that of the DL NOMA receiver in real-time propagation circumstances. The DL-based detector outperforms the MMSE for SNRs greater than 15 dB because the S-LSTM method is more robust than the clipping noise.

Algorithm based on 2‐bit adaptive delta modulation and fractional linear prediction for Gaussian source coding

A novel 2-bit adaptive delta modulation (ADM) algorithm is presented based on uniform scalar quantization and fractional linear prediction (FLP) for encoding the signals modelled by a Gaussian probability density function. The study focusses on two major areas: realization of a 2-bit adaptive quantizer based on Q-function approximation that significantly facilitates quantizer design; and implementation of a recently introduced FLP approach with the memory of two samples, which replaces the first-order linear prediction used in standard ADM algorithms and enables improved performance without increasing transmission costs. It furthermore represents the first implementation of FLP in signal encoding, therefore confirming its applicability in a real signal-processing scenario. Based on the performance analysis conducted on a real speech signal, the proposed ADM algorithm with FLP is demonstrated to outperform other 2-bit ADM baselines by a large margin for the gain in signal-to-noise ratio achieved over a wide dynamic range of input signals. The results of this research indicate that ADM with adaptive quantization based on Q-function approximation and adaptive FLP represents a promising solution for encoding/compression of correlated time-varying signals following the Gaussian distribution.

System anti-jamming technology in the design of intelligent single chip computer constant current source of field strength machine

With the development of the electric power industry, the technical level of automatic testing equipment for the reliability of electrical component circuit breakers in the transmission and distribution network is getting higher and higher. The stability and accuracy of the test power supply are the basis for ensuring the pass rate of the test product. Most of the electrical testing and testing equipment has defects such as inaccurate power supply current regulation, low power, and low level of intelligence, which are difficult to meet the testing requirements. Based on the theory of a closed-loop control system, this paper adopts embedded system design technology to realize a high-current, high-power, high-stability digital constant current source system for line detection. This paper studies the rule-based intelligent anti-jamming decision engine design and system anti-jamming performance analysis of NC-OFDM system. We give the design of an intelligent anti-jamming decision engine based on rule-based decision-making, and focus on two intelligent anti-jamming decision-making algorithms: Adaptive Modulation and Coding (AMC) algorithm based on signal-to-noise ratio difference and packet error rate and Adaptive Sub-Band Selection (ASBS) algorithm. Experimental test results show that the output current range is 200 mA to 2000 mA, the system has realized a microstep adjustment of±5 mA, and the absolute error of current measurement is less than 0.3%+4 mA. The system is stable and reliable, and has high practical value in the field of high precision and low power.

Adaptive modulation allocation algorithm in elastic optical networks

SummaryThis paper introduces a new way to allocate the modulation format and improves the energy efficiency in elastic optical networks. For every connection request, both the quality of transmission and quality of service requirements are investigated. The allocation of modulation formats is based on both the physical distance and the required number of frequency slots. To improve the energy efficiency, Yen's algorithm is executed for every connection requested, and the lightpath with the lowest power consumption is selected. Numerical results were compared with adaptive modulation algorithms presented in the existing literature. The energy efficiency was improved by up to 27%, the used bandwidth available was reduced by up to 60%, and the blocking probability was decreased by 30% using our proposed adaptive algorithm.

Novel Two-Bit Adaptive Delta Modulation Algorithms

Novel Two-Bit Adaptive Delta Modulation Algorithms

Novel Two-Bit Adaptive Delta Modulation Algorithms

Novel Two-Bit Adaptive Delta Modulation Algorithms

Adaptive Modulation and Code Strategy to Reduce Energy Consumption in Elastic Optical Network

In this paper, the novel Adaptive Modulation and Code (AMC) algorithm aims to reduce energy consumption in elastic optical network is developed. The proposed AMC algorithm adaptively allocates both, the appropriate modulation and the forward error correction (FEC), according to the actual physical distance and the optical signal to noise ratio (OSNR) of the lightpath, respectively. The algorithm compares the previous energy consumption with the actual aiming to select the lowest. The obtained results have been compared with the case when shortest path (SP) and minimum hops (MH) algorithms are employed. Simulations outcomes highlight that energy consumption decreases when AMC algorithm is adopted in comparison with SP or MH ones. The energy consumption increases proportionally with the physical lightpath distance. Finally, the blocking probability decreases whereas AMC is utilized. Â

LTE-TDD 시스템에서 혼합 ARQ 응답을 이용한 적응적 변조 코딩 구성 선택 알고리즘

LTE-TDD 시스템에서 혼합 ARQ 응답을 이용한 적응적 변조 코딩 구성 선택 알고리즘

Dither modulation based adaptive steganography resisting jpeg compression and statistic detection

In order to improve the JPEG compression resistant performance of the current steganogrpahy algorithms resisting statistic detection, an adaptive steganography algorithm resisting JPEG compression and detection based on dither modulation is proposed. Utilizing the adaptive dither modulation algorithm based on the quantization tables, the embedding domains resisting JPEG compression for spatial images and JPEG images are determined separately. Then the embedding cost function is constructed by the embedding costs calculation algorithm based on side information. Finally, the RS coding is combined with the STCs to realize the minimum costs messages embedding while improving the correct rates of the extracted messages after JPEG compression. The experimental results demonstrate that the algorithm can be applied to both spatial images and JPEG images. Compared with the current S-UNIWARD steganography, the message extraction error rates of the proposed algorithm after JPEG compression decrease from about 50 % to nearly 0; compared with the current JPEG compression and detection resistant steganography algorithms, the proposed algorithm not only possesses the comparable JPEG compression resistant ability, but also has a stronger detection resistant performance and a higher operation efficiency.

Cross‐layer queueing analysis for aggregated ON–OFF arrivals with adaptive modulation and coding

Packet loss and delay in wireless networks is a function of both the traffic characteristics (e.g. load, burstiness) and the characteristic behaviour of the fading channel. In this study, the authors propose and analyse a new queue model in which the server process is controlled by the adaptive modulation and coding (AMC) algorithm, and with aggregated ON–OFF packet arrivals to a finite buffer. In the scenarios, where users require multiple services, aggregated ON–OFF arrivals approximate bursty behaviour better than the Poisson arrivals analysed in many earlier queueing models. They focus on the packet drop probability, the packet loss probability, the average queueing delay and the throughput, showing significant differences in these metrics between the cross-layer models using aggregated ON–OFF arrivals and Poisson arrivals. This indicates that in wireless packet network performance evaluation, the traffic model is at least as important as the effect of the fading channel modelling.

Adaptive coding and modulation using imperfect CSI in cognitive BIC-OFDM systems

This work investigates adaptive coding and modulation (ACM) algorithms under the realistic assumption that the available channel state information (CSI) at the transmitter is imperfect due to estimation errors and/or feedback delays. First, we introduce an optimal performance metric for the secondary user (SU) bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) system, called the expected goodput (EGP). By using an accurate modeling approximation, we succeed in deriving a tractable and very accurate approximation for the EGP. This approximate EGP (AEGP) is then used for the derivation of several ACM algorithms which optimize the code rate and bit and energy allocation under a constraint on the interference caused to the PU network. In the numerical results, we show that the AEGP is far more accurate than previous attempts to model the GP in the presence of imperfect CSI. Further, we verify that, in spite of the imperfect nature of the available CSI, the derived ACM algorithms significantly increase the goodput of the SU network, compared to a non-adaptive selection of the transmission parameters.

Complete composite binary offset carrier modulated signals and their correlation function

The formation and structure of complete composite binary offset carrier (CBOC) modulated signals are analyzed as applied to the new-generation satellite radio navigation systems (SRNSs) (in particular, the European Galileo and, presumably, Chinese BeiDou/Compass systems). The block diagram of the CBOC-signal satellite transmitter based on the coherent adaptive subcarrier modulation (CASM) algorithm is discussed. Analytical expressions for the correlation functions (CFs) characterizing the single elements of the modulating function (MF) of CBOC signals are derived at arbitrary fractional coefficients. The CFs of the single elements of the MFs of CBOC signals are represented graphically and their properties are discussed. The Fourier transforms of CFs are used to calculate the energy spectra (power spectral densities) of the single elements of the MFs of CBOC signals. CF features and the energy spectra of CBOC signals are analyzed using signals with CBOC(6, 1, a 2/11) modulation, which are typical of the European Galileo and Chinese BeiDou/Compass systems at the carrier frequency f car = 1575.42 MHz and the fractional coefficient a 2 = 1.

On Transmission of a Continuous Signal via a Noiseless Binary Channel

We study the problem of the transmission of currently observed time variable signals via a channel that is capable of sending a single binary signal only for each measurement of the underlying process. For encoding and decoding, we suggest a modification of the adaptive delta modulation algorithm. This modification ensures tracking of time variable signals. We obtained upper estimates for the error for the case of noiseless transmission.

Reconfigurable antennas and link adaptation algorithms for MIMO-OFDM wireless systems

In this work, we study and analyze the performance of physical layer algorithms for adaptive multiple input-multiple output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems that employ a new class of adaptive antenna systems known as reconfigurable antennas. These antennas are capable of adaptively modifying their radiation characteristics and thus leverage pattern diversity to affect how the transmitter and receiver perceive the wireless channel. We propose a low complexity spatial adaptive modulation and coding (AMC) algorithm that uses the advantages of pattern reconfigurable antennas in concert with link adaptation to improve MIMO-OFDM link throughput. The algorithm operates in two main stages; first, it searches for the antenna configuration that yields the highest post processing signal-to-noise ratio (ppSNR) and, then, applies AMC to improve spectral efficiency. The performance of the proposed scheme is experimentally evaluated for a 2×2 MIMO-OFDM wireless system in an indoor environment.

A High Efficient and Robust Adaptive Coding and Modulation Algorithm for Ka Band Satellite Communications

The Ka band for satellite communications is allocated from 27GHz to 40GHz and it is possible to realize the high speed data transmission in this 13GHz bandwidth. However, the attenuation especially the rain attenuation is tremendous for the Ka band. And we need to reserve about 4~8dB link budget to guarantee the typical link reliability 99.9% for the constant coding and modulation (CCM). In this paper, we propose a adaptive coding and modulation (ACM) algorithm that can dynamically change the modulation and code efficiency according to link condition. Based on this algorithm we only need reserve about 1dB to realize the 99.7% reliability. The simulation results show that we can derive more than triple throughout compare to CCM.

QRD-Assisted Adaptive Modulation-Aided MIMO Systems

In this paper, we propose QR-decomposition (QRD)-based adaptive modulation (AM)-aided multiple-input-multiple-output (MIMO) systems. The proposed algorithm yields a tight lower bound of the free distance (FD), which determines the error probability of the detector in the high-signal-to-noise-ratio (SNR) region. Thus, this QRD-based AM algorithm is capable of achieving near-optimal performance at low complexity because the full QRD, which imposes high complexity, is performed only once for each channel realization, regardless of the number of AM modes. Our simulation results show that the proposed algorithm exhibits a better bit-error-rate (BER) performance and reduced complexity compared with the existing algorithms.