Adaptive Modulation System Research Articles

Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder

Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field.

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.

Adaptive Delta Modulation Simulation and Analysis Using MatLab


 
 
 
 The system for converting analog signals to digital signals has become a necessity, considering that the tools currently used are digital computers. One of the analog-to-digital conversion systems is the delta modulation system. In delta modulation, only one bit is used to encode each change in the input signal, where a rising signal is encoded into a one bit and a falling signal is encoded into a zero bit. This can save the bit rate which, when transmitted over the transmission channel, can save the transmission bandwidth, and when stored in computer storage media, can save storage space. However, if the slope of the signal rises and falls sharply, the delta modulation system cannot follow these signal changes, which is called slope overload. To overcome this problem, an adaptive delta modulation system was developed that has variable or adaptive pulse height for both one and zero bits. In this paper, a comparison between the delta modulation system and the adaptive delta modulation system using MatLab application will be simulated and analyzed. From the simulation results, it can be seen that the adaptive delta modulation system is able to overcome slope overload compared to the delta modulation system.
 
 
 

Reconfigurable Logic Design of CORDIC Based FFT Architecture for 5G Communications

There are numerous goals in next-generation cellular networks (5G), which is expected to be available soon. They want to increase data rates, reduce end-to-end latencies, and improve end-user service quality. Modern networks need to change because there has been a significant rise in the number of base stations required to meet these needs and put the operators’ low-cost constraints to the test. Because it can withstand interference from other wireless networks, and Adaptive Complex Multicarrier Modulation (ACMM) system is being looked at as a possible choice for the 5th Generation (5G) of wireless networks. Many arithmetic units need to be used on the hardware side of multicarrier systems to do the pulse-shaping filters and inverse FFT. The main goal of this study is to adapt complex multicarrier modulation (ACMM) for baseband transmission with low complexity and the ability to change it. We found that this is the first reconfigurable architecture that lets you choose how many subcarriers a subband has while still having the same amount of hardware resources as before. Also, under the new design with a single selection line, it selects from a set of filters. The baseband modulating signal is evaluated and tested using a Field-Programmable Gate Array (FPGA) device. This device is available from a commercial source. New technology outperforms current technology in terms of computational complexity, simple design, and ease of implementation. Additionally, it has a higher power spectrum density, spectral efficiency, a lower bit error rate, and a higher peak to average power ratio than existing technology.

Dynamic Network State Learning Model for Mobility Based WMSN Routing Protocol

The rising demand of wireless multimedia sensor networks (WMSNs) has motivated academia-industries to develop energy efficient, Quality of Service (QoS) and delay sensitive communication systems to meet major real-world demands like multimedia broadcast, security and surveillance systems, intelligent transport system, etc. Typically, energy efficiency, QoS and delay sensitive transmission are the inevitable requirements of WMSNs. Majority of the existing approaches either use physical layer or system level schemes that individually can’t assure optimal transmission decision to meet the demand. The cumulative efficiency of physical layer power control, adaptive modulation and coding and system level dynamic power management (DPM) are found significant to achieve these demands. With this motivation, in this paper a unified model is derived using enhanced reinforcement learning and stochastic optimization method. Exploiting physical as well as system level network state information, our proposed dynamic network state learning model (NSLM) applies stochastic optimization to learn network state-activity that derives an optimal DPM policy and PHY switching scheduling. NSLM applies known as well as unknown network state variables to derive transmission and PHY switching policy, where it considers DPM as constrained Markov decision process (MDP) problem. Here,the use of Hidden Markov Model and Lagrangian relaxation has made NSLM convergence swift that assures delay-sensitive, QoS enriched, and bandwidth and energy efficient transmission for WMSN under uncertain network conditions. Our proposed NSLM DPM model has outperformed traditional Q-Learning based DPM in terms of buffer cost, holding cost, overflow, energy consumption and bandwidth utilization.

Adaptive Modulation With CAZAC Preamble-Based Signal-to-Noise-Ratio Estimator in OFDM Cooperative Communication System

This paper presents an adaptive modulation in a single-input-single-output (SISO)-OFDM-based cooperative system that employs a Constant Amplitude Zero Autocorrelation (CAZAC) preamble-based SNR estimator. A novel CAZAC preamble-based SNR estimator that does not incur a throughput penalty is derived by exploiting a modified CAZAC preamble structure for the OFDM system. The proposed CAZAC SNR estimator is used to estimate the channel quality by finding the instantaneous SNR of the received cooperative data, which is sent to the transmitter via feedback channel. The modulation scheme is then changed according to the estimated SNR. The performance of the CAZAC preamble-based SNR estimator exhibits less than 0.02 dB and less than 0.2 dB bias when communicating over AWGN and SUI-5 channels, respectively. The proposed adaptive modulation scheme with the CAZAC SNR estimator significantly improved the channel throughput in comparison with the corresponding benchmarker scheme dispensing with adaptive modulation that invokes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -PSK and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -QAM. More explicitly, the proposed adaptive modulation system with the CAZAC SNR estimator that invokes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -PSK exhibits significant improvement by about 5 dB at BER=10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> in comparison to its corresponding benchmarker scheme dispensing with adaptive modulation when communicating over the SUI-5 channel. On the other hand, the relay-aided cooperative transmission exhibited an approximately 6 dB SNR gain in terms of channel throughput in comparison to the corresponding non-cooperative benchmarker scheme, and benefited from spatial diversity.

33.3: Adaptive Modulation System of the Liquid Crystal Phase Modulators

An adaptive modulation system for a liquid crystal (LC) phase modulator is demonstrated. In order to achieve the highresolution phase distribution, the information of the transmittance distribution transformed by phase retardation is applied. By the adjustment algorithm and adaptive algorithm, the phase distribution of the LC phase modulator can perfectly match the designed phase profile in a short modulation time. The adaptive modulation system shows potential application in optical communication, wearable device and display.

Tailoring Broad-Band-Absorbed Thermoplasmonic 1D Nanochains for Smart Windows with Adaptive Solar Modulation.

Controlling solar transmission through windows promises to reduce building energy consumption. A new smart window for adaptive solar modulation is presented in this work proposing the combination of the photothermal one-dimensional (1D) Au nanochains and thermochromic hydrogel. In this adaptive solar modulation system, the Au nanochains act as photoresponsive nanoheaters to stimulate the optical switching of the thermochromic hydrogel. By carefully adjusting the electrostatic interactions between nanoparticles, different chain morphologies and plateau-like broad-band absorption in the NIR region are achieved. Such broad-band-absorbed 1D nanochains possess excellent thermoplasmonic effect and enable the solar modulation with compelling features of improved NIR light shielding, high initial visible transmittance, and fast response speed. The designed smart window based on 1D Au nanochains is capable of shielding 94.1% of the solar irradiation from 300 to 2500 nm and permitting 71.2% of visible light before the optical switching for indoor visual comfort. In addition, outdoor cooling tests in model house under continuous natural solar irradiation reveal the remarkable passive cooling performance up to ∼7.8 °C for the smart window based on 1D Au nanochains, showing its potential in the practical application of building energy saving.

Adaptive modulation system for optical phase profile

Adaptive modulation system for optical phase profile

An efficient RNQ channel estimation algorithm for AMC communication techniques

ABSTRACT The adaptive communication systems require a good estimation technique to offer beneficial performances. In this work, a new robust estimation algorithm based on a non-quadratic error function (RNQ) was proposed for Bit Error Rate (BER) reduction, and improving of the spectral efficiency (SE) and transmission throughput in Adaptive Modulation and Coding-Coded Orthogonal Frequency Division Multiplexing (AMC-COFDM) systems. Also, this technique is compared to the Normalised Least Mean Square (NLMS) estimation method. The obtained results show clearly that the estimation method based on the RNQ algorithm is very efficient in term of the convergence speed and minimum average error and works very satisfactorily.

Performance of adaptive subcarrier modulated MIMO wireless optical communications in Malaga turbulence

Abstract Space diversity of multiple-input multiple-output (MIMO) system combined with the adaptive subcarrier modulation technique can effectively reduce the atmospheric channel fading effects. Malaga turbulent probability density function is obtained based on the MeijerG function. The asymptotic expression of average bit error rate in non-adaptive and adaptive modulated MIMO wireless optical systems are derived respectively, and M-ary phase shift keying (MPSK) and M-ary differential phase shift keying (MDPSK) modulation are used in these two kinds of systems. Under moderate turbulence conditions, error performances are analyzed with numerical simulations between these two systems. The results show that the average bit error performance in the adaptive modulated system can be significantly better than the non-adaptive modulated system. But under the same conditions of modulation order and target bit error rate, error performance of non-adaptive modulation is easier to be affected by transmit and receive aperture number than adaptive modulation in MIMO system.

Pneumatically adaptive light modulation system (PALMS) for buildings

This research introduces a novel approach to control light transmittance based on flexible polydimethylsiloxane (PDMS) films that have been plasma-treated such that micro-scale surface features have a visual effect as the film responds to applied strain. The effect is continuously tunable from optically clear (71.5% Transmittance over a 400–900 nm wavelength) to completely diffuse (18.1% T). Changes in the film's optical properties are triggered by bi-axial strains applied using a pneumatic system to form pressurized envelopes. This paper reports on a series of experimental studies and provides system integration research using prototypes, simulations and geometric models to correlate measured optical properties, strain, and global surface curvatures. In conclusion, a design is proposed to integrate PDMS light control within existing building envelopes.Two alternatives are investigated and compared: System A uses positive pressure featuring an exterior grid to restrain and shape the inflated film during expansion; System B uses negative pressure where the films are shaped according to the geometry of an interstitial grid that serves as a spacer between two film surfaces. Both systems can provide effective control of opacity levels using pneumatic pressure and may be suitable for use with existing glazing systems or ethylene tetrafluoroethylene (ETFE) pneumatic envelopes.

Unique faster‐than‐Nyquist transceiver of the ACM system

The link adaptive transmission system chooses the optimum combinations of modulation order and code rate according to the characteristics of wireless channels to make use of the channel resources. To improve the spectral efficiency further, the authors apply the faster-than-Nyquist (FTN) signalling to the adaptive coding and modulation (ACM) system. The ACM system based on FTN signalling can take advantage of the channel resources by adjusting the packing ratios, modulation orders and code rates. However, the variable packing ratios correspond to variable sampling rate, which challenges the receiver of the ACM system. They propose a new transceiver of the FTN-based ACM system which can detect the FTN signal at a constant sampling rate. Moreover, they search for the optimum modulation and coding schemes of ACM based on FTN by analysing the extrinsic information transfer diagram. The simulation results show that the FTN-based ACM system has additional gains compared with the traditional way and the proposed transceiver may be a better choice in the ACM system.

On Monotonicity of the Optimal Transmission Policy in Cross-Layer Adaptive &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$m$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-QAM Modulation

This paper considers a cross-layer adaptive modulation system that is modeled as a Markov decision process. We study how to utilize the monotonicity of the optimal transmission policy to relieve the computational complexity of dynamic programming (DP). In this system, a scheduler controls the bit rate of the $m$ -quadrature amplitude modulation in order to minimize the long-term losses incurred by the queue overflow in the data link layer and the transmission power consumption in the physical layer. The work is done in two steps. First, we observe the $ L^{\natural }$ -convexity and submodularity of DP to prove that the optimal policy is always nondecreasing in queue occupancy/state and derive the sufficient condition for it to be nondecreasing in both queue and channel states. We also show that, due to the $ L^{\natural }$ -convexity of DP, the variation of the optimal policy in queue state is restricted by a bounded marginal effect. The increment of the optimal policy between adjacent queue states is no greater than one. Second, we use the monotonicity results to present two low complexity algorithms: monotonic policy iteration (MPI) based on $ L^{\natural }$ -convexity and discrete simultaneous perturbation stochastic approximation (DSPSA). We run experiments to show that the time complexity of MPI based on $ L^{\natural }$ -convexity is much lower than that of DP and the conventional MPI that is based on submodularity and DSPSA is able to adaptively track the optimal policy when the system parameters change.

Proposal of an Algorithm for an Efficient Forward Link Adaptive Coding and Modulation System for Satellite Communication

This paper proposes the algorithm for forward link adaptive coding and modulation (ACM) and the detailed design for a satellite communication system to improve network reliability and system throughput. In the ACM scheme, the coding and modulation schemes are changed by as much as the channel can provide depending on the quality of the communication link. To implement the forward link ACM system in the Ka-band, channel prediction and modulation/coding decision methods are proposed and simulated. The parameters of the adaptive filter predictor based on the least mean square are optimized, the minimum mean square error of the channel predictor is 0.0608 when step size and the number of filter tap are 0.0001 and 4, respectively. A test-bed is set up to verify the forward link ACM system, and a test is performed using a Ka-band satellite (i.e., Communication, Ocean, and Meteorological Satellite [COMS]). This test verifies that the ACM scheme can increase the system throughput.

Adaptive Cooperative Food Communication System Transmission in MIMO Channels

In this study, we propose a cooperative transmission framework based on Adaptive Modulation and Coding (AMC) cooperative food communication system in Multiple-Input Multiple-Output (MIMO) fading channels. We consider the scenario that consists of one source node, one relay node and one destination node, they all equipped with multiple antennas and employed the adaptive modulation and coding schemes. The relay node follows the decode-and-forward strategy, forwarding the information received from the source to the destination in case of correct decoding. Through computer simulation, we got the effective capacity of the adaptive cooperative transmission cooperative food communication system according to different Qos requirement.

Adaptive modulation system for liquid crystal phase modulator

An adaptive modulation system for a liquid crystal (LC) phase modulator is demonstrated. The phase retardation may be modulated by resetting the driving voltage automatically by matching the measured and ideal transmittance of an LC cell sandwiched by crossed polarizers. By using this system, an LC phase modulator can get a low error function of 0.25% in a short modulation time, which is less than the 10% obtained using a conventional modulation method.

Two-Step Scheduling Scheme to Support Fairness and QoS in DVB-S2 System

The use of an efficient packet scheduling scheme for a forward link in satellite communication networks is very important to support fairness for each return channel satellite terminal (RCST) and the service differentiations for user traffics. To support fairness and QoS for each RCST with service-level agreement (SLA), the Adaptive Coding and Modulation (ACM) system in a satellite hub has to process packets with considering modulation and coding (MODCOD) and packet types. Although a DVB-S2 system with ACM scheme has higher transmission efficiency, it cannot offer fairness or quality of service (QoS) to RCSTs. Because the data are transmitted with high MODCOD in regions with clear skies, while data are transmitted using low MODCOD in regions experiencing rain events. In this paper, we propose a two-step scheduling scheme offering fairness and QoS to RCSTs, while minimizing a decrease in throughput. The proposed scheme is carried out performance evaluations using a computer simulation. As results of this simulation, the proposed scheduler was shown to support bandwidth fairness to an individual RCST, and provide a level of QoS differentiation for user traffics.

Improved Spectrum-Sharing Protocol for Cognitive Radio Networks With Multiuser Cooperation

This paper proposes an improved spectrum-sharing protocol for multiuser cooperation in cognitive radio (CR) networks. In CR networks, a secondary user (SU) can access the licensed bands of a primary user (PU) as compensation for cooperative transmission. During cooperative transmission, the SU concurrently transmits its own signal and network-coded signal from the PUs. However, detection errors at the SU cause error propagation which degrades the performance of the PU and SU. To address this problem, we develop a cooperative maximal-ratio combining scheme that mitigates the error propagation and achieves diversity gain. To evaluate the combining scheme, we derive a diversity order and closed-form bit error rate (BER) expression for arbitrary $M$ -QAM at high SNR. The analysis results show that the diversity order and BER depend on the fraction of the transmit power at the SU. Based on the dependency of the fraction factor, we propose an optimization problem to minimize the BER of the SU while guaranteeing the PU's BER. Further, we apply the BER-constrained optimization problem to the adaptive modulation system. Simulation results show that the proposed cooperation provides full diversity gain to the PU and thus improves its spectral efficiency by using the optimized fraction factor.

Low complexity minimum mean square error channel estimation for adaptive coding and modulation systems

Performance of the Adaptive Coding and Modulation (ACM) strongly depends on the retrieved Channel State Information (CSI), which can be obtained using the channel estimation techniques relying on pilot symbol transmission. Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little. In this paper, we investigate the performance of CSI prediction using the Minimum Mean Square Error (MMSE) channel estimator for an ACM system. To solve the two problems of MMSE: high computational operations and oversimplified assumption, we then propose the Low-Complexity schemes (LC-MMSE and Recursion LC-MMSE (R-LC-MMSE)). Computational complexity and Mean Square Error (MSE) are presented to evaluate the efficiency of the proposed algorithm. Both analysis and numerical results show that LC-MMSE performs close to the well-known MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.