Order Modulation Research Articles

MRP-YOLO: An Improved YOLOv8 Algorithm for Steel Surface Defects

The existing detection algorithms are unable to achieve a suitable balance between detection accuracy and inference speed. As the accuracy of the algorithm increases, its complexity also rises, resulting in a decrease in detection speed, which undermines its practicality. This issue is particularly evident in the context of surface defect detection in industrial parts, where low contrast, small target features, difficult feature extraction, and low real-time detection efficiency are prominent challenges. This study proposes a novel method for steel defect detection based on the YOLO v8 algorithm, which improves detection accuracy while maintaining low computational complexity. Firstly, the global background and edge information are adaptively extracted via the MSA-SPPF module in order to obtain a more comprehensive feature representation. Furthermore, the anti-interference ability of the model is enhanced through the deformability of attention and the large convolution kernel characteristics. Secondly, the design of Dynamic Conv and C2f-OREPA enables the model to efficiently reduce the demand for computational resources while maintaining high performance. It is further proposed that the RepHead detection head approximates the multi-branch structure of the original training by a single convolution operation. This approach not only enriches the feature representation but also maintains an efficient inference process. The effectiveness of the improved MRP-YOLO algorithm is verified using the NEU-DET industrial surface defect dataset. The experimental results demonstrate that the mAP of the MRP-YOLO algorithm reaches 75.6%, which is 2.2% higher than that of the YOLOv8n algorithm, while the FLOPs are only 2.3 G higher. It indicates that the detection accuracy is significantly improved with a limited increase in computational complexity.

Coupling a peatland hydrological model with a snowmelt module in order to model the snowmelt runoff in a boreal ombrotrophic peatland in eastern Québec (Canada).

Coupling a peatland hydrological model with a snowmelt module in order to model the snowmelt runoff in a boreal ombrotrophic peatland in eastern Québec (Canada).

Pilot Contamination Attack Detection Methods—An Exhaustive Performance Evaluation Through Probability Metrics and Statistical Classification Parameters

Among the numerous strategies that an attacker can initiate to enhance its eavesdropping capabilities is the Pilot Contamination Attack (PCA). Two promising methods, based on Phase-Shift Keying (PSK) modulation of Nth order—2-N-PSK and Shifted 2-N-PSK, can detect an existing PCA by means of analysis of the constellation that the correlation product of received pilot signals belongs to. The overall efficiency of the methods can be studied by the most commonly used probability metrics—detection probability and false alarm probability. However, this information may be insufficient for comparison purposes; therefore, to acquire a more holistic perspective on the methods’ performances, statistical evaluation metrics can be obtained. Depending on the particular application of the system in which the PCA detection methods are incorporated and the distribution of attack initiation among all samples, different classification parameters are of varying significance in the efficiency assessment. In this paper, 2-N-PSK and Shifted 2-N-PSK are comprehensively studied through their probability parameters. In addition, the methods are also compared by their most informative statistical parameters, such as accuracy, precision and recall, F1-score, specificity, and fall-out. A large number of simulations are carried out, the analyses of which indisputably prove the superior behavior of the Shifted 2-N-PSK compared to the 2-N-PSK detection method. Since a method’s performance is strongly related to the number of antenna elements at the base station, all simulations are conducted for scenarios with different antennae numbers. The most promising realization of Shifted 2-N-PSK improves the receiver operating characteristics results of the original 2-N-PSK by 7.38%, 4.33%, and 5.61%, and outperforms the precision recall analyses of 2-N-PSK by 10.02%, 4.82% and 3.86%, for the respective number of 10, 100 and 300 antenna elements at the base station.

Performance evaluation of magic square based spatial modulation in UAV communication

This study proposes a method to reduce the bit error rate (BER) in communications by unmanned aerial vehicles (UAVs). The method involves using specific active antennas that transmit at precise times with a particular mapping, ensuring orthogonality among transmitting antennas. This technique can accommodate numerous transmit antennas and amplitude-phase modulation orders. When applied to spatial modulation (SM) and generalized spatial modulation (GSM), it demonstrates clear superiority and significantly improved results compared to traditional SM and GSM methods, especially at high altitudes where conventional methods result in high BER. Introducing a new modulation method called magic square (MS) further enhances the results. The MS method provides a unique orthogonality pattern for the transmitting antenna sets. Additionally, the study introduces the utilization of magic squares in the modulation field. The MS-based SM model shows a BER rate almost 103 times lower than traditional SM at the 25 dB signal-to-noise ratio (SNR) level for various fly altitudes. Similarly, the MS-based GSM model demonstrates a BER rate almost 100 times lower than traditional GSM. The proposed method yields better results as the altitude increases, making it suitable for UAV communication at various altitudes.

Research on Rate Adaptation of Underwater Optical Communication with Joint Control of Photoelectric Domain

As the communication distance changes, the received signal strength of an underwater optical communication system will change, and the range of its variation may not only exceed the dynamic range of the photoelectric detection device but also cause the reliability of communication to change due to the change in the received signal-to-noise ratio. In order to maintain better communication over a longer distance, this paper proposes a rate-adaptive method for underwater optical communication with joint control in the photoelectric domain. In the optical domain, the incident light’s power is adaptively adjusted by controlling the transmittance of the liquid crystal light valve to reduce saturation distortion. In the electrical domain, the constellation distribution is optimized according to the desired probability mass function, and the modulation order is adjusted in real time by estimating the received signal-to-noise ratio of the link. The simulation results show that under the forward error correction (FEC) threshold, the proposed method increases the dynamic range of the photomultiplier tube (PMT) by about 10 dB and expands the dynamic range of the system’s communication distance.

Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding in MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the Tikhonov regularization parameter, as well as the step size and search direction coefficients of the conjugate gradient (CG) method, as trainable parameters within the deep learning framework. This enables dynamic adjustments based on varying channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach across a range of MIMO configurations and channel conditions, consistently achieving lower bit error rate (BER) and normalized minimum mean square error (NMSE) compared to well-known techniques like DetNet and CG. The proposed method has superior performance over CG and other model-based methods, especially with a small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations, modulation orders, and different channel conditions.

An advanced symbol detection approach for MIMO-FBMC/OQAM scheme based on migrating birds optimization algorithm

Application of multiple-input multiple-output (MIMO) technology to the filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) system provides great improvements on its robustness to channel fading effects. Nevertheless, the unique qualities of MIMO-FBMC/OQAM scheme do not make any sense without solving the symbol detection problem on its receiver part. To this end, an efficient symbol detecting strategy having the capability of recovering the transmitted symbol sequences with the highest accuracy is needed for the related system. Maximum likelihood (ML) detector is known with its excellent symbol detection performance in the literature. However, due to the usage of exhaustive search procedure, the computational complexity of the ML detector reaches extremely high levels with the increase of antenna size and modulation order. On the other hand, in the case that the exhaustive search procedure is substituted with the symbol optimization process, it becomes possible to achieve a large amount of complexity reduction in the conventional ML scheme without compromising too much on its symbol detection performance. In order to carry out an efficient symbol optimization in discrete space, we propose migrating birds optimization (MBO) algorithm based on cyclic bit flipping procedure in this paper. By virtue of employing the proposed MBO algorithm reinforced by the cyclic bit flipping mechanism for optimizing the symbol sequences, the computational complexity of the conventional ML is reduced to quite low levels. The percentages of complexity reduction achieved by the proposed scheme over the classical ML for 4 × 4, 6 × 6 and 8 × 8 MIMO configurations are equal to 29.688%, 87.188% and 98.299%, respectively. In addition to these huge complexity gains, an efficient symbol detection performance quite near to that of conventional ML is obtained thanks to the usage of aforementioned MBO-based symbol optimization procedure.

Error rates of optically pre-amplified PPM wireless systems with coding and arbitrary optical filter response

We present novel results for the uncoded and coded bit-error probability (BEP) of optically pre-amplified pulse-position modulation (PPM) wireless systems. For uncoded systems, a novel analytic method for the evaluation of the BEP is derived. The method takes into account the non-ideal optical filter response and utilizes a finite Karhunen–Loève series expansion to calculate the BEP. Using the proposed approach, it is possible to accurately evaluate the PPM BEP for arbitrarily shaped filters where the well-established χ2 method only provides approximate results. Considering a Lorentzian filter response, the discrepancy between the two methods amounts to 0.5 dB in a variety of filter bandwidths and PPM modulation orders. The Lorentzian filter response was chosen as an illustrative practical example whose series can be calculated analytically. The proposed method is also valid for any type of optical filter for which the Karhunen–Loève series expansion can be calculated analytically or numerically. Due to the finite number of terms that are required irrespective of the signal energy level, the proposed method can also be applied without loss of accuracy to assess the system performance under the effects of turbulence and adverse weather conditions. For coded systems with Lorentzian filters, Monte-Carlo simulations are utilized to evaluate the BEP performance of the 5G LDPC codes, and it is demonstrated that they impart an energy gain up to 3.3 dB for 4–PPM and 2.3 dB for 16–PPM at a target BEP of 10−5. The optimal code rates are also discussed for several combinations of the optical filter bandwidth and PPM modulation order and it is shown that in almost all of the cases the optimal code rate is 11/13. Moreover, the sum-product and min-sum decoders perform within 0.1 dB from each other for the best code rates, which points towards the utilization of the min-sum decoder in all settings, since its operation does not require knowledge of the filter parameters. Finally, the comparison between the coded systems with Lorentzian and ideal passband filters exhibits the same 0.5 dB discrepancy that was observed for uncoded systems.

Underwater acoustic FBMC communication with multiple mode index modulation

In underwater acoustic communication (UWA), there is a significant presence of multipath and Doppler spread. The integration of orthogonal frequency division multiplexing (OFDM) with index modulation (IM), utilizing subcarrier index modulation to enhance system performance underwater, has garnered considerable interest from researchers. Multiple mode modulation can enhance utilization by transmitting symbols of different modes on different subcarriers. Inspired by the concepts of index modulation and multiple mode modulation, this paper proposes a method that combines multiple mode indexing with the filter bank multicarrier system (MM-FBMC-IM), enabling the transmission of different modulation orders on each subcarrier to increase system flexibility. Simulations demonstrate that under the same spectral efficiency, multiple mode indexing FBMC offers superior bit error rate (BER) performance compared to conventional FBMC and index modulation FBMC.

Performance Enhancement for B5G/6G Networks Based on Space Time Coding Schemes Assisted by Intelligent Reflecting Surfaces with Higher Modulation Orders.

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (Eb/No) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of Eb/No by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.

Explaining outliers and anomalous groups via subspace density contrastive loss

Explainable AI refers to techniques by which the reasons underlying decisions taken by intelligent artifacts are single out and provided to users. Outlier detection is the task of individuating anomalous objects within a given data population they belong to. In this paper we propose a new technique to explain why a given data object has been singled out as anomalous. The explanation our technique returns also includes counterfactuals, each of which denotes a possible way to “repair” the outlier to make it an inlier. Thus, given in input a reference data population and an object deemed to be anomalous, the aim is to provide possible explanations for the anomaly of the input object, where an explanation consists of a subset of the features, called choice, and an associated set of changes to be applied, called mask, in order to make the object “behave normally”. The paper presents a deep learning architecture exploiting a features choice module and mask generation module in order to learn both components of explanations. The learning procedure is guided by an ad-hoc loss function that simultaneously maximizes (minimizes, resp.) the isolation of the input outlier before applying the mask (resp., after the application of the mask returned by the mask generation module) within the subspace singled out by the features choice module, all that while also minimizing the number of features involved in the selected choice. We consider also the case in which a common explanation is required for a group of outliers provided together in input. We present experiments on both artificial and real data sets and a comparison with competitors validating the effectiveness of the proposed approach.

Performance comparison of various end-to-end learning technologies with a bandwidth-limited OWC system.

Recently, end-to-end (E2E) learning methodologies have garnered significant attention as a compelling approach to attain global optimal communication within the domain of 6 G native intelligent systems. Nevertheless, a precise evaluation of the diverse E2E techniques is still lacking, leading to uncertainties regarding their applicable scenarios and effectiveness. In this paper, we present a comprehensive comparison applying three advanced E2E methods including the autoencoder-based geometric shaping (AEGS) model, comprehensive autoencoder (CAE) model, and wave-wise auto-equalization (WWAE) model in a real bandwidth-limited optical wireless communication (OWC) system. A novel attention-based comprehensive noise joint channel estimator (ACNJCE) is proposed to serve as a universal channel model adaptable to the existing E2E methods. Based on traditional carrier-less amplitude and phase modulation (CAP) modulation, AEGS, WWAE, and CAE are compared under the conditions of 2 GBaud and 3 GBaud respectively. The final results demonstrate that the CAE exhibits the capability to autonomously allocate bandwidth and achieves the highest dynamic adjustment range, which is increased by 69% compared with CAP based on neural network (NN) equalization. In contrast, AEGS has obvious advantages in terms of received optical power (ROP) gain. Based on bit-power loading discrete multi-tone modulation (DMT) modulation, WWAE can effectively compensate the signal spectrum after modulation order optimization and finally achieves the highest data rate under the condition that the -3 dB bandwidth of the channel is only close to 1 GHz. The BER of WWAE with DMT at this rate is 25.2% of that using the NN equalization. Furthermore, experimental results under turbulent conditions reveal that AEGS exhibits superior and more stable performance amidst the perturbations caused by turbulence due to its ability to achieve end-to-end autonomous optimization while integrating traditional modulation and bringing additional shaping gain. According to our knowledge, this marks the first comprehensive evaluation and comparison of existing major E2E algorithms and traditional communication algorithms in a real OWC system.

ОЦІНКА ЧАСТОТНО-ФАЗОВИХ СПОТВОРЕНЬ В ОПТИЧНИХ ТЕЛЕКОМУНІКАЦІЯХ З OFDM

The article proposes a method for monitoring the peak-to-average power ratio (PAPR), and also discusses the results of assessing the sensitivity of optical-OFDM systems to phase-frequency distortions. The results of a study of the noise immunity of an optical-OFDM system for frequency shift relative to the spacing of optical subcarriers are presented. A description of the factors causing interference and frequency-phase distortions caused by chromatic dispersion (CD) and polarization mode dispersion (PMD) on the performance of optical multiplexing systems with orthogonal frequency division is carried out. Monitoring the PAPR in OFDM systems is relevant, in particular, in the context of fiber optic communication problems that are caused by the nonlinearity of the optical fiber. It is shown that when directly deploying optical networks, the presence of frequency distortions and sensitivity to phase noise are the two main disadvantages of OFDM. Both frequency distortion and phase noise lead to interchannel interference (ICI). Due to the relatively large symbol length compared to a single carrier, OFDM is susceptible to both frequency distortion and phase noise. An additional cumulative distribution function is used for a visual description of PARP. The effect of the oversampling coefficient on the PARP value is determined. It is proposed to use the oversampled signal to obtain a more accurate PARP value. The results of the study show that the effect of phase noise on a signal in optical OFDM channels is due to the existing long symbol period compared to signal transmission on a single carrier. In addition, with an increase in the modulation order, optical OFDM becomes more sensitive to phase noise, which encourages an increase in the signal-to-noise ratio to counteract this. The results of the research showed that for coherent OFDM optical systems, the line width of the optical quantum generator is a critical parameter, especially when switching to high-order modulation. The paper discusses a diagram describing the process of convergence of radio and optical technologies in the context of the use of OFDM modulation. This technique allows for the correct choice of channel multiplexing strategy in optical OFDM telecommunications with multi-position signals.

Computer-controlled electrical stimulation of facial muscles by facial neuromuscular electrical stimulation (fNMES): Hardware and software solutions

BackgroundComputer controlled electrical stimulation of facial muscles is a promising method to study facial feedback effects, though little guidance is available for new adopters. New MethodFacial Neuromuscular Electrical Stimulation (fNMES) offers a spatially and temporally precise means of manipulating facial muscles during experiments, and can be combined with EEG to study the neurological basis of facial feedback effects. Precise delivery of stimulation requires hardware and software solutions to integrate stimulators and a stimulus-presenting computer. We provide open-source hardware schematics and relevant computer code in order to achieve this integration, so as to facilitate the use of fNMES in the laboratory. ResultsHardware schematics are provided for the building of a bespoke control module, which allows researchers to finely control stimulator output whilst participants complete computer tasks. In addition, we published code that new adopters of NMES can use within their experiments to control the module and send event triggers to another computer. These hard- and software solutions were successfully used to investigate the effects of facial muscle activation on felt and perceived emotion. We summarise these findings and discuss the integration of fNMES with EEG and peripheral physiological measures. Comparison with existing methodsOur inexpensive hardware solution allows fNMES parameters to be computer controlled, and thus allows to stimulate facial muscles with high precision. This opens up new possibilities to investigate, for example, facial feedback effects. ConclusionsWe provide tools and guidance to build a control module in order to precisely deliver electrical stimulation to facial muscles using a stimulus computer (while recording EEG or other peripheral physiology).

Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers

Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialogue systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or statistical slot-filling approaches for processing simple queries; however, the appearance of more complex utterances demanded the application of shift-reduce parsers or sequence-to-sequence models. Although shift-reduce approaches were initially considered the most promising option, the emergence of sequence-to-sequence neural systems has propelled them to the forefront as the highest-performing method for this particular task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialogue. We implement novel shift-reduce parsers that rely on Stack-Transformers. This framework allows to adequately model transition systems on the transformer neural architecture, notably boosting shift-reduce parsing performance. Furthermore, our approach goes beyond the conventional top-down algorithm: we incorporate alternative bottom-up and in-order transition systems derived from constituency parsing into the realm of task-oriented parsing. We extensively test our approach on multiple domains from the Facebook TOP benchmark, improving over existing shift-reduce parsers and state-of-the-art sequence-to-sequence models in both high-resource and low-resource settings. We also empirically prove that the in-order algorithm substantially outperforms the commonly used top-down strategy. Through the creation of innovative transition systems and harnessing the capabilities of a robust neural architecture, our study showcases the superiority of shift-reduce parsers over leading sequence-to-sequence methods on the main benchmark.

Performance Analysis of 8 × 112 Gbps (0.896 Tbps) WDM ROF Link Using 32 QAM - OFDM Modulation Scheme for Achieving Extended Range

We propose an advanced optical transport system utilizing higher order modulation formats and coherent detection. Our innovation introduces an 8 × 112 Gbps wavelength division multiplexing – radio over fiber (WDM ROF) link using a 32 quadrature amplitude modulation-orthogonal frequency division (QAM-OFDM) modulation scheme, capable for extending its reach. Our proposed research stands out from existing studies due to its distinctive use of the 32-QAM format and a 112 Gbps data rate per channel, especially when considering a channel span of 3000 km. Our analysis covers WDM-ROF link systems, including a preoptical amplifier within the link, one optical amplifier outside the channel loop span, and one within the loop span, representing distinct scenarios. We operated optical amplifiers in the gain-controlled mode. Remarkably, exclusive of employing precise dispersion or nonlinearity improvement techniques, carried out simulations reveal optimal transmission distance for our anticipated system having considered specification parameters is 3000 Km. We conducted extensive simulations to assess the system performance in terms of quality factor (Q-factor), estimated symbol error (ESE), and error vector magnitude (EVM) over link spans ranging from 1000 to 15,000 km.

Design and Implementation of Transparent Cross‐Polarization Interference Compensation in a Wideband Dual‐Polarization Satellite Receiver

ABSTRACTIn this paper, simultaneous transmission on two orthogonal antenna polarizations in a polarization division multiplexing (PDM) fashion is studied for wideband satellite communication links using dual‐polarization satellite receivers for the purpose of doubling the data rate. In order to mitigate the cross‐polarization interference (XPI), a new digital blind and transparent XPI compensation method is proposed, coined as XPI correlation learning estimation and adaptive reduction (XPI‐CLEAR). The received signal‐to‐noise‐and‐interference ratio (SNIR) and packet‐error rate (PER) performance with this non‐data‐aided and non‐decision‐directed method is assessed in a comprehensively modelled XPI channel with effects such as depolarization due to atmospheric conditions, imperfect cross‐polarization discrimination (XPD) of the antennas at the transmitter and the receiver, memory effects due to frequency selectivity of the XPD, and differential frequency offset (DFO) between the two channels. The application of the XPI‐CLEAR method presents considerable energy efficiency improvements for all the studied XPI channel effects, and is particularly beneficial for higher order modulation. A low‐complexity hardware implementation with symbol rates up to 500 MBaud validates the XPI‐CLEAR method as a practical solution to increase the data rates of the satellite air interface and to achieve the doubling of the throughput of the satellite link by the use of PDM.

Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions

To address the issues of communication failure and inefficiency in clustered drone relay communication due to external malicious interference, this paper proposes a joint optimization method for relay communication rates under interference conditions for clustered drones. This method employs the following two-step processing framework: Firstly, the Discrete Soft Actor-Critic (DSAC) algorithm is used to train the relay drones for dynamic channel selection, effectively avoiding various types of interference. Simultaneously, the Bayesian optimization algorithm is applied to optimize the hyperparameters of the DSAC algorithm, further enhancing its performance. Subsequently, the modulation order, transmission power, trajectory of the relay drones, and power allocation factors of the clustered drones are jointly optimized. This complex problem is transformed into a convex subproblem for determining a solution, aiming to maximize the communication rate of the clustered drones. The simulation’s results demonstrate that the proposed algorithm exhibits excellent performances in terms of anti-interference capability, solution convergence, and stability. It effectively improves the mission efficiency of clustered drones under interference conditions and enhances their adaptability to dynamic environments.

Join operation for the Bruhat order and Verma modules

We observe that the join operation for the Bruhat order on a Weyl group agrees with the intersections of Verma modules in type A. The statement is not true in other types, and we propose a weaker correspondence. Namely, we introduce distinguished subsets of the Weyl group on which the join operation conjecturally agrees with the intersections of Verma modules. We also relate our conjecture with a statement about the socles of the cokernels of inclusions between Verma modules. The latter determines the first Ext space between a simple module and a Verma module. We give a conjectural complete description of such socles which we verify in a number of cases. Along the way, we determine the poset structure of the join-irreducible elements in Weyl groups and obtain closed formulae for certain families of Kazhdan–Lusztig polynomials.

Atmospheric water generator: design for rural zones with low and medium humidity level

Water shortage is one of the major issues in the world. The consequences can impact negatively on the develop of a location. Puno is an arid region located in Peru. There have been different problems related to water shortage in recent years. Some communities use a waterhole filled by precipitation as a source of water. This is usually far from their communities, then they have to transport manually buckets of water by waking. In this paper, we establish a design procedure of an atmospheric water generator (AWG) located at Puno with a capacity of 40L per day in order to deal with the water shortage. Current AWGs are designed for locations with favorable climatic conditions as high relative humidity (RH). However, they wouldn't be able to operate in dried seasons as at Puno with RH under 25%. An adsorption process is implemented as an additional module in order to compensate the low ambient temperature and RH during the worst climatic conditions from June to August. An analytic model of dehumidification by a heat exchanger was developed in order to quantify influence of parameters as climatic conditions and mass fluxes. Results showed that the design is capable to produce water during all the variable climactic conditions over the year. Numerical simulations of the analytic model indicated that the system can produce water by direct cooling in most months without the support of adsorption process.