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This paper investigates an improved criterion to synthesize dissipative observer-based controllers for Markovian jump fuzzy systems under model uncertainties. Since fuzzy-basis functions include some immeasurable state variable or uncertain parameters, there are differences in the fuzzy-basis functions between controller and plant, which is a mismatched phenomenon. This work presents the first attempt for applying double-fuzzy summation-based Lyapunov functions for the observer-based control scheme of the Markov jump fuzzy system regarding the mismatched phenomenon. To be specific, the dissipative conditions are formulated in terms of uncertain parameterized bilinear matrix inequalities. Based on the improved relaxation techniques, a linear-matrix-inequality (LMI)-based algorithm is proposed in the framework of sequence linear programming matrix method. The obtained observer-based controller ensures that the closed-loop system is stochastically stable, and the dissipative performances produce less conservative results compared to preceding works via two numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with linear matrix inequality conditions to design observer-based H ∞ -controllers for discrete-time Markov jump fuzzy systems with regard to incomplete transition probabilities and sensor failures. Since some system states involved in fuzzy premise variables are immeasurable or under sensor failures, the observer-based fuzzy controller does not share the same fuzzy basic functions with plants, leading to a mismatch phenomenon. Our work contributes a new single-step LMI method for synthesizing the observer-based controller of the Markov jump fuzzy system in the presence of sensor failures with regard to the mismatched phenomenon. The non-convex H ∞ -stabilization conditions induced by the output-feedback scheme are firstly formulated in terms of multiple-parameterized linear matrix inequalities (PLMIs). Secondly, by assuming that the differences of fuzzy basic functions between the controller and plant are bounded, the multi-PLMI-based conditions are cast into linear matrix inequalities standing for tractable conditions. The designed observer-based controller guarantees the stochastic stability of the closed-loop system and less conservative results compared to existing works in three numerical examples. [ABSTRACT FROM AUTHOR]

Wireless communication technologies have profoundly impacted the interconnectivity of mobile users and terminals. Nevertheless, the exponential increase in the number of users poses significant challenges, particularly in interference management, which is a major concern in wireless communication. Machine learning (ML) approaches have emerged as powerful tools for solving various problems in this domain. However, existing studies have not fully addressed the problem of interference management for wireless communication using ML techniques. In this paper, we explore the application of recurrent neural network (RNN) approaches to address co-channel interference in wireless communication. Specifically, we investigate the effectiveness of long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU) network architectures in two different network settings. The first network comprises 10 connected devices, while the second network involves 20 devices. Our experimental results demonstrate that Bi-LSTM outperforms LSTM and GRU in terms of mean squared error, normalized mean squared error, and sum rate. While LSTM and GRU produce similar results, LSTM exhibits a marginal advantage over GRU. In addition, a combined RNN approach is also studied, and it can provide better results in dense networks. [ABSTRACT FROM AUTHOR]

The reconfiguration of wireless channels with reconfigurable reflecting surface (RIS) technology offers new design options for future wireless networks. Due to its high altitude and increased probability of establishing line-of-sight linkages with ground source/destination nodes, aerial RIS (ARIS) has greater deployment flexibility than traditional terrestrial RIS. It also provides a wider-view signal reflection. To leverage the advantages of ARIS-enabled systems, this paper defines air-to-ground linkages via Nakagami-m small-scale fading and inverse-Gamma large-scale shadowing, considering realistic composite fading channels. To construct a tight approximate closed-form formula for the outage probability (OP), a new mathematical framework is proposed. Additionally, a deep-learning-based system called the BiLSTM model is deployed to evaluate OP performance in the 3D spatial movement of the ARIS system. In the offline phase, the proposed model is trained with real-value channel state estimation sets and enhances OP performance in the online phase by learning channel information in a bidirectional manner. Simulation results demonstrate that the proposed BiLSTM model outperforms all other models in analyzing OP for the ARIS system. [ABSTRACT FROM AUTHOR]

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency. [ABSTRACT FROM AUTHOR]

Relay-assisted hybrid beamforming plays an inevitable role in enhancing network coverage, transmission range, and spectral efficiency while simultaneously reducing hardware cost, power consumption, and hardware implementation complexity. This study investigates a cognitive radio network (CRN)-based hybrid wideband transceiver for millimeter-wave (mm-wave) decode-and-forward (DF) relay-assisted multiuser (MU) multiple-input multiple-output (MIMO) systems. It is worth mentioning that the underlying problem has not been addressed so far, which is a real motivation behind the proposed algorithm. The joint optimization of hybrid processing components and the constant amplitude constraints imposed by the analog beamforming solution make this problem non-convex and NP-hard. Furthermore, the analog beamformer common to all sub-carriers is another challenging aspect of the underlying problem. To derive the frequency-flat analog processing component in the radio frequency (RF) domain and frequency-dependent baseband processing matrices in the baseband domain, the original complicated problem is reformulated as two single-hop sum-rate maximization sub-problems. Taking advantage of this decomposition, the sum spectral efficiency is maximized through RF precoding and combining. On the other hand, the impact of interference among transmitted data streams and inter-user interference (IUI) is minimized via baseband processing matrices. Finally, computer simulations are conducted by changing system parameters, considering both perfect and imperfect channel state information (CSI). Simulation results demonstrate that the proposed algorithm achieves performance close to full-complexity precoding and outperforms other well-known hybrid beamforming techniques. Specifically, more than 95% efficiency is achieved with perfect CSI, and more than 90% efficiency is attained under the assumption of 30% error in the estimated channels. [ABSTRACT FROM AUTHOR]

Graph convolutional networks (GCNs) provide an advantage in node classification tasks for graph-related data structures. In this paper, we propose a GCN model for enhancing the performance of node classification tasks. We design a GCN layer by updating the aggregation function using an updated value of the weight coefficient. The adjacency matrix of the input graph and the identity matrix are used to calculate the aggregation function. To validate the proposed model, we performed extensive experimental studies with seven publicly available datasets. The proposed GCN layer achieves comparable results with the state-of-the-art methods. With one single layer, the proposed approach can achieve superior results. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surface (RIS) is one of the most innovative and revolutionary technologies for increasing the effectiveness of wireless systems. Deep learning (DL) is a promising method that can enhance system efficacy using powerful tools in RIS-based environments. However, the lack of extensive training of the DL model results in the reduced prediction of feature information and performance failure. Hence, to address the issues, in this paper, a combined DL-based optimal decoding model is proposed to improve the transmission error rate and enhance the overall efficiency of the RIS-assisted multiple-input multiple-output communication system. The proposed DL model is comprised of a 1-dimensional convolutional neural network (1-D CNN) and a gated recurrent unit (GRU) module where the 1-D CNN model is employed for the extraction of features from the received signal with further process over the configuration of different layers. Thereafter, the processed data are used by the GRU module for successively retrieving the transmission signal with a minimal error rate and accelerating the convergence rate. It is initially trained offline using created OFDM data sets, after which it is used online to track the channel and extract the transmitted data. The simulation results show that the proposed network performs better than the other technique that was previously used in terms of bit error rate and symbol error rate. The outcomes of the model demonstrate the suitability of the proposed model for use with the next-generation wireless communication system. [ABSTRACT FROM AUTHOR]

The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is built on a variety of technologies. Wireless communication is an important part of these technologies. In particular, wireless 6G communication can be essential for the growth of the metaverse. In line with the goal of achieving higher rates in the next-generation wireless network for the metaverse, in this paper, a novel conceptualization of intelligent reflecting surface (IRS)-assisted multiple-input single output-based wireless communication in physical world environments is proposed. More specifically, this paper proposes that in the physical world, the IRS-assisted communication between a communication network and users can be reflected in the metaverse through the virtual world (such as digital avatars and the virtual environment). In the simulations, the bit-error rate and spectral efficiency of the receiver terminal were performed and calculated in the metaverse engine for future consideration. [ABSTRACT FROM AUTHOR]

In this paper, an optimization algorithm is proposed for the system, where a decode-and-forward (DaF) protocol based full duplex relay (FDR) and an intelligent reflecting surface (IRS) work together to deliver message signals from a source to a destination. The transmission is carried out in a single time frame due to the full duplex nature of the FDR and IRS. The joint optimization of the beamformers for the multiple antenna sets at the source along with the FDR antenna sets and the phase adjustment of the reflecting elements at the IRS is proposed. Though separate optimizations of the beamformers and the phase adjustment were available previously, the joint one is more challenging due to the increased number of signal paths and the impacts from these new paths. To accommodate conflicting requirements from different paths, the reflecting elements of the IRS are cleverly grouped into the corresponding number of sets, where the requirements are met separately through the optimizations of the phase values of the corresponding group. For the beamformer optimization, we parameterize the beamforming vectors, which results in two concave optimization problems for the source and for the FDR relay. These optimization sets are implemented in an alternating manner to reach a reasonable solution. Simulation results are provided to illustrate the impacts of the proposed algorithm in various implementation scenarios and parameter sets. These results identify the gains the proposed algorithm provides and the scenarios where these gains are expected. [ABSTRACT FROM AUTHOR]

This paper explores the random environment with two classes of suppliers and impulse customers. The system's greatest inventory size is S, and it has an infinitely large orbit. In this case, there are two categories of suppliers: temporary suppliers and regular suppliers. Whenever the inventory approaches r, we place on order Q 1 (= S − r ) unit items to a temporary supplier. Similarly, when the inventory level drops to s (< Q 1 < r ), we place an order for Q 2 (= S − s > s + 1 ) units of items to our regular supplier. Two types of suppliers' lead times are considered to be exponentially distributed. Here, the customers who arrive from different states of the random environment (RE) are followed by the Markovian arrival process. If there is no inventory in the system when the customer arrives, they are automatically assigned to an orbit. The model was examined in steady state by using the matrix-analytic approach. Finally, the numerical examples for our structural model are discussed. [ABSTRACT FROM AUTHOR]