Channel Gain Research Articles

Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors

Resonators are passive devices that respond to an excitation signal by oscillating at their natural frequency with exponentially decreasing amplitudes. Physical, chemical and electrical variables can modify the natural frequencies of resonators. If resonators are connected to antennas or other transducers that couple into a communication channel, they enable purely passive sensors that can be read wirelessly. In this manuscript, we use maximum likelihood estimation to analyze the measurement accuracy that can be achieved by the wireless readout of passive resonant sensors as a function of the read signal, the stimulation power and noise figure of the reader, the distance and transducer gain of the transmission channel, and the natural frequency and quality factor of the resonant passive sensor. The Crámer–Rao lower bound characterizes the minimum variance of the natural frequency and decay constant of the resonator. We show the derivation of the Crámer–Rao lower bounds from the Fisher information matrix based on a maximum likelihood estimation of discrete-time samples of an exponentially decaying phasor. This theoretical lower limit of accuracy is almost achieved by an iterative algorithm that approximates the maximum of the measured resonator spectrum with a Lorentz curve.

Enhancing Urban Connectivity: Dynamic Implementation and Integration of Multi-IRS Systems in Smart Cities

This is in preparation to stand out in urban connectivity to be used faster for Multi-Intelligent Reflecting Surfaces (Multi-IRS) in the latest thirst response. It will determine in advance the application of IRS technology for electromagnetic wave control, so that it is fine-tuned at full power to boost signal transmission and coverage across the urban areas in high-density population. It outlines flexible strategies on how to integrate the Multi-IRS system with both past and urban future establishments in a view of making connected connectivity. In reality, multi-IRS integrated with foundational smart city technologies such as IoT, 5G networks, AI, and others are nothing but a leap toward accomplishing unparalleled data flow and connectivity, both very essential for the modern urban ecosystem. Detailed case studies have demonstrated how multi-IRS systems can enable the breaking of traditional barriers in connectivity: more essentially, it can offer higher bandwidth, lower latency, and increased communication effectiveness. This development marks one of the serious steps under the concept of smart cities, where the data will be spreading and flowing without barriers between the multifarious urban systems and services. Lastly, the paper concludes with a future-looking view of urban connectivity underscored through continuous innovation and research of multi-IRS applications within the smart city landscape. The study points out the fact that dynamic IRS implementation creates an indispensable role in the pathway for upcoming development in smart city connectivity solutions, thus making a case for sustained collaborative efforts in research, policy formulating, and technological innovation for realizing the full potential of IRS technology in taming the connectivity challenges of contemporary urban settings. Performance comparison between a sequential beam search and a proposed model across varying Rician Factors, showing the proposed model's superior channel gain progression from -57 dB at 5 dB to -48 dB at 30 dB, outperforming the sequential method in environments with strong direct signals.

Machine Learning Based Throughput Enhancement in Fifth Generation Networks

The 5G Enhanced Mobile Broadband (eMBB) category offers faster data rates, network capacity, and user experiences than prior generations. This research aims to boost the 5G uplink User Equipment (UE) user data transfer rate. We use Python to build frameworks and analyze data. A 250-m-radius center-excited Picocell Base Station (PBS) is investigated to support 15 clients. Cell-range Poisson distribution determines user position. All UEs send Channel State Information (CSI) to the PBS, which evaluates signal transmission channel conditions. The study uses Rayleigh, Rician, free space path, and longdistance route loss models. This inquiry produces a channel state dataset and then it is formulated dataset is dynamic. For servicespecific requirements, UEs use K-means clustering. Clustering concatenates bandwidth, enhancing system efficiency and UE sum rate. Observations from simulation findings. UEs are grouped by channel gain, achievable data rate, and minimum servicerequired data rate. Users in cluster 3 achieve the highest cumulative rate of 9.09 Mbps after clustering with an average of 7.16 Mbps. Bandwidth concatenation increased system capacity, meeting each UEs service needs. After evaluating different clustering models performance criteria, K-means remains the best algorithm for the framework. The methodology was carefully designed to satisfy study goals. This research will investigate beamforming and dynamic clustering to improve user fairness and performance.

Joint Design of Transmitter Precoding and Optical Intelligent Reflecting Surface Configuration for Photon-Counting MIMO Systems Under Poisson Shot Noise

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to enhance link reliability in a cost-effective manner, especially for line-of-sight (LOS) link blocking caused by obstacles. In this paper, we investigate an IRS-assisted single-cell photon-counting communication system in the presence of building shadows, where one IRS is deployed to assist the communication between a multi-antenna base station (BS) and multiple single-antenna users. Photon counting has been widely adopted in sixth-generation (6G) optical communications due to its exceptional detection capability for low-power optical signals. However, the correlation between signal and noise complicates analyses. To this end, we first derive the channel gain of the IRS-assisted MIMO system, followed by the derivation of the mean square error (MSE) of the system using probabilistic methods. Given the constraints of the transmit power and IRS configuration, we propose an optimization problem aimed at minimizing the MSE of the system. Next, we present an alternating optimization (AO) algorithm that transforms the original problem into two convex subproblems and analyze its convergence and complexity. Finally, numerical results demonstrate that the IRS-assisted scheme significantly reduces the MSE and bit error rate (BER) of the system, outperforming other baseline schemes.

Spectrum and Power Efficient Anti-jamming Approach for Cognitive Radio Networks Based on Reinforcement Learning

Background: Spectrum scarcity, spectrum efficiency, power constraints, and jamming attacks are core challenges that face wireless networks. While cognitive radio networks (CRNs) enable the sharing of licensed bands when they are unoccupied, the spectrum should be used efficiently by the secondary user (SU) to ensure a high data rate transmission. In addition, the mobility of the SUs makes power consumption a matter of concern in wireless networks. Because of the open environment, the jamming attack can easily deteriorate the performance and disrupt the connections. Objectives: We aim to enhance the performance of CRN and establish more reliable connections for the SU in the presence of smart jammer by ensuring efficient spectrum utilization and extending the network lifetime. Methods: To achieve our objectives, we propose an anti-jamming approach that adopts frequency hopping. Our approach assumes that SUs observe spectrum availability and channel gain. Then, SU learns the jammer behaviour and goes for the appropriate policy in terms of the number of data and control channels that optimize jointly spectrum efficiency and power consumption. Within, the interaction between the SU and the jammer is modelled as a zero-sum stochastic game, and we employ reinforcement learning (RL) to address this game. Results: SUs learn the optimal policy that maximizes the spectrum efficiency and minimizes the power consumption in the presence of a smart jammer. Simulation results show that the low channel gain leads the SU to select a high number of data channels. However, when the channel gain is high, the SU increases the number of control channels to guarantee a more reliable connection. Taking into account the spectrum efficiency, SUs save their energy by decreasing the number of used channels. The proposed strategy achieves better performance in comparison with myopic learning and the random strategy. Conclusion: Under a jamming attack, considering the gain of utilized channels, SUs select the appropriate number of control and data channels to ensure a reliable, efficient, and long-term connection.

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Visible Light Communication

In order to satisfy the large-scale access of visible light communication (VLC) users, as well as the demand for user request rate, the resource allocation problem of visible light communication with drone-assisted non-orthogonal multiple access (NOMA) technique is investigated. An efficient scheme for joint optimization of power allocation and access point (AP) location is proposed. According to the state of user channel information, a user pairing strategy with uniform channel gain difference for any number of users is designed, and an objective function of maximizing the average user data rate with constraints is constructed. For this non-convex NP-hard problem, the optimization problem with constraints is transformed into an optimization problem without constraints by introducing the idea of a penalty function and then solved by the Harris Hawk Optimization (HHO) algorithm based on the nonlinear energy convergence factor, and ultimately, the optimal user power allocation factor, as well as the location of the AP, are found. The simulation results show that the scheme in this paper can improve the average user data rate better compared to other classical schemes. The system performance of the HHO algorithm is improved by about 20.31% compared to the Particle Swarm Optimization (PSO) algorithm. The HHO algorithm based on the nonlinear energy convergence factor improves the convergence speed by about 50% compared to the classical HHO algorithm.

User-Perceived Capacity: Theory, Computation, and Achievable Policies.

User-perceived throughput is a novel performance metric attracting a considerable amount of recent attention because it characterizes the quality of the experience in mobile multimedia services. For instance, it gives a data rate of video streaming with which a user will not experience any lag or outage in watching video clips. However, its performance limit remains open. In this paper, we are interested in the achievable upper bound of user-perceived throughput, also referred to as the user-perceived capacity, and how to achieve it in typical wireless channels. We find that the user-perceived capacity is quite limited or even zero with channel state information at the receiver (CSIR) only. When both CSIR and channel state information at the transmitter (CSIT) are available, the user-perceived throughput can be substantially improved by power or even rate adaptation. A constrained Markov decision process (CMDP)-based approach is conceived to compute the user-perceived capacity with joint power-rate adaptation. It is rigorously shown that the optimal policy obeys a threshold-based rule with time, backlog, and channel gain thresholds. With power adaptation only, the user-perceived capacity is equal to the hard-delay-constrained capacity in our previous work and achieved by joint diversity and channel inversion.

Optimizing relay node selection in cooperative wireless body area networks with modified POA

In the realm of Wireless Body Area Networks (WBANs), cooperative communications emerge as a crucial research focus, lauded for their capacity to mitigate fading and optimize spectral efficiency. Choosing the appropriate relay in cooperative communication is vital for ensuring spatial diversity, as incorrect relay selections can lower the network's overall performance. This study utilizes an optimization method referred to as M-POA(Modified Pelican Optimization Algorithm) to tackle the relay selection problem. This paper introduces a M-POA specifically designed for cooperative networks in Wireless Body Area Networks (WBAN). The core concept behind the M-POA is to mimic the hunting behavior of pelicans in nature. Here, the relay selection is determined using the M-POA, with channel gain as a fitness function. The proposed strategy integrates the Decode-and-Forward (DF) relay protocol to significantly enhance communication efficiency and reliability. The proposed method is assessed in comparison with other relay selection techniques through a Bit Error Rate (BER) analysis, evaluating the effectiveness of each approach across different Signal-to-Noise Ratio (SNR) scenarios. The simulation results analysis elucidates that M-POA provides well-balanced performance in both exploration and exploitation aspects. By calculating the Bit Error Rate (BER), we assessed the effectiveness of the optimized relay selection strategy, with the objective of minimizing BER to enhance overall communication reliability. By studying the convergence curve, we evaluated the relay selection technique's performance and gauged how effectively it improved with each iteration. The proposed scheme effectively addresses both key requirements of WBAN: improving communication system reliability and optimizing relay selection.

КЕРУВАННЯ ДИНАМІКОЮ ІМПУЛЬСНОГО ПЕРЕТВОРЮВАЧА З М’ЯКИМ ПЕРЕМИКАННЯМ, ЩО ПРАЦЮЄ НА ДУГОВЕ НАВАНТАЖЕННЯ

Issues of design and research of energy-efficient and reliable semiconductor DC voltage converters for wide application in power supply devices of arc plasmatrons used in plasma metal cutting installations are considered. An estimated structural dynamic model of a soft-switching DC converter with a closed-loop control system combining the power part and the control system, intended for use as part of the latter as a digital module, was built. A technique is proposed and methods of controlling the converter are found, which provide the specified duration of transient processes, the permissible value of load current pulsations in a quasi-steady mode and the astatism of the output current, which confirms the correctness of the proposed technique. A prototype of a pulse stabilizer with digital control was made. The results of experimental studies of the sample confirm the effectiveness of the developed control device, namely the achievement of the specified duration of transient processes caused by a step change in the load current, close to 10-12 periods of conversion and astatism of the output current. It is shown that the application of a pulse stabilizer, in which a digital control loop is used, has significant advantages compared to analog options and has the advantages of a strategic plan. The use of combined control allows you to significantly reduce the requirements for the overall gain of the main control channel, which greatly facilitates the choice of sequential digital correction. Research results may be of interest to specialists in the field of power electronics, power supply systems of autonomous objects and control systems. References 14, figures 11. Key words: automatic control system, digital controller, external disturbance, plasma, robustness, optimization.

A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services

IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area’s base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services.

Energy-Efficient Adaptive Bidirectional Transmission Strategy in Simultaneous Wireless Information and Power Transfer (SWIPT)-Enabled Cognitive Relay Network.

Introducing collaborative relay and simultaneous wireless information and power transfer (SWIPT) techniques into a cognitive wireless network, named the SWIPT-enabled cognitive relay network (CRN), is considered a promising approach to deal with insufficiency and the low utilization of spectrum resources, as well as the node's energy-constrained issues in wireless networks. In this paper, to improve the network spectrum efficiency (SE) and energy efficiency (EE) of the SWIPT-enabled CRN, we design an energy-efficient adaptive bidirectional transmission strategy. To be specific, we first select an energy-constrained best relay node with the consideration of signal-to-noise ratio and global channel gain to achieve a better bidirectional relay transmission (BRT). At the same time, we let the energy-constrained best relay node transmit a signal with the SWIPT technique, which can solve the node's energy-constrained issue and improve the network EE. Then, with the selected energy-constrained best relay node, we design a total transmit power threshold (TTPT) determining algorithm to find the TTPT, which lets the total transmission rate of the BRT be equal to the bidirectional direct transmission (BDT). Based on this TTPT, we further design an adaptive bidirectional transmission strategy and let the network achieve adaptive transmission between the BRT and BDT to obtain a higher network SE. Furthermore, to further achieve the energy-efficient transmission of the adaptive bidirectional transmission strategy, we optimize the nodes' power under the requirement of primary users' interference threshold and obtain the analytical expressions of the optimal power. Simulation results show that the transmission rate, the outage probability, and the EE of the designed energy-efficient adaptive bidirectional transmission strategy in the SWIPT-enabled CRN are, respectively, 3.01, 0.07, and 3.10 times that of the non-collaborative transmission, which show the effectiveness of our designed transmission strategy.

An effective and efficient UAV leader selection scheme in swarm of UAVs

Therefore, this study aims to introduce a novel framework for leader and coordinator selection in a swarm of UAVs that may further reduce communication delay and increase the data transmission rate. The proposed reliable framework avoids single-point failure. We use an integrated greedy approach for leader selection using better channel gain, enhanced data transmission rate, and minimal round-trip delays as metrics. Furthermore, the UAV coordinator is selected based on the nearest distance. In addition, this study also proposes an efficient and iterative algorithm for the effective selection of UAV leaders and coordinators. The experimental results indicate that our proposed protocol minimizes delay by 20% and increases the data transmission rate by 102bps as compared with existing methods.

Transmission Coil Group Shaping for Wireless Magnetic Induction MISO Communication System

Abstract Magnetic induction communication is a technology that utilizes magnetic field variations to transmit information. Compared to traditional electromagnetic wave communication, magnetic induction communication performs excellently in environments where electromagnetic waves are difficult to propagate, such as underwater and in soil. However, the current transmission rate of magnetic induction communication is relatively low. The magnetic induction MISO communication system addresses the issue of low transmission rates in cross-medium communication. However, it overlooks the impact of the transmission coil group arrangement on transmission rates. This paper investigates the effect of the transmission coil group arrangement on communication performance and proposes an arc-shaped magnetic induction MISO communication system. The transmission rate performance is analyzed, and the impact of the angle between the transmitting and receiving coils on the reception rate is studied. An expression for the channel gain increment is provided, along with its variation trend with the angle. When the position of the receiving coil is fixed, the expression yields the maximum increment of the shaped channel gain. Simulation results demonstrate that the data transmission rate of the shaped system is approximately 10% higher than that of the unshaped system.

MAC Optimization Protocol for Cooperative UAV Based on Dual Perception of Energy Consumption and Channel Gain

MAC Optimization Protocol for Cooperative UAV Based on Dual Perception of Energy Consumption and Channel Gain

On the Impact of Reactive Region on the Near-Field Channel Gain

On the Impact of Reactive Region on the Near-Field Channel Gain

The Inter-channel Interference Cancellation Algorithm for Wireless Magnetic Induction MIMO Communication System Based on Zero-forcing Precoding

Abstract The wireless magnetic induction MIMO system can enhance the data transmission rate in near-field magnetic induction communication, but it also suffers from severe inter-channel interference issues. This paper introduces precoding techniques into the wireless magnetic induction MIMO system, employing the zero-forcing precoding algorithm to eliminate interference between different channels. Firstly, leveraging the characteristic that the channel state of magnetic induction transmit-receive coils is determined solely by the physical parameters of the coils, channel estimation is performed to obtain the channel gain matrix for wireless magnetic induction communication. Subsequently, the obtained channel matrix is subjected to zero-forcing precoding to eliminate interference caused by coupling between different channels. Simulation results indicate that the data transmission rate of the wireless magnetic induction MIMO system is significantly higher after applying zero-forcing precoding compared to the rate without it. Additionally, the system’s data transmission rate decreases as the distance between the transmitting and receiving coils increases.

Transmission scheme for collaborative‐RIS‐aided NOMA system with multiple channels

AbstractThis paper investigates the combination of collaborative reconfigurable intelligent surface (RIS) with non‐orthogonal multiple access (NOMA) to enhance the quality of service (QoS) in communication systems. By introducing collaborative RIS, a virtual line‐of‐sight (LoS) link can be established between the base station (BS) and the users. In this article, the aim is to minimize the total transmit power under the constraint of each user's QoS by optimizing the phase shifts and the power allocation coefficients. To proceed, two approaches are proposed with different complexities to solve the formulated power minimization problem. Specifically, in the first method, a sub‐channel allocation scheme based on the user locations is developed, and the phase shifts are optimized to obtain the minimum transmit power for fixed channel assignment, which is further addressed by using the alternating optimization and the sequence rotation methods. In the second approach, a joint optimization algorithm that achieves the channel assignment vectors and the phase shift coefficients in an alternative manner, and a sub‐channel assignment algorithm based on the equivalent channel gain is developed. The simulation results show that the proposed transmission scheme has the capability of improving system performance significantly compared with the conventional schemes.

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.

Capacity analysis over fractional order Rayleigh fading channel under additive white generalized Gaussian noise

AbstractThis study presents an innovative fractional order Rayleigh fading model that can be used for channel capacity estimation in the presence of additive white generalized Gaussian noise. The proposed model assumes that the real and imaginary parts of channel gains are generalized Gaussian random variables, which makes it possible to consider the traditional Rayleigh fading model as a special case of fractional order Rayleigh fading. Compared to the Rayleigh model, the fractional order Rayleigh fading model offers a more precise representation of new real‐world communication, such as integrating terrestrial and underwater networks in sixth‐generation communications channels. The probability density function of the channel gain with additive white generalized Gaussian noise is analyzed here. Furthermore, the ergodic and outage capacities of the channel are determined, taking into account the assumption that the channel state information is only available at the receiver. The ergodic capacity is calculated using Meijer's G‐functions, resulting in a closed‐form expression. Numerical simulations demonstrate the superiority of the fractional order Rayleigh fading model over the Rayleigh channel. Moreover, the impact of ergodic and outage capacities under diverse channel characteristics is assessed.

Optimized Decode-and-Forward Multirelay Selection and Power Allocation in Cooperative Wireless Networks

To combat the high outage probability, high complexity calculation, and low resource utilization rate of collaborative communications, an optimal decode-and-forward (DF) multirelay selection and power allocation is proposed in cooperative wireless sensor networks. It is suitable for cooperative communications equipped with a large number of relay nodes. It uses the Lagrange multiplier method to perform the power pre-distribution of the source nodes and all relay nodes before the relay selection. In addition, it also optimally exploits the power of the distributed source and relay nodes according to statistics channel status information (CSI). By optimizing the selection of the multirelay nodes and the allocation of the power with the water-filling algorithm, the proposed scheme totally exploits the whole power to greatly reduce the resource waste. Especially, it chooses an optimal relay node in cooperative communications without a large number of instantaneous channel information, and it only need to arrange the relay nodes according to the increase in the equivalent channel gain order for the optimal relay node collection at the proper signal-to-noise ratios (SNRs). Simulation results show that the outage probability of the scheme outperforms those of the existing single selective decode-and-forward (SDF) counterparts by about 2.1 dB at an outage probability of 10−2.