Multiple-output Orthogonal Frequency Division Research Articles

Joint Power and Delay Optimization‐Based Resource Allocation in Mu‐MIMO‐OFDM System: Deep Convolutional Red Piranha Pyramid‐Dilated Neural Network

ABSTRACTIn general, Multi‐User Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MU‐MIMO‐OFDM) enables multiple users to simultaneously communicate with a single base station using multiple antennas and OFDM modulation. Nevertheless, resource allocation challenges such as power management and delay optimization arise within MU‐MIMO‐OFDM systems, requiring sophisticated solutions to ensure efficient use of resources and optimal system performance. Thus, joint power and delay optimization‐based resource allocation using a Deep Convolutional Pyramid‐Dilated Neural Network (DCPDNN) with Red piranha optimization and Optimal Delay Scheduling conflict graphs Algorithm (DCPDNN‐RPO‐ODSCGA) in MU‐MIMO‐OFDM system is proposed in this presented research. The proposed mechanism is performed in two stages: power allocation and delay optimization. In the first stage, through a Deep Convolutional Pyramid‐Dilated Neural Network (DCPDNN), which aims to maximize throughput, the network resources are distributed to user equipment (UEs) based on power and transmission rate. To reduce the loss function, Red Piranha Optimization (RPO) is proposed to optimize the layers of DCPDNN. In the second stage, the Optimal Delay Scheduling conflict graphs Algorithm (ODSCGA) is proposed for the optimizing delay in the MU‐MIMO‐OFDM system. The multiracial envelope procedure and service curve for traffic flows in the uplink transmission are used in the ODSCGA approach to estimate the delay‐bound value. Ideas like the maximal weight independent set and optimal conflict graph are also utilized. The simulations of DCPDNN‐RPO‐ODSCGA were conducted using MATLAB software. Thus, the proposed approach has attained higher spectral capacity, higher fairness index, and increased cumulative distribution function (CDF), 29.74%, 32.98%, and 16.46% lower loss rate, 28.05%, 24.09%, and 17.45% improved energy efficiency, 15.09%, 13.78%, and 12.05% lower processing time than other conventional approaches like MPQM‐SCA, Hyb‐BF‐DSA, and SIA‐FDBD methods, respectively.

Sum rate optimization in STAR-RIS assisted multiuser massive MIMO-OFDM VLC systems

Visible Light Communication (VLC) offers a promising solution for future networks, leveraging existing lighting infrastructure in indoor environments. However, VLC requires a direct line of sight to function which can be limiting. Reconfigurable Intelligent Surface (RIS) is a new technology that can bend light and radio waves, addressing this limitation in VLC. RIS come in three types: passive which reflects signals, active that boosts and reflects signals, and Simultaneous Transmission and Reflection (STAR)-RIS which can both reflect and transmit signals simultaneously. STAR-RIS offers the most control over the signal. In this paper, we propose a new multi-user VLC system with a massive Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) architecture, leveraging STAR-RIS to optimize data rates and improve coverage, particularly in non-line-of-sight scenarios. We formulate a system model and solve a convex optimization problem to determine the optimal transmission and reflection coefficients for STAR-RIS elements, aiming to maximize the total sum rate for all users. By employing maximum ratio transmission precoding, we minimize interference among users and demonstrate significant performance gains. Simulation results show that our proposed energy splitting-based STAR-RIS configuration outperforms traditional mode selection and time switching approaches with fixed or random coefficients, yielding substantial improvements in data rates and user experience. This study offers the first detailed exploration of STAR-RIS in VLC systems, highlighting its potential for future high-speed, multi-user communication networks. Our findings set the stage for further research into optimizing VLC systems using STAR-RIS, particularly in complex environments with non-line-of-sight users and massive MIMO-OFDM configurations.

A rapid fingerprint positioning method based on deep convolutional neural network for MIMO-OFDM systems

The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.

Analysis of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing with Dynamic Optimal Power Allocation

Analysis of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing with Dynamic Optimal Power Allocation

A comprehensive analysis of dynamic PAPR reduction schemes in MIMO-OFDM systems

<span>In <span>this paper, an attempt develops three different methods, namely, Hybrid Maximal-Minimum (Max-Min) model with Decomposed Selective Mapping (D-SLM) in a UFMC, Modified Enhancement Asymmetric Arithmetic Coding Scheme (M-EAAC) and Dynamic Threshold-based Logarithmic Companding (DTLC) is carried out in Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) technology to enhance the PAPR reduction. These methods allow increased data rate request through threshold limit adjustment in a desired out-of-band (OOB) range, allows data transmission for the selected for the candidate sequences for maximizing the channel utility, data capacity and computational demands and varying threshold limit to analyse the nonlinear companding effect, respectively on D-UFMC-SLM, M-EAAC SCS-TI and DTLC. The extensive analysis shows that the proposed M-EAAC SCS-TI achieves a reduced CCDF PAPR, increased average spectral efficiency and redued Bit Error Rate (BER) than the other proposed DTLC and D-UFMC-SLM methods.</span></span>

Manifold Optimization-Based Data Detection Algorithm for Multiple-Input–Multiple-Output Orthogonal Frequency-Division Multiplexing Systems under Time-Varying Channels

Recently, multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems have gained significant attention in the field of wireless communications. The utilization of the Riemannian manifold has become prevalent in MIMO-OFDM systems. However, the existing data detection algorithms for MIMO-OFDM systems are mostly designed for block fading channels. Additionally, these algorithms often suffer from high computational complexity. In this paper, we propose a data detection algorithm on the basis of Riemannian manifold optimization for MIMO-OFDM systems under time-varying channels. The core concept of this algorithm is to optimize the transmitted signals by solving the manifold optimization problem in the case of time-varying channels. In order to reduce the computational complexity of the algorithm, we improve the proposed algorithm by dividing the transmitted signals into multiple subframes for solving the optimization problem separately and using the pilots to maintain the performance of the algorithm. In the simulation, the performance of multiple proposed algorithms and the forced-zero detection algorithm under different parameter settings are compared. The simulation results show that the proposed algorithm demonstrates good bit error rate and computational complexity performances.

Dynamic thresholding logarithmic companding for PAPR reduction in MIMO-OFDM systems for 5G wireless communication

In order to advance 5G wireless communication technology, this paper presents a novel method for lowering the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems. It is called the Dynamic Thresholding-based μ-law (DT-μ-law) logarithmic companding technique, and it greatly improves the performance of the system by effectively broadcasting data streams on a single frequency across multiple channels. This technique's ability lies in compressing the amplitude of the transmitting signal beyond a specific threshold value before the parallel-to-serial conversion block in the MIMO-OFDM system. It employs the direct insertion of an offset value into the guard bits, rather than using a step function for the amplitude reduction process, which can reduce additional system complexity and data loss during signal expansion at the receiver end. This technique is more adequately suited for dynamic data rate shifting and interference within a limited threshold value. The dynamic thresholding value (α) is determined by calculating the median and standard deviation of the μ-law companding. This ensures the effective compression of higher amplitude signals at the transmitter, using μ-law log companding. To streamline the decompanding process at the receiver, an offset is introduced, facilitating a smoother transition and reducing complexity. The results demonstrate that the proposed DT- μ-law companding technique significantly improves PAPR reduction while maintaining low Bit Error Rates (BER) and high average spectral power efficiency. This efficiency is optimal when the μ value is maintained within the range of 4–4.5. Furthermore, it was observed that an increase in the μ value corresponds to a decrease in both PAPR value and BER. Specifically, at a signal-to-noise ratio of 15.2 dB, the BER stabilizes at 10−1. Compared to existing methods like the Max-Min Decomp-SLM approach, this technique offers lower Signal-to-Noise Ratio (SNR) values, thereby enhancing spectral efficiency and maximizing data capacity.

CSI-based WIFI Device Identification Solution

In recent years, wireless devices such as WIFI routers have become increasingly common in various fields. Rogue access point (RAP) is one of the threats that has persisted in wireless LAN (WLAN) for many years and can cause varying degrees of property damage and privacy leaks. In response to these threats, we propose a new security mechanism, called Rdi, which uses environment-independent features extracted from channel state information (CSI) as fingerprints for device identification. We found that the phase errors between multiple antennas on a single MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) transmitter are not the same. This phase offset is due to the I/Q imbalance and imperfect oscillator of each WiFi network card, and it will not change with factors such as environment and time. Therefore, we inferred and verified that there must be some relationship between the phase errors of multiple groups of antennas, that is, the relative phase error (RPE). In addition, RPE will also vary with different WiFi devices. Compared with some similar fingerprint detection methods in the past, the use of specific connections between group phase errors between antennas can better reveal the different attributes between devices, thereby enhancing the uniqueness of features. Therefore, we believe that RPE can be used as an effective fingerprint to detect RAP attacks. We conduct a large number of experimental demonstrations on this, and innovatively built a multi-modal convolutional neural network (CNN) model to perform efficient classification work for our solution. Experiments on 22 WiFi devices and various scenarios show that the detection rate of Rdi can reach more than 98% in both dynamic and static device states.

Ultra-reliable intelligent link scheduling based on DRL for manned/unmanned aerial vehicle cooperative scenarios

When manned/unmanned aerial vehicle (MAV–UAV) perform cooperative missions in complex and variable situations, the communication link between manned aerial vehicle (MAV) and unmanned aerial vehicle (UAV) must be extremely dependable to guarantee the reliable transfer of command and control information. To address the above issues, this study provides an intelligent link scheduling algorithm based on MAVlink protocol that leverages deep reinforcement learning (DRL) for highly reliable communication in MAV–UAV multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) communication systems. This algorithm combines a deep neural network (DNN) with a Q-learning (QL) algorithm to get a deep Q-network (DQN). The downlink communication link between MAV and UAV uses it to intelligently schedule the modulation and coding scheme (MCS) and the number of spatial multiplexing layers. Results from system simulations indicate that the proposed algorithm outperforms other representative scheduling algorithms in the communication between MAV and UAV in complex scenarios with high-speed movement and large noise interference, improving the reliability of communication in MAV–UAV cooperative missions.

TDLoc: Passive Localization for MIMO-OFDM System via Tensor Decomposition

Passive localization is an important aspect of integrated sensing and communication (ISAC). However, it is challenging to estimate the target position and velocity accurately from the receiving signals due to complex multipath propagation. This paper presents TDLoc, a multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) based passive localization and tracking system, using channel state information (CSI). We first proposed a fourth-order tensor model that contains angle-of-departure (AoD), angle-of-arrival (AoA), time-of-flight (ToF), and Doppler frequency shifts (DFS) information, followed by developing an efficient joint estimation algorithm. We also show that with more than one pair of transceivers, our method can obtain the target velocity from the relativistic Doppler effects, leading to additional DFS-based trajectory information. Moreover, the Cramér-Rao lower bound (CRLB) for multipath parameter estimation and positioning is derived for performance evaluation. Numerical results show that TDLoc outperforms state-of-the-art methods in terms of localization accuracy.

FPGA based modified multi-repeat distribution matcher for probabilistic amplitude shaping

The technique of probabilistic amplitude modulation, based on distribution matching, has garnered considerable attention in recent years as a means to enhance spectral efficiency and diminish the constellation energy of coded modulation. This paper introduces the implementation of Probabilistic Amplitude Modulation (PAS) using a Modified Multi-Repeat Distribution Matcher (MMRDM) on a Field Programmable Gate Array (FPGA). The Modified Multiple Repetition Distribution Matcher (MMRDM) is integrated into a 2×2 Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, realized through the Xilinx System Generator (XSG). Simple Zero-Forced and Minimum Mean Squared Error (MMSE) equalizers are applied to the receiver for signal detection across the MIMO channel. The system incorporates enhanced security through chaos-based scrambling with 16 and 64 Quadrature Amplitude Modulation (QAM). VHDL code files for this system are generated for the Xilinx Kintex-7 (xc7k325t-3fbg676) for hardware implementation. Performance evaluation includes an assessment of required storage capacity, complexity, and bit error rate (BER). Using Vivado 2017.4, the system is successfully routed with resource utilization, for example, 0.67% Block RAM (BRAM), 68.6% Look-Up Tables (LUT), 83% DSP 48s, and 1.5% registers for 64-QAM uniform modulation. Similarly, for 64-QAM 10 level (shaper output 60 bit) shaped modulation, the resource utilization is 0.67% BRAM, 68.8% LUT, 83% DSP 48s, and 1.6% registers on the specified device. Simulation results demonstrate an improvement in the net shaping gain of approximately (2-4 dB) at 1×10^(-4) for different equalizer cases compared to uniform QAM, along with a notable reduction in required storage capacity and computational complexity.

Enhancing channel estimation accuracy in polar-coded MIMO–OFDM systems via CNN with 5G channel models

In this paper, a novel channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO–OFDM) systems in fifth generation (5G) networks is proposed, which leverages the power of convolutional neural networks (CNNs) along with the application of polar coding. By employing polar encoding at the transmitter and polar decoding at the receiver, the proposed system enhances error correction performance, reliability, and data transmission rate in 5G networks. A novel optimized CNN-based channel estimation technique, named CNN-CENet, has been integrated into the polar-coded MIMO–OFDM receiver. This integration aims to address the interference and noise challenges in 5G systems. The proposed channel estimation method utilizes multiple layers of CNN to accurately characterize the properties of the real channel by incorporating least squares (LS) estimations. Hence, the proposed CNN-CENet provides an accurate estimation of the channel coefficients, enabling efficient signal detection at the receiver. The MATLAB simulation has been conducted using deep learning and 5G toolboxes to obtain results for various channel parameters. These results are then compared with the LS and minimum mean square error (MMSE) methods for performance evaluation. The proposed model surpasses the performance of the LS and MMSE methods, achieving a significant 95.6% reduction in mean square error (MSE) compared to LS and a substantial 59.7% reduction compared to MMSE. Additionally, it delivers notable bit error rate (BER) reductions of 59.8% and 53.2% for LS and MMSE, respectively, in low mobility scenarios, and 57.4% and 48.5% reductions in high mobility scenarios. This improvement leads to enhanced reliability and overall system efficiency.

Deep Learning based Millimeter Wave/Sub-THz RMIMO-OFDM Systems with Beamforming Wireless communication

This research paper presents Reconfigurable Multiple-input, multiple-output orthogonal frequency-division multiplexing (RMIMO-OFDM) -based finest power provision, primarily using straight practices in addition then using the Long-Range Transmission Spectrum (LRTS) procedure for Deep Learning through Neural Network Analysis. These techniques are again primary practical to deuce operators then protracted to multi-user infrastructures. And this work towards fixing this hassle via the help of the LRTS procedure, everywhere top of the line manage is allotted to the weaker person similarly minimal electricity is allotted to the more potent person. Here, the DSP algorithm-primarily based totally RMIMO-OFDM generation assists toward interpret the agency quick of intrusion, via greater correctness, in addition likewise in actual. LRTS procedure delivers superior possible cutting-edge RMIMO-OFDM knowledge through the fruitful claim of successive interference cancellation (SIC). An LRTS forecasts the site of user equipment in addition also provides the ideal power allocation. To deal with this difficulty, in this work we propose a brand-new set of foundation vectors to estimate an appropriate precoder/combiner. Apiece new foundation vector is planned to shape a radiation sample via an extensive beam to cowl the squinted beam instructions because of distinct frequencies. Imitation outcomes exhibit that the destiny approach box correctly eases the situation of multi-user communication, an appropriate strength sharing ability to the weaker consumer could be very much less as related to the two-consumer case perfect strength sharing RMIMO-OFDM. Finished the claim of the LRTS procedure, idyllic supremacy distribution volume aimed at the feebler operator remains better quality.

Theoretical Foundation and Design Guideline for Reservoir Computing-Based MIMO-OFDM Symbol Detection

In this paper, we derive a theoretical upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The specific RC implementation considered in this paper is the echo state network (ESN), and an upper bound on its generalization error is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard ERC measure and a direct application of its definition, our work uses the standard ERC measure and tools allowing fair comparison with conventional RNNs. The derived result shows that the generalization error bound obtained for ESNs is tighter than the existing bound for vanilla RNNs, suggesting easier generalization for ESNs. With the ESN applied to symbol detection in MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) systems, we show how the derived generalization error bound can guide underlying system design. Specifically, the derived bound together with the empirically characterized training loss is utilized to identify the optimum reservoir size in neurons for the ESN-based symbol detector. Finally, we corroborate our theoretical findings with results from simulations that employ 3GPP standards-compliant wireless channels, signifying the practical relevance of our work.

Reliability Achievement of MIMO-OFDM Communication Systems

Wireless communication must have high spectral efficacy and the ability to resist fading channel in an environment of channel multipath, and it is difficult to achieve these requirements with traditional modulation techniques, but the Multiple system Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) It can fulfill these requirements. Research offers a way to improve the performance of OFDM- MIMO system by decreasing the Bit Error Rate (BER) using both (DWT) Discrete Wavelet Transform and Convolutional Coding (CC). The OFDM system is modeled with a single antenna at the transmitting end and a single antenna at the receiving end (DWT) . Multiple Input Single Output (MISO) By adding a second antenna at the transmitting end, and then improving it with Output Multiple Input Single technology SIMO (by adding a second antenna at the receiving end, and with MIMO technology by adding a second antenna at the receiving end The transmitter and a second antenna at the receiving end, thus optimizing the OFDM-MIMO system using convolution coding, Then using DWT instead of FFT (Fast Fourier Transform ). The research is based on the study of BER according to bit energy to noise energy density N0/Eb. The system was modeled and tested using the MATLAB program. The use of (DWT) and (CC) in OFDM-MIMO reduces N0/Eb )4.7( dB times in the item of the Rician fading channel and 3.2 dB times in the item of the Rayleigh fading channel when BER =10-3.

MmWave 6D Radio Localization With a Snapshot Observation From a Single BS

Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, control, and also for the benefit of communications. Exploiting millimeter-wave (mmWave) signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station (BS) localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user equipment (UE), using downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one multipath component in addition to the line-of-sight (LoS), even if the position of corresponding incidence point (IP) is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the UE as well as several nuisance parameters (the UE clock offset and the positions of IPs corresponding to the multipath). The ML problem is a high-dimensional nonconvex optimization problem over a product of Euclidean and non-Euclidean manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1-dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cramér-Rao bound (CRB) is tightened using the ML estimation.

Hybrid Message Passing Channel Estimation Algorithm for Massive MIMO-OFDM Systems

In this letter, we propose the Bernoulli two-state Gaussian mixture (B-TSGM) probability model to characterize the angle-delay domain (ADD) channel of the massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. Based on the hybrid message passing (HMP) rule, we design the HMP-B-TSGM channel estimation algorithm under the structured turbo-compressed sensing (STCS) framework. Simulation results show that the considered model better captures the channel characteristics, and the proposed algorithm outperforms the state-of-the-art methods under a wide range of simulation settings while having the same complexity.

Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis

Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this paper, we consider contact-free multi-target tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6 GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets’ locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multi-pedestrian tracking experiment is conducted in a room with a size of 6.5 m×10 m to evaluate the performance of the proposed algorithm. According to experimental results, we achieve 75th and 95th percentile accuracy of 12.7 cm and 18.2 cm for single-person tracking and 28.9 cm and 45.7 cm for multi-person tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real-time, which is promising for practical MTT use cases.

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

Power Optimization of UOWC-MIMO-OFDM System Based on PSO-WF Algorithm

Under the constraint that the total transmission power is constant, when multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) technology is applied to the signal transmission design in the underwater optical wireless communication system (UOWC), the traditional water-filling algorithm (WF) uses mathematical derivation to solve the optimization problem, which requires multiple Lagrangian optimization solutions. In order to reduce the computational complexity and accelerate the convergence speed, a particle swarm optimization (PSO-WF) algorithm for water injection is proposed. It randomly selects particles in the target area, calculates the fitness, and gradually approximates the optimal solution to avoid negative power distribution. The simulation results show that, compared with the average power allocation scheme, the algorithm improves the system capacity and operation efficiency by about 80%; Compared with WF algorithm, it greatly reduces the complexity.