International Journal of Communication Systems最新文献

3D object detection leverages sensors like LiDAR and cameras to capture scene information, enabling precise determination of objects' spatial positions and orientations. This technology finds extensive applications in autonomous driving, smart homes, industrial automation, and intelligent security systems. However, high-precision 3D object detection algorithms often require substantial computational resources, posing limitations for deployment on resource-constrained devices. In this paper, we devise an efficient computation offloading and data transmission framework specifically tailored for edge computing networks to address this challenge. Our framework takes into account both the computing and communication capabilities of terminal devices and network conditions, offloading suitable computation tasks to the edge for processing. This approach mitigates the algorithm's performance requirements on terminal devices. Furthermore, we propose a data transmission scheme that incorporates attention mechanisms and hardware-accelerated coding. This scheme effectively reduces detection time and enhances overall system performance. Experimental results demonstrate that our proposed framework significantly enhances the efficiency of 3D object detection on resource-constrained devices within edge computing networks, while maintaining high detection accuracy.

Fifth generation and beyond (5GB) technology requires low latency, high capacity, and constant connectivity for safety and reliable service. Multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies can help meet these needs. However, MIMO can cause extra overhead due to massive channel feedback, and mmWave signals weaken over short distances, leading to limited coverage. Intelligent reflecting surfaces (IRSs) and highly directive active beamforming are recommended to address coverage and overhead issues. Most IRS research focuses on optimizing phase shifts in two dimensions. This paper introduces a three-dimensional model to jointly evaluate user location and IRS phase shift optimization. Additionally, phase constants are derived from optimal phase shifts to limit training overhead. A random forest learning algorithm is proposed, using optimal phase constants and codebook indices to train for each estimated location. Data transmission utilizes the Doppler effect to predict the possible locations of a user. In this way, the trained model can perform high-resolution joint beamforming for the current and future locations of the user. Simulation results show that the model accurately predicts phase shifts without needing channel state information while keeping complexity and training overhead low.

This work presents a single-layer wideband textile multiple-input–multiple-output (MIMO) antenna for wearable devices. The antenna design is made up of two rectangular-shaped monopole antennas that are mirror imaged and connected to achieve an equal voltage level in the ground surface. The antenna elements are excited by 50-Ω microstrip feed lines. By using a triangular stub decoupling element on the ground plane, greater than 22 dB of isolation is attained among antenna elements. The suggested two-element MIMO antenna has a bandwidth of 2.3–8.0 GHz and a dimension of 30 mm × 58 mm × 1 mm. In addition, four- and eight-element MIMO geometries have been designed and analyzed for massive MIMO applications. Also, an eight-element MIMO belt antenna for wearable straps is investigated. The effects of antenna bending on the human body are also investigated.

Queueing modeling and optimization of high-speed digital systems provide a tool to the system operators and administrators to build an economic system and to analyze overcrowding situations in various digital systems such as computer systems, cellular mobile webs, cognitive radio networks (CRNs), and many more. CRNs enable a more efficient and flexible use of the radio spectrum by allowing unlicensed users (ULUs) to dynamically access and share frequencies with licensed users (LUs), ensuring that spectrum resources are fully utilized while avoiding interference with licensed operations. This study aims to focus on complex, real-world challenges faced in CRNs and to resolve its few congestion issues through a queue-theoretic approach. The congestion issues faced by CRN can be resolved by modeling the Geo^X/G/1 priority retrial model with multielective services under the Bernoulli vacation schedule wherein the server's time can be better allocated to users to improve their grade of service. We can see how CRN can be seen as a discrete-time retrial queueing system according to the following formulation. In CRNs, there are two types of users: LUs and ULUs. The former is given priority over the latter in the sense that LUs can forestall the transferences (transmissions) of ULUs. In this perspective, the LU channel acts as a server that can be approachable by ULUs practically. The LU and ULU data packets, links, or sessions act as customers, which usually attach to the virtual track of blocked users if they do not get instant entrance. Moreover, each LU channel either provides access to the entering user or may cease providing service for some span of time called vacation time. This queueing process is termed as Bernoulli vacation (BV). Also, we apply an admission control policy (ACP), which controls the number of arrivals. In this study, we perform a numerical simulation through which we can conclude that the average number of data packets in CRN and expected total cost increases linearly by upgrading either the admission control probability or arrival rate. Also, our study suggests that the average system size decreases with an increase in the probability that a licensed unit joins the system. Further, multicriteria optimization is used to obtain Pareto optimal solutions of expected total system cost and expected system waiting time in CRN.

Channel estimation is a general issue for downlink transmission in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) devices. To achieve the merits of mmWave massive MIMO devices, the channel state information (CSI) is very necessary. However, it is hard to attain the downlink CSI in the corresponding device, which results in training overhead. To overcome the particular issue, this paper proposes a new method as a serial cascaded autoencoder with attention-based long short-term memory (SCA-ALSTM), where the attributes are tuned using the iterative of reptile search and dingo optimizer (IRSDO) to derive the multiobjective function with multiple constraints such as root mean square error (RMSE), mean square error (MSE), normalized mean square error (NMSE), bit error rate (BER), and spectral efficiency (SE). The proposed SCA-ALSTM model leverages the power of attention mechanisms to focus on important information within the input data, allowing for more accurate channel estimation. By incorporating the IRSDO hybrid model, the SCA-ALSTM system can efficiently fine-tune the parameters to improve channel estimation accuracy while minimizing training overhead caused by evaluating a high amount of channel factors. Finally, the experimentation is accomplished with conventional algorithms and proved that the developed model helps to improve the channel estimation accuracy while reducing training overhead. By leveraging the developed model, channel estimation may be enhanced regarding accuracy and efficiency with reduced computational complexity. Moreover, it can better handle the complexities of non–line-of-sight (NLOS) channels, leading to improved estimation accuracy. Thus, the system outperforms the channel estimation to raise the efficiency.

Concealed weapon detection has gained widespread interest in recent times due to prevalent law and order situation. There is an ever-increasing need of detection of body-worn harmful objects from a safe distance to save precious human life and to cause minimal damage to infrastructure. Most of the conventional weapon detection schemes require proximity to target for detection. To alleviate the problem of close proximity, WiFi-based wireless sensing has recently emerged as a promising technique for remote detection and sensing in different applications. The focus of this research is to implement a low cost and robust WiFi-based wireless detection system for body-worn concealed objects. The methodology is based on utilizing low-cost WiFi sensors to acquire Channel State Information (CSI), smoothening/filtering of CSI data, extraction of different statistical parameters and building a model to differentiate among two cases of body-plus-weapon and body only. Probability density functions of the variance of CSI features are computed under metal and non-metal scenarios, that are non-overlapping, which validate the effectiveness of proposed approach to separate metal and non-metal scenarios. Furthermore, heatmap images are generated and a deep learning model is trained to automate the detection process. Our deep learning-based automated detection methodology has achieved an overall accuracy of 90.5% and 87.5% on test samples, respectively, for the detection of a metallic plate and a real gun in indoor setting at 20 ft distance from the antenna.

To address the issue of low direction-of-arrival (DOA) estimation accuracy for acoustic vector sensor arrays under impulsive noise and grid mismatch conditions, a look ahead orthogonal matching pursuit algorithm based on phased fractional lower-order moments (PFLOM) is proposed. First, the algorithm uses PFLOM to suppress impulsive noise and reconstructs the PFLOM matrix using a vectorization operator. Next, it introduces off-grid deviation by performing a first-order Taylor expansion on the steering vector matrix, constructing a PFLOM-based off-grid sparse DOA model. Then, the look ahead strategy is introduced into the algorithm to select the optimal atoms by predicting their impact on the residuals. Finally, the joint sparsity of the coarse DOA estimation and the off-grid deviation vector is exploited to calculate the corresponding off-grid deviation using the alternating direction iteration method, resulting in the DOA estimation for the off-grid targets. Computer simulations validate the effectiveness of the proposed algorithm, with experimental results showing higher estimation accuracy and success rate compared with existing methods.

This paper proposes novel distributed subchannel and power allocation algorithms for heterogeneous networks. First, we introduce a subchannel allocation algorithm that minimizes the maximum effective interference experienced by users in the network. We analytically prove that the proposed subchannel allocation algorithm minimizes the effective interference in the network. Subsequently, two power control algorithms are presented for the power allocation problem, and their convergence analysis is given. Afterward, the improvement in throughput by utilizing the proposed power control algorithm is shown by simulations. Finally, the proposed subchannel and power allocation algorithms are combined to form a joint resource allocation method, which is shown to outperform existing distributed resource allocation methods in the literature.

Allocating resources in an energy-effective method for the development of 5G networks. Energy efficiency in D2D communication is another challenge. Less energy usage is needed for devices to communicate with one another. D2D communication frequently uses more energy than conventional communication techniques. This is one of the major challenges of D2D transmission that needs to be resolved. To address these challenges, a new energy-effective resource allocation system for D2D transmission in 5G WPAN is presented in this paper. The primary goal of the presented task is to enhance the average power effectiveness of the entire links of D2D, thus ensuring the service quality of the cellular user equipments (CUEs) and the constraints of energy harvesting (EH) in the D2D connections. Problems such as D2D user equipments (DUEs), spectrum, resource block allocation, and time slot allocation are used in D2D communication. To rectify these problems, this work derived an average energy efficiency issue. Moreover, this work joins the spectrum and power block allocation. Then, the EH time slot allocation is executed based on the Hybrid Gazelle with Walrus Optimization (HGWO). The extensive analysis outcomes illustrate that the implemented HGWO optimization-aided model attains more energy efficacy for distinct network attribute settings.

This paper presents the design of a simple, compact diagonal square fractal (DSF) Antenna suitable for dual-band Vehicular Communication applications. The proposed DSF antenna has etched on RT/Duroid 5880 substrate with the volume of 20.42 $��\times �$ 22.71 $��\times �$ 4.72 mm³ respectively. It consists of dual substrate layers, a square radiation patch with three iterations, and a rectangular aperture-coupled slotted in-ground plane. The proposed antenna is intended to operate at 5.9 GHz of the dedicated short-range communication (DSRC) band (IEEE 802.11p) for vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication system at 9.5 GHz in the X-band. The proposed antenna has achieved comparatively better and stable gains of 2.43 and 7.88 dBi with fractional bandwidth of 12.95% and 10.89% at DSRC-band and X-band, respectively, which provides the scope for using this antenna for various applications in vehicular communication. Desirable stable radiation patterns have been obtained in both planes' indicating relative stability at both frequencies. The efficiencies of 93.34% and 96% are observed at both resonance frequencies. A close agreement between the simulated and experimental results has been achieved. The proposed antenna can apprehend the qualities of ease of fabrication and smaller dimensions to be mounted in autonomous vehicles and vehicle networking communication system equipment and also accomplish noble wireless transmission capabilities in terms of gain, fractional bandwidth, and radiation efficiency from vehicles to vehicles and vehicle to infrastructure.