Wireless Personal Communications最新文献

Every second, millions of cells within our bodies exchange critical messages. Yet, amidst this swarming molecular dialogue, miscommunications often occur, leading to misdiagnosis and ineffective treatments. Over 30% of therapeutic molecules fail to reach their intended targets due to inadequate understanding of tissue communication pathways. Herein, we unveil a novel diffusive molecular communication (DMC) model, offering insights into concentration dynamics and channel responses across diverse tissue geometries. We employed various signaling waveforms to gain insights into how these geometries impact the propagation and reception of molecular signals in tissues under different practical scenarios. In particular, rectangular and exponential signaling waveforms under each biological rectangular deployment, Biological cylindrical deployment, and biological spherical deployments have been used to analyze the DMC system in terms of concentration and channel response. First of all, the concentration of the information signal is derived analytically under each of the signaling waveforms and deployments. Further, the presented concentration of the information signal under each signaling and deployments are employed in evaluating the channel response at the receiver. Each of the presented analytical expressions has been quantified numerically under different parameters. The results demonstrate DMC’s potential for developing new-era targeted drug delivery and bio-sensing technologies.

The immense degree of freedom (DOF), high array aperture, non-uniform linear arrays, and reduced mutual coupling have developed interest in the estimations of the direction of arrival (DOA). Due to complex previous structures, this paper investigates the bi-level sparse linear nested array (SNA) concepts to discuss element spacing and different ranges on uniform DOF. Then features of flower pollination algorithm is applied to the proposed two-level SNA to generalize and enhance the proposed structure further. In order to boost DOF, it is also investigated local and global minima of highly non-linear functions. The proposed technique for quantifying the DOA is reviewed analytically using evaluation parameters like cumulative distributive function, accuracy, root mean square error, and robustness against noise and snapshots. The simulation findings prove its validation with the analytical model and target the accuracy with fewer separations and the minimum number of physical sensors in relation to particle swarm optimization. Moreover, the strength of the proposed study further validated by comparing with Cramer Rao Bound for minimum variance which shows that the FPA outperforms.

Network densification is a key technology to meet the rapid growth of 5G traffic. Millimeter wave has rich spectrum resource, short propagation distance and obvious directivity. Therefore, the dense millimeter wave network can reduce co-frequency interference and improve network capacity. To make full use of spectrum resource, this paper proposes a user association algorithm in multi-band millimeter wave network, which maximizes the weighted total rate of network while meeting the requirement of users and backhaul bandwidth constraint of base stations (BS). To enrich resource management method of network, the resource proportion of BS allocated to each user is also jointly optimized. For the mixed integer optimization problem which is difficult to solve after modeling, we adopt penalty function and Marks–Wright algorithm to obtain solution. Simulation results show that compared with three similar algorithms, the proposed scheme can rationally utilize network resource, avoid load imbalance, and has the highest total rate of network.

This paper demonstrates the design, modeling, and analysis of a small size 180 × 165 × 1.6 mm³ printed broadband Vivaldi antenna for electromagnetic compatibility measurements. The proposed antenna is intended to be utilized as a reference antenna for emission and immunity tests inside the EMC Chamber through the band (0.8–5.5 GHz). Exponentially tapered slots were created based on mathematical equations to form an end-fire radiation antenna. Furthermore, microstrip and slot line stubs were employed to tune the impedance bandwidth. This antenna could be considered a 2-D Horn antenna with a size reduction of 64% and 67% since both antennas, Vivaldi and Horn are based on the same principle. Two rectangular slots were engraved near the feeding point to reshape and enhance the gain at lower frequency bands. Furthermore, the realized gain has been improved by approximately 3.5 dB and reached up to 10.7 dBi by introducing a pair of triangular reversal slots at the top edges of the structure. Moreover, this antenna has specifications that make it a suitable candidate to work as a reference antenna inside the EMC chamber compared to the classical Horn antenna offered for sale (PowerLOG^® PRO 30800 and TBMA4).

Mobile ad hoc networks (MANETs) are a class of wireless networks that can be operated without a fixed infrastructure. Due to the dynamics of decentralised systems, these networks are prone to different attacks like Black Hole Attack (BHA) and Gray Hole Attack (GHA). The basic requirement in this network is that all nodes are trusted nodes, but in a real-life scenario, some nodes may be malicious, so instead of transferring the data packet to the destination, it drops the data packet. Organisations have some ideas for preventing this attack but can fail due to improper methods, so the attack must be identified and addressed. This article uses the deep learning algorithm concept with a mutation-based artificial neural network (MBNN). It uses a swarm-based Cluster-Based Artificial Bee Colony (CBABC) optimisation technique to protect this network from BHA and GHA attacks. The proposed models performance has been improved by selecting the appropriate and best node for sending data packets. We have demonstrated experimental results suggesting that the proposed protocol outperforms existing work in the case of black and gray hole attacks.

The subarray partition of reconfigurable intelligent surface (RIS) can reduce the complexity and hardware cost of the RIS architecture while achieving satisfactory performance, which has great practical significance. However, existing RIS subarray partition methods do not consider the scenario with eavesdropper and are not suitable for such scenario. Therefore, this paper discusses the joint optimization design of subarray partition and reflection coefficients to maximize the system secrecy rate in the multi-antenna eavesdropper scenario. Since this problem is a nonconvex combinatorial optimization problem, we solve it by alternating optimization, and propose RIS-assisted MIMO secure communication schemes based on fixed and dynamic subarray partition respectively. In fixed subarray partition scheme, the closed expressions of the optimal transmit covariance matrix and the optimal reflection coefficients are obtained through theoretical analysis, where the reflection coefficients are optimized by coordinate descent method. The fixed subarray partition scheme is not limited by the subarray structure and the number of subarray units. In dynamic subarray partition scheme, we transform the subarray partition optimization problem into a task assignment problem, and propose an improved discrete particle swarm optimization (DPSO) algorithm to solve it. Simulations and analyses show that compared with the without partition scheme, two proposed schemes can achieve a satisfactory secrecy rate while reducing system complexity and hardware cost. In addition, the dynamic subarray partition scheme has better secrecy rate performance and better stability than fixed subarray partition scheme, but its implementation is more complex.

The main objective of the wireless industry is to satisfy the ever-growing demands of next-generation communication systems such as higher data rates and high spectral efficiency. The prominent requirement of beyond-fifth-generation (B5G) communications is to connect millions of devices around the globe to the internet for smart radio access. Besides, the spectrum allocation and the data rate improvement for B5G networks is a challenging task. This manuscript focuses on the performance enhancement of the existing physical layer design framework for B5G networks. The proposed system exploits a hybrid combination of generalized frequency division multiplexing (GFDM) and non-orthogonal multiple access (NOMA) with the multiplexing scheme in space (MS) to improve the system performance. The simulation results validate the superior performance of the proposed system in terms of enhanced data rate, sum rate, and capacity compared to the conventional GFDM-NOMA systems. More specifically, the proposed GFDM-NOMA with MS achieves a performance improvement of around 1Gbps in data rate compared to the existing system. Finally, the proposed system performance proved to be a suitable candidate for the internet of things (IoT) and device-to-device (D2D) applications in B5G communications.

The signal strength in 5G mobile communication systems is significantly influenced by the surroundings, with key factors including the path difference, operating frequency, and obstructions at specific locations. Consequently, planning a communication system that can deliver improved signal strength becomes highly challenging. To address this issue, indoor path loss models are employed to estimate signal loss in different environments, frequencies, and distances. This paper introduces an intelligent multi-objective genetic algorithm aimed at enhancing path loss and received signal power. A comparative analysis is conducted to evaluate the performance of the proposed intelligent optimization algorithm against the traditional approach. The path loss and received power of various scenarios are estimated using various path loss models. The 5GCM indoor officce, 5GCM InH shopping mall, 3GPP TR 38.91 InH office, mmMAGIC InH office, METIS InH shopping mall, and IEEE 802.11 ad InH office indoor path loss models estimates the path loss of 62.37 dB, 62.15 dB, 63.12 dB, 50 dB, 55.18 dB, and 52.89 dB in traditional approach and 36.87 dB, 35.86 dB, 36.84 dB, 68.80 dB, 36.23 dB and 33.94 dB using GA approach and received powers of (-12.17~dBm, -11.37~dBm, -12.17~dBm, -5.80~dBm,) (-12.24~dBm) and (-8.68~dBm) in traditional approach and 26.13 dBm, 27.14 dBm, 26.15 dBm, (-5.80~dBm), 26.75 dBm and 29.05 dBm using GA approach repectively. The 5GCM and 3GPP models produces the path loss difference above 25 dB and mmMAGIC, METIS and IEEE models produces a path loss below 19 dB. Except mmMAGIC model, all models produces the recceiver power difference above 37 dBm. Therefore, the highest path loss difference of 26 dB is observed in 5GCM InH shopping mall model and the highest reccieved power difference of 39.01 dBm is observed in METIS InH shopping mall model. The results clearly demonstrate that the proposed intelligent optimization approach outperforms the traditional approach across various indoor scenarios.

Designing an efficient node deployment algorithm in Underwater Acoustic Sensor Networks (UASNs) is crucial to address coverage holes and connectivity issues while meeting the Quality of Service (QoS) requirements of underwater applications. At present, the research enhances underwater communication through random deployment algorithms. However, current research has focused on designing node deployment algorithms for underwater sensors, underwater relays, or surface stations, which leads to poor network performance. Therefore, it is critical to consider all underwater nodes while designing an efficient node deployment algorithm to guarantee meeting the QoS requirements of underwater applications. To address these issues, this paper proposes the Distributed Deployment Optimization algorithm using Grid-based Depth Adjustable (DDOGDA) that relies on the minimum number of underwater nodes to maximize coverage, connectivity, and Energy Efficiency (EE) while reducing the Total Collisions (TC). In DDOGDA, the underwater nodes are placed within their Sensing Range (SR) to maintain network coverage and connectivity. The proposed algorithm takes into account multiple factors, including deployment strategies, Geographical Information System (GIS) data for the Solomon Islands, non-environmental factors, and the unique characteristics of underwater nodes. We find that the grid-based distributed node deployment algorithm can achieve higher performance than random and geometric deployment algorithms. Simulation results confirm that DDOGDA can maximize coverage and connectivity while minimizing TC. Moreover, we compare DDOGDA with random, tetrahedron, cuboid, triangular, and adaptive triangular deployment algorithms in terms of coverage, connectivity, TC, and EE and demonstrate that DDOGDA outperforms state-of-art methods.

Recently cell-free massive multiple-input multiple-output (CF-MMIMO) systems have attracted a lot of interest. It has been considered one of the main keys of current and next-generation wireless communications networks like 5G and 6G. Effectively, it can handle demand growth and maintain better spectral efficiency (SE). However, the power consumption of a large number of access points (APs) significantly influences the performance which is one of the main points in CF-MMIMO. In this research, improving energy efficiency (EE) is considered while maintaining a high quality of service in the downlink (DL) transmission by optimizing the power allocation of users. This is achieved through the use of the sequential convex approximation method to optimize DL power control coefficients with zero-forcing (ZF) precoding. Moreover, the power consumption of the backhaul lines is minimized by implementing a novel dynamic AP selection method. This method ensures that each user is assigned appropriate APs without quality-of-service degradation and reliable spectral efficiency. Reducing overall power, which leads to improving the EE, is essential for cost savings, better coverage, and carbon emissions. The outcome of the optimized power allocation demonstrates a 43.7% improvement in energy efficiency when comparing the ZF with the conjugate beamforming approach. The results of the suggested AP selection demonstrate a 20.8% improvement in EE compared to the scenario without AP selection. Additionally, there is a 7.2% enhancement in EE compared to a previous study that used fixed AP selection with a signal-to-noise ratio of 10 dB. There is a tradeoff in the total SE when AP selection is used since it tends to decrease. This degradation can be effectively controlled through the careful selection of suitable APs.