Physical Communication最新文献

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the $l_0$ and $l_1$ norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

With the growing demand for Internet of Things (IoT) applications, supporting massive access to the media is a necessary requirement in 5G cellular networks. Accommodating the stringent requirements of Ultra-Reliable Low Latency Communications (URLLC) is a challenge in massive access to the medium. The random-access procedure is of the most challenging issues in massive IoT (mIoT) networks with URLL requirements as a high number of channel access requests result in high channel access latency or low reliability. In previous works, some solutions have been proposed to solve this challenge including grant-free access, priority-based access, and grouping nodes to restrict random access requests to groups’ leaders. Particularly, previous idea that is based on grouping, clusters the devices with similar reaction against an event to a group, which is not always applicable for various IoT applications. This research proposes a novel device grouping to improve the random-access procedure of mIoT devices with URLLC requirements. In the proposed method, device grouping is accomplished based on the analysis of devices’ traffic. A similarity index is used to obtain the similarity of time series made from historical traffic patterns of devices and then, an innovative algorithm is proposed to group the devices based on this index. Grouping devices based on similar traffic patterns, provides access to the media with less complexity and more efficiency for a large number of devices. Performance of the proposed approach is evaluated using simulations and real traffic dataset. The evaluation results show higher suitability of proposed method compared to the baseline mechanism of LTE and the previous method in terms of access failures (which affects delay and reliability) and energy consumption. For a usual setting, the channel access failure decreases by about 94 % compared to the previous method and by 0.88 % compared to LTE. The energy consumption also improves by about 1.8 % compared to LTE and by 1.2 % compared to previous method. Moreover, the results show that the proposed method is appropriate for IoT applications with regular traffic patterns.

Reconfigurable intelligent surfaces (RISs) have recently gained significant attention for improving reliability in vehicular communications. However, ensuring reliable communication between far-distance vehicles remains a challenge. This work investigates an RIS-aided vehicular ad hoc network (VANET) in a road section where the distance between vehicles is too large for a single roadside unit (RSU) to provide reliable communication. We propose a novel RIS architecture where several RIS panels are connected by cables and each is equipped with a power amplifier. We then optimize vehicle power and RIS reflection coefficients to maximize the minimum bit rate of the VANET. Due to the non-convex nature of the formulated problem, we use fraction programming (FP) to reformulate it into a convex form, allowing solution using tools like CVX which is a MATLAB-based modeling system for convex optimization. The reformulated problem is then decoupled into subproblems. Block coordinate descent (BCD) is employed to optimize all variables alternately and obtain the joint optimal solution. Additionally, the alternating direction method of multipliers (ADMM) ensures that the phase shift of each reflecting element remains a unit vector. Finally, semidefinite relaxation (SDR) is used to solve the boolean quadratically constrained problem. Simulation results demonstrate the effectiveness of the proposed method and confirm that our architecture outperforms conventional approaches.

This work introduces a non-orthogonal multiple access (NOMA) scheme suitable for low-rate ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services multiplexing in downlink communication. Tailored to minimize mutual interference, URLLC information is conveyed via a modified index modulation (IM) scheme on top of quadrature amplitude modulation (QAM) transferring eMBB traffic. Aiming at providing a proof of concept of the proposed scheme, we calculate the bit error rate of both services over Rayleigh fading with diversity, as well as the eMBB service achievable rate with typical multi-input multi-output (MIMO) configuration when the URLLC service utilizes space–time coding or diversity. The proposed scheme achieves a low bit error rate for the URLLC IM signal, at the cost of a lower information rate, while affecting the performance of the eMBB user mainly due to power sharing among the IM and QAM signals. To further investigate the feasibility of the proposed scheme, we calculate the transmission energy required by the base station to support both services over a typical cellular channel model, for various service requirements and user distances, in comparison to an orthogonal multiple access (OMA) puncturing scheme utilizing time-multiplexing of QAM for the eMBB traffic and BPSK for the URLLC traffic. Overall, our results show that the proposed scheme attains better performance compared to the puncturing scheme and offers a robust solution with easy user pairing for low-rate URLLC and typical eMBB downlink service multiplexing for 5G communications and beyond.

In this article, a non-orthogonal multiple-access (NOMA) transmission system that incorporates discrete Fourier transform (DFT) spread orthogonal time-frequency space (OTFS) modulation is proposed for the downlink integrated positioning and communication (IPAC). This system is designed to decrease the Peak-to-Average Power Ratio (PAPR) within the OTFS-NOMA system while simultaneously serving both communication user (CU) and positioning user (PU) within the same time-frequency resources. Firstly, we demonstrate the applicability of the proposed DFT-spread OTFS-NOMA (DFT-s-OTFS-NOMA) system for downlink transmission. It efficiently serves both the CU and PU concurrently within the same time and frequency resources, thereby increasing spectrum utilization. Subsequently, we develop a two-stage parameter estimation algorithm focusing on accurate positioning parameter estimation for PU and precise channel estimation for CU. Then, the position of the PU is estimated using the time of arrival (TOA) model and the linear least squares (LLS) approach. Simulation demonstrates a 9 dB PAPR reduction in the proposed system compared to the existing OTFS-NOMA system. Additionally, the proposed two-stage positioning method for the PU achieves centimeter-level accuracy in position estimation and near millimeter-per-second-level accuracy in velocity estimation. What is more, the performance of channel estimation and symbol detection, aided by the PU signal in the DFT-s-OTFS-NOMA system, demonstrates resilience against Doppler effects without the need for reliance on pilots. This ensures sustained an effective Bit Error Rate (BER) even in time-variant high mobility scenarios.

In recent years, many deep learning-based methods have been utilized for the feedback of Channel State Information (CSI) in massive MIMO systems. The Transformer-based networks leverage global self-attention mechanisms that can effectively capture remote correlations between antennas, while Convolutional Neural Networks (CNNs) excel in acquiring local information. To balance the advantages of both, this paper proposes an Efficient Feature Aggregation Network called EFANet, which hybrid CNNs and Transformer. Specifically, we propose a Refined Window Multi-head Self-Attention (RW-MSA) through hybrid Convolutional Embedding Unit (CEU) and Window Multi-head Self-Attention (W-MSA) to reduce information loss between windows and achieve efficient feature aggregation. Additionally, we develop a Local Enhanced Feedforward Network (LEFN) to further integrate local information in the CSI matrix and model detailed features of different regions. Finally, the Compensation Unit (CU) is designed to further compensate for global-local features in the CSI matrix. Through the above design, the global and local features are fully interactive to reduce information loss. Numerous experiments have shown that the proposed method achieves better CSI reconstruction performance while reducing computational complexity.

Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.

In order to improve the increasing spectrum requirements and the unguaranteed quality of service (QoS) for edge users in the internet of vehicles (IoV), many techniques such as cognitive radio (CR), non-orthogonal multiple access (NOMA) and coordinated direct and relay transmission (CDRT) have been proposed. To further enhance the spectrum utilization, this paper combines the above techniques with ambient backscatter communications (AmBC), which replaces the traditional relay in the network with a backscatter device (BD). When BD meets certain energy constraints (EC), an optimal dynamic reflect coefficient (RC) is enabled to promote the reflection effect. In this work, firstly, establish a CDRT-based CR-NOMA vehicular network with an ambient backscatter relay. Secondly, the closed expression for the outage probability of each secondary user is obtained with the help of Gauss Laguerre quadrature and Gauss Chebyshev quadrature. Then the system throughput analysis and the asymptotic analysis of each user when the maximum transmitting power as well as the interference temperature constrains (ITC) tends to infinity are performed. Finally, we performed simulations to reveal the impact of AmBC and ITC on edge users. Comparisons are also made with the system using fixed RC, the conventional system and its orthogonal multiple access (OMA) counterpart, demonstrating the superiority of our proposed system in improving the performance of edge users.

The presented research examines the energy-based spectrum sensing problem of a signal transmitted over a wireless communication channel with severe fading. To account for the most challenging propagation conditions, a recently developed Lomax wireless channel model was utilized, which exhibits hyper-Rayleigh characteristics over the entire range of possible parameter values. For the channel model under consideration, the existence of the specific hyper-Rayleigh regimes (i.e., weak, strong, and full) are identified asymptotically and studied numerically for a finite signal-to-noise ratio. For the most severe fading conditions, the system’s performance was assessed in terms of the average probability of detection, receiver operating characteristics, and area under the curve. These metrics were derived in closed-form for the cases of Maximum Ratio Combining at the receiver and without diversity reception. The obtained expressions were analyzed via numerical and statistical simulation as functions of system and channel parameters, including the sensing base, average signal-to-noise ratio, and scale parameter for the signal-to-noise probability distribution. The results from numerical simulations were compared with existing regulations specified in the 5G standard for energy-based detection in medium access procedures.

The increase in data rate demands has driven the development of Cell-Free massive Multiple-Input Multiple-Output (CFmMIMO) technology in 6G Heterogeneous Networks (HetNets). This paper proposes an integration of Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communication in CFmMIMO, aiming to enhance network capacity, coverage, and service delivery in next-generation wireless networks. The paper investigates uplink transmission in a network where Access Points (APs) have imperfect channel state information. It considers Cell-free User Equipment (CUE), UAVs, and D2D pairs, deriving closed-form uplink achievable rates for each user type. Moreover, two optimization problems are formulated to enhance user data rates: one focuses on maximizing the sum data rate through pilot assignment, while the other aims to achieve weighted max–min power control through power allocation. Furthermore, to address these problem, we propose a Modified Whale Optimization Algorithm (MWOA) and a novel Pilot Assignment based on Whale Optimization Algorithm (PAWOA). Performance studies demonstrate significant improvements over state-of-the-art algorithms.