Signal-to-interference-plus-noise Ratio Research Articles

Tridiagonal Weighting‐Based Interference Cancellation Method in High‐Mobility OFDM Systems

ABSTRACTIn orthogonal frequency division multiplexing (OFDM) systems, the time‐selective fading channel causes the signal Doppler expansion in high‐mobility scenarios, leading to intercarrier interference (ICI) in the received signals. People had employed a full‐dimensional weighting matrix method to reduce the Doppler‐assisted ICI, though it required a high implementation complexity. In order to reduce the ICI and produce lower complexities, this paper proposes to exploit the tridiagonal weighting matrix to suppress ICIs, in which an optimization problem is constructed to design the tridiagonal weighting matrix, and the nonzero coefficients within the tridiagonal matrix denote optimization variables. Moreover, the optimization objective exploits the system bit error rate (BER) instead of the conventional signal to interference plus noise ratio (SINR), which results in a highly nonlinear optimization problem. Therefore, an iterative solving approach is proposed according to the successive convex approximation (SCA). Simulations show that the proposed method can achieve a good balance between the complexity and BER degradation, especially in the scenario with moderate Doppler spreads.

Maximizing energy efficiency in HetNets through centralized and distributed sleep strategies under QoS constraint

This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

Performance Evaluation of mmWave Communication for Real-Time UAV-Base Station Links

Introduction: In the context of modern communication networks, the increasing demand for high data rates, minimal latency, and improved reliability—especially in scenarios involving Unmanned Aerial Vehicles (UAVs)—calls for innovative solutions. Millimeter-wave (mmWave) communication emerges as a fitting choice due to its capacity to offer ample bandwidth and high data rates. Methods: This research paper presents a comprehensive study that compares the performance of mmWave communication links across four different frequencies (6, 28, 60, and 120 GHz) for UAV-to-Base Station (BS) communication. The investigation, conducted through simulations, adheres to atmospheric conditions such as rain and fog, different environments (urban and rural) scenarios. It evaluates the suitability of mmWave frequencies using critical performance metrics: latency, received power, Bit Error Rate (BER) and Signal-to-Interference plus Noise Ratio (SINR). These metrics play a fundamental role in assessing communication link quality. Results: Our findings reveal that leveraging higher mmWave frequencies—specifically 60 and 120 GHz—significantly enhances UAV-to-BS communication performance. Compared to lower frequencies, these higher bands exhibit reduced latency, higher data rates, and improved throughput. Consequently, mmWave frequencies beyond 60 GHz prove well-suited for UAV communication, facilitating efficient data exchange with minimal delay. Conclusion: Furthermore, the potential for reduced interference and enhanced reliability at these elevated frequencies makes them particularly advantageous in applications requiring seamless connectivity. Examples include UAV-assisted emergency response, surveillance, and remote sensing. These insights hold substantial implications for the design and deployment of UAV communication systems, highlighting mmWave technology’s transformative potential in the emerging era of aerial communication.

Optimization of Interference Suppression Algorithm for Polarization Sensitive Conformal Array in Complex Electromagnetic Environment

With the development of modern electronic technology, the electromagnetic environment is becoming increasingly complex, which poses a severe challenge to the normal work of radar, communication and other systems. Polarization sensitive array can more effectively distinguish and suppress interference signals by using polarization information of electromagnetic waves, and improve the signal-to-noise ratio (SNR) and anti-interference ability of the system. Conformal array can be closely attached to the carrier surface, reducing space occupation and improving signal receiving efficiency and accuracy. However, at present, polarization-sensitive conformal arrays still face technical problems in interference suppression, especially in complex electromagnetic environment, how to optimize interference suppression algorithms to improve system performance has become the research focus. In this paper, an adaptive interference suppression algorithm combining spatial-polarization domain processing is proposed. The algorithm digitally processes the received signal, extracts spatial and polarization domain information, constructs the joint feature vector of the signal in spatial and polarization domain, and designs a joint filter in spatial and polarization domain. The minimum mean square error (MMSE) criterion and the least mean square (LMS) algorithm are used to update the weight vector of the filter in real time, enabling the filter to automatically adapt to changes in the electromagnetic environment and achieve optimal interference suppression. Simulation experiments show that the algorithm exhibits good stability and adaptability under different electromagnetic environments, effectively suppressing interference and extracting the desired signal. Compared with traditional interference suppression algorithms based solely on spatial domain, the joint spatial-polarization domain adaptive interference suppression algorithm significantly improves the interference suppression ratio (ISR) and signal-to-interference-plus-noise ratio (SINR) performance indicators, verifying that the introduction of polarization domain information can enhance the effect of interference suppression.

Robust Multi-UAV Cooperative Trajectory Planning and Power Control for Reliable Communication in the Presence of Uncertain Jammers

Unmanned aerial vehicles (UAVs) have become a promising application for future communication and spectrum awareness due to their favorable features such as low cost, high mobility, and ease of deployment. Nevertheless, the jamming resistance appears to be a new challenge in multi-UAV cooperative communication scenarios. This paper focuses on designing trajectory planning and power allocation for efficient control and reliable communication in a ground control unit (GCU)-controlled UAV network, where the GCU coordinates multi-UAV systems to execute tasks amidst multiple jammers with imperfect location and power information. Specifically, this paper formulates a nonconvex semi-infinite optimization problem to maximize the average worst-case signal-to-interference-plus-noise ratio (SINR) among multiple UAVs by designing robust flight paths and power control strategy under stringent energy and mobility constraints. To efficiently address this issue, this paper proposes a powerful iterative algorithm utilizing the S-procedure and the successive convex approximation (SCA) method. Extensive simulations validate the effectiveness of the proposed strategy.

Joint LPI waveform and passive beamforming design for FDA-MIMO-DFRC systems

Dual-functional Radar-Communication (DFRC) systems have been recognized as one of the most promising technologies in the field of wireless communications. Nevertheless, the low probability of intercept (LPI) performance in the DFRC systems cannot be overlooked. Based on the frequency diverse array multiple-input–multiple output (FDA-MIMO) radar, a DFRC system is proposed to enhance target detection in clutter environment and achieve desirable LPI against an underlying hostile interceptor. The issue can be cast into an optimization problem that maximizes the radar signal-to-interference-plus-noise ratio (SINR) while satisfying the communication quality-of-service (QoS) requirement of each user under one of three metrics, the required LPI performance and the constant-modulus waveform constraint. To solve this challenging problem, we reformulate it into an equivalent but more tractable form by resorting to an auxiliary variable. Subsequently, we employ the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) algorithms to solve the resultant problem. Simulation results validate that the proposed LPI FDA-MIMO-DFRC scheme exhibits superior sensing performance over the conventional scheme with MIMO.

Analysis of interference effect in VL‐NOMA network considering signal power parameters performance

AbstractThis study analyses the interference effect in a visible light‐non‐orthogonal multiple access (VL‐NOMA) network that considers the signal power parameters performance for near and far users. The light‐emitting diode (LED) as a carrier transmits signals, and we investigate the interference effect. The interference effect challenge is a result of unaligned signal power parameters, thereby producing noise or echo during the signal transmission. The signal power parameters are successfully aligned, and NOMA techniques are deployed, which improves the signal performance in terms of bit‐error rate (BER), achieved data rate, and signal‐to‐interference plus noise ratio (SINR). Furthermore, the deployed NOMA techniques, such as power allocations (PA) to assign the signals appropriately, then superposition coding (SC) encodes the entire signal, and successive interference cancellation (SIC) cancels the interference within the signals. The signal behavior of the aligned and the unaligned signal power parameters performance are used to investigate the interference effect. We observed that unaligned signal power parameters reduce the signal performance of achieved data rate, BER, and SINR. Further, the aligned signal power parameter with NOMA techniques improves the signal performance. Moreover, in the aligned signal power scenario of NOMA, the near user performed better than the far user.

Joint beamforming design for STAR-RIS-assisted integrated sensing, communication, and power transfer systems

In the sixth-generation network, many low-power devices are predicted to be integrated into tasks incorporating communication and sensing. The integrated sensing and communication (ISAC) technology reuses a wireless signal for data transfer and radar sensing. Furthermore, wireless signals have the capacity to transmit energy, allowing for the simultaneous wireless information and power transfer (SWIPT). To enhance spectrum utilization, the deeper integration of SWIPT and ISAC has opened up novel investigate directions for integrated sensing, communication, and power transfer (ISCPT). This paper investigates a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) supported ISCPT system that solves the problem of traditional RIS not achieving full space wireless signal coverage. In particular, since the serious path loss issue of sensing, we propose a novel architecture for installing dedicated sensors on STAR-RIS. Under this setting, we jointly optimized the passive beamforming at STAR-RIS and the active beamforming at base station to minimize the Cramér-Rao bound (CRB) used to estimate the sensing target's two-dimensional direction of arrivals, while being constrained by the lowest signal-to-interference-plus-noise-ratio (SINR) of communication users (CUs), the lowest energy harvesting (EH) of energy users (EUs), and the maximum transmission power at base station. For complex non-convex problems, a proposed two-layer cyclic algorithm utilizes penalty dual decomposition (PDD) and block coordinate descent (BCD). Finally, the numerical outcomes verify the efficacy of our suggested design, which reveals the performance trade-off between communication, power transmission and sensing. Furthermore, compared to traditional RIS, the estimated CRB of this design is lower.

Examining the efficiency of D2D communication employing DF-enabled multiple relay systems amid co-channel interference

Device-to-device (D2D) communication is emerging as a potential paradigm in contemporary wireless communication systems. Enabling D2D communication in the mmWave range has numerous challenges that must be overcome. The biggest concern is the introduction of interference from several sources. This study investigates the impact of co-channel interference (CCI) on D2D communication when energy harvesting (EH) and decode-and-forward (DF) techniques are used across several relay nodes. It includes mathematical equations for the cumulative distribution function (CDF) of the Signal-to-Interference-Plus-Noise ratio (SINR), as well as formulas for calculating the network's outage probability (OP), throughput (TP), and availability rate (AR). Furthermore, it produces an asymptotic expression for analyzing the probability density function (PDF) of the instantaneous SINR, providing a simple and complete approach to understanding its OP, TP, and AR properties. The analytical results are supported by Monte Carlo simulations and MATLAB implementation.

Combinatorial-restless-bandit-based transmitter–receiver online selection of distributed MIMO radar with non-stationary channels

In a distributed multiple-input multiple-output (MIMO) radar for tracking moving targets, optimizing sensible selections of the transmitter–receiver pairs is crucial for maximizing the sum of signal-to-interference-plus-noise ratios (SINRs), as it directly affects the tracking accuracy. In solving the trade-off between exploitation and exploration in non-stationary channels, the optimization problem is modeled by a restless multi-armed bandits model. This paper regards the estimated SINR mean reward as the state of an arm (transceiver channel). The SINR reward of each arm is estimated based on whether it is probed. A closed loop is established between SINR rewards and the dynamic states of targets, which are estimated via the interacting multiple model-unscented Kalman filter. The combinatorial optimized selection of transmitter–receiver pairs at each time is accomplished by using the binary particle swarm optimization with the SINR index fitness function, where the index represents the upper bound on the confidence of the SINR reward. Above all, a multi-group combinatorial-restless-bandit closed-loop (MG-CRB-CL) algorithm is proposed. Simulation results for different scenarios are provided to verify the effectiveness and superior performance of MG-CRB-CL.

A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition

The performance of an Unmanned Underwater Vehicle (UUV) is significantly influenced by the magnitude of self-generated noise, making it a crucial factor in advancing acoustic load technologies. Effective noise management, through the identification and separation of various self-noise types, is essential for enhancing a UUV’s reception capabilities. This paper concentrates on the development of UUV self-noise separation techniques, with a particular emphasis on feature extraction and separation in multi-task learning environments. We introduce an enhancement module designed to leverage noise categorization for improved network efficiency. Furthermore, we propose a neural network-based multi-task framework for the identification and separation of self-noise, the efficacy of which is substantiated by experimental trials conducted in a lake setting. The results demonstrate that our network outperforms the Conv-tasnet baseline, achieving a 0.99 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and a 0.05 enhancement in the recognized energy ratio.

Active reconfigurable intelligent surface‐aided multiple‐input‐multiple‐output radar detection in the presence of clutter

AbstractReconfigurable intelligent surface (RIS) refers to a two‐dimensional smart surface consisting of massive number of reflecting elements. Both active RIS (ARIS) and passive RIS (PRIS) are promising technologies that can adapt and reconfigure the wireless environment, enhancing the reliability and capacity of wireless networks. The authors focus on enhancing the detection ability of ARIS/PRIS‐aided multiple‐input‐multiple‐output radar system in the presence of signal‐dependent clutter. Constant‐envelope constraint is imposed on the sought radar waveforms to improve the practicability of the radar system. To tackle the resultant non‐convex problem, a cyclic method based on minorisation–maximisation and alternating direction method of multipliers is derived to iteratively optimise the receive filters, the transmit waveforms, and the RIS coefficients. The major finding is that the distance between the radar and the ARIS has little to no impact on the radar signal‐to‐interference‐plus‐noise ratio (SINR). For the PRIS‐aided radar system, a simplified model can significantly improve the operational efficiency and has little impact on the radar SINR. Numerous results verify the effectiveness of the proposed algorithm.

CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming.

This paper presents a robust adaptive beamforming algorithm based on an attention convolutional neural network (ACNN) for coprime sensor arrays, named the CAWE-ACNN algorithm. In the proposed algorithm, via a spatial and channel attention unit, an ACNN model is constructed to enhance the features contributing to beamforming weight vector estimation and to improve the signal-to-interference-plus-noise ratio (SINR) performance, respectively. Then, an interference-plus-noise covariance matrix reconstruction algorithm is used to obtain an appropriate label for the proposed ACNN model. By the calculated label and the sample signals received from the coprime sensor arrays, the ACNN is well-trained and capable of accurately and efficiently outputting the beamforming weight vector. The simulation results verify that the proposed algorithm achieves excellent SINR performance and high computation efficiency.

Ergodic Rate Analysis of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface-Assisted Rate-Splitting Multiple Access Systems Based on Discrete Phase Shifts.

In this paper, we combine simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA) technology and investigate the ergodic rate performance of an STAR-assisted RSMA system. Considering the discrete phase shifts of the STAR-RIS in practice, the downlink performance of STAR-RIS-assisted RSMA with discrete phase shifts is compared to that with continuous phase shifts. Firstly, the cumulative distribution function of signal-to-interference-plus-noise ratio (SINR) of users is analyzed. Then, the total ergodic rate of the system and its approximate closed-form solution are, respectively, derived based on the cumulative distribution function of users. The simulation results validate the effectiveness of the theoretical analysis, showing good agreement between the derived theoretical ergodic rate and the corresponding simulations. Although the system performance with discrete phase shifts is inferior to that with continuous phase shifts due to quantization errors, the performance of the continuous phase shift system is well approximated when the quantization bit of the phase shift system reaches 3 in the simulations. Additionally, the impact of the number of STAR-RIS elements on the system's performance is analyzed.

An Accurate Non-contact Photoplethysmography via Active Cancellation of Reflective Interference.

Imaging Photoplethysmography (IPPG) is an emerging and efficient optical method for non-contact measurement of pulse waves using an image sensor. While the contactless way brings convenience, the inevitable distance between the sensor and the subject results in massive specular reflection interference on the skin surface, which leads to a low Signal to Interference plus Noise Ratio (SINR) of IPPG. To ease this challenge, this work proposes a novel modulation illumination approach to measure the accurate arterial pulse wave via surface reflection interference isolation from IPPG. Based on the proposed skin reflection model, a specific modulation illumination is designed to separate the surface reflections and obtain the subcutaneous diffuse reflections containing the pulse wave information. Compared with the results under ambient illumination and constant supplemental illumination, the SINR of the proposed method is improved by 4.56 and 3.74 dB, respectively.

Hybrid game theoretic strategy for optimal relay selection in energy harvesting cognitive radio network

SummaryRelay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach.

Power Allocation Scheme for Multi-Static Radar to Stably Track Self-Defense Jammers

Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance.

Resource Control in Active IRS-Aided 6G IoT Networks with Use Case in Smart Indoor Communication

The IoT ecosystem involves connected smart devices that support massive amount of data for processing and further analysis. The proliferating massive IoT network demands network connectivity, robust communication links and computational resources which puts huge load on the network. To overcome the communication overhead in the IoT landscape while offering optimal fault tolerance is the main challenge. This paper presents an intelligent solution for improving the communication efficiency of IoT network using active IRS technology. An active IRS aided communication framework is proposed which offers enhanced network connectivity by enabling controlled reflection amplitude. An algorithm is proposed which associates optimal active IRS to each AP-node link such that signal-to-interference-plus-noise ratio (SINR) is maximized. It is observed that the proposed association scheme in active IRS system offers an improvement of 19.04% in achievable rate over random association at AP transmit power [Formula: see text] of 20[Formula: see text]dBm and IRS amplification power [Formula: see text] of 4[Formula: see text]dBm. Furthermore, the system outage probability is carried out with change in [Formula: see text] and amplification gain [Formula: see text]. The mean channel power is also evaluated for different IRS densities and AP densities under different IRS reflecting elements [Formula: see text], [Formula: see text] and [Formula: see text]. In the end, the performance comparison with passive IRS system and system with no IRS assistance is carried out. A use case scenario of active IRS-aided system in smart indoor communication is also discussed.

Adaptive pulse shaping for OFDM RadCom systems in highly dynamic scenarios

Due to the spectrum and complexity efficiency, the integrated radar and communications (RadCom) systems have been widely favored, in which orthogonal frequency division multiplexing (OFDM) is the most popular signal to conduct the two functions simultaneously. However, an unoptimized pulse could suffer from severe inter-carrier interference (ICI) and high out-of-band emission (OOBE), which greatly degrades the system performance. In this paper, we introduce the pulse shaping scheme dedicated to RadCom systems, in which both transmitter and receiver can adaptively design pulses with the assistance of radar estimation. We first optimize the transmitting pulse with the weighted sum of signal-to-interference-plus-noise ratio (SINR) and OOBE by employing the popular genetic algorithm. Then, we design an improved-matched pulse at the receiver for maximizing the SINR with the fmincon solver. In this way, they both utilize the readily available radar information and keep the pulse optimal even in highly dynamic scenarios, which makes the most of RadCom systems while avoiding the overhead of channel estimation and feedback. Simulations prove the feasibility of proposed scheme and reveal that the radar image and communications SINR stay close to their optimum in most cases with much lower OOBE. An improved-matched pulse can further improve the communications performance when severe ICI occurs compared with a matched pulse.

Improved Convolutional Neural Network for Wideband Space-Time Beamforming

Wideband beamforming technology is an effective solution in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems to compensate for severe path loss through beamforming gain. However, traditional adaptive wideband digital beamforming (AWDBF) algorithms suffer from serious performance degradation when there are insufficient signal snapshots, and the training process of the existing neural network-based wideband beamforming network is slow and unstable. To address the above issues, an AWDBF method based on the convolutional neural network (CNN) structure, the improved wideband beamforming prediction network (IWBPNet), is proposed. The proposed method increases the network’s feature extraction capability for array signals through deep convolutional layers, thus alleviating the problem of insufficient network feature extraction capabilities. In addition, the pooling layers are introduced into the IWBPNet to solve the problem that the fully connected layer of the existing neural network-based wideband beamforming algorithm is too large, resulting in slow network training, and the pooling operation increases the generalization ability of the network. Furthermore, the IWBPNet has good wideband beamforming performance with low signal snapshots, including beam pattern performance and output signal-to-interference-plus-noise ratio (SINR) performance. The simulation results show that the proposed algorithm has superior performance compared with the traditional wideband beamformer with low signal snapshots. Compared with the wideband beamforming algorithm based on the neural network, the training time of IWBPNet is only 10.6% of the original neural network-based wideband beamformer, while the beamforming performance is slightly improved. Simulations and numerical analyses demonstrate the effectiveness and superiority of the proposed wideband beamformer.