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

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.

Beamforming optimization of meta-learning algorithm based on gradient descent in RIS-assisted ISAC system

In this paper, we study an integrated sensing and communication (ISAC) system assisted by reconfigurable intelligent surface (RIS). Our goal is to minimize the transmit power of the BS by jointly optimizing the active beamforming and the passive beamforming under the constraints of the signal-to-interference-plus-noise ratio (SINR) of users, the Cramer–Rao bound (CRB) of sensing and the unit-modulus of RIS reflection elements. The optimization problem is difficult to solve because the objective function is non-convex and the variables are coupled. In addition, a large number of base station (BS) antennas lead to high-dimensional transmit beamforming vectors. We first prove that the optimization variables in the optimization problem are low-dimensional coefficient matrices and passive beamforming, rather than active and passive beamforming, which reduces the complexity compared with the original problem. In addition, when the sensing channel space belongs to the communication channel space, the dedicated sensing signal is not needed. Inspired by the meta-learning technology that can solve non-convex problems, we propose a meta-learning algorithm based on gradient descent (MLAGD) to design beamforming, which tends to learn dynamic optimization strategies and iteratively update optimization variables. Specifically, we first introduce Lagrangian multipliers to transform the constrained optimization problem into the Lagrangian dual domain, and the manifold-based optimization is employed to handle the unit-modulus constraint. The meta-learning neural networks are constructed to update optimization variables through local loss function, and then network parameters are updated through global loss function. The numerical results show the effectiveness of the algorithm.

Topology optimization of UAV network for target surveillance task with support jamming

In this paper, a topology optimization method is proposed for cooperative surveillance in unmanned aerial vehicles (UAVs) under an interference environment. We introduce the regional coverage rate to evaluate the cooperative surveillance performance under interference. At its core, we derive the expression of detection probability under interference in detail, which is inherently tied to the signal to interference plus noise ratio (SINR) influenced by UAV configuration, in which the location of each UAV affects the cooperative surveillance performance. To maximize the network surveillance performance, we use the coverage rate metric as the objective function to formulate a topology optimization problem under the interference environment, while satisfying distance constraints between UAVs. As the problem is non-convex and highly nonlinear, the internal self-constrained particle swarm optimization (ISC-PSO) algorithm is used to solve this problem, which can well address the issue of range constraints while maintaining adequate convergence. The numerical studies demonstrate that the proposed algorithm can obtain good surveillance performance in terms of different jamming strategies and shapes of monitoring areas.

Performance Analysis of Weighting Methods for Handover Decision in HetNets

The increasing demand for data, driven by advancements in technology, requires expanding coverage and enhancing network capacity. This expansion presents certain challenges, such as unnecessary Handover (HO) and interference, which can lead to a degradation in Quality of Service (QoS). To provide better QoS, it is vital to precisely model the HO decision-making process with optimal cell selection ensuring service continuity with minimal disruption. This paper investigates the performance of Analytic Hierarchy Process (AHP), Entropy, Standard Deviation (STD), and Weighted Sum Model (WSM) comparatively, while considering attributes such as Reference Signal Received Power (RSRP), Signal-to-Interference-Plus-Noise Ratio (SINR), channel capacity, and cell capacity. Additionally, the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is utilized to rank candidate cells for HO decisions. The performance of the considered weighting methods has been analyzed in a dense Small Cell (SC) Long-Term Evolution-Advanced (LTE-A) Heterogeneous Network (HetNet) environment based on system Key Performance Indicators (KPIs) such as HO Rate (HOR), HO Failure (HOF), Radio Link Failure (RLF), and HO Ping-Pong (HOPP). The evaluations have shown a trade-off between the methods in different KPIs. The findings highlight the importance of the weighting methods on HO decision, considering the significance of the specific KPIs.

Joint uplink-downlink user-cell association for ultra-dense NOMA networks

As wireless communication systems evolve, the transition from homogeneous structures to heterogeneous ones intensifies. The need for continuous connectivity, fairness, and spectrum efficiency, such as in massive Internet of Things (IoT) networks and ultra reliable low latency communications (uRLLC), necessitates the utilization of base stations with diverse capabilities and coverage areas. As a result, the need for advanced interference management, user-cell association, and resource allocation techniques increases. This study proposes an innovative solution for the problem of non-orthogonal multiple access (NOMA) user-cell association in multi-tier ultra-dense heterogeneous networks (UD-HetNet). The proposed network constitutes a general framework that can be applied to next generation cellular communications, massive IoT, non-terrestrial and uRLLC type communications. Differing from previous studies that ignore interference and primarily focus on the received signal powers for user-cell association, this investigation advances the subject by considering the interplay between downlink received signal/interference power and uplink inter-cell interference. Due to the non-convex integer programming nature of the problem, it is proposed to employ an innovative relaxation algorithm to obtain a sub-optimal solution for the total signal-to-interference-plus-noise ratio (SINR). As verified through simulation results, the proposed association method permits concurrent communication participation of multiple users while managing the interference, rendering it as an ideal approach for systems emphasizing fairness and continuous connectivity.

Secrecy Analysis of a Mu-MIMO LIS-Aided Communication Systems under Nakagami-m Fading Channels.

This study evaluates the performance of large intelligent surface (LIS) technology in the context of a multi-user MIMO mobile communication system (Mu-MIMO) proposed for the sixth generation (6G). LIS employs digitally controlled reflectors to enhance Signal-to-Interference plus Noise Ratio (SINR) and establish line of sight (LoS) connectivity in non-LoS environments, improving transmission security. Analytical expressions are derived to assess LIS performance metrics, including distribution parameters, bit error probability, and secrecy outage probability, considering the presence of eavesdroppers and environmental fading. The study highlights the potential of LIS technology to enhance the confidentiality and reliability of digital communication systems in next-generation networks.

A jamming power control game with unknown user’s communication metric

We consider a jamming problem in which a jammer aims to degrade a user’s communication in which the user might differ in applied applications or communication purposes. Such differences are reflected by different communication metrics used by the user. Specifically, signal-to-interference-plus-noise ratio (SINR) is used as a metric to reflect regular data transmission purposes. Meanwhile, as another metric, latency, modeled by the inverse SINR, is used to reflect emergency communication purposes. We consider the most difficult scenario for the jammer where it does not know which application (metric) the user employs. The problem is formulated as a Bayesian game. Equilibrium is found in closed form, and the dependence of equilibrium on network parameters is illustrated.

Product manifold optimization-based joint beamforming for RIS-aided MIMO systems: A fairness based approach

Reconfigurable intelligent Surface (RIS) can effectively improve the communication quality and extend the signal coverage range by reconfiguring the wireless communication transmission environment, which is very suitable for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, a product manifold optimization-based beamforming design (PMO-BD) algorithm for a RIS-assisted multi-user MIMO system is proposed to enhance fairness among users. We establish a lower bound for the minimum signal-to-interference-plus-noise ratio (SINR) among users as the optimization objective, which is subjected to multiple constraints. By maximizing the lower bound to ensure the quality of service (QoS) for each user, fairness among users get improved. The PMO-BD algorithm exploits the internal structure of the product manifold to deal with the high dimension and nonlinear characteristics of the problem effectively, thereby reducing algorithm complexity and enhancing convergence rates. Simulation results verify the advantages of the proposed PMO-BD algorithm in comparison to other algorithms.

Experimental full-duplex amplify-and-forward relay scheme for OFDM with power gain control

The fundamental challenges for full-duplex (FD) relay networks are the self-interference cancellation (SIC) and the cooperative transmission design at the relay. This paper presents a practical amplify-and-forward (AF) FD one-way relay scheme for orthogonal frequency division multiplexing (OFDM) transmission with multi-domain SIC. It is found that the residual self-interference (SI) signals at the relay can be regarded as an equivalent multipath model. The effects of the residual SI at the relay are incorporated into the equivalent end-to-end channel model, and the inter-block interference can be removed at the destination by using cyclic prefix (CP) protection. Based on the equivalent multipath model, we present a solution for optimizing the amplification factor on the performance of signal-to-interference-plus-noise ratio (SINR) when the equivalent multipath length is longer than the CP. Furthermore, a practical one way FD relay network with 3 nodes is built and measured. The simulation and measured results verify the superior performance of the proposed scheme.

MUSCAT: Distributed multi-agent Q-learning-based minimum span channel allocation technique for UAV-enabled wireless networks

We consider a minimum span channel allocation problem (MS-CAP) to overcome spectrum scarcity and facilitate the efficiency of unmanned aerial vehicle (UAV)-enabled wireless networks. Basically, the MS-CAP minimizes the difference between the maximum and minimum used frequency, i.e., the required total bandwidth, while guaranteeing the quality-of-service (QoS) requirements for each wireless link in the network. The conventional optimal minimum span channel allocation (MS-CA) scheme is based on a centralized approach, assuming that global network information is available at the central controller. In practice, however, this may not be feasible for dynamic environments like UAV-enabled wireless networks since the real-time exchange of network information and channel allocation results with dynamically moving UAVs is formidable. Hence, we propose a novel practical MS-CA algorithm based on distributed multi-agent reinforcement learning (MARL), where each agent independently learns its best strategy with its local observations. To the best of our knowledge, the proposed technique is the first work of designing a distributed MARL for the MS-CAP for multi-UAV-enabled wireless networks in the literature. Numerical results reveal that the proposed distributed MS-CA technique can efficiently save the required total bandwidth while ensuring the QoS requirements of each link, represented by the signal-to-interference plus noise ratio (SINR) threshold, even in dynamic wireless networks. It validates the applicability of the proposed distributed MS-CA framework to dynamic networks.

Joint Base Station Selection and Power Allocation Design for Reconfigurable Intelligent Surface-Aided Cell-Free Networks

Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers an RIS-aided CF system where each user is supported by a devoted RIS and can establish connections with multiple BSs for coherent transmission. Specifically, each RIS can enhance signal transmission between users and their selected BSs through passive beam-forming, but also randomly scattered signals from other non-selected BSs to users, causing additional signals and interference in the network. To gain insights into the system performance, we first derive the average signal-to-interference-plus-noise ratio (SINR) received by each user in a closed-form expression. Subsequently, we formulate an optimization problem aimed at maximizing the weighted sum-SINR of all users in the RIS-CF network. This optimization considers both BS transmit power allocation and BS selections as variables to be jointly optimized. To tackle the complexity of this nonconvex optimization problem, we develop an innovative two-layer iterative approach that offers both efficiency and efficacy. This algorithm iteratively updates the transmit power allocation and BS selections to converge to a locally optimal solution. Numerical results demonstrate significant performance improvement for the RIS-CF network using our proposed scheme. These results also highlight the effectiveness of jointly optimizing BS transmit power allocation and BS selections in the RIS-CF network.

Handover management procedures for future generations mobile heterogeneous networks

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks.

In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays.

Investigation of the dependence of the multi-linear model parameters on the user mobility pattern in O-RAN in the context of user groups classification

The paper presents the results of a study on the influence of user mobility patterns in the O-RAN (Open Radio Access Net-work) on the parameters of a multi-linear model. The considered multi-linear model is represented as three-dimensional tensors that describe the changes of received signal power or SINR (Signal-to-Interference-plus-Noise Ratio). Factor matrices after the CP (Canonycal Polyadic) decomposition are exploited to separate users by their mobility using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. A model and a set of scenarios are developed for simulating the O-RAN segments and to study the influence of the user mobility on the parameters of the multi-linear model. Simulation results indicate that the number of users in groups with different mobility patterns does not affect the parameters of the multi-linear model and the performance of the DBSCAN algorithm. For the considered scenarios, the best classification results are achieved when the order of the multi-linear model equal to three. Moreover, it is revealed that the topology of a network segment effects on the order of a multi-linear model.

Wireless Body Area Nanonetworks via Vascular Molecular Communication.

Advancements in biotechnology and molecular communication have enabled the utilization of nanomachines in Wireless Body Area Networks (WBAN2) for applications such as drug delivery, cancer detection, and emergency rescue services. To study these networks effectively, it is essential to develop an ideal propagation model that includes the channel response between each pair of in-range nanomachines and accounts for the interference received at each receiver node. In this paper, we employ an advection-diffusion equation to obtain a deterministic channel matrix through a vascular WBAN2. Additionally, the closed forms of inter-symbol interference (ISI) and co-channel interference (CCI) are derived for both full duplex (FDX) and half duplex transmission (HDX) modes. By applying these deterministic formulations, we then present the stochastic equivalents of the ideal channel model and interference to provide an innovative communication model by simultaneously incorporating CCI, ISI, and background noise. Finally, we evaluate the results with numerous experiments and use signal-to-interference-plus-noise ratio (SINR) and capacity as metrics.

An optimal calibration factor for mutual information effective SINR mapping scheme in 5G NR

AbstractIn this letter, an optimal calibration factor for mutual information effective signal‐to‐interference‐plus‐noise ratio (SINR) mapping (MIESM) scheme in fifth‐generation new radio (5G NR) is provided. By applying the optimal calibration factor to the MIESM scheme, the estimation accuracy of the effective SINR can be improved. The optimal calibration factor minimizes the mean squared error (MSE) of the difference between the estimated effective SINR and the required SNR under additive white Gaussian noise (AWGN) at a block error rate (BLER) of 10%. Simulation results show that the optimized MIESM scheme outperforms the non‐optimized MIESM scheme.

OTFS narrowband interference suppression based on energy concentration

Orthogonal Time Frequency Space (OTFS) modulation has significant Doppler resistance in non-geostationary orbit (NGSO) satellite communications, but is extremely affected by single-tone interference (STI). In this paper, a Fast Harris Hawk Optimization-based Energy Concentration Interference Suppression (FHHO-ECIS) algorithm is proposed to remove STI. First, the algorithm designs the translation matrices in the Delay-Doppler (DD) domain based on the interference center. The STI energy is concentrated by the translation matrices to a single point in the OTFS data block and removed by a zero-setting operation. Subsequently, an interference center pre-estimation module is proposed to improve the accuracy and speed of the HHO algorithm for interference center estimation. Finally, the theoretical analysis of the algorithm's interference suppression gain and the effect of the zero-setting operation on the data is performed. Simulation results show that the FHHO-ECIS algorithm improves the signal to interference plus noise ratio (SINR) by more than 1.7 dB and the bit error rate (BER) performance is improved by more than 0.3 dB.

An Approach to Maximize the Admitted Device-to-Device Pairs in MU-MIMO Cellular Networks

Due to the shortage of wireless resources and the emergence of a large number of users, determining how to guarantee the quality-of-service (QoS) requirements of users and make more users work in the same spectrum has become an urgent research topic. In this paper, we study a multi-user MIMO (MU-MIMO) cellular network system model in which cellular users (CUs) share the same spectrum resource with multiple device-to-device (D2D) pairs. To maximize the number of admitted D2D pairs sharing the same spectrum with the CUs, a joint power allocation and channel gain (JPACG) algorithm is proposed. The optimization problem is divided into two steps to be solved. First, the power allocation of CUs without D2D pairs admitted is solved. Then, the optimization problem is transformed into minimizing the interference to CUs when CUs are treated as primary users. The admittance order of D2D pairs is determined by the transmission power and channel gain. The proposed algorithm uses a convex optimization algorithm to solve the problem of power allocation joint interference channel gain in order to maximize the number of admitted D2D pairs under the constraints of the signal-to-interference-plus-noise ratio (SINR) threshold and maximum transmission power. In addition, the effect of the number of admitted D2D pairs on the total sum rate of all users is also analyzed. The simulation results show that the proposed JPACG algorithm can achieve better performance in admitting D2D pairs.

Impact of different receiver geometries on a reconfigurable intelligent surface assisted multi-cell VLC system in the presence of light path blockage.

We report the effect of integrating metasurface-aided reconfigurable intelligent surfaces (RISs) on the signal-to-interference-plus-noise ratio (SINR) and data rate of a multi-cell visible light communication (VLC) system. RIS has been deployed in the channel between transmitter and receiver to redirect the reflected light in the desired directions, even in the absence of line-of-sight (LoS) links. Results show that the introduction of RIS has improved average SINR but reduced average illumination level compared to a no-RIS system. As the quantity of RIS increases, a discernible improvement in the maximum SINR value is observed. Here, three different receiver geometries, namely, a photodiode (PD), freeform diversity receiver (FDR), and modified FDR (MFDR), have been adopted. The impact of individual receivers has been reported in the presence of light path blockage. MFDR geometry is found to be most suitable with more coverage probability compared to the other two receivers. With (40c m×24c m) RIS area, during blockage, MFDR maintains an average SINR of 21.95dB, which is 97.29% and 14.24% greater than PD and FDR, respectively.