Signal To Interference Noise Ratio Research Articles

A joint resource allocation strategy in a radar-communication coexistence network for target tracking and user serving

With the rapid development of commercial communications, the research on Radar-Communication Coexistence (RCC) systems is becoming a hot spot. The resource allocation techniques play a crucial role in the RCC systems. A performance-driven Joint Radar-target and Communication-user Assignment, along with Power and Subchannel Allocation (JRCAPSA) strategy, is proposed for an RCC network. The optimization model aims to minimize the sum of weighted Bayesian Cramér-Rao Lower Bounds (BCRLBs) of target state estimates for radar purpose. This is subject to constraints such as the Communication Data Rate (CDR) for communication purpose, the total power budget in each RCC system, assignment relationships, and the number of available subchannels. Considering that such a problem falls into the realm of Mixed Integer Programming (MIP), a Three-stage Iteratively Augment-based Optimization Method (TIAOM) is developed. The Communication-User Assignment (CUA), Communication Subchannel Allocation (SCA), and Radar-Target Assignment (RTA) feasible solution domains are iteratively expanded based on their importance, leading to the efficient acquisition of a suboptimal solution. Simulation results show the outperformance of the proposed JRCAPSA strategy, compared to the other benchmarks and the OPTI toolbox. The results also imply that the Bayesian Cramér-Rao Lower Bound (BCRLB) is a more stringent optimization metric for the achieved Mean Square Error (MSE), compared to Mutual Information (MI) and Signal-to-Interference-Noise Ratio (SINR).

Synergistic optimization strategy for beamforming and power allocation in dual-functional radar-communication systems

The dual-functional radar-communication (DFRC) integrated system presents an ideal solution to address the challenge of spectrum resource congestion in future networks. This paper explores an adaptive power allocation technique based on beamforming to enhance the word error probability (WEP) performance of the DFRC system. Initially, a joint optimization model is developed to minimize the WEP while adhering to constraints on radar signal-to-interference-noise ratio (SINR), peak-to-average-power ratio, sidelobe level, and total transmit power. This model incorporates dual-function transmit beam, radar, and communication receive beam patterns. Subsequently, the proposed subproblem convex relaxation alternating update (SCRAU) algorithm is introduced to achieve a locally optimal solution for multi-carrier power allocation. This algorithm decomposes the original non-convex optimization problem into three sub-problems with lower complexity and iteratively optimizes them. Simulation experiments validate that the SCRAU algorithm can simultaneously fulfill radar and communication functions. The SCRAU algorithm demonstrates superior WEP performance compared to current advanced algorithms.

Autonomous radar interference detection and mitigation using neural network and signal decomposition

<p>Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference-noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A neural network (NN) with four inputs, one hidden layer, and one output is trained with various signal-to-noise (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed neural network can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2dB higher than the NN-CFAR-Z algorithm.</p>

An SIC-free NOMA-VLC system enhanced by multi-user rate allocation

Visible light communication (VLC) becomes a potential basic inter-connection technology for Internet of Things (IoT) services due to the advantages of ubiquitous light emitting diodes (LEDs). In this paper, a novel non-orthogonal multiple access (NOMA) scheme without serial interference cancellation (SIC) process is employed in baseband VLC system. The scheme is based on rate allocation and corresponding gain which could enhance the received signal to interference noise ratio (SINR). Simulation results show that the proposed scheme could achieve better bit error ratio and avoid error propagation (EP) compared to the conventional NOMA scheme.

Improving 4G LTE network quality using the automatic cell planning

The growing demand for network services leads to an increase in traffic load on eNodeB, resulting in decreased network quality and performance, necessitating optimization. This research analyses the results of optimising 4G reference signal received power (RSRP), signal to interference noise ratio (SINR) and throughput parameters using the automatic cell planning (ACP) method. ACP has been shown to significantly improve the performance and quality of 4G LTE networks compared to traditional cell planning methods. Based on the standard parameter RSRP, increased after ACP optimisation which is dominant in the range ≥ -100 s.d ˃ -85 dBm and obtained an average value of -98.59 dBm with good category. The average SINR has increased by 18.23 dB with a good category. The dominant throughput is in the 14,000 Kbps range with an average value of 50,241.08 Kbps with the excellent category. The ACP method can enhance the performance of 4G LTE networks, potentially addressing operator issues of unstable network quality due to poor coverage. The ACP method significantly enhances 4G LTE network performance, coverage, and user experience, potentially addressing unstable network quality due to poor coverage. This research is crucial for both users and the telecoms industry.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

Revolutionizing healthcare 5.0: Blockchain-driven optimization of drone-to-everything communication using 5G network for enhanced medical services

The healthcare industry has witnessed a notable surge in the adoption of drone technology for the swift and efficient transportation of vital medical supplies. In the expansive and uncontrolled realm where drones operate, challenges arise concerning authentication and secure data sharing. In the era of Healthcare 5.0, characterized by the integration of cutting-edge technologies, this article explores a groundbreaking approach to revolutionize medical services through the synergy of blockchain technology, drone-to-everything (D2X) communication, and the upcoming 5G network that provides enhanced broadband, extra reliable, low latency communication and massive machine type communication. This research addresses the critical task of delivering medical supplies using drone technology, focusing on ensuring the security, reliability and demand for a large storage capacity during the transportation of sensitive items.This research introduces an optimized communication link by using a blockchain network and the Internet of Things (IoT). To achieve such a goal two different algorithms are introduced and three different scenarios are implemented to include all possibilities in real applications. Three potential routes for data transmission, drone to drone, drone to ground user devices, and drone to base station, are explored to enhance communication efficiency. The contribution lies in optimizing the communication link to ensure a low bit error rate (BER), high signal-to-interference noise ratio (SINR) and maintain fixed signal power during data transmission. The outcomes of this study have significant implications for advancing healthcare logistics through the integration of cutting-edge technologies. The performance of the proposed method has been proven by simulations using parametric measurements.

Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR.

An Improved Interference Mitigation Scheme for Femtocell Networks

Femtocell networks have gained significant attention in recent years due to their ability to improve indoor wireless coverage and capacity. However, they are susceptible to interference from neighboring cells, which significantly degrade their performance and quality of service to the end-user. The study introduced an adaptive power control technique that causes the power level of the femtocell base station to adjust dynamically to reduce the interference caused by adjacent femtocell networks, thereby adjusting the system bandwidth, carrier frequency, path loss and femtocell Base Station (BS) transmission power based on the request made by the users. The interference mitigation scheme was optimized by tuning the parameters of the power control algorithm, such as the transmit power levels and the interference thresholds. The algorithm was simulated with MATLAB simulation tools. Throughput, Signal to Interference Noise Ratio (SINR), coverage enhancement and user satisfaction were used as measurement metrics for the model using descriptive statistics. The results showed that the average performance obtained for the throughput increased by 25.88% from 3.40 X 1010 (bps) to 4.28 X 1010 (bps), and the SINR increased by 35.29% from 1.70 X 109 dB to 2.30 X109 dB for the benchmarked power control and improved power control techniques respectively. Coverage enhancement and user satisfaction were nearly 0, which indicates that the technique was of average performance. This study concluded that the new scheme enhances wider coverage to reach more users and mitigate interference while maintaining a higher level of femtocell user satisfaction.

Model order estimation method for user mobility classification in heterogeneous RAN systems

The performance of modern heterogeneous Radio Access Networks (RANs) can be improved by introducing new methods and algorithms for network resource management. In particular, user mobility information can be used to control RAN resources. To determine user mobility, information about the variation of the Signal to Interference + Noise Ratio (SINR) between base stations and users can be collected and analyzed. These data are multi-linear and can be represented as tensors to extract information from them. However, for the tensors decomposition, it is necessary to know the order of the multi-linear model or the rank of the tensors. Inspired by the LaRGE (LineAr Regression of Global Eigenvalues) method, we propose a novel method for the model order estimation of the multi-linear data based on the analysis of the Complimented Global Eigenvalues (CGE) profile. We have denoted our method as Enhanced LaRGE (EnLaRGE). We have compared the EnLaRGE method with the classical methods AIC, MDL and their multi-linear versions, as well as with the M-EFT and N-D EFT methods. EnLaRGE shows good performance for the tensors with small one of the tensors dimensions. EnLaRGE method is implemented for the automatic model order estimation of multi-linear data received from the Network Simulator 3 with RAN Intelligent Controller (RIC). The results show the high precision of the multi-linear model order estimation in comparison with state-of-the-art methods. The estimated model order can be used for user mobility classification and improving heterogeneous RANs performance. This research was funded by Russian Science Foundation, grant number 23-69-10084, 318, https://rscf.ru/project/23-69-10084/

Heterogeneous cyber-physical network coexistence through interference contribution rate and uplink power control algorithm (ICR-UPCA) in 6G edge cells

Optimizing power control for interference mitigation at the network cell edge is pivotal in enhancing capacity within a heterogeneous cyber-physical infrastructure, such as smart cities, manufacturing, healthcare, energy grids, transportation, and agriculture, among others. In this paper, we consider the intricate dynamics of Internet of Things (IoT) 5/6G edge users, with a particular focus on the Interference Contribution Rate (ICR), where macro and femtocells are critical network infrastructures. Existing approaches has drawbacks such as computational complexity, overhead, and co-channel interference, among others. However, to fully address interference challenges from the coexistence of diverse network hierarchies, preserving the Quality of Service for femtocell users is prioritized. The paper concurrently enhances the handoff mechanism of cell edge users in the macro cell network. A two-tier heterogeneous network (HetNet) is utilized to initially assess the contribution of edge user equipment (UE) to interference levels during its active state while quantifying it as ICR. Game theory is used to formulate a cohesive model for the coexistence of macro cell (MUE) and femtocell users (FUE). ICR-based uplink power control and reference signal received quality (RSRQ)-based handoff algorithms are deployed to regulate interference levels and enhance the Signal-to-Interference-Noise Ratio (SINR) of the MUE at the cell edge. This is achieved through coordinated transmit power adjustments by both user types. Results indicate a 6.67 % channel capacity loss (interference tolerance) by the FUE, leading to a 12.5 % improvement, translating to approximately 4 Mbps and 1 Mbps channel enhancements, respectively. The MUE and FUE can effectively coordinate power control with minimal overhead, accepting compromises in network channel quality. This approach facilitates improved MUE data access rates while ensuring the preservation of FUE. We show that interference is successfully mitigated through power control in heterogeneous networks with lower computational complexity.

Analisa Pengaruh Issue Overshooting Cell Terhadap Coverage dan Quality Jaringan 4G LTE Kelurahan Jati Baru Kota Padang

The increase in service users causes network problems, such as poor signal quality, sudden signal loss, or no network at all, To improve network quality, improvements are made to service coverage. One of them is by adding eNodeB. With the addition of eNodeB, network maintenance must be carried out to ensure identification and improvement related to network coverage and performance. If maintenance is not performed, it can cause bad spots, one of which is cell overshooting. The purpose of this research is to analyse the effect of cell overshooting on 4G LTE network coverage and quality parameters. 4G network coverage parameters are: Reference Signal Received Power (RSRP) and 4G network quality parameters namely Signal to Interference Noise Ratio (SINR). From the drive test results obtained 80.77% of 546 samples on RSRP parameters ≥ -100 dBm and 32.78% of 546 samples on SINR parameters ≥ 3 dB. The low SINR parameter of the drive test results is caused by bad spot overshooting cell interference that occurs at site 4253364E and site MC425CC02E with sites around the bad spot area. From the analysis, it can be seen that bad spot overshooting cell affects network quality, while network coverage parameters do not. Where the RSRP parameters at the bad spot point are -87 dBm and -92 dBm and SINR parameters at the bad spot point are -1 dB and -3 dB.

Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory

Link-adaptation (LA) is one of the most important aspects of wireless communications where the modulation and coding scheme (MCS) used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a single-user SISO (SU-SISO) system with out-of-cell interference, LA is performed by computing the post-equalization signal-to-interference-noise ratio (SINR) at the receiver. The same technique can be employed in multi-user MIMO (MU-MIMO) receivers that use linear detectors. Another important use of post-equalization SINR is for physical layer (PHY) abstraction, where several PHY blocks like the channel encoder, the detector, and the channel decoder are replaced by an abstraction model in order to speed up system-level simulations. However, for MU-MIMO systems with non-linear receivers, there is no known equivalent of post-equalization SINR which makes both LA and PHY abstraction extremely challenging. This important issue is addressed in this two-part paper. In this part, a metric called the bit-metric decoding rate (BMDR) of a detector, which is the proposed equivalent of post-equalization SINR, is presented. Since BMDR does not have a closed form expression that would enable its instantaneous calculation, a machine-learning approach to predict it is presented along with extensive simulation results.

LORAWAN GATEWAY PLANNING USING AS923-2 FREQUENCY IN TASIKMALAYA FOR MONITORING ODC

This study aims to design a LoRaWAN network and find out how many gateways are needed to cover the research area and to design an IoT-based monitoring system for ODC devices on the FTTH network based on data at PT. Telkom Witel Tasikmalaya. The method used is a simulation using Atoll software version 3.40 and several calculation stages to predict the parameters of RSSI (Received Signal Strength Indicator) and SINR (Signal to Interference Noise Ratio) in a planning area of ​​358.66 km2. This study using AS923 frequency with a bandwidth of 125 kHz and a Spreading factor of 10. The results obtained are signal strength (RSSI) and signal quality (SINR) parameters. Based on the results of calculations and planning simulations, it produces 20 gateways using SF 10 with an RSSI parameter of -69.53 dBm and a SINR parameter of 20.21 dBm, each gateway can cover 4-5 km2 in the planning area.

Spatial Modulation Aided Physical Layer Security for NOMA-VLC Systems

We consider the physical layer security (PLS) problem in multi-user non-orthogonal multiple access (NOMA) enabled multiple-input multiple-output (MIMO) visible light communication systems intercepted by a passive eavesdropper (Eve). We propose a novel transmit precoding scheme based on receive spatial modulation (RSM) to degrade the signal-to-interference-noise ratio (SINR) of Eve by exploiting only the slow-fading characteristics of the visible light channel of the legitimate users (Bobs). The proposed PLS precoder is reinforced with secret parameter exchange with Bobs and a CSI acquisition model is proposed to reduce the PLS algorithm's computational load substantially at the transmitter. The closed-form expressions for the achievable secrecy rates and their upper and lower bounds are derived. Via Monte Carlo simulations, we confirm that Bobs can successfully decode their information in various user configurations while Eve's received SINR is significantly worsened by the jamming signal induced by the proposed precoder with secret key exchange. It is also shown that Eve's bit error rate (BER) is increased to the 0.5-level for almost any position in the considered indoor environment. Finally, we corroborate the derived secrecy expressions by computer simulations and show that the proposed scheme provides PLS for Bobs.

Downlink Massive MIMO Systems: Reduction of Pilot Contamination for Channel Estimation with Perfect Knowledge of Large-Scale Fading

Massive multiple-input multiple-output (MIMO) technology is considered crucial for the development of future fifth-generation (5G) systems. However, a limitation of massive MIMO systems arises from the lack of orthogonality in the pilot sequences transmitted by users from a single cell to neighboring cells. To address this constraint, a proposed solution involves utilizing orthogonal pilot reuse sequences (PRS) and zero forced (ZF) pre-coding techniques. The primary objective of these techniques is to eradicate channel interference and improve the experience of end users who are afflicted by low-quality channels. The assessment of the channel involves evaluating its quality through channel assessment, conducting comprehensive evaluations of large-scale shutdowns, and analyzing the maximum transmission efficiency. By assigning PRS to a group of users, the proposed approach establishes lower bounds for the achievable downlink data rate (DR) and signal-to-interference noise ratio (SINR). These bounds are derived by considering the number of antennas approaches infinity which helps mitigate interference. Simulation results demonstrate that the utilization of improved channel evaluation and reduced loss leads to higher DR. When comparing different precoding techniques, the ZF method outperforms maximum ratio transmission (MRT) precoders in achieving a higher DR, particularly when the number of cells reaches 𝛶𝛶𝑝𝑝=7.

Novel EBBDSA based Resource Allocation Technique for Interference Mitigation in 5G Heterogeneous Network

Telecom operators have applied the cell association and load balancing technique with femtocell (HeNB) in urban dense 5G Heterogeneous networks (HetNets) for reducing loads of macrocells (MeNBs) and improving the total capacity. However, this deployment creates an interference scenario; mainly, the problem occurs severely from the users of the connected and autonomous vehicle in MeNBs coverage to HeNBs coverage. The main reason is that deploying HeNBs in MeNBs applies the co-subchannels by HeNB and MeNB, leading to severe cross-tier interference (CSI) in the OFDMA subcarrier. Due to this CSI occurrence, the spectral efficiency, Signal to Interference Noise Ratio (SINR), and system outages affect severely. As a result, the MeNB User Equipment (MUEs) and HeNB User Equipment (HUEs) suffer from service disruption, and the entire system’s performance degrades. This challenge has attracted the attention of many researchers and the telecom industries. As a result, several interferences controlling methods such as enhanced frequency allocation strategies, power management, control technique, resource scheduling, spectrum sharing, and Cognitive Radio (CR) techniques have been suggested for 5G communications. However, the study suggests that downlink interference is still challenging in dense urban zones, especially in multilevel and high-rise complex buildings. This paper proposes the EBBDSA-CSI technique where a novel Evolutionary Biogeography-based Dynamic Subcarrier Allocation (EBBDSA) algorithm is introduced for reducing the cross-tier Subcarriers interference (CSI) issues in MeNB and HeNB. The system-level simulation results suggest that the proposed algorithm reduces the system outage to 88.1%, and total spectral efficiency achieves 83.6% compared to the existing techniques.

Sum Throughput Maximization Scheme for NOMA-Enabled D2D Groups Using Deep Reinforcement Learning in 5G and Beyond Networks

Device-to-Device (D2D) communication underlaying cellular network is a capable system for advancing the spectrum’s efficiency. However, in this condition, D2D generates cross-channel and co-channel interference for cellular and other D2D users, which creates an excessive technical challenge for allocating the spectrum. Despite this, massive connectivity is another issue in the 5G and beyond networks that need to be addressed. To overcome this problem, non-orthogonal multiple access (NOMA) is integrated with the D2D groups (DGs). In this paper, our target is to maximize the sum throughput of the overall network while maintaining the signal-to-interference noise ratio (SINR) of the cellular and D2D users. To achieve the target, a discriminated spectrum distribution framework dependent on multi-agent deep reinforcement learning (MADRL), termed a deep deterministic policy gradient (DDPG) is proposed. Here, it shares the global historical states, actions, and policies using the duration of central training. Furthermore, the proximal online policy scheme (POPS) is used to decrease the computation complexity of training. It utilized the clipping substitute technique for the modification and reduction of complexity at the training stage. The simulation results demonstrated that the proposed scheme POPS attains 16.67%, 24.98%, and 59.09% higher performance than the DDPG, Deep Dueling and deep Q-network (DQN).

Performance of Static Power Allocation in Indoor Room on VLC-NOMA System Using Modulation PPM


 
 
 Visible light communication (VLC) is an emerging and promising technology that exploits the visible light spectrum for data transmission. However, one of the major challenges in VLC is how to efficiently allocate the scarce modulation bandwidth to multiple users while avoiding interference and maintaining signal quality. To tackle this challenge, we propose a novel scheme that combines non-orthogonal multiple access (NOMA) with static power allocation (SPA) and pulse position modulation (PPM) in VLC. We conduct simulations in a realistic indoor scenario with a 9x9x3 m room and a single 12-watt LED at the center, using a line of sight (LOS) channel with a field of view (FOV) of 70o. The results show that our scheme achieves superior performance, with user 1 and user 2 obtaining signal-to-interference noise ratio (SINR) values of 20 dB and 74 dB, respectively. Our scheme can effectively overcome the limitations of VLC, such as low data rate, limited coverage area, and high sensitivity to ambient light noise, and pave the way for future VLC applications.
 
 

Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications

This is the second part of a two-part paper that focuses on link-adaptation (LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems with non-linear receivers. The first part proposes a new metric, called bit-metric decoding rate (BMDR) for a detector, as being the equivalent of post-equalization signal-to-interference-noise ratio (SINR) for non-linear receivers. Since this BMDR does not have a closed form expression, a machine-learning based approach to estimate it effectively is presented. In this part, the concepts developed in the first part are utilized to develop novel algorithms for LA, dynamic detector selection from a list of available detectors, and PHY abstraction in MU-MIMO systems with arbitrary receivers. Extensive simulation results that substantiate the efficacy of the proposed algorithms are presented.