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

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.

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.

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.

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.

Effect of a vehicle's mobility on SNR and SINR in vehicular optical camera communication systems.

The widespread use of light-emitting diodes (LEDs) and cameras in vehicular environments provides an excellent opportunity for optical camera communication (OCC) in intelligent transport systems. OCC is a promising candidate for the Internet of Vehicles (IoV), and it uses LEDs as the transmitter and cameras as the receiver. However, the mobility of vehicles has a significant detrimental impact on the OCC system's performance in vehicular environments. In this paper, a traffic light that uses multiple-input multiple-output (MIMO) technology serves as the transmitter, and the receiver is a camera mounted on a moving vehicle. The impact of vehicle mobility on the vehicular MIMO-OCC system in the transportation environment is then examined using precise point spread function (PSF) analysis. The experimental results are used to evaluate the proposed PSF. A good agreement between the laboratory's recorded videos and this PSF model's simulations is observed. Moreover, the signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR) values are evaluated. It is shown that they are greatly influenced by the vehicle's speed, direction of motion, and position of the camera. However, since the angular velocity in a typical transportation environment is low, it does not have a significant impact on the performance of the vehicular OCC systems.

Constellation design for PD-NOMA-based mmWave inter-satellite communication

The upcoming sixth-generation (6G) networks are supposed to utilize frequencies beyond the fifth-generation (B5G) and serve for satellite communications. During inter-satellite millimeter wave (mmWave) communications, the transmitting satellites may have identical simultaneous power transmissions, leading to the erroneous decoding of their information. This paper describes the application of a power-domain (PD) non-orthogonal multiple-access (NOMA) scheme for both terrestrial and inter-satellite communications consisting of two users or satellites. Specifically, the user (or satellite) symbol decoding is addressed under identical power transmissions. Both the user’s (transmitting satellite’s) data are extracted by minimizing the average probability of symbol errors using a designed constellation. Analytical/graphical results along with the simulation verify decent symbol decoding at a low average noise variance. At a noise variance of 0.01, the achieved average symbol-error probability reaches an order of up to 10−5. The proposed system results in a negligible error-vector magnitude (EVM) of −10 dB at higher end of the considered signal-to-interference-plus-noise ratio (SINR) range. Further, the proposed method alleviates the successive interference cancellation (SIC) pre-requisite of NOMA, strengthening the application of NOMA with mmWaves in next-generation satellite communication systems.

Interference Situational Aware Beam Pointing Optimization for Dense LEO Satellite Communication System

Recently, the low earth orbit (LEO) mega-constellation faces serious time-varying interferences due to spectrum sharing, dense deployment, and high mobility. Therefore, it is important to study the interference avoidance techniques for the dense LEO satellite system. In this paper, the interference situational aware beam pointing optimization technique is proposed. Firstly, the angle of departure (AoD) and angle of arrival (AoA) of the interfering links are obtained to represent the time-varying interference. Then, the interference avoidance problem for dense LEO satellite systems is modeled as a non-convex optimization problem, and a particle swarm optimization (PSO) based method is proposed to obtain the optimal beam pointing of the user terminal (UT). Simulations show that the relative error of the mean signal-to-interference plus noise ratio (SINR) obtained by the proposed method is 0.51%, so the co-channel interference can be effectively mitigated for the dense LEO satellite communication system.

On autoregressive and neural methods for massive-MIMO channel de-noising

In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.

Genetic Algorithm for Mode Selection in Device-to-Device (D2D) Communication for 5G Cellular Networks

The widespread use of smart devices and mobile applications is leading to a massive growth of wireless data traffic. With the rapidly growing of the customers’ data traffic demand, improving the system capacity and increasing the user throughput have become essential concerns for the future fifth-generation (5G) wireless communication network. In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular spectrum and all communications take place through the base stations (BS) and core network. Device-to-Device (D2D) communication refers to a technology that enables devices to communicate directly with each other, without sending data to the base station and the core network. This technology has the potential to improve system performance, enhance the user experience, increase spectral efficiency, reduce the terminal transmitting power, reduce the burden of the cellular network, and reduce end to end latency. In D2D communication user equipment’s (UEs) are enabled to select among different modes of communication which are defined based on the frequency resource sharing. Dedicated mode where D2D devices directly transmit by using dedicated resources. Reuse mode where D2D devices reuse some resources of the cellular network. Outband mode where D2D communication uses unlicensed spectrum (e.g. the free 2.4 GHz Industrial Scientific and Medical (ISM) band or the 38 GHz millimetre wave band) where cellular communication does not take place. Cellular mode where the D2D communication is relayed via gNode B (gNB) and it is treated as cellular users. In this work, the target was to reach the optimal mode selection policy and genetic algorithm method was used with the objective of maximizing the total fitness function. Optimal mode selection policy was presented and analysed amongst cellular, dedicated, reused and outband mode. In the present study of mode selection issues in D2D enabled networks, genetic algorithm was proposed for the case when the cellular user equipment (UE) moves in the network. Quality of service (QoS) parameters, mobility parameters and Analytic Hierarchy Process (AHP) method were used to define the mode selection algorithm. To evaluate the performance of the proposed genetic algorithm, a study of the convergence of the algorithm and the signal-to-interference plus noise ratio (SINR) was done.

On energy conservation for fixed-wing UAV assisted practical full-duplex relaying with service requirement and bank angle limit

This paper studies a full-duplex (FD) amplify-and-forward relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as a mobile relay to serve a source-destination communication pair so as to satisfy their service requirement. Here, the UAV relay flies in a circular path and hence it must continuously change its heading of flight which leads to a bank angle of the aircraft. In order to ensure flight safety, therefore, a bank angle limit is imposed on the UAV relay. By considering a practical FD relaying, namely, there exists both residual loop interference (RLI) and processing time-delay at the relay side, novel signal to interference plus noise ratio (SINR) received at the destination is analyzed, giving a closed-form expression in addition to a concise lower-bound. Armed with the SINR analysis, the size of data received by the destination during a period of flight time of the UAV is calculated, leading to a closed-form lower-bound of the size of data. Taking bank angle limit into consideration and using the calculated lower-bound of the size of data, an optimization problem with the purpose of energy conservation is solved, leading to a novel method for joint adjustment of the UAV's flight parameters. Computer simulation experiments are conducted, and the results demonstrated that the proposed optimization method performs better in terms of energy conservation compared to the benchmark techniques regardless of the value of RLI and/or the service requirement of the source-destination communication pair.

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.

Performance Analysis of UAV RF/FSO Co-Operative Communication Network with Co-Channel Interference

The unmanned aerial vehicle (UAV) communication network has emerged as a promising paradigm capable of independent operation and as a relay to enhance communication coverage and efficiency. However, densely distributed terrestrial base stations with shared communication frequencies inevitably generate co-channel interference (CCI). The interference effect can be effectively eliminated by implementing free-space optical (FSO) communication in the UAV communication network. This paper proposes a solution for the UAV communication network to address interference effectively, specifically by employing a hybrid millimeter-wave radio frequency (RF)/FSO communication system. The RF links serve as the primary means of communication, while the FSO links act as a backup means of communication in the case of CCI. The exact outage probability (OP) and average symbol error rate (SER) expressions are derived for the hybrid RF/FSO communication network. The decision to switch between them depends on the signal-to-interference-plus-noise ratio (SINR). Furthermore, the SINR switching threshold value, which satisfies the target SER, has been calculated numerically for the proposed model. Simulation results indicate that the proposed network notably enhances the OP and attains a signal-to-noise ratio gain of approximately 4.6 dB in the average SER, particularly in scenarios where the RF links are subjected to severe interference or adverse weather conditions, as opposed to a pure RF communication network.

A Nyström-based low-rank unitary MVDR beamforming scheme

A Nyström-based scheme is devised to approximate the real symmetrical covariance matrix of the popular unitary MVDR (U-MVDR) beamformer with low-rank kernel matrix, based on which a novel lightweight and robust Nyström-based U-MVDR (NU-MVDR) beamformer is developed. Subsequently, the full-dimensional Nyström covariance matrix estimator’s signal subspace can be calculated by eigenvalue decomposition (EVD) of two reduced-dimensional matrices, which will save a significant amount of computing load at the EVD stage. This scheme has obvious computational efficiency and lightweight storage overheads. Meanwhile, we derive the effect of the Nyström covariance matrix’s rank on the output signal-to-interference-plus-noise ratio (SINR). Numerical results demonstrate that the proposed beamformer performs well in less snapshots and steering vector errors cases.

An approach to ensure joint illumination & communication performance of a forward error corrected indoor visible light communication (VLC) system in presence of ambient light interference

Abstract In recent time of looming radio frequency (RF) spectrum crisis, visible light communication using lighting infrastructure emerged as a potential alternative at an indoor environment. This paper addresses the setback associated with ambient light interference in an indoor Visible Light Communication (VLC) system to ensure joint communication and illumination performance inside an office room. A novel VLC architecture with suitable white light emitting diode (WLED) luminaire arrangement is presented to minimize the dispersion of signal to interference plus noise ratio (SINR) across the room. Luminaires are categorized in two groups viz. data transmitting illuminants and illuminants for lighting purpose. The first group is dedicated to transmit data as well as serves the purpose of illumination. The other set creates only ambient illumination to achieve quality lighting attributes. The proposed forward error corrected receiver configuration discards the ambient light noise originated by the illuminants that serve the ambient illumination. Tail biting convolutional encoder and viterbi decoder are used at the encoding section of the transmitter and decoding section of the receiver respectively to improve bit error rate. Results obtained through MATLAB simulation shows better average bit error rate (BER) in the order of 10−8 measured at uniformly distributed 25 grid points over the working plane. At the same time achieved average horizontal illuminance with good uniformity comply with ISO recommendation.

Mainlobe deceptive jammer suppression with DEPC-MIMO radar with joint transmit–receive design

This paper investigates the problem of mainlobe deceptive jammer suppression via joint transmit–receive design in a multiple-input multiple-output (MIMO) radar. At the design stage, a discrete element-pulse coding (DEPC) phase is implemented in both the slow-time pulses and transmit array antennas. After decoding and compensation of the delayed pulses, the true and false targets can be identified in the joint transmit–receive (JTR) spatial frequency domain. Furthermore, to eliminate the false targets, a data-dependent beamforming technique is adopted, where an optimization problem is constructed to maximize the output signal-to-interference-plus-noise ratio (SINR) by optimizing the DEPC coefficients and the weight vector of the receive filter in an alternating way. Specifically, two methods, including the Single Jammer Suppression (SJS)-Alternating Direction Method of Multipliers (ADMM) and Multiple Jammers Suppression (MJS)-Gradient Descent (GD) methods are developed for the cases of a single jammer and multiple jammers, respectively. A series of numerical results are utilized to verify the effectiveness of the proposed methods for mainlobe deceptive jammer suppression.

Power Resource Allocation Algorithm for Dual-Function Radar–Communication System

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power.